An adaptive linear neural network with least mean M-estimate weight updating rule employed for harmonics identification and power quality monitoring

Abstract

This paper describes a combined adaptive linear neural network and least mean M-estimate (ADALINE-LMM) algorithm for estimating the amplitude and phase of the individual harmonic contained in a distorted power system current signal. The weight vector of the ADALINE is updated iteratively by LMM algorithm. A Hampel’s three parts redescending M-estimator function is incorporated in the instantaneous cost function to provide thresholds for identifying and eliminating the effect of temporary fluctuation owing to the presence of impulsive noise. This type of combined approach shows more accurate and faster tracking capability than the combined ADALINE and variable step size least mean square (ADALINE-VSLMS) algorithm. In addition to this, the proposed algorithm is suggested in shunt hybrid active power filter (SHAPF) for extracting the harmonics and reactive power components from the distorted load currents. Extensive time domain simulation is carried out to evaluate the performance of the SHAPF for maintaining the power quality of a system under various demanding situations. Moreover, an experimental setup is developed in the laboratory for verification of the proposed control technique in a real-time application using a Spartan 3A DSP processor.