PV Module Soiling Detection Using Visible Spectrum Imaging and Machine Learning

Boris I. Evstatiev (), Dimitar T. Trifonov, Katerina G. Gabrovska-Evstatieva, Nikolay P. Valov and Nicola P. Mihailov
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Boris I. Evstatiev: Faculty of Electrical Engineering, Electronics, and Automation, University of Ruse “Angel Kanchev”, 7004 Ruse, Bulgaria
Dimitar T. Trifonov: Faculty of Electrical Engineering, Electronics, and Automation, University of Ruse “Angel Kanchev”, 7004 Ruse, Bulgaria
Katerina G. Gabrovska-Evstatieva: Faculty of Natural Science and Education, University of Ruse “Angel Kanchev”, 7004 Ruse, Bulgaria
Nikolay P. Valov: Faculty of Electrical Engineering, Electronics, and Automation, University of Ruse “Angel Kanchev”, 7004 Ruse, Bulgaria
Nicola P. Mihailov: Faculty of Electrical Engineering, Electronics, and Automation, University of Ruse “Angel Kanchev”, 7004 Ruse, Bulgaria

Energies, 2024, vol. 17, issue 20, 1-20

Abstract: During the last decades photovoltaic solar energy has continuously increased its share in the electricity mix and has already surpassed 5% globally. Even though photovoltaic (PV) installations are considered to require very little maintenance, their efficient exploitation relies on accounting for certain environmental factors that affect energy generation. One of these factors is the soiling of the PV surface, which could be observed in different forms, such as dust and bird droppings. In this study, visible spectrum data and machine learning algorithms were used for the identification of soiling. A methodology for preprocessing the images is proposed, which puts focus on any soiling of the PV surface. The performance of six classification machine learning algorithms is evaluated and compared—convolutional neural network (CNN), support vector machine (SVM), random forest (RF), k-nearest neighbor (kNN), naïve-Bayes, and decision tree. During the training and validation phase, RF proved to be the best-performing model with an F1 score of 0.935, closely followed by SVM, CNN, and kNN. However, during the testing phase, the trained CNN achieved the highest performance, reaching F1 = 0.913. SVM closely followed it with a score of 0.895, while the other two models returned worse results. Some results from the application of the optimal model after specific weather events are also presented in this study. They confirmed once again that the trained convolutional neural network can be successfully used to evaluate the soiling state of photovoltaic surfaces.

Keywords: soiling; photovoltaic; convolutional neural network (CNN); machine learning; imaging; classification (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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