FUZZY LOGIC & ITS APPLICATION TO

DISTRIBUTION SYSTEM

SUBMITTED TO: DR. RANJAN KU JENA DR.ABHIMANYU MAHAPATRA

 

SUBMITTED BY: PRANAYA PIYUSHA JENA REGD NO: 0901106213 ELECTRICAL ENGG

Definition of fuzzy

Fuzzy – “not clear, distinct, or precise; blurred”

Definition of fuzzy logic

1. it deals with reasoning that is approximate rather

than fixed and exact. In contrast with traditional

logic theory, where binary sets have two-valued

logic, true or false, fuzzy logic variables may have a

truth value that ranges in degree between 0 and 1.

2. Fuzzy logic has been extended to handle the concept

of partial truth, where the truth value may range

between completely true and completely false.

Fuzzy sets Fuzzy sets are sets whose elements have degrees of membership.

Binary set :

1 T>40°

High= 0 T≤40°

Fuzzy set:

1 T>40°

High= T−30 ∕ 10 30°< T≤40°

0 T≤30°

Membership Function

A curve that defines how each point in the input

space is mapped to membership value between 0 and 1.

Types Of Membership Function

1. Triangular Function

2. Trapezoidal Function

3. Bellshaped Function

Linguistic Variable It is a variable whose values are in words or in a natural

language. Ex: speed=(fast, slow, moderate, very slow etc.)

FUZZY LOGIC SYSTEM

FUZZIFICATION Input values are translated to linguistic concepts, which

are represented by fuzzy sets. In other words, membership functions are applied to the

measurements, and the degree of membership in each premise is determined.

FUZZY INFERENCE Fuzzy inference is a computer paradigm based on

fuzzy set theory, fuzzy if-then-rules and fuzzy reasoning.

Linguistic rules describing the control system consist of two parts; an antecedent block (between the IF and THEN) and a consequent block (following THEN)

DEFUZZIFICATION A fuzzy system will have a number of rules that transform

a number of variables into a "fuzzy" result, that is, the result is described in terms of membership in fuzzy sets.

extraction of a crisp value that best represents the fuzzy set.

OPTIMAL CAPACITOR PLACEMENT IN DISTRIBUTION SYSTEM USING FUZZY TECHNIQUES

The power loss in a distribution system is

significantly high because of lower voltage and hence

high current, compared to that in a high voltage

transmission system.

The pressure of improving the

overall efficiency of power delivery has forced the

power utilities to reduce the loss, especially at the

distribution level This can be achieved by placing

the optimal value of capacitors at proper locations

in radial distribution systems.

The objective of the capacitor placement

problem is to determine the locations and sizes of

the capacitors so that the power loss is minimized

and annual savings are maximized.

fuzzy logic is a powerful tool in meeting challenging such problems in power systems .

Node voltage measures and power loss in the network branches have been utilized as indicators for deciding the location and also the size of the capacitors in fuzzy based capacitor placement methods.

The fuzzy system take two input variable as

1. Power loss reduction index(PLRI)

2. Bus voltage

And one output variable as

1. Capacitor placement suitability index(CPSI)

Decision matrix/Rule base

Based on these two values capacitor placement

suitability index (CPSI) for each bus is determined

by using fuzzy toolbox in MATLAB.

The bus which is in urgent need of balancing will give maximum CPSI.

Buses which are already balanced will give lesser values.

Bus location for capacitor placement

REFERENCE I.J.Nagrath & M. Gopal. ‘control system engineering’ .5th

edition. S.K.Bhattacharya, and S.K.Goswami, “Improved Fuzzy Based

Capacitor Placement Method for Radial Distribution System”.IEEE Trans. Power Apparatus and Systems, vol. 108, no. 4, pp.741–944, Apr. 2008.

http://en.wikipedia.org/wiki/Fuzzy_logic  C. Chin, W. M. Lin, “Capacitor Placements for Distribution

Systems with Fuzzy Algorithm”, Proceedings of the 1994

Region 10 Ninth Annual International Conference, 1994, pp-

1025 - 1029.

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