A PSO method with nonlinear time-varying evolution based on neural network for design of optimal harmonic filters

Abstract

A particle swarm optimization method with nonlinear time-varying evolution based on neural network (PSO-NTVENN) is proposed to design large-scale passive harmonic filters (PHF) under abundant harmonic current sources. The goal is to minimize the cost of the filters, the filters loss, and the total harmonic distortion of currents and voltages at each bus, simultaneously. In the PSO-NTVENN method, parameters are determined by using a sequential neural network approximation. Meanwhile, based on the concept of multi-objective optimization, how to define the fitness function of the PSO to include different performance criteria is also discussed. To show the feasibility of the proposed method, illustrative examples of designing optimal passive harmonic filters for a chemical plant are presented.