Open document
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General information |
| Author |
Fei, Juntao; Wang, Zhe |
| Published |
InTech Open Access Publisher, 2013
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| 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. |
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International Journal of Advanced Robotic Systems
Author: Ottaviano, Erika; Ceccarelli, Marco; Husty, Manfred; Yu, Sung-Hoon; Kim, Yong-Tae; Park, Chang-Woo; Hyun, Chang-Ho; Chen, Xiulong; Feng, Weiming; Sun, Xianyang; Gao, Qing; Grigorescu, Sorin M.; Pozna, Claudiu; Liu, Wanli; Zhankui, Wang; Guo, Meng; Fu, Guoyu; Zhang, Jin; Chen, Wenyuan; Peng, Fengchao; Yang, Pei; Chen, Chunlin; Ding, Rui; Yu, Junzhi; Yang, Qinghai; Tan, Min; Polden, Joseph; Pan, [...]
Published: 2004
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