Stability analysis and enhancement in Wind Power System using Fuzzy Controller with Hopfield Neural Net

Abstract This paper intends to propose a method to improve the stability and power control in two area power system by tuning fuzzy control vector with help of Hopfield Neural Net Algorithm (HNNA). Because of the sudden load variations and wind power disparity, there will be frequency fluctuation problem in interconnected power system and it leads to loss of stability. This developed control strategy gives the solution for large speed variations of renewable energy source of plant like wind power plant. When fuzzy logic controller (FLC) is implemented for stability and control problem, data from control vector is considered. Proposed method is concentrated to obtain new set of dada from fuzzy logic controller used in interconnected power system. According to discrepancy in wind plant speed variations, Hopfield Neural Net algorithm is used for getting the modified control vector values from fuzzy controller. To obtain correctness in data of controlled output from parent data from wind plant is taken as input, and hence new set of data points are obtained from Hopfield Neural Net.