Adaptive RBF Neural Network Control for Three-Phase Active Power Filter

Juntao Fei,Zhe Wang

doi:10.5772/56535

Abstract

Abstract An adaptive radial basis function (RBF) neural network control system for three-phase active power filter (APF) is proposed to eliminate harmonics. Compensation current is generated to track command current so as to eliminate the harmonic current of non-linear load and improve the quality of the power system. The asymptotical stability of the APF system can be guaranteed with the proposed adaptive neural network strategy. The parameters of the neural network can be adaptively updated to achieve the desired tracking task. The simulation results demonstrate good performance, for example showing small current tracking error, reduced total harmonic distortion (THD), improved accuracy and strong robustness in the presence of parameters variation and nonlinear load. It is shown that the adaptive RBF neural network control system for three-phase APF gives better control than hysteresis control.

Highlights

IntroductionThe harmonic current in electrical systems increases with nonlinear loads, which has degraded power quality
The harmonic current in electrical systems increases with nonlinear loads, which has degraded power quality.Active power filter (APF) is effective to deal with the harmonic current and power factor of the varying loads
active power filter (APF) is shown in Fig. 1, where vsavsbvsc are voltages of the three‐phase power system, r is the resistance from the power source to inductance on the AC side of APF, L is the inductance on the AC side of APF, vdc is the capacitor voltage on the DC side, is is line current, iL is non‐linear load current, i*c is command current ic is compensation current, and as the basis of compensation current

Summary

Introduction

The harmonic current in electrical systems increases with nonlinear loads, which has degraded power quality. Marconi et al [11] developed a robust nonlinear controller to compensate harmonic current for shunt active filters. Neither systematic analysis nor controller design for APF utilizing RBF neural network based on Lyapunov stability theory, have been discussed in the literature. An adaptive control method based on RBF neural network is proposed to overcome the shortcomings of traditional methods, improve the current tracking performance and guarantee the Lyapunov stability of the closed‐loop system. This method features high control accuracy, real‐time operation, and a wide range of applications, and can reduce current total harmonic distortion effectively. This is a successful application example using adaptive control, neural network control, with application to the APF

Basic Principles of Active Power Filter

RBF Neural Network

Design of RBF Neural Controller

Simulation Study

Findings

Conclusion