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A Machine Learning-Based Communication-Free PV Controller for Voltage Regulation

Author

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  • Shabib Shahid

    (Electrical Engineering Department, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia)

  • Saifullah Shafiq

    (Electrical Engineering Department, Prince Mohammad Bin Fahd University, Khobar 31952, Saudi Arabia)

  • Bilal Khan

    (Electrical Engineering Department, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia)

  • Ali T. Al-Awami

    (Electrical Engineering Department, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia
    Interdisciplinary Research Center for Smart Mobility and Logistics, King Fahd University of Petroleum & Minerals, P.O. Box 5067, Dhahran 31261, Saudi Arabia)

  • Muhammad Omair Butt

    (Electrical Engineering Department, Prince Mohammad Bin Fahd University, Khobar 31952, Saudi Arabia)

Abstract

Due to the recent advancements in the manufacturing process of solar photovoltaics (PVs) and electronic converters, solar PVs has emerged as a viable investment option for energy trading. However, distribution system with large-scale integration of rooftop PVs, would be subjected to voltage upper limit violations, unless properly controlled. Most of the traditional solutions introduced to address this problem do not ensure fairness amongst the on-line energy sources. In addition, other schemes assume the presence of communication linkages between these energy sources. This paper proposes a control scheme to mitigate the over-voltages in the distribution system without any communication between the distributed energy sources. The proposed approach is based on artificial neural networks that can utilize two locally obtainable inputs, namely, the nodal voltage and node voltage sensitivity and control the PV power. The controller is trained using extensive data generated for various loading conditions to include daily load variations. The control scheme was implemented and tested on a 12.47 kV feeder with 85 households connected on the 220 V distribution system. The results demonstrate the fair control of all the rooftop solar PVs mounted on various houses to ensure the system voltage are maintained within the allowed limits as defined by the ANSI C84.1-2016 standard. Furthermore, to verify the robustness of the proposed PV controller, it is tested during cloudy weather condition and the impact of integration of electric vehicles on the proposed controller is also analyzed. The results prove the efficacy of the proposed controller.

Suggested Citation

  • Shabib Shahid & Saifullah Shafiq & Bilal Khan & Ali T. Al-Awami & Muhammad Omair Butt, 2021. "A Machine Learning-Based Communication-Free PV Controller for Voltage Regulation," Sustainability, MDPI, vol. 13(21), pages 1-22, November.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:21:p:12208-:d:672786
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    References listed on IDEAS

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    1. Khalid, Muhammad & Ahmadi, Abdollah & Savkin, Andrey V. & Agelidis, Vassilios G., 2016. "Minimizing the energy cost for microgrids integrated with renewable energy resources and conventional generation using controlled battery energy storage," Renewable Energy, Elsevier, vol. 97(C), pages 646-655.
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    Cited by:

    1. Muhammed Sait Aydin & Sahban W. Alnaser & Sereen Z. Althaher, 2022. "Using OLTC-Fitted Distribution Transformer to Increase Residential PV Hosting Capacity: Decentralized Voltage Management Approach," Energies, MDPI, vol. 15(13), pages 1-19, July.

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