Co-Active Neuro-Fuzzy Inference System for Governing Control and Excitation Control of Power System Stability
This paper presents the application of extended adaptive neuro-fuzzy inference system (ANFIS) for solving the power system stability problem. The issue of power system stability is becoming more crucial . The excitation and governing control of generator play an important role in improving the dynamic and transient stability of power system. In this paper, the authors present an extended Neuro-fuzzy based method called coactive neuro-fuzzy inference system (CANFIS), for the excitation control and governing control. Among these techniques, generator control is one of the most widely applied in power industry. In this paper a coordination of governing control and excitation control using multiple-output ANFIS with nonlinear fuzzy rules called CANFIS model compensate their control inputs during faults . The proposed CANFIS automatically coordinates the behavior of the two compensations. The observations show its satisfactory behavior for proposed control. A generalized ANFIS which is tuned by a Multilayer Perceptron (MLP) neural network has a modified version of ANFIS and it improves the power system response by fast damping of oscillations of internal angle of generator following different types of faults and different location of transmission line.