A FUZZY WAVELET NEURAL NETWORK LOAD FREQUENCY CONTROLLER BASED ON GENETIC ALGORITHM

In this paper, a self tuning load frequency controller based on Fuzzy Wavelet Neural Network (FWNN) and Genetic Algorithm (GA) is developed to quench the deviations in frequency and tie line power due to load disturbances in an interconnected power system. The error between desired system output and output of control object is employed to tune the network parameters. Tuning rule is accomplished based on GA approach by minimizing a compound of control error. For the purpose of the proposed method’s evaluation, the proposed method is applied to a two area power system with considerations regarding governor saturation and the results are compared to the one obtained by a classic PI controller. Moreover, the robustness of the proposed method is tested against change of parameters. The simulation studies show that the designed controller by proposed method has a very desirable dynamic performance, better operation and improved system parameters such as settling time and step response rise time even when the system parameters change.