Machine learning approaches for automatic defect detection in photovoltaic systems

• URL: http://arxiv.org/abs/2409.16069v1
• Date: Tue, 24 Sep 2024 13:11:05 GMT
• Title: Machine learning approaches for automatic defect detection in photovoltaic systems
• Authors: Swayam Rajat Mohanty, Moin Uddin Maruf, Vaibhav Singh, Zeeshan Ahmad,
• Abstract summary: Solar photovoltaic (PV) modules are prone to damage during manufacturing, installation and operation. Continuous monitoring of PV modules during operation via unmanned aerial vehicles is essential. Computer vision provides an automatic, non-destructive and cost-effective tool for monitoring defects in large-scale PV plants.
• Score: 1.121744174061766
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Solar photovoltaic (PV) modules are prone to damage during manufacturing, installation and operation which reduces their power conversion efficiency. This diminishes their positive environmental impact over the lifecycle. Continuous monitoring of PV modules during operation via unmanned aerial vehicles is essential to ensure that defective panels are promptly replaced or repaired to maintain high power conversion efficiencies. Computer vision provides an automatic, non-destructive and cost-effective tool for monitoring defects in large-scale PV plants. We review the current landscape of deep learning-based computer vision techniques used for detecting defects in solar modules. We compare and evaluate the existing approaches at different levels, namely the type of images used, data collection and processing method, deep learning architectures employed, and model interpretability. Most approaches use convolutional neural networks together with data augmentation or generative adversarial network-based techniques. We evaluate the deep learning approaches by performing interpretability analysis on classification tasks. This analysis reveals that the model focuses on the darker regions of the image to perform the classification. We find clear gaps in the existing approaches while also laying out the groundwork for mitigating these challenges when building new models. We conclude with the relevant research gaps that need to be addressed and approaches for progress in this field: integrating geometric deep learning with existing approaches for building more robust and reliable models, leveraging physics-based neural networks that combine domain expertise of physical laws to build more domain-aware deep learning models, and incorporating interpretability as a factor for building models that can be trusted. The review points towards a clear roadmap for making this technology commercially relevant.

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