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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Cheng-I Chen & Yeong-Chin Chen & Chung-Hsien Chen & Yung-Ruei Chang, 2020. "Voltage Regulation Using Recurrent Wavelet Fuzzy Neural Network-Based Dynamic Voltage Restorer," Energies, MDPI, vol. 13(23), pages 1-19, November.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:23:p:6242-:d:451827
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    References listed on IDEAS

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    1. Emiyamrew Minaye Molla & Cheng-Chien Kuo, 2020. "Voltage Quality Enhancement of Grid-Integrated PV System Using Battery-Based Dynamic Voltage Restorer," Energies, MDPI, vol. 13(21), pages 1-16, November.
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    Cited by:

    1. Cheng-I Chen & Sunneng Sandino Berutu & Yeong-Chin Chen & Hao-Cheng Yang & Chung-Hsien Chen, 2022. "Regulated Two-Dimensional Deep Convolutional Neural Network-Based Power Quality Classifier for Microgrid," Energies, MDPI, vol. 15(7), pages 1-16, March.
    2. Zhenyu Li & Ranchen Yang & Xiao Guo & Ziming Wang & Guozhu Chen, 2022. "A Novel Voltage Sag Detection Method Based on a Selective Harmonic Extraction Algorithm for Nonideal Grid Conditions," Energies, MDPI, vol. 15(15), pages 1-21, July.
    3. Cheng-I Chen & Yeong-Chin Chen & Chung-Hsien Chen, 2022. "Recurrent Wavelet Fuzzy Neural Network-Based Reference Compensation Current Control Strategy for Shunt Active Power Filter," Energies, MDPI, vol. 15(22), pages 1-23, November.

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