Automatic soiling and partial shading assessment on PV modules through RGB images analysis

Cavieres, Robinson; Barraza, Rodrigo; Estay, Danilo; Bilbao, José Ignacio; Valdivia, Patricio

doi:10.1016/j.apenergy.2021.117964

Cited by 42 publications

(10 citation statements)

References 29 publications

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“…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] ].…”

Section: Failure Modes In Pv Systems and Existing Approachesmentioning

confidence: 99%

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

Michail,

Livera,

Tziolis

et al. 2024

Heliyon

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Section: Failure Modes In Pv Systems and Existing Approachesmentioning

confidence: 99%

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

Michail,

Livera,

Tziolis

et al. 2024

Heliyon

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“…Ref. [22] determines the power losses due to partial shading from visible spectrum RGB images based on the Region-based Convolutional Neural Network (CNN). Ref.…”

Section: Simulation and Experimentsmentioning

confidence: 99%

Photovoltaic Array Reconfiguration under Partial Shading Conditions Based on Short-Circuit Current Estimated by Convolutional Neural Network

et al. 2022

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Partial shading conditions (PSC) have negative effects on the operation of photovoltaic (PV) systems. In this paper, a PV array reconfiguration method is developed to minimize power losses of PV arrays under partial shading conditions. The proposed reconfiguration method is based on equalizing the reduction of the short-circuit current of the PV modules in the PV array. Eight state-of-the-art Convolutional Neural Network models are employed to estimate the effect of shading on the short-circuit current of a PV module. These models include LeNet-5, AlexNet, VGG 11, VGG 19, Inception V3, ResNet 18, ResNet 34, and ResNet 50. Among eight models, the VGG 19 achieves the best accuracy on 1842 sample images. Therefore, this model is used to estimate the ratio of the actual short-circuit current and the estimated short-circuit current in four studied shading scenarios. This ratio decides the switching rule between PV modules throughout the PV array under PSC. A 2×2 experimental PV array shows that the proposed reconfiguration method improves the output power from 5.81% to 25.19% in four shading patterns. Accordingly, the power losses are reduced from 1.32% to 13.75%. The power improvement and the reduction of power losses of the proposed dynamic PV array reconfiguration system under four case studies demonstrates its effectiveness in addressing the effects of PSC on the PV array.

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“…CNNs have been employed to identify multiple fault modes in PV modules through electroluminescence imaging 28–30 and infrared imaging 31,32 . It has also been used to analyze visible spectrum images for soiling detection 33–35 . These techniques enable the extraction of more information from the image set and achieve greater precision in the classification.…”

Section: Introductionmentioning

confidence: 99%

“…31,32 It has also been used to analyze visible spectrum images for soiling detection. [33][34][35] These techniques enable the extraction of more information from the image set and achieve greater precision in the classification. However, the hidden layer nature of these algorithms prevents access to the main feature being analyzed, and the method of processing the information remains unclear.…”

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confidence: 99%

Estimation of the impact of natural soiling on solar module operation through image analysis

Cavieres

Salas

Barraza

et al. 2023

Progress in Photovoltaics

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This study presents a novel algorithm to evaluate the power losses in photovoltaic (PV) modules that are exposed to naturally accumulated soiling. The proposed algorithm uses red, green, and blue (RGB) images of the PV modules captured in the visible spectrum and measures features such as the average color intensity and standard deviation from every color channel. These features are then ranked based on the module performance by using statistical tools, and the most relevant set of features is selected. The selected features are used as an input, and the module power generation is used as the label to train an artificial neural network that outputs a continuous value that represents the instantaneous performance of the evaluated module when compared with a clean module operating under the same environmental conditions. The proposed method is effective for images captured within an irradiance ranging from 700 to 1000 (W/m2), with the predictions achieving an R2 score of 0.96 and RMSE of 0.74% for power losses ranging from 0% to 20% in natural soiling. This percentage corresponds to a clean condition, where irradiance is not used as an input, making it a cheap and reliable solution to monitor soiling conditions. This is the first work of its kind to demonstrate a correlation between the extracted color features of RGB images and module performance under outdoor conditions for the studied dataset. The proposed algorithm presents less accurate results when it is tested in modules exposed to non‐homogenous soiling, suggesting that the proposed methodology might be effective only for naturally accumulated soiling.

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Automatic soiling and partial shading assessment on PV modules through RGB images analysis

Cited by 42 publications

References 29 publications

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

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

Photovoltaic Array Reconfiguration under Partial Shading Conditions Based on Short-Circuit Current Estimated by Convolutional Neural Network

Estimation of the impact of natural soiling on solar module operation through image analysis

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