TY - JOUR

T1 - A Control Strategy of Levelling Load Power Fluctuation Based on Fuzzy-Neural Network by Tuning Coefficients of Learning Rate with Genetic Algorithm

AU - Fujii, Toshinori

AU - Funabiki, Shigeyuki

PY - 1997

Y1 - 1997

N2 - 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 magnet energy storages (SMES) was proposed for this purpose. The control results depend on the values of coefficients of learning rate in fuzzy neural network. Therefore, it is desired to obtain better control results that the coefficients of learning rate are tuned to the optimum value. In this paper, the control strategy based on an auto-tuning of scaling factors with neural network and tuning of coefficients of teaming rate of neural network with genetic algorithm is proposed for leveling load fluctuation. Encoding and decoding of coefficients of learning rate and selection, crossover and mutation of genetic operation are shown and crossover rate, mutation rate is discussed. Then, we can achieve the better leveling of load power fluctuation by using fuzzy neural network with genetic algorithm.

AB - 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 magnet energy storages (SMES) was proposed for this purpose. The control results depend on the values of coefficients of learning rate in fuzzy neural network. Therefore, it is desired to obtain better control results that the coefficients of learning rate are tuned to the optimum value. In this paper, the control strategy based on an auto-tuning of scaling factors with neural network and tuning of coefficients of teaming rate of neural network with genetic algorithm is proposed for leveling load fluctuation. Encoding and decoding of coefficients of learning rate and selection, crossover and mutation of genetic operation are shown and crossover rate, mutation rate is discussed. Then, we can achieve the better leveling of load power fluctuation by using fuzzy neural network with genetic algorithm.

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U2 - 10.1541/ieejias.117.552

DO - 10.1541/ieejias.117.552

M3 - Article

AN - SCOPUS:78951479351

SN - 0913-6339

VL - 117

SP - 552

EP - 557

JO - IEEJ Transactions on Industry Applications

JF - IEEJ Transactions on Industry Applications

IS - 5

ER -