Improved Neural Network Algorithms with Time-Varying Widrow-Hoff Learning Rule for Harmonic Estimation

Abstract

Estimation of harmonics is the main process in harmonic reduction. Algorithms are very well developed for estimation of harmonic components. In power electronic applications, objectives like fast response of the system are of primary importance. Effective methods for estimation of instantaneous harmonic components are presented in this paper. Originally, Fourier transformation is used to analyse a distorted waveform. In order to improve the convergence rate and processing speed an adaptive neural network algorithm, called ADALINE, has been used. Further improvements in the existing ADALINE algorithm have been made and two new algorithms, namely Modified ADALINE and Constricted ADALINE, are proposed by incorporating time-varying learning factor into the weight updating rule of ADALINE. The proposed methods stay effective as they converge to a minimum error and bring out finer estimation. The proposed methods are validated through harmonic estimation of PC load current. Then the results are compared with the existing ADALINE algorithm to illustrate their effectiveness.