Abstract— In this paper, we describe a Cyber-Physical system approach to fault detection in Photovoltaic (PV) arrays. More specifically, we explore customized neural network algorithms for fault detection from monitoring devices that sense data and actuate at each. . And this is exactly where AFCI technology comes into play: it detects arcs early on, thereby helping to minimize potential fire risks. The safety of photovoltaic systems is ensured not only by strict standards. . The I-V characteristics of a photovoltaic (PV) system reveal its actual state and performance, and are used for fault detection and diagnosis in PV systems. Photovoltaic (PV) panel faults caused by weather, ground leakage, circuit issues, temperature, environment, age, and other damage can take many forms but often symptomatically exhibit temperature. .
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[pdf] This paper presents an innovative explainable AI model for detecting anomalies in solar photovoltaic panels using an enhanced convolutional neural network (CNN) and the VGG16 architecture. Object detection with YOLOv5 models and image segmentation with Unet++, FPN, DLV3+ and PSPNet. To address the shortcomings of existing photovoltaic defect detection technologies, such as high labor costs, large workloads. .
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