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

Cheng-I Chen, Yeong-Chin Chen, Chung-Hsien Chen and Yung-Ruei Chang
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Cheng-I Chen: Department of Electrical Engineering, National Central University, Taoyuan 32001, Taiwan
Yeong-Chin Chen: Department of Computer Science and Information Engineering, Asia University, Taichung 41354, Taiwan
Chung-Hsien Chen: Metal Industries Research and Development Centre, Taichung 40768, Taiwan
Yung-Ruei Chang: Institute of Nuclear Energy Research, Taoyuan 32546, Taiwan

Energies, 2020, vol. 13, issue 23, 1-19

Abstract: 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.

Keywords: voltage regulation; dynamic voltage restorer (DVR); power quality; recurrent wavelet fuzzy neural network (RWFNN)-based controller; positive-sequence voltage analysis (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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