Adaptive Neural Sliding Mode Control of Active Power Filter

Juntao Fei,Zhe Wang

doi:10.1155/2013/341831

Abstract

A radial basis function (RBF) neural network adaptive sliding mode control system is developed for the current compensation control of three-phase active power filter (APF). The advantages of the adaptive control, neural network control, and sliding mode control are combined together to achieve the control task; that is, the harmonic current of nonlinear load can be eliminated and the quality of power system can be well improved. Sliding surface coordinate function and sliding mode controller are used as input and output of the RBF neural network, respectively. The neural network control parameters are online adjusted through gradient method and Lyapunov theory. Simulation results demonstrate that the adaptive RBF sliding mode control can compensate harmonic current effectively and has strong robustness to disturbance signals.

Highlights

Active power filters are commonly used to deal with the increasing harmonic current in electrical system nowadays, which can degrade the quality of power system
The motivation of this paper is to investigate the combination of adaptive control, neural network control and sliding mode control applied to active power filter (APF) based on Lyapunov analytical method
An adaptive radial basis function (RBF) neural sliding mode control method is proposed for three-phase active power filter

Summary

Introduction

Active power filters are commonly used to deal with the increasing harmonic current in electrical system nowadays, which can degrade the quality of power system. Kandil et al [17] developed a novel three-phase active filter based on neural networks and sliding mode control, but RBF neural network and Lyapunov stability analysis are not used in the paper. We will design an adaptive controller for shunt active power filter by combining the advantages of adaptive control, RBF neural network, and sliding mode control strategies. (3) The adaptive neural network sliding mode control is proposed to deal with nonlinear load in APF system and to improve the performance of current tracking. This is a successful example of using adaptive control, RBF neural network control, and sliding mode control with application to three-phase APF

Dynamics of Active Power Filter

Adaptive RBF Neural Sliding Control and Stability Analysis

L dVs dt dVdc dt

Simulation Study

Conclusions