DocumentCode :
3470207
Title :
Optimal algorithm of distribution network planning including distributed generation
Author :
Wang, Yanjun ; Zhang, Yun
Author_Institution :
Sch. of Electr. Eng. & Autom., Tanjin Univ., Tianjin
fYear :
2008
fDate :
6-9 April 2008
Firstpage :
872
Lastpage :
876
Abstract :
The genetic algorithm has some defects for optimizing multi-objective, such as slow convergent speed and easy to premature. So this article proposes an improved self- adaptive genetic algorithm, improving terminal criterion and method of selection, make a self-adaptive disposal in crossover and mutation probability. Considering multi-objective of distribution network planning including distributed generation, this article introduces total satisfied degree by employing the fuzzy optimal theory, which makes a good way to transform multi-objective into single objective. Results of a system simulation show that this algorithm can seek the best result of overall situation effectively, increase the convergence speed obviously, also has favorable self-adaptive characteristic.
Keywords :
distributed power generation; fuzzy set theory; genetic algorithms; power distribution planning; crossover probability; distributed generation; distribution network planning; fuzzy theory; genetic algorithm; mutation probability; optimal algorithm; self-adaptive disposal; terminal criterion; Cost function; Distributed control; Genetic algorithms; Genetic mutations; Investments; Power system reliability; Power system security; Reactive power; Stability; Voltage; Distributed generation; distribution network planning; fuzzy optimization; improved self-adaptive genetic algorithm; multi-objective optimization;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Electric Utility Deregulation and Restructuring and Power Technologies, 2008. DRPT 2008. Third International Conference on
Conference_Location :
Nanjuing
Print_ISBN :
978-7-900714-13-8
Electronic_ISBN :
978-7-900714-13-8
Type :
conf
DOI :
10.1109/DRPT.2008.4523529
Filename :
4523529
Link To Document :
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