OPTIMIZE LOAD SHEDDING USING FUZZY LOGIC CONTROLLER … · School of Engineering. When SSE Campus is supplied by TNEB& PV syst em and there will be difference between power generation

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]

2, [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

International Journal of Pure and Applied Mathematics Special Issue

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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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OPTIMIZE LOAD SHEDDING USING FUZZY LOGIC CONTROLLER … · School of Engineering. When SSE Campus is supplied by TNEB& PV syst em and there will be difference between power generation