Adaptive Neuro-Fuzzy inference system for automatic generation control of interconnected hydrothermal plant

Abstract

This paper presents the analysis of Load Frequency Control (LFC) of a two-area hydrothermal system under deregulated environment by considering Adaptive Neuro-Fuzzy Inference System (ANFIS). Fixed gain controllers for LFC are designed at nominal operating conditions and fail to provide best control performance over a wide range of operating conditions. So, in order to keep system performance near its optimum, it is desirable to track the operating conditions and use updated parameters to compute control gains. Open transmission access and the evolving of more socialized companies for generation, transmission and distribution affects the formulation of AGC problem. So the traditional LFC two-area system is modified to take into account the effect of bilateral contracts on the dynamics. A control scheme based on ANFIS, which is trained by the results of off-line studies obtained using genetic algorithm, is proposed in this paper to optimize and update control gains in real-time according to load variations. The efficiency of the proposed method is demonstrated through computer simulations.