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

Toshinori Fujii, Shigeyuki Funabiki

Research output: Contribution to journalArticle

1 Citation (Scopus)

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 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.

Original languageEnglish
Pages (from-to)552-557
Number of pages6
JournalIEEJ Transactions on Industry Applications
Volume117
Issue number5
DOIs
Publication statusPublished - 1997

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Fuzzy neural networks
Tuning
Genetic algorithms
Neural networks
Superconducting magnets
Reactive power
Energy storage
Decoding

ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering
  • Electrical and Electronic Engineering

Cite this

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