Voltage regulation using recurrent wavelet fuzzy neural network-based dynamic voltage restorer

Cheng I. Chen, Yeong Chin Chen, Chung Hsien Chen, Yung Ruei Chang

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

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.

Original languageEnglish
Article number6242
JournalEnergies
Volume13
Issue number23
DOIs
StatePublished - 1 Dec 2020

Keywords

  • Voltage regulation
  •  dynamic voltage restorer (DVR)
  •  positive-sequence voltage analysis
  •  power quality
  •  recurrent wavelet fuzzy neural network (RWFNN)-based controller

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