elnet 2016 - katedra informatiky fei vŠb-tuo · the conference elnet 2016 was held on 22nd...

ELNET 2016Proceedings of the 13th Workshop

Faculty of Electrical Engineering and Computer ScienceVSB – Technical University of Ostrava

ISBN 978–80–248–4008–6

ELNET 2016http://www.cs.vsb.cz/elnet/

13th WorkshopOstrava, 22nd November 2016Proceedings of papers

Organized by

VSB – Technical University of OstravaFaculty of Electrical Engineering and Computer Science

ELNET 2016c© Radomır Gono, editor

ISBN 978–80–248–4008–6

This work is subject to copyright. All rights reserved. Reproduction or publication ofthis material, even partial, is allowed only with the editors’ permission.

Technical editors:

Peter Chovanec [email protected]

Michal Kratky [email protected]

Faculty of Electrical Engineering and Computer Science,VSB – Technical University of Ostrava

Page count: 81Impression: 100Edition: 1st

First published: 2016

This proceedings was typeset by PDFLATEX.

Published by Faculty of Electrical Engineering and Computer Science, VSB – Technical

University of Ostrava

Preface

The conference ELNET 2016 was held on 22nd November 2016 at VSB-TechnicalUniversity of Ostrava, Czech Republic. This is the thirteenth conference.

The conception of ELNET conferences was a response to increasing interestin Energy and Power Systems and related aspects in the Czech Republic andSlovakia, in the last few years. An important point is the interdisciplinary natureof key topics of the conference:

– Energy and Power Systems– Distributed Power Generation– Fault Diagnosis– Power Breakdown Analysis– Survivable Network System Analysis– Energy Data Storing and Analysis– Visualisation– Structure and Grow of Networks

ELNET is a workshop intended for meeting of promoters of Energy andPower Systems and related aspects. It is focused on theoretical and technicalfoundations of information technologies, time-proven methods and developmenttrends. It also serves as a place for discussion about new ideas.

Conference provided an excellent opportunity for faculty, scholars, and prac-titioners to meet renowned researchers and to discuss innovative ideas, resultsof research, and best practices on various conference topics.

I would like to cordially thank the authors and PC members for their effort,materialised in this volume. Special thanks go to the Organising Committeemembers for their arduous editing work.

In conclusion, I would like to thank all contributing authors for their excellentresearch papers.

November 2016 Zdenek HradılekProgram Committee Chair

ELNET 2016

Organization

Evaluation Committee

Chair:Zdenek Hradılek (VSB – Technical University of Ostrava, Czech Republic)

Members:Vaclav Snasel (VSB – Technical University of Ostrava, Czech Republic)Stanislav Rusek (VSB – Technical University of Ostrava, Czech Republic)Ales Horak (Masaryk University in Brno, Czech Republic)

Program Committee

Anna Gawlak (Technical University Czestochowa, Poland)Radomır Gono (VSB – Technical University of Ostrava, Czech Republic)Przemyslaw Janik (Technical University Wroclaw, Poland)Michal Kolcun (Technical University Kosice, Slovak Republic)Zbigniew Leonowicz (Technical University Wroclaw, Poland)Zbynek Martınek (University of West Bohemia, Czech Republic)Harald Schwarz (BTU Cottbus, Germany)Jerzy Szkutnik (Technical University Czestochowa, Poland)Petr Toman (VUT Brno, Czech Republic)Ladislav Varga (Technical University Kosice, Slovak Republic)Jirı Tuma (CVUT Praha, Czech Republic)

Organizing Committee

Peter Chovanec (VSB – Technical University of Ostrava, Czech Republic)Michal Kratky (VSB – Technical University of Ostrava, Czech Republic)Yveta Geleticova (VSB – Technical University of Ostrava, Czech Republic)

VII

Workshop Location:

Campus of VSB – Technical University of Ostrava17. listopadu 15, 708 33 Ostrava–Poruba, Czech Republic22nd November 2016

http://www.cs.vsb.cz/elnet/

VIII

Sponsor

Workshop ELNET 2016 is supported by Skupina CEZ.

http://www.cez.cz/

Table of Contents

Actual Results of the Reliability Computation in 2016 . . . . . . . . . . . . . . . . . 1Radomır Gono, Stanislav Rusek, Pavel Bednar, Peter Chovanec,Michal Kratky

Analysis and Simulation of the Causes of Power Quality Disturbances . . . 10Dung Vo Tien, Veleslav Mach, Radomır Gono, Zbigniew Leonowicz

Draft Virtual Model of BGS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20Ladislav Novosad, Zdenek Hradılek

Comparison of Dynamic Models of Asynchronous Machines . . . . . . . . . . . . 29Martin Kral, Radomır Gono

Modelling of Cogeneration Unit 400kW . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39Michal Spacek, Zdenek Hradılek

Innovative Possibility of using Waste Heat from Biogas Plants . . . . . . . . . . 51Jirı Jansa, Zdenek Hradılek

On Indexing of Multidimensional Space for Efficient Range QueryProcessing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60Peter Chovanec, Michal Kratky

Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69

Actual Results of the Reliability Computation in2016

Radomır Gono1, Stanislav Rusek1, Pavel Bednar2, Peter Chovanec2, MichalKratky2

1 Department of Electrical Power Engineering2 Department of Computer Science

FEECS, VSB – Technical University of Ostrava,17. Listopadu 15/2172, 708 33 Ostrava-Poruba, Czech Republic

radomir.gono, stanislav.rusek, pavel.bednar, peter.chovanec,

[email protected]

Actual Results of the Reliability Computation in 2016

Radomir Gono1, Stanislav Rusek1, Pavel Bednar2, Peter Chovanec2, Michal Kratky2

1Department of Electrical Power Engineering 2Department of Computer Science

VŠB - Technical University of Ostrava, 17. listopadu 15, 708 33 Ostrava – Poruba radomir.gono, stanislav.rusek, pavel.bednar, peter.chovanec,

[email protected]

Abstract. The paper deals with the computation of distribution network com-ponents reliability parameters. Actual value of the component reliability param-eters in distribution network is used for the reliability computation and also for reliability-centered maintenance system. Reliability indices are possible to re-trieve only from accurate databases of distribution companies. Such a database includes records of outages and interruptions in power networks. The main problem for an analysis of these databases is the heterogeneity feature: data-bases of various distributors differ from one another. It is impossible to retrieve reliability parameters from this data in a direct way. In this paper there is ap-plied a framework for the retrieving of parameters from various outage data-bases in the Czech and Slovak republics. There are also actual results.

Key Words: Component reliability, failure rate, mean time to repair, distribu-tion network, and outage database

1 Introduction

This work deals with the component reliability. It is necessary to observe outages and interruptions in the transmission and distribution of electrical energy for retrieving the component reliability [1]. Furthermore, electrical energy unsupplied to consumers is possible to compute. A statistical significance of an outage database depends on the number of records in the database. A larger database would describe the real condition of network equipment more accurately. Therefore, it is necessary to merge databases of various distributors and distribution areas. The main problem of the merging is the heterogeneity feature: databases of various distributors differ from one another, be-cause they have different database systems and also different approaches for evalua-tion of outages and interruptions in their networks.

In [2] there is introduced a framework that makes it possible to retrieve parameters from these various databases. This idea is developed and new results are shown here.

c© Radomır Gono (Ed.): ELNET 2016, pp. 1–9, ISBN 978–80–248–4008–6.VSB – Technical University of Ostrava, FEECS, 2016.

2 Radomır Gono et al.

2 History of Outage Monitoring

Component failure rates tend to vary with a component work life. A bathtub curve is

commonly applied to represent the time-dependent failure rate changes of a compo-

nent. Many parameters in the field of reliability vary for a specific component and the

condition in which a component works. These random variables are represented by

probability distribution functions [3], [4], [5], [6].

Failure rates of overhead distribution equipment are, in general, very system specif-

ic due to their dependence on geography, weather, animals and other factors [7]. Typ-

ical reliability values for pieces of distribution equipment have been introduced in [8],

[9], [10], [11], [12], [13].

Outage monitoring in the former Czechoslovakia started in 1975 according to regu-

lations 2/74 [14]. These regulations unified interruptions, outages and damaged

equipment monitoring options for all distribution companies in Czechoslovakia.

Unfortunately, database building has ceased since 1990 because of political and so-

cial changes. The expert group, CIRED Czech, has introduced a discussion on relia-

bility issues. The first calls for integration of particular outage databases were already

claimed at the first meeting of this group in 1997. In 1999, distributors opted for uni-

fied monitoring of global reliability indices and the reliability of selected pieces of

equipment [15]. Data for the reliability computation is centrally processed and ana-

lyzed at the Technical University of Ostrava. This data has been handled and pro-

cessed since the year 2000.

3 Reliability Analyses

A majority of reliability computations is performed in the following way. The reliabil-

ity computation of the whole system is executed on the basis of components reliability

that is included in the system. That is the reason why the reliability is computed in two

phases. The first phase represents the retrieving of component reliability parameters

and the second phase is the reliability computation itself. Other phases may include

the evaluation of computed results and an improvement of the supply quality.

In virtue of experience, it is necessary to state that in most cases, the retrieving reli-

ability parameter is far more complicated than the reliability computation itself.

3.2 Input Data for Computations

There are various methods for input data retrieval which are based on the type of an

examined object, available data of an examined object, etc. Reliability is divided into

two basic groups in compliance with the method of input data retrieval:

• Empirical reliability – input data for the reliability computation is retrieved from

data on equipment, or similar equipment operating under similar conditions

• Predetermined reliability – the probability of outage-free operation is expressed

on the basis of knowledge about component status.

Actual Results of the Reliability Computation in 2016 3

Obviously, incorrect input data leads to poor results, even when a correct computa-

tion method is applied. Moreover, in many cases of reliability computations in electri-

cal power engineering, we face the problem of insufficient data size for a component,

e.g. an insufficient number of historical records.

3.3 Reliability Computation

In the case of empirical reliability, we need data on operations and outages of compo-

nents occurring in the reliability diagram, or data on components of the same type

operating under similar operating conditions. The more extensive the database, the

more reliable the results are. In the case of power system components, data must be

available for outages of breakers, disconnectors, transformers, lines, etc. for a set type

and voltage level. Moreover, there is another type of data necessary for the reliability

computation. We need to have knowledge of the power network itself. For example,

we must know the number of pieces of equipment for a set type, the total length of a

line type, voltage level and so on.

Consequently, retrieval of the failure rate for a power system is the basis of the em-

pirical reliability computation. This method is mostly employed in retrieving reliabil-

ity parameters for the reliability computation because the application of predetermined

methods requires different approaches to each power system component.

On the other hand, empirical methods require accurate records of outages. Conse-

quently, for statistically significant results of reliability computations, data on outages

dating back to many years in the past is required. It is possible to compute basic relia-

bility parameters of particular components from this database - annual failure rate and

time to repair.

The number of outages per period is retrieved from the database. The period is

usually defined depending on requirements concerning the reliability computation. An

additional value necessary for the failure rate computation is the number of compo-

nents for a set type and area. This value is possible to retrieve from the equipment

owner (usually system operator). As the numbers of components change in the real

power network during a period, we update it annually. Other important information is

possible to retrieve in more detailed databases, e.g. the most frequent cause of outag-

es, areas of the greatest amounts of undelivered energy, etc.

Regulations 2/74 include reliability parameters for basic equipment. These parame-

ters were set in 1980 and are very outdated. It is necessary to update these parameters

using an analysis of outage databases.

3.4 Heterogeneous Outage Data

In the case of electrical power networks, each distributor produces incompatible out-

age data. Although a data model of this data may be the same (e.g. relational data

model), such data is not necessarily compatible. For example, sets of relations for two

distributors belong to different relation schemes. Moreover, each scheme includes

different attributes expressing the same feature of an entity type.


A common way of addressing the problem is to develop a common relation scheme

and different data transform into the relation. It enables querying and analysis. We

have selected 31 attributes [16]. For the component reliability only few attributes are

necessary:

• Distribution Company - anonymous code of distributor

• Outage Identification - unique code of event

• Outage Type - accidental, planned or forced

• Equipment Voltage - 0.4 kV, 22 kV...

• Outage Cause - foreign influences, causes before starting operation...

• Equipment Type - overhead line, underground line...

• Failed Equipment - specific device - conductor, switch, pole, fuse...

• Failed Equipment Type - further specification - wooden pole, steely pole...

• Amount of Failed Equipments

• Producer - Siemens, ABB...

• Production Year - age of the component

• Beginning of outage

• End of outage - time of restoration of supply to all consumers

• End of equipment failure - time of repair of the device

• Failure Type - with or without equipment damage

Some other attributes are included for continuity of supply analyses and some for

future expansion purposes.

4. Results

The basic reliability data of particular elements may be computed from the data-

base of outages and interruptions stored at the VSB – Technical University of Ostrava.

The results include the rates and mean durations of equipment outages.

4.1 Database Range

The actual data collection includes outage data from distributors from the Czech Re-

public and one from the Slovak Republic. We have retrieved data from eight distribu-

tion areas (Table 1).

Distributors have delivered their data in xls files twice a year. Today database con-

tains more than 400 thousand records (from 2000 to 2016) on voltage levels 110 kV,

MV and partially LV.


Table 1. Database range (months)

4.2 Framework Results

The graphic representation of all distribution regions reliability indices from the

above-mentioned data for the 22 kV cable is given in Fig. 1. From the significant

differences in particular years it is possible to observe the contribution of our anal-

yses. The divergence of reliability indices is eliminated during long-term observation.

Fig. 1. The value tendency of reliability indices of the 22 kV cable


These parameters could update reliability indices from old Regulations 2/74 [14].

There is a comparison of both databases, 1975 - 1979 and 2000 - 2016, in Table 2.

Table 2. Comparison of results

In Table 2, we can observe that the current reliability indices are rather more supe-

rior.

One of the results of analyses is structuring failures according to their causes (Fig.

2). The most common cause of outages is “Operation and maintenance causes”.


Causes before starting operation Operation and maintenance causes

Foreign influences Forced outage

Cause not explained Other causes

Fig. 2. Structuring outages according to their causes

It is possible to provide also comparison of distribution regions - REAS (Fig. 3).

The Energy Regulatory Office could find these results useful for justifying of renewal

costs among distribution system operators.

Fig. 3. Comparison of distribution regions


0

20000

40000

60000

80000

100000

120000

140000

160000

180000

200000

0 - 1 min 2 - 3 min 4 - 10 min 11 - 60 min 61 min - 1 month

Failure duration

Nu

mb

er

of

fail

ure

s

Fig. 4. Number of outages distributed according to their duration

Fig. 4 shows distribution of outages according to their duration. The most of outag-

es are longer than 1 hour and shorter than 1 month.

We can also obtain other information important for operators, such as the faulty

equipment series from a specific producer, areas of the greatest amounts of unsupplied

energy, etc.

5. Conclusion

A statistical significance of an outage database depends on the number of records in

the database. A larger database would describe the real condition of the network

equipment more accurately. Therefore, it is necessary to merge databases of various

distributors. The main problem of the fusion is the heterogeneity feature: databases of

various distributors differ from one another.

The framework result may include the rates and mean durations of equipment out-

ages. We can also obtain other significant information for operators. The result proves

the framework is appropriate for analyzing such data. We compared the new results to

the original results in this paper.

Acknowledgements: This research was partially supported by the SGS grant from

VSB - Technical University of Ostrava (No. SP2016/95) and by the project

TUCENET (No. LO1404).


References

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models (New York, USA: Holt, Rinehart and Winston, 1975)

2. M. Kratky, R. Gono, S. Rusek, & J. Dvorsky, A framework for an analysis of failures data in

electrical power networks. Proc. PEA Conf. on Power, Energy, and Applications, Gaborone,

BW, 2006, 45-46

3. W.H. Beyer, Crc standard mathematical tables (Boca Raton, USA: CRC Press, 1984)

4. R. Ramakumar, Engineering reliability: fundamentals and applications (Upper Saddle River,

USA: Prentice-Hall, 1996)

5. T. Gonen, Electric power distribution system engineering (New York, USA: Mcgraw-Hill

College, 1985)

6. S. Asgarpoor & M.J. Mathine, Reliability evaluation of distribution systems with non-

exponential down times, IEEE Transactions on Power Systems, 12(2), 1997, 579-584

7. R.E. Brown & J.R. Ochoa, Distribution system reliability: default data and model validation,

IEEE Transaction on Power Systems, 13(2), 1998, 704-709

8. W.F. Horton, S. Goldberg, & R.A. Hartwell, A cost/benefit analysis in feeder reliability

studies, IEEE Transaction on Power Delivery, 4(1), 1989, 446-452

9. R. Brown, S. Gupta, S. Venkata, R. Christie, & R. Fletcher, Distribution system reliability

assessment using hierarchical Markov modeling, IEEE Transaction on Power Delivery,

11(4), 1996, 929-1934

10. R. Brown, S. Gupta, S. Venkata, R. Christie, & R. Fletcher, Distribution system reliability

assessment: momentary interruptions and storms, IEEE Transaction on Power Delivery,

12(4), 1997, 1569-1575

11. H.L. Willis, Power Distribution Planning Reference Book (Boca Raton, USA: CRC Press,

1997)

12. P. Save, Substation reliability - practical application and system approach, IEEE Transac-

tion on Power Systems, 10(1), 1995, 380-386

13. D. Karlsson, H.E. Olovsson, L. Walliin, & C.E. Slver, Reliability and life cycle cost esti-

mates of 400 kV substation layouts, IEEE Transaction on Power Delivery, 12(4), 1997,

1486-1492

14. J. Piskac, & J. Marko, Regulations for electric power system no. 2 – failure statistics at

electricity distribution (Prague, CZ: CEZ, 1974)

15. Distribution companies of the Czech Republic, Distribution network grid code, appendix

no. 2 - methodology of reliability determination of electric power supply and distribution

network equipments (Prague, CZ: ERU, 2005)

16. R. Goňo, M. Krátký, & S. Rusek, Analysis of Distribution Network Failure Databases.

Przegląd elektrotechniczny (Electrical Review), 86(8), 2010, 168-171

17. R. Cimbala, J. Kurimský, I. Kolcunová: Determination of thermal ageing influence on

rotating machine insulation system using dielectric spectroscopy, Przegląd Elektrotech-

niczny, Vol. 87, no. 8 (2011), p. 176-179

Analysis and Simulation of the Causes of PowerQuality Disturbances

Dung Vo Tien1, Veleslav Mach1, Radomır Gono1, Zbigniew Leonowicz2

1 FEECS, VSB – Technical University of Ostrava, Czech Republic2 Wroclaw University of Science and Technology, Polanddung.vo.tien, veleslav.mach, [email protected],

[email protected]

adfa, p. 1, 2011. © Springer-Verlag Berlin Heidelberg 2011

Analysis and Simulation of the Causes of Power Quality Disturbances

Dung Vo Tien1, Veleslav Mach1, Radomir Gono1 and Zbigniew Leonowicz2

1VSB - Technical University of Ostrava

[email protected], [email protected], [email protected]

2Wroclaw University of Science and Technology

[email protected]

Abstract. This paper summarizes disturbances phenomena that may occur on a power system, some typical causes of them and the potential impact on equipment. This paper presents techniques to simulate various power quality disturbances in medium voltage grid such as voltage sag, voltage swell, transient, harmonic, distortion by using ATP/EMTP software.

Keywords: Power quality, voltage disturbances, power system, simulation model, EMTP/ATP software.

1 Introduction

Nowadays, industries with sensitive electrical loads have become more dependent on the quality of power supply systems, and the electric power quality has become an important issue for electric utilities and their customers. Power Quality (PQ) events such as sag, swell, transients, harmonics, notch, fluctuation and flicker are the most common types of disturbances that occur in a power system. The ultimate goal to deal with PQ issues is to find a proper characteristic from PQ events and to provide a suitable solution to both utilities and users. In order to solve PQ problems, the cause of power quality phenomenon must be detected.

This paper introduces the cause of various power quality disturbances and some practical power disturbances further to mentioned, such as sag, swell, interruption, harmonic, transient and simulations performed with the help of EMTP/ATP.

2 Power Quality Disturbance and some typical causes of them

The development of model industrial devices requires ensure the quality of power because the electric device are very sensitive to disturbances. These concerns are


Analysis and Simulation of the Causes of Power Quality Disturbances 11

reflected in the newer versions of power quality standards, such as IEEE1159-1995

[1] and IEC6100-4-30 [2]. There are a number of different types of power quality

disturbances and also a number of different ways to define and categorize them. Here

follows one possible list of power quality disturbances types, categorized in one of

many possible ways. Table 1 provides a broad classification of the disturbances that

may occur on a power system, some typical causes of them and the potential impact

on equipment [3].

Table 1. Power Quality issues

Category Causes Impacts

Voltage sags

(dips)

- Local and remote faults

- Inductive loading

- Switch on of large loads

- Tripping of sensitive equipment

- Resetting of control system

- Motor stalling/tripping

Voltage surges - Capacitor switching

- Switch off of large loads

- Phase faults

- Tripping of sensitive equipment

- Damage to insulation and windings

- Damage to power supplies for electronic

equipment

Overvoltage - Load switching

- Capacitor switching

- System voltage regulation

- Problems with equipment that requires

constant steady-state voltage

Undervoltage - Heavy network loading

- Loss of generation

- Poor power factor

- Lack of var support

- All equipment without backup supply

facilities

Harmonics - Industrial furnaces

- Non-linear loads

- Transformers/generators

- Rectifier equipment

- Many operation of sensitive equipment

and relays

- Capacitor fuse or capacitor failures

- Telephone interference

Power frequency

variation

- Loss of generation

- Extreme loading conditions

- Negligible most of time

- Motors run slower

- De-tuning of harmonic filters

Voltage

fluctuation

- AC motor drives

- Inter-harmonic current

components

- Welding and arc furnaces

- Flicker in Fluorescent lamps

- Flicker in Incandescent lamps

Rapid voltage

change

- Motor starting

- Transformer tap changing

- Light flicker

- Tripping of equipment

Voltage

unbalance

- Unbalanced loads

- Unbalanced impedances

- Unbalanced faults

- Overheating in motors/generators

- Interruption of 3-phase operation

12 Dung Vo Tien et al.

Short and long

voltage

interruptions

- Power system faults

- Equipment failures

- Control malfunctions

- CB tripping

- Loss of supply to customer equipment

- Computer shutdowns

- Motor tripping

Transients - Lightning

- Capacitive switching

- Non –linear switching loads

- System voltage regulation

- Control system resetting

- Damage to sensitive electronic

components

- Damage to insulation

According to [4], the results from a number of power quality studies in the United

States and Europe have identified a number of common and power quality

disturbances, drawing from a range of end users (Fig. 1 and 2).

Fig. 1. Most common power quality issues

(U.S.)

Fig. 2. Most common power quality issues (EU-

25 countries.)

3 Modeling approach

The distribution voltage grid in this paper was presented in [5].

3.1 Line faults model

a. Single line to ground fault model

Single line to ground faults is the most popular fault in power system, (about 80%

of the total faults in power systems of Viet Nam). Fig. 3 shows single line to ground

(SLG) fault (A-g) at 22 kV line EMTP/ATP model. The simulated voltage sag

waveforms caused by a single line to ground (SLG) fault on phase A at overhead lines

at 0.098 (at the phase A voltage peak) to 0.2 seconds with a fault resistance of 10 Ω. It

can be observed that the 22 kV line voltage sag at phase A and the 22 kV line slight

voltage swell at unfaulted phases B and C a due to fault resistance. At the 0.4 kV load

voltage, voltage sag at phase A and phase B (due to the phase shift during fault)

slighter than 22 kV line due to the transformer and line.


Fig. 3. Phase to ground fault (A-g) at 22 kV line EMTP/ATP model.

Table 2. System parameters

Components Details

1 Source 250 MVA,110kV, 50Hz, X/R=10.5

2 HV/MV Transformer 25 MVA, 110/22kV, D/Y0

3 MV/LV Transformer 25 MVA, 22/0.4kV, Y/Y

4 Line Overhead line, AC70

5 Load 1 22 kV, 1.58 MW and 1.02 Mvar

6 Load 2 0.4 kV, 1.67 MW and 0.58 Mvar

In power quality studies, voltage sag waveform magnitude is commonly presented

in RMS waveform and normalized for better recognition. The RMS analysis is

simulated by model RMS1F and a user-specified library file (RMS1F.SUP) will be

created in ATPDraw. Fig. 4c, 4d shows the RMS analysis of single line to ground fault

voltage sag waveforms in fig. 4a, 4b. The sag magnitudes for each phase can be

clearly visualized. It can be observed that the 0.4 kV load voltage sag at phase A, B is

slight due to short-circuit the remote location and isolated by transformer.

Fig. 4a,b. Instantaneous waveform of 22kV feeder voltage and 0.4 kV load voltage

Fig. 4c,d. RMS waveform of 22 kV feeder voltage and 0.4 kV load voltage

Fig. 4. Voltage sag and swell caused by phase to ground fault at load voltage and 22 kV

feeder voltage


b. Phase B-C Fault Evolving to a Three-Phase

Fig. 5 shows multistage voltage sag instantaneous waveforms, the presence of

heavy wind caused a phase-to-phase fault (from 0.1 to 0.168 second) that evolved to a

three-phase fault by spreading of the ionized cloud and fault clearing at 0.25sec. The

changes of fault impedance during a fault create a multistage voltage sag power

quality disturbance.

Fig. 5. Phase B-C Fault evolving to a three- phase EMTP/ATP model.

Fig. 6a,b. Instantaneous waveform of 22 kV feeder voltage and 0.4 kV load voltage

Fig. 6c,d. RMS waveform of 22 kV feeder voltage and 0.4 kV load voltage

Fig. 6. Voltage sag and swell caused by Phase B-C Fault evolving to a three- phase at load

voltage and 22 kV feeder voltage

3.2 Transformer energizing model

The transformer energizing model developed in EMTP/ATP is shown in Fig. 7. It

is used to simulate voltage sag caused by transformer inrush current and core

saturation during energizing. The model consists of source 22 kV, 25 MVA, 50Hz

connect to a three-phase breaker, 22/0.4 kV, 1 MVA saturable core transformer and

200 kW resistive, 150 kVar inductive load. The saturable core transformer are

modeled by BCTRAN component. The switch is set to open at initiate stage and close

at 0.2 second, the voltage sag usually takes more than 1 second to rise back to its

nominal voltage level.


Fig. 8 shows the RMS analysis of three phases voltage sag caused by transformer

energizing. It can also be clearly seen that three phase experience unbalanced voltage

sag and gradually rise to its nominal voltage level. In this case, shallow voltage sag is

2%, but it can up to 15% from its nominal magnitude, it is dependent on the source

feeder power rating and transformer power rating. The higher the transformer power

rating, the lower the sag magnitude [6].

Fig. 7. Transformer energizing model in EMTP/ATP.

Fig. 8. Instantaneous waveform and waveform of voltage sag caused by transformer

energizing at 22 kV bus.


3.3 Lightning impulse model

According to [7], [8], [9], surge arresters model was proposed by Pincenti and

Giannettoni [8], and ATP simulation by Francisco J. Peñaloza [9].

The lightning impulse model developed in ATPDraw is shown in fig. 9, 10. Lines

are modeled by JMarti routine. The lightning is represented by a Heidler source. The

surge impedance results from the inductance and capacitance per unit length of the

line, disregarding the ohmic resistance per unit length and the conductance of the

insulation.

0

0

C

LZC

L0 : Inductance per unit length in H/km.

C0 : Capacitance per unit length in F/km.

Fig. 9. Three-phase connection

of the surge arresters

Fig. 10. Lightning impulse model within ATPDraw

Fig. 11, 12 shows the simulated impulsive transient waveform for all three phase

at the arrester terminal and the 22 kV bus after lightning in phase B.

Fig. 11. Voltage at the arrester terminal after lightning in phase A

Fig. 12. Voltage at the bus after lightning in phase A


3.4 Three phase nonlinear load model

The three phase nonlinear model developed in EMTP/ATP is shown in Fig. 13. It

is used to simulate harmonic voltage disturbance caused by three phase bridge

rectifier (six- pulse bridge rectifier). The system parameters in Table 2 but low load

(load 2) have 40 kW resistive and 30 kvar inductive, 1 Ω resistive and 500 μF

capacitive filter behind the three phase rectifier.

Fig. 13. Three phase nonlinear load model

Fig. 14 shows the harmonic waveforms at 22 kV and 0.4 kV bus. Using RMS

analysis is not recognize power disturbance because it is nominal. The harmonics can

also be calculated by fast Fourier transform (FFT) as shown in Fig. 15. Fig. 15 clearly

shows that at 0.4 kV and 22 kV phase A consists of high 5th

, 7th

, 11th

, 13th

, 17th

,

19th

, 23rd, 25th

and 29th

harmonic order [6]. The characteristic harmonics are based on

the number of rectifier (pulse number) used in a circuit and can be determined by the

following equation: h= 6n 1.

Fig. 14. Harmonic distortion waveforms of 22 kV feeder voltage and 0.4 kV load voltage


THD= 1.64% THD= 3.85%

Fig. 15. Harmonic analysis of phase a harmonic distortion at 22 kV bus (left) and 0.4 kV

load (right).

4 Conclusion

The paper summarizes disturbances phenomena that may occur on a power system,

some typical causes of them and the potential impact on equipment. This paper also

presents technique to simulate various power quality disturbances in medium voltage

grid by using ATP/EMTP software. The models presented include distribution line

fault, transformer energizing that are used to simulate various types of voltage sag and

swell event, lightning impulse model used to simulate impulsive transient event,

nonlinear load models used to simulate triple harmonic and more [5].

Acknowledgement: This research was partially supported by the SGS grant from

VSB-TU Ostrava (No. SP2016/95) and by the project TUCENET (No. LO1404).

References

1. IEEE Recommended Practice for Monitoring Electric Power Quality, IEEE Std.

1159-1995, 1995.

2. Testing and Measurement Techniques—Power Quality Measurement Methods,

IEC 61000-4-30, 2003.

3. Detailed Overview of Power System Disturbances (Causes and Impacts),

http://electrical-engineering-portal.com/detailed-overview-of-power-system-

disturbances-causes-and-impacts

4. Report of schneider-electric. http://blog.schneider-electric.com/power-

management-metering-monitoring-power-q,ality/2015/06/11/power-quality-

measuring-to-manage/

5. Dung Vo Tien, Veleslav Mach, Radomir Gono, Zbigniew Leonowicz, Analysis

and Simulation the Causes of Power Quality Disturbances, Wofex 2016, pp.73-78.


6. Rodney H.G. Tan and Vigna K. Ramachandaramurthy, A Comprehensive

Modeling and Simulation of Power Quality Disturbances Using

MATLAB/SIMULINK, InTech 2015.

7. Durbak W.D.: Zinc-Oxide Arrester Model for Fast Surges, EMTP Newsletter, Vol.

5, No. 1, January 1985.

8. Pinceti P., Giannettoni M.: A Simplified Model for Zinc Oxide Surge Arresters,

IEEE Trans. On Power Delivery, Vol. 14, No. 2, p. 393-397, April 1999.

9. M. Popov, ATPDraw-based Models: Nonlinear Elements, Surge Arresters and

Circuit Breakers, EEUG meeting 2008, European EMTP- ATP Conference, p.15-

28.

10. Mack Grady, Understanding Power System Harmonics, IEEE Power Engineering

Review, April 2012.

11. Lõszlú prikler, Hans Kristian Hoidalen, ATPDRAW version 5.6, 2009.

12. Jorge Blanes, Low voltage model for the study of slow disturbances in medium

voltage grids, Electric Power Systems Research 99 (2013), p 64–70.

Draft Virtual Model of BGS

Ladislav Novosad, Zdenek Hradılek

Department of Electrical Power Engineering,FEECS, VSB – Technical University of Ostrava,

17. Listopadu 15/2172, 708 33 Ostrava-Poruba, Czech [email protected], [email protected]

adfa, p. 1, 2011.

© Springer-Verlag Berlin Heidelberg 2011

Draft Virtual Model of BGS

Ladislav Novosád, Zdeněk Hradílek

Department of Electrical Power Engineering

VŠB – Technical University of Ostrava

17. listopadu 15/2172, 708 33 Ostrava-Poruba, Czech Republic

[email protected],[email protected],

Abstract. This article focuses on analysing the operation of a biogas station

(BGS), or rather a draft model simulating the operation of a biogas station. The

main aim of this model will be the establishment of a course of electrical power

which is supplied to the 22kV distribution network. The article therefore conta-

ins the basic principle of the draft biogas station model and describes its main

parts. Subsequently, it will be possible to use this BGS model in combination

with other models and simulations of renewables and 22kV system. The result

of cooperation of the draft model with models of other resources should be cla-

rifying the possibility of connecting biogas stations to 22kV networks.

KEYWORDS: virtual BGS model, measurement database, biogas station, re-

newable source, co-generation, co-generation unit

1 Introduction

In recent years, there are increased shares of electricity produced from renewable

energy sources (RES) in distribution grids. The vast majority of these sources operate

in a decentralized manner in the distribution grid (DG) which brings a range of new

problems with itself. These problems are mainly related to the very nature of some of

these sources, typically for example a dependence on weather regarding photovoltaic

and wind power plants. A question then is how should be a thus created system safely

and stably operated or developed. Therefore, it is necessary to know the characteris-

tics of individual RES, including the BGS as well. Precisely for this reason, this ar-

ticle deals with the issues of a BGS operation. This article aims to outline the possibi-

lities of creating a virtual model of the electrical part of BGS and use it to simulate the

time dependence of the output power supplied to DG. The outcome of such a model

will subsequently be a waveform of supplied power to DG. Thus created course can

be combined with other DG models, RES, simulating the operation of a comprehensi-

ve distribution network part. For the above reasons, the article will deal with the

electrical part of BGS only. BGS power heat balance will not be considered in the


Draft Virtual Model of BGS 21

model and we will not simulate the actual process of fermentation, or the operation of

a non-electric BGS part.

2 Model functional units

The BGS virtual model will have two main objectives:

1. Evaluation of measured BGS data

2. Simulation of BGS output electric power

2.1 Evaluation of measured data

The aim of this block will be automatic data processing. The output will be a set of

basic information about the measured electrical quantities. Measured data will be

loaded into a simulator from a text file (.txt). A prerequisite for successful data loa-

ding will be complying with the style of arrangement of individual values and quan-

tities in the data file. A set of 10 data files from various BGS will be possible to insert

into the model at one time.

The outcome of the model evaluation will be both graphical information (diagrams

and waveforms of selected quantities) and statistical information (the number of ou-

tages, decreases, total operating time, etc.), as well as the juxtaposition of individual

measurements at various BGSs. Examples of evaluation model outcomes can be seen

in the following examples.[6,7]

Table 1. Example of output of the fundamental values of CGU operation

Basic Information on CGU Operation

BGS 1 BGS 2 BGS 3

Total measurement time (h) 720 720 720

Period of decreased power supply (less than

the nominal value) (h) 73.7 3.5 46

Total period of full power failures (h) 1.15 0.25 0.045

Total period of failure-free operation (h) 645.15 716.25 673.96

Number of full failures 2 1 1

22 Ladislav Novosad, Zdenek Hradılek

Table 2. Example of output of the fundamental values of CGU operation

Summary

Statistics

for: P (W) U1 (V) U2 (V) U3 (V) I1 (A) I2 (A) I3 (A)

Count 71864 71864 71864 71864 71864 71864 71864

Average 703646 237.136 239.09 237.551 955.749 983.909 1026.01

Median 716440 237.26 239.22 237.68 970.925 999.99 1041.2

Mode 726310 237.02 239.33 237.67 1000.4 1013.6 1050.8

Geometric

mean - 237.131 239.08 237.546 951.221 979.352 1021.46

Variance 2.90E+09 2.5862 2.5525 2.44337 5526.77 5660.24 6002.78

Standard

deviation 53882.5 1.60818 1.5976 1.56313 74.3423 75.2346 77.4776

Coeff. of

variation 7.66% 0.68% 0.67% 0.66% 7.78% 7.65% 7.55%

Standard

error 200.998 0.00599 0.0059 0.00583 0.27731 0.28064 0.28901

Minimum -31861 227.59 229.21 227.7 40.94 37.082 37.107

Maximum 731080 242.21 244.69 242.86 1032.2 1072.3 1110.5

Range 762941 14.62 15.48 15.16 991.26 1035.22 1073.39

Stnd. ske-

wness -3820.18 -393.824 -431.8 -431.18 -3682.3 -3794.5 -3754.3

Stnd. kur-

tosis 2240.08 46.4467 60.668 52.2646 2100.7 2248.86 2185.3


Fig. 1. Example of graphical representation of measurement data in Excel

Fig. 2. Example of graphical representation of measurement data in Excel

2.2 Simulation of BGS output electric power

However, the main purpose of the model will be, as already mentioned, to create a

simulation of electric power supply course from the BGS. The model itself will be

processed in MS Office Excel.


The simulator will consist of several functional units, see the following diagram.

Model evaluation part

Fig. 3. Simplified diagram of the principle of the model operation

The model itself is composed of the following basic parts:

Information on BGS

Simulation time

Measured data

Model evaluation

Simulation model

Model outputs

Measured data evaluation outputs

Model evalua-

tion part

Information on BGS

type and number of

CGUs, gas supply,

CGU output, etc.

OUTPUT 2:

courses of electric quanti-

ties

OUTPUT 1:

courses of electric quanti-

ties – measured data eval-

uation

Simulation

time

Measured

data


Information on bgs

This block is used to set the basic parameters of the model biogas station. In terms of

electrical quantities, these parameters include the size (installed electrical power) and

the number of co-generation units (CGU) located in the BGS, as well as their type, the

consumption of biogas and other operational information. An important factor in the

BGS operation is the supply of produced biogas - entered as a slack operation of all

CGUs at 100% power in this case. (Note: The vast majority of CGUs in the Czech

Republic are not equipped with gas tanks. Consequently, they are able to operate the

co-generation units when a failure of fermentation process occurs for a limited period

only, in tens of minutes depending on the fermenter size.)

Simulation time

Basic time data on the total simulation time of CGU operation will be entered in this

segment. The first draft simulator assumes the maximum simulation time of about 0-

30 days. This time will be subsequently expanded if necessary.

Measured data

The aim of this block will be to retrieve measurement data from BGS and their sub-

sequent processing and evaluation. Due to the fact that there is not the necessary data-

base of BGS operation data, or the operation of co-generation units in biogas stations,

several long-term measurements at BGS were carried out.

BGS Loděnice

BGS Tošanovice

BGS Hodoňovice

These BGSs are located in three different locations in the Czech Republic. The mea-

surement done at the biogas stations was carried out using a digital automatic measu-

ring instrument – a BK ELCOM ENA 330 network analyser. It is a complete system

for the monitoring and analysis of the quality of electricity. Using this device, effecti-

ve values of phase voltages and currents were recorded at one-minute intervals. Vol-

tage was always measured directly on the busbars of main BGS distributor, current

was measured indirectly using flexible coils – AmpFLEX. The remaining quantities,

such as active, reactive and apparent powers, were automatically calculated by this

instrument and stored in its memory.

The electricity produced was led into the main distributor connected to a 0.4/22kV

transformer for all three BGS and connected to a 22kV distribution grid (DG) through

it. [1,2,3,4]


The measurement of electrical variables included the assessment of power quality

according to CSN EN 50160. Voltage size and dips, total harmonic distortion, flicker,

and unbalance were primarily evaluated. The BK ENA 330 analyser used meets the

requirements for measuring instruments and measurement procedure given by the

CSN EN 50160, CSN EN 61000-4-7, CSN EN 61000-4-15 and CSN EN 61000-4-30

standards.

The method described above yielded large amounts of data. These data were then

adjusted to the desired shape and stored in MS Office Excel file. Given that measure-

ment times for each of the BGSs were different, the data were shortened to a total

period of 30 days of measurement, while omitting the first and the last day of mea-

surement. The data were not complete for the entire 24 hours in this period. It will be

necessary to keep the exact shape and write of the measured data for a proper function

of the model. [5,6,7]

Fig. 4. Example of the measured and processed data

Thus processed data and subsequent evaluation are essential prerequisite for the BGS

simulator realization.

The measured data are important for simulation in the model because they provide the

basic assumptions and characteristics for draft simulation. Since the reliability of

CGU was not further examined, the measured data section will be much more impor-

tant.

Calculation model part

This part of the model is designed for the actual calculation/generation of the electric

power supply simulated course from the BGS. Basic input information for the calcula-

tion part is retrieved from information on BGS, reliability data or measurement data.


Given that the basic data on the reliability of the CGU operation are not available

currently, the evaluation results of the three BGSs will be used for calculations. The

result of this section will therefore be a data set which can be characterized and repre-

sented as virtual supplied power from BGS.

The created course can be considered as a theoretical course of power supply from

CGU and can be used for other simulations, e.g. in combination with electric power

renewable sources.

However, the above simulation process of the so called general course of power

supply from CGU has several basic drawbacks. These include the dependence of

power supplied on the CGU operation, or on the sufficient amount of biogas. Due to

the CGU high operational reliability, the lack of biogas is one of the main reasons for

DG power supply failures. This factor may be difficult to evaluate since it is a diffe-

rent factor among all the available measured BGS as it depends on many factors

(CGU operation, type of biomass, climatic conditions, etc.).

Fig. 4. Fig. 2. Example of graphical representation of Excel generated data

3 Conclusion

This article gives a basic idea about the draft simulation of biogas station electric part.

The result of such simulation should therefore be a comprehensive set of data or a

graphical dependency describing the course of power supply from BGS to 22kV

network. Due to the measured power characteristics at three different BGSs, it can be


assumed that the course will be nearly constant from the viewpoint of the power

course dependency on time. Despite this fact, the model described above will be reali-

zed in order to verify these assumptions.

Further research will continue to examine the connection of BGS to DG in terms of

possible support services, including a close cooperation of BGS with other RES. The

cooperation with PVP is primarily expected, thus providing additional options of

network stabilizations for 22kV level.

Work was partially supported by SGS grant VŠB-TU Ostrava No. SP2016/95.

References

1. Data measured in TOZOS Tošanovice BGS

2. Data measured in Hodoňovice BGS

3. Data measured in Loděnice BGS

4. TOZOS Tošanovice BGS operation book and operation rules

5. L. Novosad, Z. Hradilek, “Power Analysis of Co-Generation Units at Biogas Station”, Pro-

ceedings of the 16th International Scientific Conference on Electric Power Engineering

(EPE) 2015 Dlouhé Stráně, 2015, ISBN 978-1-4673-6788-2

6. L. Novosad, Z. Hradilek, P. Moldřík, “Analysis of data measured in Tosanovice biogas sta-

tion”, ELNET 2015: 12th workshop : Ostrava, 24th November 2015, pp. 28-36

7. L. Novosad, Z. Hradilek, “Analysis of Energy Balances Three Biogas Stations in Czech Re-

public”, Proceedings of the 16th IEEE International Conference on Environment and

Electrical Engineering, 2016

Comparison of Dynamic Models ofAsynchronous Machines

Martin Kral, Radomır Gono



Comparison of Dynamic Models of Asynchronous

Machines

Martin Král1, Radomír Goňo1

1Department of Electrical Power Engineering, FEECS, VŠB - Technical University of Ostrava, 17. listopadu 15, 708 33 Ostrava – Poruba

[email protected], [email protected]

Abstract. This paper deals the comparison mathematical descriptions of an asynchronous machines. Differential equations are then used to create a dynamic models of asynchronous machines. For the possibility of evaluation the quality of the created models are created at the conclusion the basic characteristics of representing the asynchronous machines.

Keywords: asynchronous machine; dynamic model; nonlinear system; simulation; MATLAB

1 Introduction

The foundation of this contribution is to find good mathematical model of asynchronous machines among different mathematical models of asynchronous machines, which will be the least demanding for simulation. The comparison is made, because our subsequent activities will be creation a complex mathematical model, which will be necessary to optimize.

For comparison, we chose the most widely used mathematical equations describing asynchronous machines. The mathematical equations describing the three-phase models with variable angle of rotation between the rotor and the stator [1], a three-phase models with invariable angle of rotation between rotor and stator [2], two-phase models [3]. Two-phase models are simplistic models of asynchronous machines to simulate. We chose the two-phase model αβ, the coordinate system which is fixedly connected to the stator. The second two-phase model is based on the aforementioned model αβ. This model dq against αβ will move synchronous the speed of the rotating magnetic field ωs. The last two-phase model, which we will deal with in this paper, is a model kl, which is tied with the mechanical rotor speed.


30 Martin Kral, Radomır Gono

2 Mathematical models of asynchronous machine

Due to the design we consider asynchronous machine (ASM) for the nonlinear system with a number of parameters. We tried to find a system of differential equations that would sufficiently and accurately describe the properties of the machine. When we design a mathematical model, we use a series of assumptions that simplify the Assembly model. In particular, the following assumptions:

• Stator and rotor winding is three-phase, coils of phases are spread out

symmetrically along the air gaps. • Three-phase stator and rotor winding is connected into the star. • Magnetic induction along the air gap is ideal sine. • Magnetic circuit has a linear characteristic. • Resistances are constant. • Losses in magnetic circuits are zero [4].

2.1 Three-phase models

Fig. 1. Three-phase symmetric induction motor [1]

Comparison of Dynamic Models of Asynchronous Machines 31

Three-phase model with variable angle of rotation between the rotor and the

stator

Simplified view of three-phase asynchronous motor is shown in Fig.1. In this model is a time-variable angle between the rotor and stator windings.

Basic equations for calculating voltages rotor and stator are shown in equations (1).

Stator voltage - ∙ Rotor voltage - ′ ′ ∙ ′

where: – resistance of stator windings ′ – recalculated resistance of rotor windings to stator windings – one-phase current flows over stator windings ′ – one-phase current flows over rotor windings – stator flux linkages ′ – rotor flux linkages

(1)

For example, equation (2) shows stator flux linkage of phase “a”. ∙ ∙ ∙ ∙ ∙∙

(2)

Where is stator sefl-inductance, this inductance is , other

inductances are mutual inductances between stator-stator, stator-rotor windings.

Mutual inductance between the stator windings (or rotor) are negative, as the axis of the winding makes an angle α = 2π/3 (cos2π/3 =-1/2) [5].

Fig. 2. The mutual inductances stator windings

The matrix of flux linkages:

′ ′′ ′ ∙ ′ (3)


Where the matrix of self- and mutual inductances and : − −− −− − ,

′ = ′ + − −− ′ + −− − ′ +

(4)

The matrix of stator-rotor mutual inductances:

′ =+ 23 − 23− 23 + 23+ 23 − 23

(5)

The matrix of rotor-stator (transposition) mutual inductances:

′ =− 23 + 23+ 23 − 23− 23 + 23

(6)

Three-phase model with invariable angle of rotation between the rotor and the

stator

Model without variable angle of rotation between the rotor and the stator is almost same lime model with variable angle of rotation between the rotor and the stator. There is one difference and that is the rotation angle of the rotor relative to the stator winding. In this case is = 0, that means all of mutual inductances ( ′ , ′ ) will be changed.

The matrix of stator-rotor mutual inductances:

′ =1 − 12 − 12− 12 1 − 12− 12 − 12 1

= ′ (7)


The matrix of rotor-stator (transposition) mutual inductances:

′ =1 − 12 − 12− 12 1 − 12− 12 − 12 1

= ′ (8)

It is seen that the model without variable angle of rotation between the rotor and

the stator is much simpler as model with variable angle of rotation between the rotor and the stator [5].

2.2 Two-phase models

The scalar form of equations for voltage stator and rotor after the coordinate transformation:

Stator voltages - = ∙ + − ω ∙

= ∙ + + ω ∙ Rotor voltage - ′ = ′ ∙ ′ + − ω − ∙

′ = ′ ∙ + + ω − ∙

where: r – resistance of stator windings ′ – recalculated resistance of rotor windings to stator windings – one-phase current flows over stator windings ′ – one-phase current flows over rotor windings – stator flux linkages ′ – rotor flux linkages ω – rotation of the rotor relative to stator ωk – rotation of the system

(9)

Two-phase model αβ In this case the new coordinate system will be tightly bound to the stator (ωk = 0). The coordinates of this system are labelled α (the real axis) and β (the imaginary axis).


Fig. 3. The transformation of the current vector from the three-phase (abc) to two-phase (αβ) system.

Voltage equations in the αβ stationary system, after putting ωk = 0 to (9) are following:

Stator voltages - = ∙

∙ Rotor voltage - ′ ′ ∙ ∙

′ ′ ∙ ′ − ∙

(10)

Where:

′′0 00 0

0 0 0 0 ∙ ′′

(11)

Two-phase model dq Now we start from a stationary αβ system, against which the system dq moved at the same synchronous speed of the rotating magnetic field (ωS) in the air gap of the motor.


Fig. 4. The transformation from system αβ to system dq.

Voltage equations in a synchronously rotating coordinate system are:

Stator voltages - = ∙ − ∙

∙ ∙ Rotor voltage - ′ ′ ∙ − − ∙

′ ′ ∙ − ∙

(12)

Where:

′′0 ∙ − ∙0 ∙ ∙∙− ∙ ∙∙ 0 0 ∙ ′′

(13)


Two-phase model kl This system will rotate together with the rotor mechanical speed ωk = ω.

The voltage equations for this system are as follows:

Stator voltages - = ∙ + − ∙

= ∙ + + ∙ Rotor voltage - ′ = ′ ∙ +

′ = ′ ∙ +

(14)

Where:

′′ = 0 ∙ − ∙0 ∙ ∙∙− ∙ ∙∙ 0 0 ∙ ′′ (15)

3 Simulation of asynchronous machine in MATLAB Simulink

To create dynamic models, we took advantage of the differential equation, which is described above. As has been written, these differential equations describe the three-phase and two-phase asynchronous machine, if the rotor voltages are ( ′ ) = 0, this is a machine with a squirrel-cage.

By above-listed differential equations were created to simulate the five models of asynchronous motors: three-phase models with variable angle of rotation between the rotor and the stator, three-phase models with invariable angle of rotation between the rotor and the stator, two-phase model αβ, two-phase model dq and two-phase model kl.

Results of the above described models appeared almost identical, so only one graph is shown of each variable.

The parameters of the machines in the simulation: Stator voltages: = 220 , rotor voltages ′ = 0 (squirrel-cage), = 2

(poles), = 2.1 (resistance of stator circuit), = 2.51 (resistance of rotor circuit), = 0.129 (mutual inductance), = 0.008 (stator inductance), ′ = 0.008 (rotor inductance), = 0.003 (magnetic induction), = 0.013 ∙ (moment of inertia), = 50 (frequency).


The simulation results:

Fig. 5. Stator current

Fig. 6. Torque

Fig. 7. Rotation

Fig. 8. Stator voltage

4 Conclusion

At the beginning we have written differential equations describing the asynchronous machine by basic ways: three-phase models with variable angle of rotation between the rotor and the stator, three-phase models with invariable angle of rotation between the rotor and the stator, two-phase model αβ, two-phase model dq and two-phase model kl.

Using these equations we developed dynamic models in the mathematic lab MATLAB. In all models was modelled the same state of asynchronous motor with squirrel-cage. It was the start of the motor from rest to the rated no load speed of the motor shaft. The simulation results are described and shown in the previous chapter.

According to the simulation results, which were identical result, we can choose a simpler model of asynchronous machine. This simple model has sufficiently accurate simulation results. Thanks to the selection of a simple model for the creation of a


comprehensive model, which we mentioned in the introduction, will be less demand computing power. Acknowledgement: This research was partially supported by the SGS grant from VSB - Technical University of Ostrava (No. SP2016/95) and by the project TUCENET (No. LO1404).

References

1. Victor Giurgiutiu, Sergey Edward Lyshevski: Micromechatronics: Modeling, Analysis, and Design with MATLAB, Second Edition CRC Press, 2009. 920 s. ISBN 9781420065626.

2. FIRAGO, B.I., PAVJAČIK, L. B.: Regulirujemyje elektroprivody pěreměnnogo toka. ZAO „Těchnoperspektiva“ Minsk 2006. ISBN 985-6591-37-6.

3. Petr Kadaník: Řízení asynchronního motoru bez použití snímače rychlosti Doctoral thesis (ČVUT Praha), 2004.

4. Josef Běloušek: Trakční pohony s asynchronním motorem Doctoral thesis (VUT Brno), 2013. 136 s.

5. Sergey Edward Lyshevski: Electromechanical Systems and Devices CRC Press, 2008. ISBN 9781420069723.

Modelling of Cogeneration Unit 400kW

Michal Spacek, Zdenek Hradılek



Modelling of Cogeneration Unit 400kW

Michal Špaček1, Zdeněk Hradílek2

1VŠB – TU Ostrava, Katedra elektroenergetiky, 17. listopadu 15, 708 33 Ostrava,

http://fei1.vsb.cz/kat410/

tel: +420 597 329 321, email: [email protected], 2VŠB – TU Ostrava, Katedra elektroenergetiky, 17. listopadu 15, 708 33 Ostrava,


tel: +420 597 321 235, email: [email protected],

Abstract. This article deals with general modelling of cogeneration unit. It pro-

vides substantial information about cogeneration units. Development of cogen-

eration units has experienced an evident boom over the recent years. These are

significant sources of both heat and electric power. That is the reason for seek-

ing the fastest and the cheapest method to design a cogeneration unit with op-

timal performance. The fastest and still the cheapest method is modelling, as

desribed by this article. It shows a model of a cogeneration unit with the electric

power of 401kWe and the heating power of 549kWt.

Keywords- power factor,cogeneration unit, modeling, regulation, electric pow-

er, heating power, electrical efficiency)

1 Introduction

When designing a cogeneration unit for any type of technology requires the elec-trical efficiency assessment. Another decision shall consider the suitability of particu-lar cogeneration unit for the specific technology.

A cogeneration unit may be operated in the range of thermal power from 100% to

63% of the nominal value. It is appropriate to have each cogeneration unit modelled

so as to allow energy balance at each operating state in this range. Modelling of the

cogeneration units is also appropriate with respect to altitude. Altitude, depending on

the air temperature, affects engine performance and thus the electric and thermal pow-

er cogeneration unit.


40 Michal Spacek, Zdenek Hradılek

2 Cogeneration unit

Cogeneration unit with piston engines comprie the engine itself to drive the power

generator, together with a set of heat exchangers using the heat from engine cooling system and lubrication oil (glycol/water or water/water exchanger types) or exhaust gases (exhaust/water exchanger type).

Combustion engines are heat machines converting the heat generated by fule dur-ing the combustion process into mechanical energy using the pressure of exhaust gases exerted on the piston moving inside the cylinder. The exhaust gases produced by combustion of fuel geneate a high pressure and temperature ranging within 2 000 – 2 500 °C at the beginning of combustion process and 1 000 – 1 500 °C at the end of expansion. The reverse motion of piston works gradually to convet heat energy into mechanical work (rotary motion of the combustion engine crank shaft) via the piston rod and crank assembly. The main advantages of combustion engines include fast actuation, permanent standby, higher efficiency and small outer dimensions. The effi-ciency of current combustion engines ranges around 45%. Their disadvantages include mainly the top performance threshold (35 MW), low engine overload potential, the inability to actuate the engine when overloaded [1].

For basic classification of combustion engines, see the description below.

Per operation principle:

four-stroke – one operation cycle comprises 4 strokes of the piston (two rota-tions of the crank shaft),

two-stroke – one operation cycle comprises two strokes of the piston (one ro-tation of the crank shaft).

Per the fuel used:

liquid fuel engines (petrol, diesel, ethanol, biofuels),

gaseous fuel engines (natural gas, biogas),

dual engines, those are basically compression ignition engines with fuel con-taining approx. 10 % of diesel to ignite the fuel mixture with air (the primary fuel of natural gas, the ignition fuel is diesel),

multi-fuel engines (operating with several types of liquid fuels - from light to heavy).

Per the cylinder boost method:

low-speed engines (with the mean piston velocity below 6.5 m/s),

Modelling of Cogeneration Unit 400kW 41

high-speed engines (with the mean piston velocity of 6.5 m/s or higher).

Per the cylinder boost method:

boostless engines (intak of air or fuel mixture operated by pressure generated during the intake stroke of piston inside the engine cylinder)

supercharged engines (supply of air or fuel mixture into the cylinder is ensured by

means of supercharger

generating overpressure to increase the power-to-displacement ratio).

Per mixture forming method:

with external mixing (the mixture is formed outside the working cylinder, con-taining easily evaporating liquid fuel with air or gas with air),

with internal mixing (the fuel mixture is completed inside the working cylin-der, by spreading the fuel inside the cylinder under high pressure, whis cylin-der is fed with fuel and air separately).

Power generators in cogeneration units with lower performance are asynchronous, higher performance if linked with synchronous generators.

The fuel, mostly natural gas, is blended with air in the mixing unit to be drawn in and compressed using the supercharger driven by exhaust gases emitted by the com-bustion engine. The compressed hot mixture passes through the cooler (fuel/glycol exchanger) on its way to the combustion engine. Cooling of glycol is ensured by means of an enclosed air cooler. Waste heat produced by cooling of fuel mixture rep-resents approx. 8% of the total waste heat amount and it is not included in the total heating power of this unit due to lower temperature. It can be used for conditioning of adjacent premises, if required. The other balance circuit generates heat from the en-gine block heating, heat from cooling of engine oil and heat from exhaust gases. The heat from cooling of engine block and oil is drawn by glycol (water) and brought into the glycol-water (water/water) exchanger to produce warm water or hot water in the warm-water (hot-water) circuit. The war water from the exchanger then enters the exhaust-water exchanger connected in series with the previous glycol-water (wa-ter/water) exchanger for final heating of warm (hot) water for the service circuit. The electrical efficiency of cogeneration unit, depending on its size, will range within approx. 27% - 42%. The effciency value is determined using the ratio of electric pow-er of the unit to the heat brought with fuel (fuel energy input). Heat efficiency of a combustion engine ranges within 43% - 53%. It is determined as the ratio of cogenera-tion unit performance (the sum of waste heat from the engine block cooler, the lubri-cation oil cooler and the exhaust cooler) to the fuel energy input (fuel heat). The waste heat from exhaust gases represents approx. 40% of the total waste heat [2] [3].


Fig. 1. Basic configuration of a cogeneration unit with combustion engine


3 Modeling Cogeneration Unit 400kW

The input data recorded in type data sheets will be used for retrieval of the basic

nominal values necessary for processing of energy balance details as:

engine performance,

generator efficiency,

electrical output,

heat output,

fuel consumption per hour,

heating, electrical and overall efficiency.

Determination of operating parameters of cogeneration units depends on the aver-age annual load of cogeneration units, certain manufacturers declare output and effi-ciency data even for loads as 75 % and 50 %. Polynomial equations of 2nd degree can be then used to enumerate the operating characteristics of cogeneration units within the load range between 100 % and 50 % [4].

The cogeneration unit selected for modelling was the unit with electrical output of 401kWe and the heat output of 549kWt (401kWe/549kWt) Fig. 2 and 3. This unit may be operated 24 hours per day, yet this scope may not exceed 3,000 hours per year in order to secure the so called green bonuses.


Fig. 2. Cogeneration unit

Fig. 3. Cogeneration unit


The form of this equation for the 401kWe/549kWt cogeneration unit can be seen in the Figure No. 4.

Fig. 4. Relation between the cogeneration efficiency on the load of cogeneration unit and

the polynomial equation

Such value defined for the load range between 100 % and 50 % will be used to de-termine the heat output of cogeneration unit with the known electrical output and vice versa. The form of this equation for the 401kWe/549kWt cogeneration unit can be seen in the Figure No. 5.


Fig. 5. Relation between the output ratio of PT/PE and the load of cogeneration unit and

the polynomial equation

The fuel consumption is considered with respect to normal conditions, i.e. the am-bient temperature of 0 °C and pressure of 101 325 Pa or under business conditions, that is the temperature of 15 °C and pressure of 101 325 Pa, all within the cogenera-tion unit load range between 100 % and 50 %. This consumption of natural gas deter-mined in [Nm3/h or m3/h] for any operating load of the 401kWe/549kWt cogeneration unit is expressed with the equation in form shown in the Figure No. 6.


Fig. 6. Relation between fuel consumption and the load of cogeneration unit and the poly-

nomial equation

Pursuant to nominal and estimated operating output and efficiency, as well as the estimated amount of operating hours of cogeneration unit per day or year, the material and energy balances of the cogeneration unit will be determined accordingly. That concerns mainly power generation, heat production, fuel consumption, and inherent power consumption.

The values entered for modelling of cogeneration unit are shown in the Chart I. The balance data calculated for various operation regimes can be found in the Chart II. This chart contains modelled data for cogeneration units within the nominal electri-cal output of 100%, 75% and 50% respectively. Modelling can be conducted under any load, while it must be done in compliance with the nominal output range from 50% to 100%.

TABLE I. VALUES ENTERED FOR COGENERATION UNIT MODELLING

Maximum daily operation (hours/day) 24

Maximum annual operation (hours/year) 3000

Shaft engine power (kW) 147.5

Generator efficiency (%) 94.9

Electrical output (kW) 140

Heat output (kW) 207


TABLE II. VALUES CALCULATED WITH THE COGENERATION UNIT LOADS AT 100%, 75% AND 50%

LEVELS OF THE NOMINAL ELECTRICAL OUTPUT

Cogeneration unit power loading (%) 100 75 50

Heat and electrical output ratio 1.37 1.41 1.58

Electric power produced per year (MWh) 1 203 903 601

Heat produced per year (GJ) 5 929 4 575 3 412

Natural gas consumption (m3/h) 111.5 88.1 64.5

Cogeneration unit energy input (combustion engine)

(MJ/h)

3 790.7 2 995.7 2 192

Electric efficiency (%) 38.08 36.21 32.92

Heat efficiency (%) 52.14 50.91 51.90

Overall efficiency (%) 90.22 87.12 84.82

Specific heat consumption to generate 1kWh of electric

power (MJ/kWh)

9.453 9.943 10.935

Specific heat consumption to generate 1kWh of heat

energy (MJ/kWh)

6.905 7.072 6.937

4 Cogeneration unit modelling depending on altitude

The rise of altitude, or in other words, the decrease of atmospheric pressure results in reduction of the torque and performance of a combustion engine. That is mainly due to the fact that the power gain resulting from a single stroke of the piston corresponds with the amount of energy gained by full combustion of one cylinder load. The power produced by the cylinder contents is limited by the amount of oxygen drawn inside pursuant to the intake stroke. The atmospheric pressure with respect to altitude has been calculated using the formula 4. The comparison of values gained with/without supercharger clearly show that the engine output coefficients are higher that data gen-erated by the supercharged engine.


TABLE III. IMPACT OF ALTITUDE AND TEMPERATURE OF INTAKE AIR ON THE OUTPUT OF ENGINE

WITHOUT SUPERCHARGER (ENGINER OUTPUT REDUCTION COEFFICIENT)

Altitude Intake air temperature

25°C 35°C 45°C

0 m 1.000 0.950 0.900

100 m 0.990 0.940 0.890

200 m 0.980 0.930 0.880

300 m 0.970 0.920 0.870

400 m 0.960 0.910 0.860

500 m 0.950 0.900 0.850

600 m 0.940 0.890 0.840

700 m 0.930 0.880 0.830

800 m 0.920 0.870 0.820

900 m 0.910 0.860 0.810

1000 m 0.900 0.850 0.800

1100 m 0.890 0.840 0.790

1200 m 0.880 0.830 0.780

1300 m 0.870 0.820 0.770

1400 m 0.860 0.810 0.760

1500m 0.850 0.800 0.750

TABLE IV. IMPACT OF ALTITUDE AND TEMPERATURE OF INTAKE AIR ON THE OUTPUT OF THE

SUPERCHARGED ENGINE (ENGINER OUTPUT REDUCTION COEFFICIENT)

Altitude Intake air temperature

25°C 35°C 45°C

0 m 1.000 0.950 0.900

100 m 0.992 0.942 0.892

200 m 0.984 0.934 0.884

300 m 0.976 0.926 0.876

400 m 0.968 0.918 0.868

500 m 0.960 0.910 0.860

600 m 0.952 0.902 0.852

700 m 0.944 0.894 0.844

800 m 0.936 0.886 0.836

900 m 0.928 0.878 0.828

1000 m 0.920 0.870 0.820

1100 m 0.912 0.862 0.812

1200 m 0.904 0.854 0.804

1300 m 0.896 0.846 0.796

1400 m 0.888 0.838 0.788

1500m 0.880 0.830 0.780


7 Conclusion

The modelling process enables demonstration of any cogeneration unit with any electrical output.

This article concerns modelling of the cogeneration unit delivering 401kWe/549kWt. The electrical output levels were set at 100%, 75% and 50% of the nominal value. These are clearly entered in the Chart 2 for particular calculations. Operation of cogeneration units was further assessed, considering their location al various altitudes.

References

This work was supported by the Ministry of Education, Youth and Sports of the

Czech Republic (No. SP2016/95).

Innovative Possibility of using Waste Heat fromBiogas Plants

Jirı Jansa, Zdenek Hradılek



Innovative possibility of using waste heat from biogas

plants

Jiří Janša1, Zdeněk Hradílek

2

1 VŠB – TU Ostrava, Katedra elektroenergetiky, 17. listopadu 15, 708 33 Ostrava,


tel: +420 597 329 321, email: [email protected], 2 VŠB – TU Ostrava, Katedra elektroenergetiky, 17. listopadu 15, 708 33 Ostrava,


tel: +420 597 325 919, email: [email protected],

Abstract. This article describes the innovative possibilities of using waste heat from bio-

gas plants. The first described possibility is the use of heat for growing aquaculture. This possi-

bility offers an exciting opportunity to grow exotic species of fish and shellfish in colder areas

without access to the sea. The second possibility is then heat storage in containers namely in

both forms. The first are the latent heat storage systems and the second one, then the thermody-

namic systems. They are described in the simplicity of both the principles and the possibility of

their use and limitations. The last possibility is the use of heat for cold production. It is de-

scribed here primarily absorption cooling technology. It are also mentioned the possibility of

remote cooling. All these possibilities are called innovative because of the small number of

realizations, but in the future with technologies developed may become quite common.

Keywords: waste heat, aquaculture, cooling, heat storage, biogas plant


52 Jirı Jansa, Zdenek Hradılek

Aquaculture

There are many opportunities for integrated aquaculture systems. For instance in an

Integrated Fish Farming & Irrigation system (IFFI) a fish farm facility is set-up

between the water source and the irrigated field providing nutrients to the field. In the

example of the Aquaponic concept, the aim is to develop a sustainable eco-technology

to integrate and combine aquaculture and horticulture with minimized emissions and

optimizing reusable water flows. An aquaponic system is a food production system

that combines aquaculture (raising fish and other aquatic animals) and hydroponics

(cultivating plants in water, without soil) in one integrated symbiotic environment.

There exist many other similar concepts. In general win-win situations can be created

if biogas plants and aquacultures are linked. In some systems, the digestate is used as a

fertilizer for the aquaculture. In other systems, the waste from aquaculture is used as

feedstock material for biogas plants. In the last years, a new concept was developed

which gained increasing interest in Europe, namely the use of the waste heat of biogas

plants for heating aquacultures. Fish and shrimps from the sea or other water bodies

are generally becoming scarcer. Thus, they are increasingly cultivated artificially and

often with high environmental impacts. Heated aquacultures are still rare in Europe

due to the high energy costs. The use of the waste heat from biogas plants offers new

opportunities for farmers to produce additional high-quality products. Aquaculture can

be an interesting new income source which also allows the cultivation of tropical

species under European climates. Another crucial parameter is the energy

consumption, whereas about one third of energy supplied is needed as electricity and

about two thirds as heat [1]. Heat is needed to heat the water and to acclimatize (heat

and cool) the halls. Temperatures for heating the ponds vary depending on the shrimp

or fish species. Ideal water temperatures range between 20°C and 32°C.

Heat transport in containers

In several cases it may not be possible to install district heating systems either as

the distances are too far or as it is not possible due to legal or other framework condi-

tions. In these cases, the heat transport via storage systems in containers may be con-

sidered. However it must be noted that this technology is not yet widely applied. Only

few manufacturers are currently offering heat storage systems in containers. The idea

is to store the heat of the biogas plant in mobile containers, usually in standardised

non-insulated 20 feet containers (6.10 m x 2.44 m). Theoretically, the containers do

not have to be insulated as the energy is primarily chemically stored and not by in-

creased temperature as in other storage systems. However, in practice, they are insu-

lated, as besides the chemically stored heat, also the temperature increases during the

Innovative Possibility of using Waste Heat from Biogas Plants 53

loading process and contributes to the overall heat storage, Once the container is load-

ed, it can be transported by trucks to the heat consumer. Transport distances could be

between 1 and 30 km for a 500 kWel biogas plant [2]. According to [3] the distance

should not be longer than 20 km, if the maximum operational workload is 4,000 hours.

The challenge is the storage technology inside the container. There exist two main

technologies for the heat storage:

Latent heat storage systems

Thermodynamic storage systems

In latent heat storage systems the heat is stored by using the melting heat of a sub-

stance that is called phase change material (PCM). During the loading phase, the PCM

changes its phase from solid to liquid whereas the temperature is not increased (iso-

thermal phase change). If the process is reversed, the heat can be used again. The

available and desired temperature levels influence the selection of the PCM which is

characterised by its melting temperature. In latent heating storage systems for biogas

plants, PCM can be, for instance, dissolved sodium acetate (trihydrate) which is a non-

hazardous salt. Dissolved sodium acetate has a melting point of 58°C. The heating or

loading circle is separated from the PCM, so thermal energy has to be transferred

within the storage material. For the loading process a temperature difference of at least

10°C should be available, thus 68°C are needed at the heat source for heat storage in

dissolved sodium acetate systems. The low melting temperature allows only the use of

this system for applications that need low temperatures of about 48°C. Thus, applica-

tions for this system are limited. A 20 feet and about 26 t container has a heat storage

capacity of about 2.5 MWh which is equivalent to about 250 l heating oil [1]. The

load capacity is about 250 kW at temperatures of 70/90°C and the loading time about

10 hours (ibid.). The consumption capacity is about 125 kW at temperatures of

48/38°C and the consumption time about 20 hours (ibid.). Another suitable PCM is

dissolved barium hydroxide (octahydrat) with a melting point of 78°C. Due to its

hazardous characteristics, special safety requirements are needed. Cost effective stor-

age systems demand high internal heat fluxes, which depend mainly on the heat con-

ductivity of the storage material. Non-metallic storage materials usually show low heat

conductivities, especially the solid phase behaves like a thermal isolator. The increase

of the effective heat conductivity in the storage material is essential for the develop-

ment of cost effective storage systems [4] Technology providers currently include the

companies LaTherm (www.latherm.de) (Figure 3) or Transheat (www.transheat.de).

Transheat offers a container (Figure 2, Figure 1) in which the heat is transferred by a

heat exchanger to a thermal oil. This oil is pumped into the tank where it is mixed with

sodium acetate, thereby transferring the heat and storing the heat by melting the salt.


Figure 1: Scheme of a latent heat storage system (adapted from TransHeat GmbH)

Figure 2: Railway wagon with a latent heat storage system (Source: TransHeat

GmbH)

Figure 3: Trailer with a container and a latent heat storage system (Source: LaTh-

erm GmbH)

In thermodynamic storage systems (sorptive thermal storage) Zeolithes are used.

Zeolithes are microporous, aluminosilicate minerals commonly used as commercial

adsorbents. Due to its porous structure, Zeolithes have a very large surface area. A

single gram of Zeolithe pellets has a surface area of up to 1,000 m² (Fraunhofer 2012).

When water vapour passes Zeolithe material, the vapour is adsorbed and heat re-

leased. Therefore these systems are not only suitable for heat storage, but also at the

same time for drying purposes. The system is re-loaded by dry and hot air. According

to [5], the system can store three to four times the amount of heat that can be stored by

water. Thus, it only requires storage containers around a quarter the size of water

tanks. Furthermore, the heat can be stored for a long period. Energy losses occur only

in the charging and de-charging process of the container, but not during the storage

duration itself, as the energy is chemically bound. Nevertheless, this system is not yet

commercially available.


Cooling

Waste heat from biogas plants can be also used to create cooling capacity. There exist

two main principles of cooling devices, namely absorption and vapour-compression

chillers.

Vapour-compression chillers are the most widely used devices for air-conditioning

as well as for chilling in domestic and commercial refrigerators. Core of this system is

the compressor that is operated with electricity. In contrast to the operation with main-

ly electric power in vapour-compression chillers, absorption chillers principally use a

heat source as main energy for the cooling process. Absorption chillers are an alterna-

tive to regular compressor chillers where electricity is unreliable, costly, or unavaila-

ble, where noise from the compressor is problematic, or where surplus heat is availa-

ble as it is the case of biogas plants. Generally, absorption chillers are characterized

by the following main benefits when compared to vapour compression chillers [6]:

Lower electrical requirements for chiller operation

Lower sound and vibration levels during operation

Ability to utilize recovered heat and convert it to cooling energy

Refrigerant solutions typically do not pose a threat to ozone depletion of the at-

mosphere.

Both, absorption and compressor chillers use a refrigerant liquid, usually with a very

low boiling point (often less than −18°C). In both types, heat is extracted from one

system and thus creating the cooling effect, when the refrigerant liquid evaporates.

The main difference between the two systems is the way the refrigerant is changed

from the gaseous phase back into a liquid so that the cycle can repeat. The compres-

sion chiller changes the gas back into a liquid by increasing pressure levels through a

(electrically operated) compressor. An absorption chiller changes the gas back into a

liquid by absorption of the refrigerant in another liquid and adjacent desorption with

heat. The other difference between the two types is the refrigerant used. Compressor

chillers typically use hydrochlorofluorocarbons (HCFCs) or hydrofluorocarbons

(HFCs), while absorption chillers typically use ammonia or lithium bromide (LiBr).

Generally, absorption chillers are categorised as direct or indirect-fired, and as single,

double or triple-effect. For using waste heat of biogas plants, only indirect-fired chill-

ers are relevant, although theoretically, also direct-fired chillers could be operated

with the direct combustion of biogas. Absorption and compressor chillers can be also

combined (cascade or hybrid cooling). The use of absorption chillers depends on the

waste heat temperature, the used refrigerant and transport medium, as well as on the

desired cooling temperature. LiBr/H2O absorption chillers are able to cool down to

6°C and NH3/H2O absorption chillers from 0°C down to -60°C. In order to compare

chillers, the energy efficient ratio (EER) is used which is similar to the coefficient of

performance (COP) of heat pumps. It is the ratio of the cooling capacity (Q C) to the

heat input capacity (Q H). Thereby, the capacity of the pump (PP) is negligible. The

EER of actual absorption refrigeration systems is usually less than 1. Typical EERs


for commercially available chillers range from 0.65 to 0.8 for single effect units and

0.9 to 1.2 for double effect units [6].

The general process of a typical ammonia-water absorption chiller is shown in Figure

4. In this process, ammonia (NH3) serves as the refrigerant and water (H2O) as the

transport (absorbent) medium. In the evaporator the refrigerant pure ammonia in liq-

uid state produces the cooling effect. It absorbs the heat from the substance to be

cooled and gets evaporated. From here, the ammonia vapour is pumped to the absorb-

er. In the absorber a weak solution of ammonia-water is already present. The water,

used as the transport medium in the solution, is unsaturated and it has the capacity to

absorb more ammonia gas. As the ammonia from evaporator enters the absorber, it is

readily absorbed by water and the strong solution of ammonia-water is formed. During

the process of absorption, heat is liberated which can reduce the ammonia absorption

capacity of water; hence the absorber is cooled by the cooling water. Due to the ab-

sorption of ammonia, a strong solution of ammonia-water is formed in the absorber.

This solution is pumped by the pump at high pressure to the generator in which it is

heated by the waste heat from the biogas plant while ammonia is vaporized. Ammonia

vapour leaves the generator, but some water particles also get carried away with am-

monia refrigerant due to the strong affinity of water for ammonia. Therefore, it is

passed through the separator, similar to a distillation column. Water goes back

through the regenerator and expansion valve to the generator. The weak ammo-

nia/water solution goes back form the generator to the absorber. Pure ammonia vapour

enters the condenser at higher pressure where it is cooled by water. It changes its

phase into a liquid state and then passes through the expansion valve where its tem-

perature and pressure falls down suddenly. Ammonia finally enters the evaporator

again, where it produces the cooling effect. Thereby the cycle is closed.


Figure 4: Process of a typical ammonia-water absorption refrigerator

District cooling

Figure 5: Annual CO2 savings in selected European cities due to district cooling

(Source: Euroheat & Power)


District cooling is similar to district heating, but distributes chilled water instead of

heat. Although the demand for cooling is increasing steadily, due to higher comfort

standards and higher temperatures related to climate change, district cooling is not as

applied as district heating. Several European cities have introduced district cooling

systems, in order to save greenhouse gas emissions (Figure 5).

The source of chilling can be from absorption chillers, vapour-compression chillers,

and other sources like ambient cooling, or from deep lakes, rivers, aquifers and

oceans. Different cooling systems can be also combined. A general advantage of using

waste heat from biogas plants for the operation of absorption chillers is the high sea-

sonal availability of heat in summer, combined with the high demand for cooling in

summer. Depending on contracts with consumers, cooled water may be provided for

both basic and peak demand. Due to the higher investment costs of absorption chillers,

additional vapour-compression chillers may be operated during peak demand in order

to guarantee peak supply.

Conclusion

This article deals with unconventional possibilities of using waste heat from biogas

plants. Nontraditional because of the small extension of the implementation, but with

great potential use in the future. Use of heat for aquaculture production appears to be

having regard to rising prices of fish and other marine organisms as ever more promis-

ing. The disadvantage is, as with any new device types high purchase price, but that

will decrease with realizations and may one day be completely normal. Another inter-

esting possibility is the transfer of heat in the containers. This possibility is useful for

remote BGS that have no other possibility utilization of waste heat, and simultaneous-

ly to close the enterprise needing heat. In addition, this heat is very limited by two

factors. The first is the relatively low temperature of the discharge container, which

prevents wider use in industry. The second factor is then the sheer size of the contain-

er, which limits both the maximum thermal performance and the useful life on a single

charge. The last of unconventional possibilities of using waste heat is paradoxically its

use for the production of cold. In terms of energy balance BGS, this option seems like

the perfect solution, because most of the waste heat produced by BPS in the summer

months, when Even obviously greatest demand for cold. Coldness can be utilized for

example to maintain constant temperature in storage of vegetables that can be pro-

cessed within BGS, while the waste from its processing as input biomass for biogas

production. The last possibility is then the use of remote cooling. Here, however,

raises the same issues as in district heating. The main problem is the location of BGS

in remote areas where there is not a large population and thus not need for cold or

heat.


Acknowledgement

This work was supported by the Ministry of Education, Youth and Sports of the Czech

Republic (No. SP2016/95).

References

[1] Schulz W., Heitmann S., Hartmann D., Manske S., Erjawetz S.P., Risse S.,

Räbiger N.,Schlüter M., Jahn K., Ehlers B., Havran T., Schnober M., Leit-

faden Verwendung von Wärmeüberschüssen bei landwirtschaftlichen Bio-

gasanlagen. – Bremer Energie Institut; Bremen, Germany, 2007

[2] Gaderer M., Lautenbach M., Fischer T. (2007) Wärmenutzung in kleinen

landwirtschaftlichen Biogasanlagen. – Bayerisches Landesamt für Umwelt-

schutz (LfU), Augsburg, Germany;

http://www.lfu.bayern.de/energie/biogas/doc/machbarkeitsstudie_abwaermen

utzung.pdf [10.07.2012]

[3] Kralemann M. (2007) Einleitung: Wärmenutzung in Biogasanlagen. - In:

Schröder D. Wärmenetze an Biogasanlagen Ein Leitfaden. – Fachkongress

am 20 November 2007, Hitzacker; Region Aktiv Wendland/Elbtal;

http://www.projektbioenergie.com/wcms/ftp//p/projektbioenergie.com/upload

s/leitfaden_biogaswrmenetze_11071.pdf [10.07.2012] [4] DLR (2012) http://www.dlr.de/tt/desktopdefault.aspx/tabid-2872/4415_read-6487/

[10.07.2012]

[5] Fraunhofer (2012)

http://www.fraunhofer.de/en/press/research-news/2012/june/compactand-flexible-

thermal-storage.html [10.07.2012]

[6] Skagestad B., Mildenstein P. (no date) District Heating and Cooling Connection

Handbook. – International Energy Agency (IEA) District Heating and Cooling.

http://dedc.dk/sites/default/files/programme_of_research_development_and_demonst

ration_on_district_heating_and_cooling.pdf [10.07.2012]

[7] New Buildings Institute (1998) Guideline: Absorption Chillers. - New Buildings

Institute; Fair Oaks; Canada; http://www.stanford.edu/group/narratives/classes/08-

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On Indexing of Multidimensional Space forEfficient Range Query Processing*

Peter Chovanec, Michal Kratky

Department of Computer Science,FEECS, VSB – Technical University of Ostrava,

17. Listopadu 15/2172, 708 33 Ostrava-Poruba, Czech Republicpeter.chovanec, [email protected]

On Indexing of Multidimensional Space forEfficient Range Query Processing?

Peter Chovanec, Michal Kratky

Department of Computer ScienceVSB – Technical University of Ostrava, Czech Republic

peter.chovanec, [email protected]

Abstract. The reliability computation is applied for the maintenance ofequipment in power networks. The reliability computation is calculatedfrom a database of outages in electrical power networks. In the case ofthe outage database described in this paper, data are stored in a relationwith 31 attributes. The reliability computation requires to process up tohundreds of range queries over this data. Therefore, the efficient process-ing of these queries is necessary. Since multidimensional range queriesare used, a multidimensional data structure, the R-tree, has been ap-plied in our previous work. The efficiency of the R-tree decreases withthe increasing dimensionality of a space. This issue is commonly knownas the curse of dimensionality. In this article, we focus on the space di-mensionality of the outage database; we show that the reduction of theindexed attributes leads to more efficient range query processing.

Key words: power networks, reliability computation, outage database,R-tree

1 Introduction

Institutional changes taking place all over the world drastically effect the ap-proach to power supply quality. It is developing towards a purely commercialmatter between suppliers and their customers. The supply that does not complywith agreed qualitative parameters will lead to trade disputes and financial set-tlements. Undelivered energy, including its valuation, has arrived on the scene.The two following aspects of supply quality can be considered:

1. Supply reliability – relating the availability of electricity in a location.2. Voltage quality – relating to the purity of characteristics of the voltage wave-

form, including the absolute level of the voltage and frequency.

This document deals with the first aspect in more detail. Worldwide centersof reliability computation1 provide databases of information about the availabil-ity of electronic and non-electronic components and distribution functions for

? This work was supported by the Ministry of Education, Youth and Sports of theCzech Republic (SGS, No. SP2016/95).

1 For example Alion System Reliability Center, http://src.alionscience.com/


On Indexing of Multidimensional Space for Efficient Range Query Processing 61

various failure types. They include the result failure rate and we can retrieveinformation about the producer, operation conditions, etc. These databases areapplicable to the availability prediction of complicated systems. However, thesedatabases do not include data about power equipments.

The IEEE standards define a host of reliability indices applied to distributionreliability. IEEE P1366 [15] explains the reliability indices applied to the mea-surement of distribution system reliability, and a way of calculating reliabilityindices. Although authors introduce a discussion about some factors influenc-ing these indices, reliability parameters for power system equipment are notdepicted. The Canadian Electrical Association2 introduces a collection of relia-bility parameters for power system equipment. This is useful for North America;however, it is almost impossible to compare conditions and equipment in NorthAmerica and Central Europe.

In many cases, it is necessary to compute electrical energy not supplied toconsumers; probability computation of not supplied energy is only possible onthe basis of the reliability computation results. Consequently, we need to observefailures and outages in the transmission and distribution of electrical energy3 forretrieving the component reliability [1].

In [12], we introduced a framework for retrieving reliability parameters in dis-tribution networks. Consequently, we improved the approach by a new embeddedDBMS, called RadegastDB, presented in [10]. In [6], we depicted preliminary re-sults of multiple range queries over the outage database. Since we store outagedata in a relation with 31 attributes and we use multidimensional range queriesto query the relation, a multidimensional data structure, the R-tree, is utilizedas a storage of the data. Unfortunately, the efficiency of the R-tree decreaseswith the increasing dimension of a space. This issue is known as the curse of di-mensionality. In this paper, we show that the reduction of the indexed attributesleads to more efficient range query processing.

This paper is organized as follows. In Section 2, we briefly introduce thedatabase framework RadegastDB, a multidimensional data structure called theR-tree and describe its major advantages as well as disadvantages. In Section 3,we introduce the outage database and describe the reliability computation. More-over, we show the new approach at the end of the section. In Section 4, we putforward preliminary results of the new approach. In the last section, the papercontent is resumed and the possibility of our future work is outlined.

2 Database System for Handling Outage Data

2.1 Introduction

In [12], we have introduced a framework for storage and querying outage data [8,7]. Databases of various distributors are transformed into a common relation

2 http://www.canelect.ca/3 We have used the term ’outage database’ instead of the preferred phrase ’database

of failures and outages in the transmission and distribution of electrical energy’ inthis paper.

62 Peter Chovanec, Michal Kratky

scheme with 31 attributes. Since then, several works have been presented [11,2, 4]. In [10], we introduced a new data storage based on multidimensional datastructures [13]. A variant of the R-tree [9], called the R∗-tree [3], has been appliedfor the implementation. In [6], we depicted preliminary results of multiple rangequery processing over the outage database. In [5], we introduced algorithms, costmodel, and results of multiple range queries.

2.2 R-tree and its Variants

Since 1984 when Guttman proposed his method, R-trees [9] have become themost cited and most used as the reference data structure in this area. The R-tree is a height-balanced tree based on the B+-tree with at least 50% utilizationguaranteed. This data structure supports point and range queries and some formsof spatial joins as well. A general structure of the R-tree is shown in Figure 1.

R1 R2

R3 R4 R5 R6

p2 p4 p10 p6 p9 p1 p7 p3p8 p5 p11

R1

R2

R3

R4

R5

R6

p2p4

p8

p10

p6

p9

p1

p5

p7

p3

p11

Fig. 1. A planar representation and general structure of the R-tree

It is a hierarchical data structure representing spatial data by a set of nestedn-dimensional minimum bounding rectangles (MBR). If N is an inner node, itcontains pairs (Ri, Pi), where Pi is a pointer to a child of the node N . If R isthe inner node MBR, then the rectangles Ri corresponding to the children Ni

of N are contained in R. Rectangles at the same tree level may overlap. If N isa leaf node, it contains pairs (Ri, Oi), so called index records, where Ri containsa spatial object Oi.

Many variants of the R-tree have been proposed during the last decades.Although original algorithms of the R-tree tried to minimize the area covered byMBRs, R∗-tree [3] takes other objectives into account, e.g. the overlap amongMBRs. R+-tree [14] was introduced as a variant that avoids overlapping MBRsin intermediate nodes of the tree and an object can be stored in more than oneleaf node. Although, a lot of variants have been presented, the query processingstill shares some performance issues:

1. Indexing of dead space, some areas of MBRs included in the index containno data.


2. Random accesses to pages when a query is processed, especially in thecase of physical accesses to the secondary storage.

3. MBR overlap, MBRs of sibling nodes can share a common space.4. High number of irrelevant MBRs, MBRs containing no tuples matched

by a query are accessed.

When the dimensionality of a space increases, the volume of the space ex-ponentially increases and data become sparse. As a result, these negative issuesmore and more influence the efficiency of query processing. This effect is knownas the curse of dimensionality [16].

3 Reliability Computations in Power Networks

3.1 Outage Database

Databases of various distributors are transformed into the common relationscheme with 31 attributes (see Table 1). We see that some attributes are foreignkeys of codebooks: these codebooks are labeled with an order number. Code-books are often produced by an energy regulatory office, e.g. ERU4 in the caseof the Czech Republic.

3.2 Reliability Computations

The majority of reliability computations is performed in the following way. Thereliability computation of the whole system is executed on the basis of compo-nents reliability that are included in the system [7]. That is the reason why thereliability is computed in two phases. The first phase represents the retrievingof component reliability parameters and the second phase is the reliability com-putation itself. Other phases can include an evaluation of computed results andan improvement of the supply quality.

In virtue of experience, it is necessary to state that in most cases, retrievinga reliability parameter is far more complicated than the reliability computa-tion itself. It consists from a set of non-trivial queries over the data collection,e.g. Figure 2 shows a form for the reliability computation generating 120 rangequeries.

3.3 New Approach in Reliability Computations

As mentioned before, the efficiency of the multidimensional data structures de-creases with the increasing dimensionality of a data. Since the dimensionality ofthe outage database is rather high, this issue significantly affects the range queryprocessing. Although the database contains 31 attributes, only 7 attributes areused for the reliability computation. The list of the attributes used during reli-ability computation is presented in Table 2. The new approach indexes only 7attributes necessary for the computation, all others attributes are stored in leafnodes of the R-tree as non-indexed attributes.4 http://www.eru.cz/


Table 1. The Outage relation scheme

Order Attribute Data Foreign Key/Type Codebook Order

1 distributor NUMBER yes/012 event order CHAR –3 event type NUMBER yes/024 distribution point NUMBER yes/035 area CHAR –6 network type NUMBER yes/057 network voltage NUMBER yes/048 equipment voltage NUMBER yes/049 original event order CHAR –10 event cause NUMBER yes/0611 equipment type NUMBER yes/0712 damaged equipment NUMBER yes/0813 damaged equipment type NUMBER yes/1014 amount NUMBER –15 short type NUMBER yes/0916 producer NUMBER yes/1117 production date DATE –18 T0 DATE –19 T1 DATE –20 T2 DATE –21 T3 DATE –22 T4 DATE –23 TZ DATE –24 P1 NUMBER –25 P2 NUMBER –26 D1 NUMBER –27 D2 NUMBER –28 Z1 NUMBER –29 Z2 NUMBER –30 LxT NUMBER –31 failure type NUMBER yes/13

Table 2. The list of attributes used for reliability computation

Order Attribute Data Foreign Key/Type Codebook Order

1 distributor NUMBER yes/013 event type NUMBER yes/028 equipment voltage NUMBER yes/0411 equipment type NUMBER yes/0712 damaged equipment NUMBER yes/0818 T0 DATE –31 failure type NUMBER yes/13


Fig. 2. A form of the reliability computation

4 Preliminary Results

In our experiments5, we compare the R-tree indexing all 31 attributes of the out-age database and the R-tree indexing only 7 attributes necessary for the reliabil-ity computation. As a storage, we used embedded RadegastDB presented in [10].The embedded DBMS has been implemented in C++. The outage database cur-rently contains 319,825 records. In our test, we measure processing times of thepassportization computation for all distributors for damaged equipment andequipment types. It typically includes 670 range queries per one computation.The efficiency of the reliability computation has been measured by the through-put of queries in the data structure.

We compare the performance of the passportization computation in the years2013, 2014, and 2015. All tests have been executed 10×, the average results areshown in Tables 3 and 4. As we can see, the query processing is up-to 15% moreefficient when only 7 attributes are indexed.

5 Conclusion

The outage database is a collection of outages in power networks in the Czechand Slovak Republics. Its existence is necessary for the reliability computationof a wholesale-consumer connection; therefore, the demand for this computa-tion increases. A significant number of complex queries is necessary to process

5 The experiments were executed on an Intel Xeon E5430 2.66Ghz, 12.0 MB L2 cache;8GB of DDR333; Windows 2003 Server R2.


Table 3. The computation of passportization for damaged equipments and variousyears

Year Throughput [q/s]

31 indexed attributes 7 indexed attributes

2013 10,736 11,477

2014 15,279 18,330

2015 18,020 21,368

Table 4. The computation of passportization for equipment types and various years

Year Throughput [q/s]

31 indexed attributes 7 indexed attributes

2013 23,263 26,005

2014 34,359 39,090

2015 41,020 46,140

during the computation; a sophisticated storage of the data and efficient queryprocessing are necessary. In [10], we introduced a new embedded DBMS, calledRadegastDB, for handling the outage database. The R-tree data structure hasbeen used as a storage of the data. In this paper, we showed that the reductionof the indexed attributes leads to the more efficient range query processing: thethroughput is up-to 15% more efficient when only attributes necessary for thereliability computation are indexed.

References

1. R. E. Barlow and F. Proschan. Statistical Theory of Reliability and Life Testing:Probability Models. Holt, Rinehart and Winston, Inc., 1975.

2. R. Baca, M. Kratky, and V. Snasel. Bulk-loading of Compressed R-tree withFailure Data. In Proceedings of the 4th Workshop ELNET 2007. FEECS, VSB –Technical University of Ostrava, 2007.

3. N. Beckmann, H.-P. Kriegel, R. Schneider, and B. Seeger. The R∗-tree: An efficientand robust access method for points and rectangles. In Proceedings SIGMOD 1990,pages 322–331. ACM Press, 1990.

4. P. Chovanec and M. Kratky. Benchmarking of Lossless R-tree Compression forData of Failures in Electrical Power Networks. In Proceedings of the 7th Workshopof ELNET, Czech Republic, 2010.

5. P. Chovanec and M. Kratky. On the Efficiency of Multiple Range Query Processingin Multidimensional Data Structures. In Proceedings of the 17th InternationalDatabase Engineering & Applications Symposium, IDEAS ’13, pages 14–27, NewYork, NY, USA, 2013. ACM.


6. P. Chovanec, M. Kratky, and P. Bednar. Querying Outage Data using MultiQueries - Preliminary Results. In Proceedings of the 9th Workshop of ELNET,Czech Republic, 2012.

7. R. Gono and S. Rusek. Analysis of Power Outages in the Distribution Networks.In Proceedings of the 8th International Conference on Electrical Power Quality andUtilisation (EPQU2003), Cracow, Poland, 2003.

8. R. Gono, S. Rusek, and M. Kratky. Reliability analysis of distribution networks.In Proceedings of the 9th International Conference on Electrical Power Quality andUtilisation, EPQU 2007. Barcelona, Spain. IEEE Press, 2007.

9. A. Guttman. R-Trees: A Dynamic Index Structure for Spatial Searching. In Pro-ceedings of the International Conference on Management of Data, ACM SIGMOD1984, Boston, USA, pages 47–57. ACM Press, 1984.

10. M. Kratky, R. Baca, and P. Chovanec. Efficiency of the Embedded DatabaseSystem for Handling Outage Data. In Proceedings of the 8th Workshop of ELNET,Czech Republic, 2011.

11. M. Kratky, R. Gono, and S. Rusek. A Framework for Querying and IndexingElectrical Failure Data. In Proceedings of ELNET 2006. Ostrava, Czech Republic,2006.

12. M. Kratky, R. Gono, S. Rusek, and J. Dvorsky. A Framework for an Analysis ofFailures Data in Electrical Power Networks. In Proceedings of the InternationalConference on Power, Energy, and Applications Conference, ELNET/PEA 2006.IACTA Press/IASTED, 2006.

13. H. Samet. Foundations of Multidimensional and Metric Data Structures. MorganKaufmann, 2006.

14. T. K. Sellis, N. Roussopoulos, and C. Faloutsos. The R+-Tree: A Dynamic IndexFor Multi-Dimensional Objects. In Proceedings of VLDB 1997, pages 507–518.Morgan Kaufmann, 1997.

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Author Index

Bednar, Pavel, 1

Gono, Radomır, 1, 10, 29

Hradılek, Zdenek, 20, 39, 51

Chovanec, Peter, 1, 60

Jansa, Jirı , 51

Kral, Martin, 29Kratky, Michal, 1, 60

Leonowicz, Zbigniew, 10

Mach, Veleslav, 10

Novosad, Ladislav, 20

Rusek, Stanislav, 1

Spacek, Michal, 39

Tien, Dung Vo, 10

Editor: Radomır Gono

Title: ELNET 2016

Place, year, edition: Ostrava, 2016, 1st

Page count: 81

Edit: VSB – Technical University of Ostrava,17. listopadu 15, 708 33 Ostrava-Poruba, Czech Republic

Impression: 100

ISBN 978–80–248–4008–6

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