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Recurrent Wavelet Fuzzy Neural Network-Based Reference Compensation Current Control Strategy for Shunt Active Power Filter

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)

Abstract

The usage of a shunt active power filter (SAPF) is one of the helpful means to mitigate the reactive power and harmonic current of a power grid. The compensation performance of the SAPF is related to the accuracy of the reference voltage extraction from the utility grid, the control stability of the DC-link voltage regulation, and the synchronization between the source voltage and the reference compensation current. To modify the performance of the SAPF for the harmonic compensation, the control strategy of the SAPF reference compensation current based on the recurrent wavelet fuzzy neural network (RWFNN) is proposed in this paper. There are three sections in the proposed control strategy, including the regulated fundamental positive-sequence extraction (section A), DC-link voltage regulation (section B), and calculation of reference compensation current (section C). By regulating the analysis mechanism with the variation of fundamental frequency in the section A, the accurate reference voltage would be obtained. The control stability for the regulation of the DC-link voltage can be accomplished by applying the RWFNN-based controller in the section B. With the synchronized reference voltage in the section A and the estimated control current in the section B, the reference compensation current can be correctly obtained in the section C. From the case studies with the real-time simulator produced by OPAL-RT Technologies Inc., the effectiveness of proposed control strategy for the SAPF reference compensation current can be verified.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:22:p:8687-:d:977648
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    References listed on IDEAS

    as
    1. 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.
    2. Cheng-I Chen & Chien-Kai Lan & Yeong-Chin Chen & Chung-Hsien Chen, 2019. "Adaptive Frequency-Based Reference Compensation Current Control Strategy of Shunt Active Power Filter for Unbalanced Nonlinear Loads," Energies, MDPI, vol. 12(16), pages 1-14, August.
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    Cited by:

    1. Minh Ly Duc & Petr Bilik & Radek Martinek, 2023. "Harmonics Signal Feature Extraction Techniques: A Review," Mathematics, MDPI, vol. 11(8), pages 1-36, April.

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