Application of fuzzy neural network controller in hydropower generator unit

Abstract

Purpose

The purpose of this paper is to develop a new hybridized controller based on fuzzy reasoning and neural network (NN) for hydropower generator unit (HGU).

Design/methodology/approach

The approach contains fuzzy neural networks controller (FNNC), RBF network identification (RBFNI) and HGU system. FNNC may give control value to control HGU via fuzzy NN reasoning and computing according to HGU rotate speed error and error varying rate. RBFNI is used to identify the character of HGU system and predict its output. FNNC may adjust parameters and member function according to the identifying and predictive outcome of RBFNI.

Findings

Sees that the hybridized control system is feasible and stable, and the controlling performance of the hybridized system is superior to conventional fuzzy controller.

Research limitations/implications

The theoretical proof of stability of the proposed scheme still remains to be studied. Accessibility and availability of membership functions and control rules is also a limitation applied.

Practical implications

The main advantage of the proposed method is that FNNC has reasoning, learning, and optimizing capability which can control effectively HGU. This will be useful for control engineers to control complex industrial plants.

Originality/value

The paper proposes new combined approach to optimal control of HGU using FNNC, and it is aimed at operational researches and engineers, especially those who dealt with HUG controller.

Citation

Xiao, Z., Guo, J., Zeng, H., Zhou, P. and Wang, S. (2009), "Application of fuzzy neural network controller in hydropower generator unit", Kybernetes, Vol. 38 No. 10, pp. 1709-1717. https://doi.org/10.1108/03684920910994079