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Adaptive Fuzzy Approximation Control of PV Grid-Connected Inverters

Author

Listed:
  • Myada Shadoul

    (Department of Electrical and Computer Engineering, Sultan Qaboos University, Muscat-123, Oman)

  • Hassan Yousef

    (Department of Electrical and Computer Engineering, Sultan Qaboos University, Muscat-123, Oman)

  • Rashid Al Abri

    (Department of Electrical and Computer Engineering, Sultan Qaboos University, Muscat-123, Oman)

  • Amer Al-Hinai

    (Department of Electrical and Computer Engineering, Sultan Qaboos University, Muscat-123, Oman
    Sustainable Energy Research Center, Sultan Qaboos University, Muscat-123, Oman)

Abstract

Three-phase inverters are widely used in grid-connected renewable energy systems. This paper presents a new control methodology for grid-connected inverters using an adaptive fuzzy control (AFC) technique. The implementation of the proposed controller does not need prior knowledge of the system mathematical model. The capabilities of the fuzzy system in approximating the nonlinear functions of the grid-connected inverter system are exploited to design the controller. The proposed controller is capable to achieve the control objectives in the presence of both parametric and modelling uncertainties. The control objectives are to regulate the grid power factor and the dc output voltage of the photovoltaic systems. The closed-loop system stability and the updating laws of the controller parameters are determined via Lyapunov analysis. The proposed controller is simulated under different system disturbances, parameters, and modelling uncertainties to validate the effectiveness of the designed controller. For evaluation, the proposed controller is compared with conventional proportional-integral (PI) controller and Takagi–Sugeno–Kang-type probabilistic fuzzy neural network controller (TSKPFNN). The results demonstrated that the proposed AFC showed better performance in terms of response and reduced fluctuations compared to conventional PI controllers and TSKPFNN controllers.

Suggested Citation

  • Myada Shadoul & Hassan Yousef & Rashid Al Abri & Amer Al-Hinai, 2021. "Adaptive Fuzzy Approximation Control of PV Grid-Connected Inverters," Energies, MDPI, vol. 14(4), pages 1-22, February.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:4:p:942-:d:497486
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    References listed on IDEAS

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    1. Zahedi, A., 2011. "A review of drivers, benefits, and challenges in integrating renewable energy sources into electricity grid," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(9), pages 4775-4779.
    2. Sidra Mumtaz & Saghir Ahmad & Laiq Khan & Saima Ali & Tariq Kamal & Syed Zulqadar Hassan, 2018. "Adaptive Feedback Linearization Based NeuroFuzzy Maximum Power Point Tracking for a Photovoltaic System," Energies, MDPI, vol. 11(3), pages 1-15, March.
    3. Monica, P. & Kowsalya, M., 2016. "Control strategies of parallel operated inverters in renewable energy application: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 65(C), pages 885-901.
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    Cited by:

    1. Roy Chaoming Hsu & Tzu-Hao Lin & Po-Cheng Su, 2022. "Dynamic Energy Management for Perpetual Operation of Energy Harvesting Wireless Sensor Node Using Fuzzy Q-Learning," Energies, MDPI, vol. 15(9), pages 1-22, April.
    2. Yeqin Wang & Yan Yang & Rui Liang & Tao Geng & Weixing Zhang, 2022. "Adaptive Current Control for Grid-Connected Inverter with Dynamic Recurrent Fuzzy-Neural-Network," Energies, MDPI, vol. 15(11), pages 1-20, June.
    3. Miao Zhang & Keyu Zhuang & Tong Zhao & Xianli Chen & Jingze Xue & Zheng Qiao & Shuai Cui & Yunlong Gao, 2022. "Bus Voltage Control of Photovoltaic Grid Connected Inverter Based on Adaptive Linear Active Disturbance Rejection," Energies, MDPI, vol. 15(15), pages 1-20, July.
    4. Grzegorz Dec & Grzegorz Drałus & Damian Mazur & Bogdan Kwiatkowski, 2021. "Forecasting Models of Daily Energy Generation by PV Panels Using Fuzzy Logic," Energies, MDPI, vol. 14(6), pages 1-16, March.
    5. Adolfo Dannier & Gianluca Brando & Marino Coppola, 2022. "Special Issue on Power Converter of Electric Machines, Renewable Energy Systems, and Transportation," Energies, MDPI, vol. 15(3), pages 1-3, January.

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