1. FUZZY LOGIC & ITS APPLICATION TO DISTRIBUTION SYSTEMSUBMITTED TO: SUBMITTED BY:DR. RANJAN KU JENA PRANAYA PIYUSHA JENADR.ABHIMANYU MAHAPATRA REGD NO: 0901106213 ELECTRICAL ENGG
2.  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.
3. 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°
4.  Membership Function A curve that defines how each point in the input space is mapped to membership value between 0 and 1.
5.  Types Of Membership Function1. Triangular Function2. Trapezoidal Function3. Bellshaped Function
6.  Linguistic Variable It is a variable whose values are in words or in a natural language. Ex: speed=(fast, slow, moderate, very slow etc.)
7. FUZZY LOGIC SYSTEM
8. 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.
9.  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)
10.  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.
11. OPTIMAL CAPACITOR PLACEMENT INDISTRIBUTION SYSTEM USINGFUZZY TECHNIQUES
12.  The power loss in a distribution system is significantly high because of lower voltage and hencehigh current, compared to that in a high voltagetransmission system. The pressure of improving theoverall efficiency of power delivery has forced thepower utilities to reduce the loss, especially at thedistribution level This can be achieved by placingthe optimal value of capacitors at proper locationsin radial distribution systems.
13.  The objective of the capacitor placementproblem is to determine the locations and sizes ofthe capacitors so that the power loss is minimizedand 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.
14.  The fuzzy system take two input variable as1. Power loss reduction index(PLRI)2. Bus voltage And one output variable as1. Capacitor placement suitability index(CPSI)
15.  Decision matrix/Rule base
16. Based on these two values capacitor placementsuitability index (CPSI) for each bus is determinedby 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.
17.  Bus location for capacitor placement
18.  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.