Water photovoltaic plant contaminant identification using visible light images

A comprehensive review of unmanned aerial vehicle-based approaches to support photovoltaic plant diagnosis

“…As reported in the literature [ 62 , [72] , [73] , [74] , [75] , [76] ], the inspection of RGB images is usually performed along with IRT for aerial plant inspection. Over the past years, researchers and operators have been using RGB cameras embedded to UAV as a standalone fault diagnosis technique [ 25 , [77] , [78] , [79] , [80] , [81] ].…”

A Survey of Photovoltaic Panel Overlay and Fault Detection Methods

“…A simple and reliable method. Zhou et al [68] developed a method for the automatic segmentation and classification of polluted PV panels using visible spectrum images captured by drones. The method used a flexible threshold segmentation scheme that combined grayscale color space and S component.…”

“…However, when the algorithm parameters and thresholds are not selected, It will overfit or underfit when appropriate. The method in [68] can not only identify the photovoltaic panels in the floating photovoltaic power station, but can also give their location and pollution status, providing intuitive support for daily maintenance.…”

“…Therefore, future improvements can consider introducing particle size analyzers or image processing technology to obtain information on dust particle size and distribution uniformity and incorporate it into the model. The method in [68] uses VGG16 as a classifier. Although it achieves high accuracy and recall on the test set, VGG16 is a more complex and redundant model that requires more parameters and computing resources.…”

“…Ref. [68] uses visible light images to detect contaminated PV panels in floating PV power plants. Texture features and pixel features also need to be considered, as well the as evaluation and transfer learning of various network models, and it may also be affected by factors such as camera quality, module angle, and site terrain.…”