# An Overview of CNN-Based Image Analysis in Solar Cells, Photovoltaic Modules, and Power Plants
## 1 Introduction  
Conventional fossil fuel supplies could run out within a few decades [1, 2, 3], and their prices could easily rise in times of military and economic competition, as we have seen in recent years [4]. Humanity must do its utmost to develop and deploy renewable energy sources as widely as possible. In the last decade, solar and wind energy capacity has expanded at the fastest rate in the world [5]. In Hungary, the rapid expansion of solar energy continued in 2024. The capacity of industrial solar power plants has increased to 3478 megawatts, and nearly 260,000 residential solar systems are already in operation with a total capacity of 2362 megawatts. With an additional 260 megawatts of captive solar capacity, the total solar capacity has reached 6100 megawatts. The National Energy and Climate Plan originally set a target of 6000 megawatts by 2030 [6]. The focus of this article is to examine the possibility of supporting the quality assurance of PV modules and systems from production through installation to maintenance with the use of artificial intelligence. Nowadays, the production of photovoltaic (PV) modules is a highly automated, mechanized process that requires a certain degree of precision. However, the pressure from the necessity of high production metrics acts as a detriment to this precision and the quality that goes with it [7]. According to Ref. [8], in China alone, the installed solar capacity has increased by several gigawatts per year over the last decade, while the global demand has required the production and installation of hundreds of millions of solar panels per year. With such a high production volume, it is very difficult to minimize the number of faults affecting electrical performance. The quality assurance process for solar cell production includes checking the condition of the panels while they are still in the factory. Furthermore, condition assessments are necessary even after the solar system or power plant has been built [7; 8; 9; 10; 11; 12]. Condition assessments may be affected by inadequate or for some known reason altered electricity generation rates, may take place during installation to collect reference data for warranty validation, or may be part of a predefined maintenance process [13; 14]. Usually, a team of experts will visit the plant on the ground during a visual inspection, but the number of defects able to be observed by the naked eye is small. To increase efficiency, the use of a thermal camera is essential [15; 16], and also provides the possibility of creating predictive models, as in the study by [PERSON] et al. [17]. The process can be accelerated by using a UAV (unmanned aerial vehicle) drone equipped with a thermal camera [18; 19; 20; 21; 22]. It should be noted that some power plants prohibit the use of drones for safety reasons. Thermal imaging is an excellent method to detect hot-spot failures and bypass diode failures patchwork failures, and this method can be automized [23]. Once a faulty module has been identified, it is possible to measure electrical parameters and record I-V curves, even for daylight outdoor electroluminescence studies, for example with special InGaAs cameras [24; 25; 26; 27; 28]. Condition surveys typically cover the entire solar plant. An all-encompassing health check allows for the condition of solar cables, connectors, inverters and transformers to be checked, in addition to the solar modules. Random inspections should be carried out every six months to once a year, and full condition surveys every three to five years. The frequency of maintenance is influenced by climate or even air pollution levels. Regular cleaning can reduce the effect of the latter [29]. Imperfections within PV modules can be vastly different, ranging from physical to chemical and structural anomalies. One of the most common problems is the presence of microcracks [30], which are often invisible to the naked eye. These defects usually result in a small energy loss, but if left unchecked, they can spread and lead to failures.