Open AccessAdaptive RBF Neural Network Control for Three-Phase Active Power Filter
Author(s) -
Juntao Fei,
Zhe Wang
Publication year - 2013
Publication title -
international journal of advanced robotic systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.394
H-Index - 46
eISSN - 1729-8814
pISSN - 1729-8806
DOI - 10.5772/56535
Subject(s) - control theory (sociology) , computer science , artificial neural network , total harmonic distortion , harmonics , robustness (evolution) , radial basis function , nonlinear system , three phase , tracking error , adaptive control , voltage , artificial intelligence , control (management) , engineering , biochemistry , chemistry , physics , quantum mechanics , electrical engineering , gene
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