Adaptive artificial neural network based control strategy for shunt active power filter

Abstract

Shunt active power filter (SAPF) is used to mitigate the current harmonics and to improve the power factor. In this paper, Adaptive linear-neuron (ADALINE) based phase lock loop (PLL) controlling scheme is presented for SAPF. ADALINE networks estimate the fundamental supply frequency. This scheme detects the phase information of the supply voltage and also used for parallel computing as it provides faster convergence. This algorithm is trained by least-mean squares (LMS) rule which offers low computational burden on the system. In this work, ADALINE is tuned using particle swarm optimization (PSO) technique to improve the dynamic performance of the system. The results obtained are compared with conventional PLL control technique and are found to be significantly better. The performance of the proposed ADALINE based control algorithm is validated using MATLAB/Simulink.