Intelligent Monitoring of Photovoltaic Panels Based on Infrared Detection

Wei, Kexiang; Wang, Xing; Yang, Wenxian; Yang, Bo; Ma, Yunyu; Zhang, Daibing

doi:10.2139/ssrn.4024959

Cited by 1 publication

(1 citation statement)

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“…A common drawback of these methods is that they do not handle background interference well. Wei [14] used unmanned aerial vehicles to collect infrared images of photovoltaic arrays and proposed using Faster RCNN to detect hot spot defects, improving detection accuracy, but not solving the problem of low detection accuracy and missed detection of small hots pots.…”

Section: Related Workmentioning

confidence: 99%

SK-FRCNN: A Fault Detection Method for Hot Spots on Photovoltaic Panels

Wu,

Hao

2023

IEEE Access

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Photovoltaic power generation is clean and environmentally friendly, and has been widely used. Hot spots on photovoltaic panels, caused by shading and leading to heating, reduce the efficiency of photovoltaic power generation and even damage the panels. To address the problem of low detection accuracy in existing models for hot spot detection on photovoltaic panels, a method for detecting hot spot faults on photovoltaic panels, called SK-FRCNN (Selective Kernel-Faster RCNN), based on the Faster RCNN network is proposed. The feature extraction module in the Faster RCNN network uses an attention mechanism to adaptively adjust the receptive field for hot spots of different sizes, enhancing the feature extraction capability. A feature pyramid structure is adopted with a bottom-up fusion to obtain higher resolution features from low-resolution feature maps through upsampling and fusion operations, thereby preserving multi-scale information of the feature maps and improving the recognition effect for small hot spot faults. The ROI Align unit is used to replace the pooling layer in the Faster RCNN model, canceling the quantization operation and using bilinear interpolation algorithm to obtain floating-point pixel image values, more accurately calculating the feature representation of ROI regions and improving the localization accuracy of hot spot recognition. Experimental results show that the improved algorithm achieves an average detection accuracy of 79.98% for hot spot faults on photovoltaic panels, which is 1.82% higher than that of the original Faster RCNN network.INDEX TERMS Hot spot fault, faster RCNN, SK attention mechanism module, feature fusion, ROI align unit.

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Section: Related Workmentioning

confidence: 99%