From Pixels to Insights: A Software Prototype for AI-Driven Complete Diagnostics of PV Plants
Year: 2024
Type: conference-paper
Abstract: Although aerial infrared (aIRT) imagery-based solutions for diagnostics of PV plants demonstrate impressive time-efficiency and spatial resolution, they also suffer from considerable drawbacks: limited automation (hence, expert dependence) and insufficient quantitative insights. In this paper, we introduce a software prototype, evolved from an innovative diagnostics framework researched and developed by CEA-INES over the last years, which integrates aIRT imagery with deep learning-based algorithms and physical/electrical modeling. With such approach, unlike conventional ones, we worked on reaching both qualitative fault detection and quantitative (power loss) insights, with a focus on various spatial granularity levels within PV systems. Leveraging advanced deep learning techniques, first results show that we can achieve automated PV module detection and fault identification/classification, with associated power loss analysis at PV system, string/inverter or module level. Further real-life validation efforts are underway, in utility-scale PV plants. Future developments aim to enhance further our PV diagnostic framework, through data fusion with SCADA outputs and integration with maintenance and end-of-life (EoL) management tools.
Authors: Tsanakas, John (Ioannis) A., Stepec, Murielle, Marechal, Philippe, Ha, Duy-Long
Language: English
Field: Energy
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6891624404
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