Automatic Inspection of Photovoltaic Power Plants Using Aerial Infrared Thermography: A Review

Abstract

In recent years, aerial infrared thermography (aIRT), as a cost-efficient inspection method, has been demonstrated to be a reliable technique for failure detection in photovoltaic (PV) systems. This method aims to quickly perform a comprehensive monitoring of PV power plants, from the commissioning phase through its entire operational lifetime. This paper provides a review of reported methods in the literature for automating different tasks of the aIRT framework for PV system inspection. The related studies were reviewed for digital image processing (DIP), classification and deep learning techniques. Most of these studies were focused on autonomous fault detection and classification of PV plants using visual, IRT and aIRT images with accuracies up to 90%. On the other hand, only a few studies explored the automation of other parts of the procedure of aIRT, such as the optimal path planning, the orthomosaicking of the acquired images and the detection of soiling over the modules. Algorithms for the detection and segmentation of PV modules achieved a maximum F1 score (harmonic mean of precision and recall) of 98.4%. The accuracy, robustness and generalization of the developed algorithms are still the main issues of these studies, especially when dealing with more classes of faults and the inspection of large-scale PV plants. Therefore, the autonomous procedure and classification task must still be explored to enhance the performance and applicability of the aIRT method.

Review of technology specific degradation in crystalline silicon, cadmium telluride, copper indium gallium selenide, dye sensitised, organic and perovskite solar cells in photovoltaic modules: Understanding how reliability improvements in mature technologies can enhance emerging technologies

“…A hot spot can occur in a PV module due to various causes, such as cells mismatching, partial shading or interconnection failures. 9,[43][44][45][46][47] Indeed, when the cell is affected by partial shading, it results in a short circuit condition or a reverse bias. 45,48 An entire cell can form a hot spot in a module; alternatively, the cell temperature can also be non-uniform.…”

“…A hot spot forms when one or more solar cells generate less current than the string current of a PV module. A hot spot can occur in a PV module due to various causes, such as cells mismatching, partial shading or interconnection failures 9,43–47 . Indeed, when the cell is affected by partial shading, it results in a short circuit condition or a reverse bias 45,48 …”

Autonomous Monitoring and Analysis of Photovoltaic Systems

“…The third published paper is a review article in which the authors presented a comprehensive review on automatic inspection of PV power plants using aerial infrared thermography (aIRT) [33]. This review covered several related aerial PV inspection topics, including infrared thermography (IRT), unmanned aerial vehicles (UAVs), aerial infrared thermography (aIRT), aerial inspection algorithms (e.g., digital image processing (DIP), machine learning (ML), deep learning (DL)), and applications of automatization algorithms (e.g., automatic path planning, detection of PV systems and modules, orthomosaicking, fault detection and classification).…”

A Review on Machine Learning Applications for Solar Plants

“…Thus, ML methods have been proposed to approximate this complex dynamic. The recent studies [ 1 , 2 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 ] prove that ML technologies for a solar plant’s design, forecasting, maintenance, and control increase the effectiveness and reliability of the solar plant as compared to conventional methods. In smart sensor systems of solar plants, ML methods are crucial units to increase the quality of datasets processing the solar plant’s design, forecasting, maintenance, and control.…”