Fuzzy logic
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Transcript of Fuzzy logic
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.
THANK YOU