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Intelligent Controlled DSTATCOM for Power Quality Enhancement

Author

Listed:
  • Jun-Hao Chen

    (Department of Electrical and Electronic Engineering, Chung Cheng Institute of Technology, National Defense University, Taoyuan 335, Taiwan)

  • Kuang-Hsiung Tan

    (Department of Electrical and Electronic Engineering, Chung Cheng Institute of Technology, National Defense University, Taoyuan 335, Taiwan)

  • Yih-Der Lee

    (Nuclear Instrumentation Division, Institute of Nuclear Energy Research, Taoyuan 325, Taiwan)

Abstract

In this study, a three-phase four-wire distribution static compensator (DSTATCOM) is proposed to improve power quality, including the compensation of the three-phase unbalanced grid currents, the total harmonic distortion (THD) reduction of the grid current, and the power factor (PF) correction. Moreover, when different types of loads vary in the power system, the instantaneous power follows into or out of the DC-link capacitor in the DSTATCOM and results in poor transient responses of the grid current and DC-link voltage and performance deterioration. Hence, the DC-link voltage control plays a significant part in the DSTATCOM under load variation. For the purpose of mending the transient responses of the grid currents and DC-link voltage control and the performance of the DSTATCOM, the conventional proportional-integral (PI) controller is substituted with a novel online trained wavelet Takagi-Sugeno-Kang fuzzy neural network (WTSKFNN) controller in this study. Furthermore, the network structure and the online learning method of the proposed WTSKFNN controller are described in detail. Finally, the experimental results are given to certify the feasibility and effectiveness of the DSTATCOM using the proposed WTSKFNN controller for the power quality enhancement and the DC-link control improvement under load variation.

Suggested Citation

  • Jun-Hao Chen & Kuang-Hsiung Tan & Yih-Der Lee, 2022. "Intelligent Controlled DSTATCOM for Power Quality Enhancement," Energies, MDPI, vol. 15(11), pages 1-19, May.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:11:p:4017-:d:827706
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    Citations

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    Cited by:

    1. Oscar G. Duarte & Javier A. Rosero & María del Carmen Pegalajar, 2022. "Data Preparation and Visualization of Electricity Consumption for Load Profiling," Energies, MDPI, vol. 15(20), pages 1-30, October.
    2. Zehui Yuan & Zheng Liao & Haiyan Tu & Yuxin Tu & Wei Li, 2022. "Analysis of Wide-Frequency Dense Signals Based on Fast Minimization Algorithm," Energies, MDPI, vol. 15(15), pages 1-18, August.
    3. Umme Mumtahina & Sanath Alahakoon & Peter Wolfs, 2023. "A Literature Review on the Optimal Placement of Static Synchronous Compensator (STATCOM) in Distribution Networks," Energies, MDPI, vol. 16(17), pages 1-38, August.

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