An efficient hybrid tunicate swarm algorithm and radial basis function searching technique for maximum power point tracking in wind energy conversion system

Abstract

Purpose The purpose of this paper is to track the maximal power of wind energy conversion system (WECS) and enhance the search capability for WECS maximum power point tracking (MPPT). Design/methodology/approach The hybrid technique is the combination of tunicate swarm algorithm (TSA) and radial basis function neural network. Findings TSA gets input parameters from the rectifier outputs such as rectifier direct current (DC) voltage, DC current and time. From the input parameters, it enhances the reduced fault power of rectifier and generates training data set based on the MPPT conditions. The training data set is used in radial basis function. During the execution time, it produces the rectifier reference DC side voltage that is converted to control pulses of inverter switches. Originality/value Finally, the proposed method is executed in MATLAB/Simulink site, and the performance is compared with different existing methods like particle swarm optimization algorithm and hill climb searching technique. Then the output illustrates the performance of the proposed method and confirms its capability to solve issues.