Wide area adaptive hybrid fuzzy STATCOM controller for dynamic stability enhancement

Abstract

Purpose The purpose of this paper is to develop an adaptive fuzzy controller for STATCOM to damp low-frequency inter-area oscillation over wide operating range using wide area signals in multimachine power system. Design/methodology/approach In this paper tuneable fuzzy model is proposed where the parameters of the fuzzy inference system are tuned by using the adaptive characteristic of the artificial neural network. Based on back propagation algorithm and method of least square estimation, the fuzzy inference rule base is tweaked according to the data from which they are modelled. The wide area control signals, for the proposed controller, available in the power system are selected on the basis of eigenvalue sensitivity defined in terms of participation factor. Findings The effectiveness of the proposed controller with wide area signals is tested on two test cases, namely, two area network and IEEE 12 bus benchmark system. The comparative analysis of the proposed adaptive fuzzy controller is carried out with conventional STATCOM controller along with fuzzy-and neural-based supplementary controller all using selected wide area signals. The results show that neural network tuned fuzzy controller leads to better system identification and have enhanced damping characteristics over wide operating range. Originality/value In the available literature, numerous researchers have indicated the use of fuzzy logic controller and neural controller along with their hybrid schemes as STATCOM controller for improving the dynamics of the multimachine power system using local signals. The main contribution of the paper is in using the hybrid intelligent control scheme for STATCOM using wide area signals. The advantage of proposed scheme is that the performance of well-designed fuzzy system can be enhanced with the same training data that are used for designing a neural controller thus giving enhanced performance in comparison to individual intelligent control scheme.