Abstract
In recent years, the overwhelming growth of solar photovoltaics (PV) energy generation as an alternative to conventional fossil fuel generation has encouraged the search for efficient and more reliable operation and maintenance practices, since PV systems require constant maintenance for consistent generation efficiency. One option, explored recently, is artificial intelligence (AI) to replace conventional maintenance strategies. The growing importance of AI in various real-life applications, especially in solar PV applications, cannot be over-emphasized. This study presents an extensive review of AI-based methods for fault detection and diagnosis in PV systems. It explores various fault types that are common in PV systems and various AI-based fault detection and diagnosis techniques proposed in the literature. Of note, there are currently fewer literatures in this area of PV application as compared to the other areas. This is due to the fact that the topic has just recently been explored, as evident in the oldest paper we could obtain, which dates back to only about 15 years. Furthermore, the study outlines the role of AI in PV operation and maintenance, and the main contributions of the reviewed literatures.
Highlights
The rapid development of technology and social advancements has led to the skyrocketing of energy demand, which has, in turn, resulted in an increase in fossil fuel generation of energy [1,2] This has raised concerns of high CO2 emission into the atmosphere due to the combustion of fossil fuels [3,4], which leads to global warming, GHG emissions, climate change, and other environmental issues [5]
These types of faults include maximum power point trackers (MPPT) and inverter faults that mostly occur due to inverter components failure, such as IGBTs, capacitors, and converter switch failure [51,52,53]; bypass diode faults resulting from a massive reverse current flow during faults, which leads to short-circuits [54,55]; blocking diode faults, as a result of a reverse current flow [39]; open-circuit faults caused by items falling on PV panels, physical failure of panel-panel cables or joints, and sloppy termination of cables, plugging, and unplugging connectors at junction boxes [56]; faulty connections damage of connecting cables or a wrong connection of panels [57]; battery bank failures due to abnormal charging conditions [39]; and blackouts caused by natural disasters, such as a storm and lightning [58]
Evolutionary computation is currently a distinct branch of artificial intelligence inspired by nature [78,79], with smart methods based on evolutionary algorithms targeted at solving various real life problems through natural processes involving live things [79]
Summary
The rapid development of technology and social advancements has led to the skyrocketing of energy demand, which has, in turn, resulted in an increase in fossil fuel generation of energy [1,2] This has raised concerns of high CO2 emission into the atmosphere due to the combustion of fossil fuels [3,4], which leads to global warming, GHG emissions, climate change, and other environmental issues [5]. The model considers the uncertainty of future scenarios across the buildings lifecycle Another AI application is PV systems, which is utilized in [26] for the optimal dispatch of PV inverters in unbalanced distribution systems using reinforcement learning, while the authors of [27] used AI for the optimal design of a phase change material integrated renewable system with on-site PV, radiative cooling, and hybrid ventilations.
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