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

Aline Kirsten Vidal De Oliveira,Ricardo Rüther,Mohammadreza Aghaei

doi:10.3390/en15062055

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.

Highlights

This review has shown that different automatization algorithms, including digital image processing (DIP), Deep Learning (DL) and classification techniques, have been employed for automating different tasks of the aerial infrared thermography (aIRT)
This paper has conducted a comprehensive review of the literature for methods of automating different tasks of the aIRT framework of PV power plants, since it is a subject that has been intensely investigated by researchers in recent years
The use of DL algorithms has provided good results with an accuracy of up to 90% in the detection and classification of faults in 10 different anomaly types detected in module segments extracted from aIRT images

Summary

Introduction

As the world experiences a continuously growing share of photovoltaics (PVs) in the energy mix, increasing the performance and reliability of PV installations is of utmost importance. In this context, infrared thermography (IRT) has become a well-established and competitive fault detection method for the condition monitoring and maintenance of PV systems [1]. It provides reliability and accuracy in the detection of typical PV module faults such as bypassed or disconnected substrings, microcracks, soldering problems, shunted cells and disconnected modules. Another feature of this technique is the possible large-scale applicability, through the combination of IRT cameras with an unmanned aerial vehicle (UAV), configured for aerial infrared thermography (aIRT) [1,2]

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Conclusion