Towards Smart Monitoring Systems: Fault Detection and Diagnosis-Based Artificial Intelligence Algorithms in Solar PV Power Plants

Authors Mohammed Ali Jallal1, Abdessalam El Yassini1, Samira Chabaa1,2, Abdelouhab Zeroual1

1I2SP Research Team, Physics Department, Faculty of Sciences Semlalia, Cadi Ayyad University, 40000, Marrakesh, Morocco

2Industrial Engineering Department, National School of Applied Sciences, Ibn Zohr, 80000, University, Agadir Morocco

Issue Volume 14, Year 2022, Number 5
Dates Received 11 May 2022; revised manuscript received 21 October 2022; published online 28 October 2022
Citation Mohammed Ali Jallal, Abdessalam El Yassini, et al., J. Nano- Electron. Phys. 14 No 5, 05004 (2022)
PACS Number(s) 07.05.Mh, 84.60. − h
Keywords Fault detection and diagnosis, Artificial Intelligence, Solar photovoltaic, Energy transition, Microgrids, Smart grids.

Solar photovoltaic (PV) power plant reliability and efficiency are always hot topics. During the operating period as all industrial systems, these plants are susceptible to malfunctions and failures in their equipment or operations. Faults occurrence in solar PV system components can significantly affect the efficiency, power generation yield, safety, and stability of the entire PV power plant if not detected and corrected promptly. Furthermore, if any faults persist, they can increase the fire hazard. For these reasons, incorporating a smart diagnostic system is required, where its primary goal is to provide accurate indicators for detecting faults and thus maintaining the stability of the solar PV power plant energy production. Given the importance of this topic, the present literature starts with a description of various fault mechanisms that occur in solar PV power plants before providing a consistent review about fault detection and diagnosis strategies-based artificial intelligence to boost the progress and the transition towards smart grid-based green energies.

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