Abstract
Automatic detection of solar array faults reduces maintenance costs and increases efficiency. In this paper, we address the problem of fault detection, localization, and classification in utility-scale photovoltaic (PV) arrays using machine learning methods. More specifically, we develop a series of customized neural networks for detection and classification of solar array faults. We evaluate fault detection and classification using metrics such as accuracy, confusion matrices, and the Risk Priority Number (RPN). We examine and assess the use of customized neural networks with dropout regularizers. We develop and evaluate neural network pruning strategies and illustrate the trade-off between fault classification model accuracy and algorithm complexity. Our approach promises to elevate the performance and robustness of PV arrays and compares favorably against existing methods.
Highlights
F AULTS in utility-scale solar arrays [1]–[4] often lead to increased maintenance costs and reduced efficiency
Our results show that the 2× pruned networks perform better than standard machine learning (ML) classifiers and concrete dropout has the best performance among all methods examined
We propose the use of a concrete dropout and compare the results with uniform dropout [18] neural network architecture for fault classification using PVWatts
Summary
F AULTS in utility-scale solar arrays [1]–[4] often lead to increased maintenance costs and reduced efficiency. Smart monitoring devices (SMDs) [5] (Figure1.(b)) that have remote monitoring and control capability have been proposed [6] to provide data from each panel and enable detection and localization of faults and shading. The presence of such SMDs renders the solar array system as a cyber-physical system [7] that can be monitored and controlled in real-time with algorithms and software. Even with the presence of SMDs, fault detection and classification is challenging and requires statistical analysis of PV data Traditional methods such as the Support Vector Machines (SVM) [4], decision tree based approach [9], FIGURE 1. We study the faults and their diagnosis from an operations’ and management perspective, as described in Section II.B and provide efficient representations of the neural network for deployment on hardware
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