Voltage Regulation Using Recurrent Wavelet Fuzzy Neural Network-Based Dynamic Voltage Restorer

被引：7
|
作者：
Chen, Cheng-, I [1 ]
Chen, Yeong-Chin [2 ]
Chen, Chung-Hsien [3 ]
Chang, Yung-Ruei [4 ]
机构：
[1] Natl Cent Univ, Dept Elect Engn, Taoyuan 32001, Taiwan
[2] Asia Univ, Dept Comp Sci & Informat Engn, Taichung 41354, Taiwan
[3] Met Ind Res & Dev Ctr, Taichung 40768, Taiwan
[4] Inst Nucl Energy Res, Taoyuan 32546, Taiwan
来源：
ENERGIES | 2020年 / 13卷 / 23期
关键词：
voltage regulation; dynamic voltage restorer (DVR); power quality; recurrent wavelet fuzzy neural network (RWFNN)-based controller; positive-sequence voltage analysis; COMPENSATION; IMPROVEMENT; STRATEGY;
D O I：
10.3390/en13236242
中图分类号：
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号：
0807 ; 0820 ;
摘要：
Dynamic voltage restorers (DVRs) are one of the effective solutions to regulate the voltage of power systems and protect sensitive loads against voltage disturbances, such as voltage sags, voltage fluctuations, et cetera. The performance of voltage compensation with DVRs relies on the robustness to the power quality disturbances and rapid detection of voltage disturbances. In this paper, the recurrent wavelet fuzzy neural network (RWFNN)-based controller for the DVR is developed. With positive-sequence voltage analysis, the reference signal for the DVR compensation can be accurately obtained. In order to enhance the response time for the DVR controller, the RWFNN is introduced due to the merits of rapid convergence and superior dynamic modeling behavior. From the experimental results with the OPAL-RT real-time simulator (OP4510, OPAL-RT Technologies Inc., Montreal, Quebec, Canada), the effectiveness of proposed controller can be verified.
引用
收藏
页数：19
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