An optimized fuzzy controller for a synchronous generator in a multi-machine environment

In this paper, an optimized neuro-fuzzy power-system stabilizer (NF PSS) is proposed to improve the transient and dynamic stability of synchronous mac hines. The NF PSS employs a five-layer fuzzy-neural network (FNN). The lear ning scheme of this FNN is composed of three phases. The first phase uses a clustering algorithm for coarse identification of the initial membership f unctions of the fuzzy controller (FC). The second phase extracts the lingui stic-fuzzy rules from the available training data. In the third phase, a mu lti-resolutional dynamic genetic algorithm (MRD-GA) is used to fine-tune an d optimize the membership functions of the FC. Extensive simulation studies have been carried out to show the performance of the NF PSS and to compare it with a Conventional PSS (CPSS) in a multi-machine power-system environm ent. (C) 1999 Published by Elsevier Science B.V. All rights reserved.