DocumentCode
1429456
Title
Enhancement of restoration service in distribution systems using a combination fuzzy-GA method
Author
Hsiao, Ying-Tung ; Chien, Ching-Yang
Author_Institution
Dept. of Electr. Eng., Tamkang Univ., Tamsui, Taiwan
Volume
15
Issue
4
fYear
2000
fDate
11/1/2000 12:00:00 AM
Firstpage
1394
Lastpage
1400
Abstract
This work presents a combination fuzzy-GA method to resolve the service restoration problem. The problem formulation proposed herein considers five different objective functions related to maximizing the amount of total load to be restored as well as minimizing the number of the switching operations, deviation of the bus voltage, the feeder´s current and transformer´s loading. Meanwhile, the operational constraints, radial structure of the network configuration and sequence of the switching operations are included in the problem formulation. These objective functions are modeled with fuzzy sets to evaluate their imprecise nature. In the interactive method, the dispatcher provides his or her anticipated value (the degree of preference) of each objective, then the optimization problem is solved by the genetic algorithm, Analyzing the results from the former interactive and updating the expected value of each objective function via the interactive procedure allow us to derive the compromised or satisfied solution efficiently. Simulation results obtained from the Tai-Power system demonstrate the effectiveness of the solution algorithm
Keywords
distribution networks; fuzzy set theory; genetic algorithms; power system restoration; switching; Tai-Power system; bus voltage deviation; combination fuzzy-GA method; dispatcher; distribution power flow; distribution systems; feeder current; fuzzy sets; genetic algorithm; interactive fuzzy satisfying method; interactive method; multi-objective programming; operational constraints; radial structure; restoration service enhancement; switching operations; total load maximisation; transformer loading; Control systems; Councils; Expert systems; Fuzzy sets; Genetic algorithms; Instruction sets; Load modeling; Optimization methods; Power system restoration; Transformers;
fLanguage
English
Journal_Title
Power Systems, IEEE Transactions on
Publisher
ieee
ISSN
0885-8950
Type
jour
DOI
10.1109/59.898118
Filename
898118
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