# A Generative Adversarial Network-Based Fault Detection Approach for Photovoltaic Panel
## 2 Related Work
### PV Panel Fault Detection  
PV panel fault detection roughly goes through the following phases: an initial phase, the phase of physical approaches and the phase of intelligent detection. In the initial phase, manual inspection is the most primitive method for detecting PV panel faults. The worker uses an image of the PV panel taken by a camera to check whether there is a defect. Traditional manual inspection evaluates a single PV panel just by sight, which is usually high cost, high error rate and low efficiency [24], and has severe subjective inclines for different defects. In the phase of physical approaches, [PERSON] et al. [25] used laser scanning technology to aid the fault-detection process. It displays the continuity of resistance in the crystalline silicon of the forward conversion of laser scanning. If there exists a crack, there will be a discontinuity of resistance. [PERSON] et al. [26] proposed the use of sound waves for fault detection, which makes PV panels vibrate to generate sound waves. Based on a comparative analysis with sound waves generated by non-defective solar cells, the fault detection can be realized. Physical detection [25; 26; 27] has high detection accuracy for specific types of PV panel faults, but it cannot effectively detect other faults. In the intelligent phase of fault detection, the intelligent PV panel fault detection method based on machine vision is now being gradually adopted by users. By machine vision detection, [PERSON] et al. [28] proposed a method for texture analysis of dark defect areas in electroluminescence images to classify defective and non-defective cells. [PERSON] et al. [29] first reported the UAV inspection system to perform non-destructive inspection of large-scale PV power plants. However, the effect of fault diagnosis cannot meet the requirements of practical use, and many researchers are further studying fault diagnosis methods. [PERSON] et al. [30] first extracted features by calculating the first derivative of the Gaussian function on the collected images, and then performed defect detection through feature matching. This method verifies the effectiveness of the fault analysis algorithm based on image processing to a certain extent, but strictly requires good quality of the collected images. However, due to various reasons such as the wind effect, the obtained image resolution is low, and the performance of this method is significantly reduced. In addition, traditional pattern recognition algorithms often fail to extract fault features with acceptable complexity from the acquired aerial images. With the evolution of deep convolutional neural networks, the introduction of artificial intelligence algorithms into the intelligent patrol system [9] can improve its robustness and reliability [10]. [PERSON] et al. [11] proposed an intelligent diagnosis method of aerial PV module image defects based on the deep convolutional neural network (CNN). This method uses CNN to classify multiple deep features and diverse states. Compared with traditional methods, it can flexibly and reliably solve the problem of PV module images with low quality and distortions. [PERSON] [12] proposed using a generative adversarial network combined with data augmentation to increase training samples; this achieves high classification accuracy, but cannot precisely locate defects. [PERSON] [13] presented a deep learning-based automatic detection of multitype defects to fulfill inspection requirements of the production line. These studies used CNN to solve PV panel fault detection, and the models were based on supervised learning. However, due to the lack of abnormal panel samples, supervised learning is not suitable for this kind of problem. [PERSON] et al. [15] proposed an unsupervised anomaly detection method based on the generative adversarial network (AnoGAN) only using standard panel samples; it uses continuously iterative optimization to find a certain point in latent space. However, due to the need for iterative optimization, the AnoGAN algorithm is bound to consume a lot of time.