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公开(公告)号：US11621668B2
公开(公告)日：2023-04-04
申请号：US16868050
申请日：2020-05-06
摘要： Solar array fault detection, classification, and localization using deep neural nets is provided. A fault-identifying neural network uses a cyber-physical system (CPS) approach to fault detection in photovoltaic (PV) arrays. Customized neural network algorithms are deployed in feedforward neural networks for fault detection and identification from monitoring devices that sense data and actuate each individual module in a PV array. This approach improves efficiency by detecting and classifying a wide variety of faults and commonly occurring conditions (e.g., eight faults/conditions concurrently) that affect power output in utility scale PV arrays.
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公开(公告)号：US20210390413A1
公开(公告)日：2021-12-16
申请号：US17348410
申请日：2021-06-15
摘要： Dropout and pruned neural networks for fault classification in photovoltaic (PV) arrays are provided. Automatic detection of solar array faults leads to reduced maintenance costs and increased efficiencies. Embodiments described herein address the problem of fault detection, localization, and classification in utility-scale PV arrays. More specifically, neural networks are developed for fault classification, which have been trained using dropout regularizers. These neural networks are examined and assessed, then compared with other classification algorithms. In order to classify a wide variety of faults, a set of unique features are extracted from PV array measurements and used as inputs to a neural network. Example approaches to neural network pruning are described, illustrating trade-offs between model accuracy and complexity. This approach promises to improve the accuracy of fault classification and elevate the efficiency of PV arrays.
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