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Probabilistic Wavelet Fuzzy Neural Network based reactive power control for grid-connected three-phase PV system during grid faults

Author

Listed:
  • Lin, Faa-Jeng
  • Lu, Kuang-Chin
  • Ke, Ting-Han

Abstract

This study presents a reactive power controller using Probabilistic Wavelet Fuzzy Neural Network (PWFNN) for grid-connected three-phase PhotoVoltaic (PV) system during grid faults. The controller also considers the ratio of the injected reactive current to meet the Low Voltage Ride Through (LVRT) regulation. Moreover, the balance of the active power between the PV panel and the grid-connected inverter during grid faults is controlled by the dc-link bus voltage. Furthermore, to reduce the risk of over-current during LVRT operation, a current limit is predefined for the injection of reactive current. The main contribution of this study is the introduction of the PWFNN controller for reactive and active power control that provides LVRT operation with power balance under various grid fault conditions. Finally, some experimental tests are realized to validate the effectiveness of the proposed controller.

Suggested Citation

  • Lin, Faa-Jeng & Lu, Kuang-Chin & Ke, Ting-Han, 2016. "Probabilistic Wavelet Fuzzy Neural Network based reactive power control for grid-connected three-phase PV system during grid faults," Renewable Energy, Elsevier, vol. 92(C), pages 437-449.
    • Handle: RePEc:eee:renene:v:92:y:2016:i:c:p:437-449
    DOI: 10.1016/j.renene.2016.02.036
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    Citations

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    Cited by:

    1. Qamar Navid & Ahmed Hassan & Abbas Ahmad Fardoun & Rashad Ramzan & Abdulrahman Alraeesi, 2021. "Fault Diagnostic Methodologies for Utility-Scale Photovoltaic Power Plants: A State of the Art Review," Sustainability, MDPI, vol. 13(4), pages 1-22, February.
    2. Li Wang & Teng Qiao & Bin Zhao & Xiangjun Zeng & Qing Yuan, 2020. "Modeling and Parameter Optimization of Grid-Connected Photovoltaic Systems Considering the Low Voltage Ride-through Control," Energies, MDPI, vol. 13(15), pages 1-23, August.
    3. Woon-Gyu Lee & Thai-Thanh Nguyen & Hyeong-Jun Yoo & Hak-Man Kim, 2018. "Low-Voltage Ride-Through Operation of Grid-Connected Microgrid Using Consensus-Based Distributed Control," Energies, MDPI, vol. 11(11), pages 1-18, October.
    4. Zeb, Kamran & Uddin, Waqar & Khan, Muhammad Adil & Ali, Zunaib & Ali, Muhammad Umair & Christofides, Nicholas & Kim, H.J., 2018. "A comprehensive review on inverter topologies and control strategies for grid connected photovoltaic system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 94(C), pages 1120-1141.
    5. Muhammad Yasir Ali Khan & Haoming Liu & Zhihao Yang & Xiaoling Yuan, 2020. "A Comprehensive Review on Grid Connected Photovoltaic Inverters, Their Modulation Techniques, and Control Strategies," Energies, MDPI, vol. 13(16), pages 1-40, August.
    6. Chao-Rong Chen & Faouzi Brice Ouedraogo & Yu-Ming Chang & Devita Ayu Larasati & Shih-Wei Tan, 2021. "Hour-Ahead Photovoltaic Output Forecasting Using Wavelet-ANFIS," Mathematics, MDPI, vol. 9(19), pages 1-14, October.
    7. Athari, Hamed & Niroomand, Mehdi & Ataei, Mohammad, 2017. "Review and Classification of Control Systems in Grid-tied Inverters," Renewable and Sustainable Energy Reviews, Elsevier, vol. 72(C), pages 1167-1176.

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