A control strategy for leveling load power fluctuations with a successive learning fuzzy-neural network based on prediction of average load power

Toshinori Fujii, Shigeyuki Funabiki

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

The effective usage of power facilities can be realized by leveling the fluctuating active power and compensating the reactive power. A fuzzy control strategy of superconducting magnetic energy storage (SMES) has been proposed for this purpose. The control results depend on the values of the scaling factors in fuzzy reasoning. Therefore, to obtain better control results, the scaling factor should be successively adjusted according to the load power fluctuations. In this paper, a control strategy based on autotuning of scaling factors and a fuzzy singleton reasoning method using backpropagation in a neural network is proposed for leveling load fluctuations. The prediction and revision of the teaching signal in terms of the energy of the SMES is proposed. The learning rate and the revision of the teaching signal are discussed. Better leveling of load power fluctuation are shown to be achievable by using fuzzy logic and neural networks.

Original languageEnglish
Pages (from-to)72-80
Number of pages9
JournalElectrical Engineering in Japan (English translation of Denki Gakkai Ronbunshi)
Volume120
Issue number2
Publication statusPublished - 1997

Fingerprint

Fuzzy neural networks
Energy storage
Teaching
Neural networks
Fuzzy control
Reactive power
Backpropagation
Fuzzy logic

Keywords

  • Fuzzy inference
  • Neural network
  • Power leveling
  • SMES
  • Successive learning

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

Cite this

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abstract = "The effective usage of power facilities can be realized by leveling the fluctuating active power and compensating the reactive power. A fuzzy control strategy of superconducting magnetic energy storage (SMES) has been proposed for this purpose. The control results depend on the values of the scaling factors in fuzzy reasoning. Therefore, to obtain better control results, the scaling factor should be successively adjusted according to the load power fluctuations. In this paper, a control strategy based on autotuning of scaling factors and a fuzzy singleton reasoning method using backpropagation in a neural network is proposed for leveling load fluctuations. The prediction and revision of the teaching signal in terms of the energy of the SMES is proposed. The learning rate and the revision of the teaching signal are discussed. Better leveling of load power fluctuation are shown to be achievable by using fuzzy logic and neural networks.",
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