Artificial neural network-based harmonics extraction algorithm for shunt active power filter control

Abstract

This paper presents a harmonics extraction algorithm using artificial neural network methods. The neural network algorithm was used due to the simpler calculation process compared with conventional method such as fast Fourier transform (FFT). Two types of neural network, i.e., multi-layer perceptron (MLP) and radial basis function (RBF) were employed to extract harmonics current component from its distorted wave current. Further, the extracted harmonics current was used as reference current for shunt active power filter (APF) control. This paper compared the performance of MLP and RBF for harmonics extraction. The advantages of RBF are simpler shape of the network and faster learning speed. Unfortunately, the RBF need to be trained recursively for various harmonics component. MLP can be used to extract various harmonics component in specific data range but need large number of data training hence slower training process.