optimize load shedding using fuzzy logic controller … · school of engineering. when sse campus...
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OPTIMIZE LOAD SHEDDING USING FUZZY
LOGIC CONTROLLER IN SAVEETHA SCHOOL
OF ENGINEERING
S.Rajesh1, R.Hariharan
2, T.Yuvaraj
3
P.G. Scholar, Department of Electrical and Electronics Engineering, Saveetha School of Engineering, Saveetha Institute of Medical And Technical Science, Chennai
1
Assistant Professor, Department of Electrical and Electronics Engineering, Saveetha School of Engineering, Saveetha Institute of Medical And
Technical Science, Chennai 2,3
[email protected], [email protected]
3
ABSTRACT:
In this paper, we propose a new intelligent load shedding strategy applying fuzzy control
algorithms. This strategy is based on the estimate, in real time, of the load quantity to shed. Even
though, Shedding exact amount of load is not possible and end up with more than necessary or
insufficient at power system that begin to be sustain system safe, secure, strength. This paper
shows an intelligent load shedding strategy in electrical system of Savee tha School of
Engineering Campus consisting different type and size of loads and being supplied by a
distributed generator with electricity board. DG & TNEB which supply with Saveetha School of
Engineering Campus can‘t meet energy requirement when there is a disturbance in power
system. In case of 280Kw from TNEB & 300 KW Solar power plant to be building at University
Campus supply to SSE Faculty, the difference between power generation and demand can
decrease. In this case, load shedding becomes necessary to improve reliability of power supply
and sustain system stability. Loads are sorted by importance priority and optimal load shedding
International Journal of Pure and Applied MathematicsVolume 119 No. 12 2018, 15889-15900ISSN: 1314-3395 (on-line version)url: http://www.ijpam.euSpecial Issue ijpam.eu
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method is applied. The fuzzy logic is engaged for optimal load shedding solution. Strategy is
applied on SSE Faculty loads which have different importance level.
Key words: Fuzzy Logic, Load shedding, Importance priority, Optimal load shedding, Distribution generation, Tamil Nadu Electricity Board (TNEB).
1. INTRODUCTION
Power system workability’s consist of generation, transmission, and distribution functions. In the
most recent decade, technological developments and a changing financial and regulatory
environment have resulted in a renewed interest for distributed generation [1]. In an electrical
competitive market, load shedding decision support systems are needed to find the ways to
process load shedding to satisfy both economic and technical conditions. Distributed generation
is an electric power plant which is generally connected to the distribution network and located
close to customers [2]. It is small scale power generation units which include different types of
technologies such as wind turbine, photovoltaic arrays, fuel cells, biomass or micro turbines. In
the future, distributed generation is expected to make a major contribution to the existing electric
power systems [3]. Now a days distributed generation access has been developed into substation
networks and have positive conditions such as improved reliability, loss reduction [4].
Nevertheless it will change the distribution system and problems can occur in utility power
system. That is load shedding in islanded mode. Distributed generation system process
autonomous from grid system in islanded mode [5].
Disconnecting a certain amount of loads at a feeder is defined as load shedding. It is an
emergency management type which protects the power system. The power systems should
supply continuous, quality and reliable electric energy to end user [6]. But, any problem in
distribution system it could not meet energy condition because of difference between load
demand & power generation [7]. From Tamil Nadu Electricity Board (TNEB) 350 kVA is taken
as a case study. It was responsible for electricity generation, distribution and transmission, and it
regulated the electricity supply in the state. The demand increases, the price or rate automatically
increases and Power scarcity is now a critical problem because of the increasing per capita
energy consumption. The price and demand becomes unsound. Electricity power is needed for
consumer routine work. Due to power shortage, power cut also happening frequently [8].
To support the system stability, load shedding techniques are used. Various investigates
have been presented to load shedding problem in distributed generation. In [9], efficient load
shedding strategy based on fuzzy logic for islanding operation of a distribution network and
generator tripping in distribution network is presented. In [10], a genetic algorithm (GA) based
optimal load shedding that can apply for electrical distribution networks with and without
dispersed generators (DG). The objective is to minimize the sum of curtailed load and also
system losses within the frame-work of system operational and security constraints. In [11], an
optimal load shedding strategy for power systems with multiple DGs is presented and in this
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paper the load shedding is formulated as an optimization problem subject to system, operation
and security constraints.
This paper determines an intelligent load shedding strategy in electrical system of Saveetha
School of Engineering. When SSE Campus is supplied by TNEB& PV system and there will be
difference between power generation and demand. In this case, load shedding becomes essential
to improve reliability of power supply and sustain system stability. Loads are sorted by
importance priority and optimal load shedding method is applied. The fuzzy logic technique is
employed for optimal load shedding. In [12], a load shedding method to provide safe operation
of islanded distribution network is proposed. The proposed method determines magnitude of
disturbance via swing equation.
2. PROPOSED SYSTEM
This paper describes an intelligent load shedding strategy in islanded mode electrical system
which has a distributed generator and EB supply. From the fig. 1, It is consist of PV Generation,
Load demand, Fuzzy logic controller such as Fuzzification, Rule Base, Inference System,
Defuzzification and Load shedding. The fuzzy logic controller is operated to guess amount of
loads to be shed. Load to be shed is estimated according to power generation of distributed
generator and system generators and load demand of system. [13-22] Fuzzy logic system is
designed by Virtual Instrumentation.
Fig 1 Block diagram of fuzzy logic controller
The basic components of fuzzy logic controller are fuzzification, inference mechanism, rule base
and defuzzification units. The first stage of the fuzzy logic is fuzzification. The fuzzification is to
convert crisp numbers to fuzzy values which is indicated as linguistic variables. The input values
are classified as to membership functions. The fuzzy values are evaluated according to rules
which provide relations between inputs and outputs and are related with master information. The
evaluated data by inference mechanism is sent to defuzzification unit. In the last stage, the fuzzy
data should transform to real output values. This stage is called as defuzzification. The fuzzy
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logic controller is operated to guess amount of loads to be shed. Load to be shed is estimated
according to power generation of distributed generator and EB supply.
Two fuzzy logic inputs which are daily power generation of PV plant with EB and daily load
demand of system are continuously checked by the fuzzy logic controller. Total generator power
generation is calculated according to inputs of fuzzy logic. If the total generator power and PV
plant with EB power can meet the energy requirement of system, load shedding is not necessary.
Otherwise an exact amount of load guessed by fuzzy logic controller should be shed to sustain
system stability. Load shedding method is applied according to importance priority of loads to
keep working of critical loads which are most important loads. Loads are sorted by importance
priority as critical, semi-critical and noncritical. An exact amount of load is determined by fuzzy
logic controller and SCADA system or manually sheds the loads according to their importance
priority.
3. SYSTEM FORMATION
A fuzzy system is a system of variables that are associated using fuzzy logic. A fuzzy controller
uses defined rules to control a fuzzy system based on the current values of input variables. Fuzzy
systems consist of three main parts: linguistic variables, membership functions, and rules. A
fuzzy controller requires at least one input linguistic variable and one output linguistic variable.
The linguistic variable load shedding might output include critical, non-critical, semi-critical, No
load shedding. Membership functions are numerical functions corresponding to linguistic terms.
A membership function represents the degree of membership of linguistic variables within their
linguistic terms. The degree of membership is continuous between 0 and 1, where 0 is equal to
0% membership and 1 is equal to 100% membership. The linguistic variable load shedding
might have membership functions of inputs are Low, Medium, and High.
For designing of fuzzy logic load shedding system, it consists of two inputs and one output.
Saveetha School of Engineering campus has load demand nearly 450kW. Total solar power
system capacity of Saveetha School of Engineering is 300kW. During summer season we get
around 200kW per day and during winter season we get around Min 33.33kW. SSE has 350KVA
transformer in its campus. SSE gets power supply from Tamil Nadu Electricity Board nearly
280kW. SSE consumes supply from TNEB, minimum 167kW and maximum 200kW per day
and PV System. These parameters might change with the changes in college working hours and
climatic condition.
The linguistic variables of output are NOCR (Noncritical), SMCR (Semi-critical) and CR
(Critical) because of loads to be shed are sorted by importance priority as shown Table 1 & 2.
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Table 1 Output of Linguistic variables
Table 2 Output of Linguistic variables
The inputs are daily PV power generation and daily load demand of system. The output is
amount of load to be shed. Controller aims to keep working important loads when there is a
difference between power generation and load demand. The linguistic variables membership
functions of inputs are Low, Medium and High. The linguistic variables of output are NOLS (No
Load Shedding), NOCR (Noncritical), SMCR (Semi-critical) and CR (Critical) because of loads
to be shed are sorted by importance priority as shown figure 1.
Figure 1 Importance priority of load shedding
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Fuzzy rules describe, in words, the relationships between input and output linguistic variables
based on their linguistic terms. A rule base is the set of rules for a fuzzy system. The rule base is
equivalent to the control strategy of the controller. We can use fuzzy controllers to control fuzzy
systems.
To create a rule for, you must specify the antecedents, or IF portions, and consequents, or THEN
portions, of the rule. Associate an input linguistic variable with a corresponding linguistic term to
form an antecedent. Associate an output linguistic variable with a corresponding linguistic term
to form a consequent. The consequent of a rule represents the action you want the fuzzy
controller to take if the linguistic terms of the input linguistic variables in the rule are met. When
constructing a rule base, avoid contradictory rules, or rules with the same IF portion but different
THEN portions. A consistent rule base is a rule base that has no contradictory rules. And we
create fuzzy rules for load shedding as shown Table 3 At summer & Table 4 At winter and
Figure 2.
Table 3 At summer Table 4 At winter
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Figure 2 Rules Window
SIMULATION RESULTS:
Saveetha University SSE Campus has the most important building because this campus has a
hostel at the same time. Installed power of hostel is 230 kW. In case of 280Kw EB supply and
300 kW Solar power plant to be building at SSE Campus supplies to hostel, the difference
between power generation and load demand will decrease. However PV plant can meet college
& hostel needed power when load demand is low. During peak load demand, load shedding is
required to sustain stability and to keep working important loads which are varies labs, air
conditioner system etc.
Various loads of SSE are sorted by importance priority as critical, semi-critical and noncritical as
indicated in Table 5 (Day time) &Table 6 (Night time). If load shedding is necessary, Loads will
be shed respectively noncritical, semi-critical. Critical loads must always keep working because
these loads are vital for routine life.
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Table 5 Day time Table 6 Night time
If PV generation and maximum generator power aren‘t enough for load demand, load shedding
is performed according to importance priority of loads. Obtained results for different hours are
summarized in Figure 3.
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Figure 3 At Day time-Load Demand is minimum, PV (summer) is medium, EB supply is
High
Obtained results for different hours are summarized in Table 7. Loads are shed according to fuzzy results. For example, guess of fuzzy logic controller is 165kW at 11:00 a.m. First 5 noncritical loads should be shed since sum of them is approximately equal to guess of fuzzy.
Table 7 Obtained fuzzy results according to daily data of PV-Load Demand
CONCLUSION:
This project proposes an intelligent load shedding strategy for islanded electrical system which
has a distributed generation. The strategy is applied on Saveetha University SSE Campus. The
amount of loads to be shed is determined by fuzzy logic based on acquired real data of PV power
generation with EB supply and load demand. According to importance priority, these loads are
shed by SCADA system or manually. The proposed method provides vital loads in the buildings;
such as labs, computers to work continuously and primarily sheds noncritical loads. The system
reliability is raised and optimal load shedding is provided by this proposed method.
FUTURE SCOPE:
Using Fuzzy logic controller through PLC & SCADA should be digitalize the automation load
shedding process in SSE Campus .It will reduce the tariff of power units and it will be using for
water management system through sensors, and maintenance can easy.
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