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Control strategy of leveling load power fluctuations based on fuzzy neural networks by tuning coefficients of learning rate with genetic algorithm
Author(s) -
Fujii Toshinori,
Funabiki Shigeyuki
Publication year - 1998
Publication title -
electrical engineering in japan
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.136
H-Index - 28
eISSN - 1520-6416
pISSN - 0424-7760
DOI - 10.1002/(sici)1520-6416(199810)125:1<65::aid-eej8>3.0.co;2-h
Subject(s) - artificial neural network , crossover , genetic algorithm , control theory (sociology) , fuzzy logic , computer science , power (physics) , fuzzy control system , scaling , mutation , algorithm , artificial intelligence , mathematics , control (management) , machine learning , physics , biology , quantum mechanics , biochemistry , geometry , gene
Abstract The effective usage of the power facilities can be realized by leveling the fluctuating active power and compensating the reactive power. The fuzzy and fuzzy neural network control strategy of superconducting magnetic energy storages (SMES) was proposed for this purpose. The control results depend on the values of coefficients of learning rate in fuzzy neural networks. Therefore, it is desirable to obtain better control results by tuning the coefficients of learning rate to their optimum values. In this paper, the control strategy based on an autotuning of scaling factors with neural network and tuning of coefficients of learning rate of neural network with genetic algorithm is proposed for leveling load fluctuations. Encoding and decoding of coefficients of learning rate and selection, crossover, and mutation within genetic operations are shown, and crossover rate and mutation rate are discussed. Through these methods, we can achieve a better leveling of load power fluctuation by using fuzzy neural network with genetic algorithm. © 1998 Scripta Technica, Electr Eng Jpn, 125(1): 65–72, 1998