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Overview of Intelligent Inverters and Associated Cybersecurity Issues for a Grid-Connected Solar Photovoltaic System

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  • Sai Nikhil Vodapally

    (Department of Electrical and Computer Engineering, The University of Memphis, Memphis, TN 38152, USA)

  • Mohd Hasan Ali

    (Department of Electrical and Computer Engineering, The University of Memphis, Memphis, TN 38152, USA)

Abstract

The major problem associated with the grid-connected solar photovoltaic (PV) system is the integration of the generated DC power into the AC grid and maintaining the stability of the system. With advancements in research on these PV inverters, artificial intelligence (AI)-based control models are replacing the existing linear methods. These smart PV systems are prone to a variety of attacks, ranging from physical attacks on the PV plants to data integrity attacks and communication-based attacks. This paper provides an overview of the cybersecurity issues with smart PV inverters, their impacts on the grids, and control methods that exist to detect and identify cyber-attacks on a smart PV grid system. An extensive bibliography is provided on grid-forming and grid-following inverters with a variety of control techniques like Proportional–Integral–Derivative (PID) control, fuzzy-based control, and their performances under different fault situations. Multi-level inverter design approaches with various intelligent control techniques to overcome switching failures and other system faults are reviewed. Moreover, some recommendations for future research on intelligent inverters for grid-connected PV systems are proposed. This work will benefit researchers, scientists, and engineers who are working in the field of intelligent inverters for the grid-connected PV system.

Suggested Citation

  • Sai Nikhil Vodapally & Mohd Hasan Ali, 2023. "Overview of Intelligent Inverters and Associated Cybersecurity Issues for a Grid-Connected Solar Photovoltaic System," Energies, MDPI, vol. 16(16), pages 1-19, August.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:16:p:5904-:d:1214021
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    References listed on IDEAS

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    1. Suliang Ma & Mingxuan Chen & Jianwen Wu & Wenlei Huo & Lian Huang, 2016. "Augmented Nonlinear Controller for Maximum Power-Point Tracking with Artificial Neural Network in Grid-Connected Photovoltaic Systems," Energies, MDPI, vol. 9(12), pages 1-24, November.
    2. Tariq Kamal & Murat Karabacak & Vedran S. Perić & Syed Zulqadar Hassan & Luis M. Fernández-Ramírez, 2020. "Novel Improved Adaptive Neuro-Fuzzy Control of Inverter and Supervisory Energy Management System of a Microgrid," Energies, MDPI, vol. 13(18), pages 1-22, September.
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