A deep learning-based framework was introduced in this paper to autonomously identify defective solar PV panels in EL images for four state-of-the-art algorithms: YOLO v8, MobileNet v2,
Abstract—Accurate fault diagnosis and quantification are es- sential for the reliable operation and intelligent maintenance of photovoltaic (PV) arrays. However, existing fault quantification methods
Thus, this paper introduces the types, causes, and impacts of PVS faults, and reviews and discusses the methods proposed in the literature for PVS fault diagnosis, and in particular, failures in PV arrays.
HAL is a multi-disciplinary open access archive for the deposit and dissemination of sci-entific research documents, whether they are pub-lished or not. The documents may come from teaching and
In this study, we concentrate only on the techniques employed for the detection of faults on the DC side. Many researchers have suggested a number of diagnostic approaches specifically
Once a fault is located and detected, an appropriate diagnosis method needs to be used to rectify it. In this paper, a comprehensive review of diverse fault diagnosis techniques reported in
At present, numerous significant review studies have examined various aspects of PV fault detection and diagnosis methods. While the field is rich with valuable contributions, we highlight
Consequently, it is imperative to implement efficient methods for the accurate detection and diagnosis of PV system faults to prevent unexpected power disruptions. This paper introduces a
By fitting the measured I-V curves from the PV system and diagnosing potential faults and their severity based on the fitted model parameters, the approach proposed in this study offers a
Fault detection and diagnosis (FDD) methods are critical for PV plant system stability, high performance operation and safety.
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