# A Generative Adversarial Network-Based Fault Detection Approach for Photovoltaic Panel
###### Abstract  
Photovoltaic (PV) panels are widely adopted and set up on residential rooftops and photovoltaic power plants. However, long-term exposure to ultraviolet rays, high temperature and humid environments accelerates the oxidation of PV panels, which finally results in functional failure. The traditional fault detection approach for photovoltaic panels mainly relies on manual inspection, which is inefficient. Lately, machine vision-based approaches for fault detection have emerged, but lack of negative samples usually results in low accuracy and hinders the wide adoption of machine vision-based approaches. To address this issue, we proposed a semi-supervised anomaly detection model based on the generative adversarial network. The proposed model uses the generator network to learn the data distribution of the normal PV panel dataset during training. When abnormal PV panel data are put into the model in the test phase, the reconstructed image generated by the model does not equal the input image. Since the abnormal PV panel data do not obey the data distribution learned by the generator, the difference between the original image and its reconstructed image exceeds the given threshold. So, the model can filter out the fault PV panel by checking the error value between the original image and its reconstructed image. The model adopts Gradient Centralization and SmoothL1 loss function to improve its generalization performance. Meanwhile, we use the convolutional block attention module (CBAM) to make the model pay more attention to the defective area and greatly improve the performance of the model. In this paper, the photovoltaic panels dataset is collected from a PV power plant located in Zhejiang, China. We compare the proposed approach with state-of-the-art semi-supervised and unsupervised approaches (i.e., AnoGAN (Anomaly Detection with Generative Adversarial Networks), [PERSON]'s method, GANomaly, and f-AnoGAN), and the result indicates that the Area Under Curve (AUC) increases by 0.06, 0.052, 0.041 and 0.035, respectively, significantly improving the accuracy of photovoltaic panel fault detection. photovoltaic power generation; abnormal detection; generator adversarial networks; deep convolutional; gradient centralization +
Footnote †: journal: Applied  
0
Footnote †: journal: Applied  
0
Footnote 0: _Applied_  
0
Footnote 0: _Applied_  
The overall goal of the Paris Agreement was to achieve a peak in global greenhouse gas emissions around 2020, and net zero greenhouse gas emissions in the second half of this century. Photovoltaic power generation is a technology that directly converts light energy into electric energy by using the photovoltaic effect at the semiconductor interface. Abundant solar radiant energy is an important energy that is inexhaustible, pollution-free, cheap and free to be used by human beings. Therefore, solar energy has become the focus of attention because of its unique advantages [1; 2]. In the global promotion of clean and low-carbon energy transition, the adoption of photovoltaic (PV) panels in clean energy is increasing year by year [3]. According to International Energy Agency (IEA) statistics, since 2018, the cumulative installed capacity of global PV power generation has reached 480 GW and is expected to reach 1721 GW in 2030 [4]. In order to increase the exposure time of PV panels, large-scale PV power plants are located in areas without apparent shade, such as the roof of buildings, plains, hills and over fish ponds, as shown in Figure 1. Long-term exposure to ultraviolet rays, high temperature and humid environments accelerate the oxidation of PV panels, which results in functional failure [5]. Severe sealant delamination increases reflection, decreases irradiance, sinks moisture in the module, and accelerates panel oxidation [6]. The accumulation of dust directly reduces light transmittance and affects the efficiency of the PV power generation system, sometimes up to about 50% or, even worse, 80% [7].