wide-area based intelligent and adaptive transmission system …silicon.ac.in/smart-2015/intelligent...

Wide-area based Intelligent and Adaptive Transmission System

Protection

S. R. Samantaray School of Electrical Science,

Indian Institute of Technology Bhubaneswar, Email- [email protected]

Smart-grid, Silicon Institite of Technology, Bhubaneswar

12/05/2015 1

Contents

• Introduction to Transmission system relaying

• Relaying attributes

• Challenges due to FACTs and Wind-integration

• PMU and WAMs

• Proposed Intelligent relaying schemes

• Proposed Adaptive Relay setting

• Conclusions


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Distance Relaying and its basic functions:

• Fault Detection • Fault Classification • Fault Location

Typical current and voltage waveforms

Evolution of Relays:

• Electromechanical relays - (1900-present)-1st Generation

• Solid state relays-(1970-1990)- 2ndGeneration

• Digital relays-micro-processor based relay (1982-Present)- 3rd Generation

• Intelligent Relays - (Adaptive Relays) Computer/ DSP/ FPGA based relays with intelligent algorithms- 4thGeneration

Smart-grid, Silicon Institite of Technology,

Bhubaneswar 12/05/2015 5

Attributes of the Relay: • Reliability: Reliability is generally understood to measure the degree of

certainty that a piece of equipment will perform as intended (a reliable relaying system must be dependable and secure)

• Dependability: Dependability is defined as the measure of the certainty that the relays will operate correctly for all the faults for which they are designed to operate.

• Security: Security is defined as the measure of the certainty that the relays will not operate incorrectly for any fault. As a relaying system becomes dependable, its tendency to become less secure increases.

• Speed: Speed of operation is the key indicator for relay performance and it provides the operating time of the relay :

Instantaneous Time delay High speed (50 milliseconds) Ultra high speed (4 milliseconds or less)

Stepped Distance Relay

A B C

Zone 1

Zone 2

Zone 3

F2

A B C

jX

R

Zone 1

Zone 2

Zone 3


12/05/2015 7

Possible Challenges

• Inclusion of FACTs devices in the modern transmission network.

• Integration of off-shore wind-farms in the transmission network.

• Distinguishing Faults from other conditions such as Power Swings.


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FACTs embedded in to the power system

TCSC

UPFC

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• The presence of the TCSC in fault loop not only affects the steady-state components but also the transient components .

• While the use of the UPFC improves the power transfer capability and stability of a power system, certain other problems emerge in the field of power system protection, in particular the transmission line protection, affecting greatly the reach of the distance relay.

• In the FACTS-based transmission line, if the fault does not include FACTS device, then the impedance calculation is like an ordinary transmission line, and when the fault includes FACTS, then the impedance calculation accounts for the impedances introduced by FACTS device.

• Thus, it is really challenging to build relays which can be intelligent enough to consider the aforementioned issued due to inclusion of FACTs.

Impact of FACTs devices

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Performance with STATCOM

Performance with UPFC

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• The difficulty that arises in integrating wind-farms is primarily due to uncontrollable wind speed as it continuously varies throughout a day resulting fluctuation in wind-farm output power.

• The output power of a generating unit has a nonlinear relationship with the wind speed and when such a farm is connected to the grid through a transmission line, the transmitted power and the relay end voltage (with respect to grid voltage) fluctuate continuously.

• Further, wind-farm generation capacity also greatly affects the tripping boundary of the distance relay. Thus, fixed setting approach in such an environment will lead to significant error in relaying decision.

• Wind-farm integration to the transmission line may also bring problems such as weak feed or weak source condition and some machines like doubly fed induction generator (DFIG) contributes only about 1.1 p.u (110% of full generation) after a few cycles of the fault inception, resulting into a very weak source

• Moreover, the integration of doubly fed induction generator (DFIG) based wind-farm has serious impacts on the existing distance and differential relaying schemes.

Impact of off-shore wind integration

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PMU and Wide-Area Measurement

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Need for Wide-Area Monitoring (WAM)

• Deregulation, competition and increase in complexity of today’s power networks have exacerbated power system stability issues including wide disturbances, which are not ably covered by existing protection and network control systems.

• As power grids get even more heavily loaded by sudden bulk power transfers, the system becomes very vulnerable and even minor equipment failures can result in cascade tripping and eventually, blackouts.

• To ensure system stability in a heavily loaded system, all or most installed components should remain in service and right actions must be taken quickly if the system has not recovered after a serious event.

• To cater to this requirement, the solution is to have real time monitoring. Such a wide area measurement system provides operators with real time knowledge of various instability issues and events as and when they occur.

• A typical wide area measurement or WAMS system is built up on a reliable communication system connecting power stations, network control centers and sub stations.


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Wide Area Monitoring System(WAMS)

GPS satellite

PMU

PMU PMU

PMU

Wide Area Monitoring System use a GPS satellite signal to time-synchronize from phasor measurement units (PMUs) at important nodes in the power system, send real-time phasor (angle and magnitude) data to a Control Centre.

The acquired phasor data provides dynamic information on power systems, which helps operators to initiate corrective actions to enhance the power system reliability.


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PMU based Wide Area Monitoring


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PMU based WAMs

Phasor Measurement Units (PMU)

• Synchronized phasor measurements are traced from their origins in computer relaying to present applications in power system operation, protection, and control.

• The start of the modern EMS systems based upon state estimators can be said to have begun with the aftermath of the 1965 catastrophic failure of the North-Eastern power grid in North America.

• There was a great deal of research conducted in techniques for determining the state of the power system in real time based upon real-time measurements.

• Of course, there was not the possibility of achieving synchronized measurements in those days, and instead a technique was devised whereby measurements could be obtained by sequential scan and from them the state of the power system estimated by anon-linear state estimator.

• It was recognized that the state obtained in this manner at best described a quasi-steady state approximation to the actual state of the network.


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Phasor measurement

Phasor Measurement Units (PMU) uses the GPS to synchronize the sampling clocks, so that the calculated phasors would have a common reference.

Phasor representation of sinusoid Phasor estimation using DFT


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Synchronized Measurements

Basic PMU connectivity to Power System


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PMU and GPS

Functional Block diagram of the PMU

GPS satellite transmission for achieving synchronization of sampling clocks in PMUs


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Real-Time PMUs and PDC

SEL PMUs

SEL PDC


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C37.118 Standard for PMU

Accuracy Index Total Vector Error (TVE)

Standard PMU Rates

Where and are measured values

and are theoretical values of the input signal


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C37.118 Compliance for PMU

The level-1 is intended as standard compliance level and evel-0 is provided for applications those can not be served by Level-1


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Point to Point Communications: Protection application


Integrated Impedance based protection scheme

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Integrated Impedance based protection scheme for TCSC compensated Line

GTNET PMU Block of RSCAD library


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Proposed Dedicated Scheme for TCSC compensated Line

int_

( )

( )

sa ra

asa ra

U UZ

I I

int_

( )

( )

sb rb

bsb rb

U UZ

I I

int_

( )

( )

sc rc

csc rc

U UZ

I I

Integrated Impedance of each phase is defined as:


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Imaginary part of integrated impedance(IPII) During External Fault:

/ /

where 2/Y

2/Y / /

IPII img(2/Y) Large negative value

s r sg rg s sg r rg

sg rg

s r s r

s r s sg r rg

I I I I U Z U Z

Z Z

U U U U

I I U Z U Z


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Imaginary part of integrated impedance(IPII) During Internal Fault:

ACEs Er

sU rU

sgZ rgZ

lsZ TCSCZ

sZ rZ

sI rI

sgI rgIRf

fI

AC

lrZF

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Imaginary part of integrated impedance(IPII) During Internal Faults:


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Observations: • In normal operating conditions (or in external fault condition), the

sign of the IPII (which reflects the impedance of the line capacitance) is negative with large absolute value.

• In case of internal faults (faults on the line to be protected), the sign of the IPII is mostly positive but may become negative with smaller absolute value for some fault conditions

• Thus, based on the sign and absolute value of IPII, the external and internal faults can be distinguished.

• However, the magnitude of IPII varies while subject to changes in operating parameters of the power system during fault conditions. Thus, setting a threshold on the magnitude of IPII will never work considering wide variations in faulted conditions.


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Variations in Parameters:

• Variation in fault resistance (RF) from 0 to 300Ω

• Variation in fault location: 20%, 30%, 50%,70%, 80%, and 95% of the total line length

• Variation in fault inception angle(FIA): 0,30,60,90

• Different types of fault: a-g, b-g, c-g, a-b, b-c, c-a, ab-g, bc-g, ca-g, a-b-c etc.

• Total cases simulated=6000

Smart-grid, Silicon Institite of Technology,

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• To alleviate the above mentioned problems, the decision making process is further enhanced by cascading the data-mining algorithm such as DT.

• Data mining is defined as the process of discovering patterns in data and is a form of inductive learning.

• DTs are grown through a systematic process known as recursive binary partitioning; a “divide and conquer” approach where successive questions with yes /no answers are asked in order to partition the sample space.

Application of Data-mining

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Advantages of Data Mining Model (Decision Tree)

• It promote decision making

• It can handle high dimension data

• It does not required any domain knowledge or parameter setting, and is therefore suitable for exploratory knowledge discovery

• Learning and classification steps are simple and fast

• Good accuracy

• It perform well despite noisy or missing data (robustness).

• It convert result to a set of easily interpretable rules

• Simple to understand and implement.

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Data Mining Model Building for the proposed scheme:


• In the proposed scheme, imaginary parts of integrated impedance imgZint _ a, imgZint _ b and img Zint _ c are used as input to the DT.

• The target outputs (classes) are categorized as 0(normal or external fault), 1(a-g fault ), 2(b-g fault), 3(c-g fault), 4(a-b/a-b-g fault), 5(b-c/b-c-g fault), 6(c-a/c-a-g fault) and 7(a-b-c fault).

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Flowchart:


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Real-Time Digital Simulator (RTDS) Implementataion RTDS unit

PC interface

Ethernet communication


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Transmission system developed on RTDS


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DATA Collection….


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Decision Tree generated with (70-30)% training-testing ratio:


Img Zint_a< -115

Img Zint_b< -110 Img Zint_b< -266

Img Zint_c< -100 Img Zint_a>= -989 Img Zint_c< -500Img Zint_c< -254

0 (No Fault) 3(c-g Fault) 2(b-g Fault) 5(b-c Fault) 1(a-g Fault) 6(c-a Fault) 4 (a-b Fault) 7(a-b-c Fault)

Yes No

Yes No YesNo

Yes No No NoYesYes No Yes

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Confusion Matrix for 30% of test data set:


Predicted

Actual

0

(Normal)

1

(a-g)

2

(b-g)

3

(c-g)

4

(a-b/a-b-g)

5

(b-c/b-c-g)

6

(c-a/c-a-g)

7

(a-b-c)

0(Normal) 88 0 0 0 0 0 0 0

1(a-g) 0 432 0 0 0 0 0 0

2(b-g) 0 0 499 0 0 0 0 0

3(c-g) 0 0 0 453 0 0 0 0

4(a-b/a-b-g) 0 0 0 0 114 0 0 0

5(b-c/b-c-g) 0 0 0 0 0 118 0 0

6(c-a/c-a-g) 0 0 0 0 0 0 142 0

7(a-b-c) 0 0 0 0 0 0 0 128

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Simulation Results on RTDS

IPII magnitude of different phases during remote end internal a-g fault

Trip signals of different phases during remote end internal a-g fault

Fault Inception

(Sec)

10ms

(Sec)

10ms


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IPII magnitude of different phases during remote end internal a-c fault

Trip signals of different phases during remote end internal a-c fault

Fault Inception



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IPII magnitude of different phases during during remote end internal a-b-c fault

Trip signals of different phases during remote end internal a-b-c fault

Fault Inception

Fault inception

Trip Signal from DT

Trip Signal from DT

Trip Signal from DT



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Scheme D(%) (fault at10% of line)

D(%) (fault at60% of line)

D(%) (fault at90% of line)

Distance Relaying

100 62 10

Proposed Intelligent Relaying 100 100 100

Dependability Comparison between Distance Relaying and proposed relaying scheme for different fault locations

Scheme D(%) (RF=1Ω)

D(%) (RF=100Ω)

D(%) (RF=300Ω)

Current differential

100 85 45

Proposed Intelligent Relaying

100 100 100

Dependability Comparison between current differential scheme and proposed relaying scheme for different fault resistances

Scheme S(%) (Power Swing)

S(%) (External fault)

Integrated Impedance based Pilot protection

scheme[16]

50 92

Proposed Intelligent Relaying

100 100

Security comparison between integrated impedance based pilot protection scheme and proposed relaying scheme

Performance Statistics

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Fault Type

Fault Resistance

Fault Location

(%)

Operation

Proposed Scheme Distance Relaying Current Differential

Scheme

Trip Response Time

(ms)

Trip Response Time

Trip Response Time

A-G 1 10 Yes 15.25 Yes 18.35 Yes 16.15

B-G 10 95 Yes 15.15 No - Yes 16..05

C-G 200 50 Yes 15.05 Yes 18.16 No - A-B 20 20 Yes 14.35 Yes 19.25 Yes 15.64

B-C 30 90 Yes 15.03 No - Yes 15.64

C-A 250 25 Yes 15.23 Yes 18.63 No - A-B-C 300 95 Yes 15.05 No - No -

Performance Comparison for different types of faults in case of tested 9-Bus system

Scheme D (%)

30%

Compensation

D (%)

40%

Compensation

Bypass

Mode

(50cases)

Vernier

mode

(50cases)

Bypass

Mode

(50cases)

Vernier

mode

(50cases)

Proposed

Intelligent

Relaying

100 100 100 100

Effect of TCSC mode and compensation level on Dependability of the proposed relaying scheme

Performance Statistics

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Synchrophasors-Assisted IPII-Based Intelligent Relaying for Transmission Lines Including UPFC

AC

EsEr

sV rV

sgZ rgZ

lZ

sZ rZ

sIrI

sgI rgI

Rf

fI

AC

Ish

Zsh

AC

seVZse

UPFC Transmission Line

Equivalent circuit of the UPFC compensated transmission system for an external fault


IPII for external faults

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IPII is defined as follows

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IPII for internal faults

AC

EsEr

sV rV

sgZ rgZ

lZ

sZ rZ

sIrI

sgI rgI

Rf

fI

AC

Ish

Zsh

AC

seVZse

UPFC Transmission Line

Equivalent circuit of the UPFC compensated transmission system for an internal fault

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UPFC

Substation-1 Substation-2

500kV

Transmission line

Bus-I Bus-II

EXTRACTION OF IPII OF EACH PHASE

FINAL RELAYING DECISION

TRAINED DT

USED FOR TESTING

CO

MM

UN

ICA

TIO

N

CH

AN

NE

L

CO

MM

UN

ICA

TIO

N

CH

AN

NE

L

PMU-I PMU-II

CTCT

PT

PT

To Circuit Breaker

EXTRACTION OF IPII OF

EACH PHASE

Training period of DT

TRAINED DT

Offline Process using Intel®

Core(TM)i5-2400 [email protected]

Online Process on RTDS platform using

Real-Time PB5 card

Data used as input file to

Rattle software package

To be used on-line

CB

Trip SignalProposed Scheme

12/05/2015 54


System Model on RTDS

• Variation in fault resistance (RF) from 0 to 300Ω • Variation in fault location: 20%, 30%, 50%,70%, 80%, and

95% of the total line length • Variation in fault inception angle(FIA): 0,30,60,90 • Different types of fault: a-g, b-g, c-g, a-b, b-c, c-a, ab-g,

bc-g, ca-g, a-b-c • UPFC mode of operations: Automatic power flow control

mode(APFC) and Bypass mode

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DT Building


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Trip signals of different phases during remote end internal a-g fault

Fig: IPII magnitude of different phases during remote end internal a-g


Performance assessment

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Trip signals of different phases during remote end internal a-c fault

IPII magnitude of different phases during remote end internal a-c fault



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Trip signals of different phases during remote end internal a-b-c fault

IPII magnitude of different phases during remote end internal a-b-c fault



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Dependability comparison


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Performance with Different Fault Resistance

Effect of mode and compensation level


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Comparison with existing relay


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Phase Angle of the Positive sequence integrated impedance (PAPSI) based wide-area back-protection scheme

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Positive sequence integrated impedance (PSII) based wide-area back-protection scheme


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Positive sequence diagram for an internal fault

Wide-Area adaptive transmission system protection


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PAPSII for External fault

Positive sequence diagram for an external fault

In case of external faults, the positive sequence currents flowing into the protection zone is very less as compared to the line charging current which flow into the zone of protection (transmission line). The sign of the angle becomes negative as the current involved is the line charging current. This situation remains same for no-fault condition as is very small as compared to the fault current, resulting in similar PAPSII condition as external fault situation.


12/05/2015 66

Operating criteria

If -20<PAPSII<180 Then, there is an internal fault And if -180<PAPSII<-30 Then, there is an external fault/No-Fault If -20<PAPSII<-30 Then, it's a dead zone

0 deg

90 deg

-90 deg

180 deg

-30deg

Zones of

Internal Fault

Zones of External

Fault/No-Fault

-20deg

Dead Zone

Generator-3Load A

BUS-7

BUS-1

BUS-4

BUS-3BUS-9BUS-8BUS-2

Generator-1

Generator-2

BUS-5BUS-6

PMU PMU

PMU

PMU

PMU

PMU

Load B Load C

Validation on RTDS platform

Schematic of modified WSCC-9-bus system Time(s)

Threshold

Ma

gn

itu

de

of

PA

PS

II(d

eg

)

PAPSII values during voltage inversion following an a-g fault in line 7-8.


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PAPSII during unbalanced Fault with voltage Inversion:

Phase voltages before(upper figure) and after(lower figure) the capacitor following an a-g fault at 0.04 sec.

PAPSII values during voltage inversion following an a-g fault in line7-8.


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PAPSII during Unbalanced Fault with Current Inversion:

Phase currents at both ends of line 7-8 during current Inversion following an a-g fault at 0.04 sec.

PAPSII values during current inversion following an a-g fault in line 7-8.


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PAPSII during Load Encroachment:

Impedance trajectory of relay at bus-7 during load encroachment

Response of PAPSII during Load encroachment


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PAPSII during Results for Power Swing:

Response of PAPSII during stable power swing Impedance trajectory of relay at bus-7 during power swing


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Intelligent Differential Relaying Scheme

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• Wind-farm integration to the transmission line may also bring problems such as weak feed or weak source condition.

• The fault current in case of phase faults depends on the amount of generation at the instance of fault and the fault contribution characteristics of the machines.

• Some machines like doubly fed induction generator (DFIG) contributes only about 1.1 p.u (110% of full generation), after a few cycles of the fault inception, resulting into a very weak source.

• Further, the fault current contribution of wind turbines with crowbar protected DFIG affects the performance of existing current differential and pilot protection schemes .

• When both UPFC and Wind-farms are integrated together in the transmission lines, the system becomes more complicated and the performance of the conventional relaying scheme is greatly affected.

• Thus, there is a strong motivation in developing a dedicated relaying strategy for transmission line protection including UPFC and wind-farms together.

Motivation

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Decision tree-induced fuzzy rule-based differential relaying for transmission line including unified power flow controller and wind-farms


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Operating parameter variation

• Variation in fault resistance (Rf) from 0 to 100Ω • Variation in fault location: 20%, 30%, 50%,70%, 80%, 85%, and

90% of the total line length • Variation in fault inception angle(FIA): 0,30,60,90 • Variations in source impedance angle: 30 % from normal value. • Different types of fault: a-g, b-g, c-g, a-b, b-c, c-a, ab-g, bc-g, ca-

g, a-b-c • UPFC series injected voltage (Vse) varied for 0-15% of the

normal voltage • UPFC voltage phase angle(θse) varied from 0-360 • UPFC Control mode (Automatic power flow control mode and

bypass mode) • Variation in wind speed: 10m/s, 15m/s, 20m/s • Reverse power flow • Remote in-feed • Noisy Environment (Singal to Noise Ration: SNR 20dB)

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The System Studied: A 500kV, 50Hz power system: Single circuit Transmission Line with UPFC and Wind-farm). In this power system, there are two substations (sending end and receiving end), and one UPFC located at the mid-point of the transmission line (distributed model). Wind-farm is connected at the receiving end of the studied system. Hence, the system consists of two sources, UPFC and its associated components and a 400 km transmission line.

Line parameters:

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Relay

Substation-1

(Sending end)Substation-2

(Receiving end)

400 km,500kV

Transmission

line

Feature

Extraction at

Bus-1

Feature

Extraction at

Bus-2

CT CT

PT PT

Zse

Vse

Zsh

Vsh

UPFC

Wind-

Farm

Relay

Substation-1

(Sending end)

Substation-2

(Receiving end)Zse

Vse

Zsh

Vsh

UPFC Wind-

Farm

Single circuit transmission line with UPFC and Wind-farm

Double circuit transmission line with UPFC and Wind-farm

The System Studied

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Initial Features Used:


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X15< -312

0 (No Fault)

9(c-a-g Fault)

2(b-g Fault) 8(b-c-g Fault)

5(b-c Fault) 6(c-a Fault) 4 (a-b Fault)

10(a-b-c

Fault)

Yes No

1(a-g Fault)

7(a-b-g Fault)

5(b-c Fault)

YesYes

Yes

YesYes

Yes

Yes

YesYes

No

NoNo

NoNo

No

No

NoNo

X11>=1.6 X12>= 1.6

X12>=1.6X10>=1.6 X11< 1.6 X10>=1.6

X12>=1.5 X10>=1.6X11>=1.6

X12>=3

3 (c-gFault)

Yes

No

DT Building

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DT-Fuzzy Transformation

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Fuzzy Rule base

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(i) Dependability (D): Total number of fault cases predicted / Total number of actual fault cases.

(ii) Yield (Y): Total number of correct fault cases predicted / Total number of fault cases predicted. (iii) Security(S) = Total number of external faults predicted as external fault / Total number of external faults.

Performance Assessment

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Scheme Dependability (Fault at 10%

of the line)

Dependability (Fault at 80 %

of the line)

Dependability (Fault at 95% of the line)

DT with one end data

100 40 10

DT-Fuzzy 100 100 100

Scheme D (%) (fault at10% of

line)

D (%) (fault at60% of

line)

D (%) (fault at90% of

line) Distance

Relaying(Mho Characteristics)

100 62 12

Proposed Relaying Scheme

100 100 100

DT-induced Fuzzy rule base for fault classification of transmission line including UPFC and Wind-farm

Dependability Comparison between Conventional distance relaying and proposed DT-Fuzzy based relaying


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Features without noise

Actual class Predicted class Dependability (%)

Yield (%)

L-G L-G 100 100

L-L L-L 100 100

L-L-G L-L-G 100 100

L-L-L L-L-L 100 100

Features with SNR 20dB

L-G L-G 100 100

L-L L-L(49cases) + L-L-G(1cases)

100 98

L-L-G L-L-G 100 100

L-L-L L-L-L 100 100

Dependability and Yield comparison for different types of faults

Scheme D (%) S (%) Distance Relay (Mho

Characteristics) 65 75

Presented Scheme in [22]

72 72

Presented Scheme in[25]

83 88

Proposed Scheme 100 100

Dependability and Security comparison for different types of faults

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Types of fault Dependabilityof DT_Fuzzy(%)

Yield of DT_Fuzzy(%)

L-G 99.95 100

L-L 100 100

L-L-G 100 98.96

L-L-L 100 99.68

Performance Assessment for Remote Infeed Line

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Cross-Differential Protection scheme for Transmission Lines including UPFC

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N

πkni

eN-

k NT

nT,j-kwkTx

NT

njT,S

21

0

The expression for discrete ST becomes

The window function in the discrete domain is chosen as:

22

2

2

2r

c

NT

nbajTexp

πr

c

NT

nba

NT

njT,w

Fast discrete S-Transform (FDST) can be achieved by :

Appropriate choice of the frequency scaling is important for fast computation of the discrete S-Transform algorithm.

Fast discrete S-Transform

))n,k(W(FFT*)k(X)n,k(G

1

0

2N

k

)N/ikexp()n,k(G)n,k(G

FFT

Inverse FFT

)n,k(ie)n,k(A)n,k(S

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CUSUM-based cross-differential detection

Fault detection time (or sample point)

CUSUM for any signal is shown as follows

Cross Differential Energy

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System Studied

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CUSUM-based cross-differential fault detection in the parallel transmission system with UPFC, for ACG fault on Circuit-1: (a) currents in Circuit-1, (b) currents in Circuit-2, (c) |CS (I1)| - |CS (I2)| when fault distance = 70%, Rfault = 10 Ω, Vse = 6%, θse = 60°, (d) |CS (I2)| - |CS (I1)| when fault distance = 70%, Rfault = 10 Ω, Vse = 6%, θse = 60°.


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FDST and spectral energy based fault classification for ACG fault on Circuit-1, over a window of one cycle: (a) three-phase currents in Circuit-1, (b) FDST contours for phase-a current, (c) FDST contours for phase-b current, (d) FDST contours for phase-c current, (e) fault classification for phase-a using spectral energies,

Spectral energy based fault classification

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(f) fault classification for phase-b using spectral energies, (g) fault classification for phase-c using spectral energies.

Spectral energy based fault classification

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Fault Type

Condition-1 Condition-2

a b c a b c

AG 0.6136 0.0000 0.0000 0.1599 0.0009 0.0006

ABG 0.4026 0.2156 0.0002 0.1256 0.1186 0.0006

BC 0.0003 0.3567 0.2221 0.0000 0.2175 0.1863

ABCG 0.4589 1.1658 1.068 0.4134 0.3662 0.5569

Change in Energy during fault situation

Fault

Type

Rfault

(Ω)

Loca-

tion

(%)

Relay Unit Operation time

(ms) Faulted

phases a b c

AG 5 10 9.12 - - a2

BG 10 20 - 9.86 - b2

ABG 20 25 11.25 11.62 - a2, b2

BCG 15 15 - 11.49 10.53 b2, c2

ACG 5 20 9.58 - 9.58 a2, c2

AB 5 5 11.25 11.62 - a2, b2

BC 2 25 - 10.59 10.59 b2, c2

ABCG 8 30 11.25 11.49 10.57 a2, b2, c2

Response of the proposed algorithm for near end fault in Circuit-2


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Fault

Type

Rfault

(Ω)

Loca-

tion

(%)


(ms) Faulted

phases a b c

BG 100 85 - 16.47 - b1

CG 50 75 - - 12.28 c1

BCG 150 95 - 18.45 16.31 b1, c1

CAG 40 80 12.40 - 12.40 c1, a1

AB 100 70 13.20 15.66 - a1, b1

BC 80 75 - 15.66 16.54 b1, c1

CA 120 65 15.79 - 12.70 c1, a1

ABCG 100 70 13.20 15.66 12.70 a1, b1, c1

Response of the proposed algorithm for far end fault in Circuit-1

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FDST and spectral energy based fault classification for BG fault on Circuit-1, over a window of one cycle: (a) three-phase currents in Circuit-1, (b) FDST contours for phase-a current, (c) FDST contours for phase-b current, (d) FDST contours for phase-c current, (e) fault classification for phase-a using spectral energies, (f) fault classification for phase-b using spectral energies, (g) fault classification for phase-c using spectral energies.


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Sl. No.

Vse

(in

%)

Rfault

(Ω)


(ms) Faulted

phases a b c

1 0 10 12.18 13.95 11.52 a1, b1, c1

2 3 10 12.18 13.95 11.52 a1, b1, c1

3 5 10 11.94 12.57 10.03 a1, b1, c1

4 7 10 11.16 13.09 10.09 a1, b1, c1

5 10 10 11.70 12.56 10.49 a1, b1, c1

6 0 140 14.01 - 14.01 c1, a1

7 3 140 14.70 - 12.11 c1, a1

8 5 140 15.85 - 13.26 c1, a1

9 7 140 16.68 - 15.70 c1, a1

10 10 140 17.98 - 16.44 c1, a1

RESPONSE OF THE PROPOSED ALGORITHM FOR A FAR-END FAULT ON CIRCUIT-1

WITH VARIATION IN UPFC SERIES VOLTAGE

Sl. No. θse

(in °)

Rfault

(Ω)


(ms) Faulted

phases a b c

1 0 5 - - 12.18 c1

2 45 5 - - 12.18 c1

3 90 5 - - 12.18 c1

4 180 5 - - 11.94 c1

5 270 5 - - 13.09 c1

6 360 5 - - 12.56 c1

7 0 130 - 10.12 14.57 b1, c1

8 45 130 - 10.12 14.57 b1, c1

9 90 130 - 11.69 15.73 b1, c1

10 180 130 - 15.85 15.85 b1, c1

11 270 130 - 15.60 16.88 b1, c1

12 360 130 - 16.23 17.78 b1, c1

RESPONSE OF THE PROPOSED ALGORITHM FOR A NEAR-END FAULT ON CIRCUIT-1 WITH VARIATION IN

UPFC SERIES VOLTAGE PHASE ANGLE

Performance Assessment UPFC parameter ariation

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0.5 0.505 0.51 0.515-0.1

-0.05

0

0.05

0.1

Time [s]

(E1

,a-E

2,a

),(E

2,a

-E1

,a)

SET = 0.0884

(E2,a

-E1,a

)

(E1,a

-E2,a

)No trip

signal issued

0.508 0.51 0.512 0.514 0.516 0.518 0.52 0.522-0.1

-0.05

0

0.05

0.1

Time [s]

(E1

,a-E

2,a

),(E

2,a

-E1

,a)

SET = 0.0884

(E2,b

-E1,b

)

(E1,b

-E2,b

)

No trip signal issued

0.56 0.561 0.562 0.563 0.564 0.565-0.1

-0.05

0

0.05

0.1

Time [s]

(E1

,c-E

2,c

),(E

2,c

-E1

,c) SE

T = 0.0884

(E2,c

- E1,c

)

(E1,c

- E2,c

)

No trip signal issued

Response to External Fault

FDST and spectral energy based fault classification for external ABCG fault in section B1-B3

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Sl. No. Vse

(in %)

Rfault

(Ω)


(ms) Faulted

phases a b c

1 0 10 - - 13.12 c1

2 3 30 13.85 - - a1

3 5 50 14.24 13.45 - a1, b1

4 7 100 14.47 - 15.89 a1, c1

5 10 150 15.45 14.21 13.64 a1, b1, c1

θse

(in °)

Rfault

(Ω) Relay Unit Operation time (ms)

Faulted

phases

1 0 10 - 14.12 - b1

2 45 30 - 13.45 15.84 b1, c1

3 90 50 - 15.23 14.59 a1, c1

4 180 100 - 15.12 14.89 b1, c1

5 270 150 16.12 15.99 15.32 a1, b1, c1

RESPONSE OF THE PROPOSED RELAYING ALGORITHM FOR A FAR-END FAULT ON

CIRCUIT-1 ON RTDS PLATFORM

Performance Testing on RTDS platform

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Adaptive Distance Relay Setting

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Power system with UPFC and Wind-integration

Fault before UPFC

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Fault after UPFC

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Adaptive Relay Setting

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Performance testing during power swing

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Conclusions:

• Integrated impedance-based intelligent relaying is a new paradigm for EHV lines including TCSC and UPFC.

• It can identify effectively faulty phases and distinguish external faults from internal faults with high degree of dependability and security.

• DT-Fuzzy based differential relaying based on multiple parameters provides fast and accurate fault classification for line employing FACTs and Wind integration.

• Cross differential protection scheme provides reliable, fast and accurate relaying scheme based on time-frequency domain.

• Implementation of the above schemes on RTDS platform establishes the potential ability of each scheme for respective protection measures.

• The proposed adaptive relay setting is highly promising for line with FACTs and Wind-integration.


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The true sign of intelligence is not knowledge but imagination

Albert Einstein

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http://www.brainyquote.com/quotes/authors/a/albert_einstein.html


Thank You

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wide-area based intelligent and adaptive transmission system …silicon.ac.in/smart-2015/intelligent...

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