Series arc fault diagnosis method of photovoltaic arrays based on GASF and improved DCGAN

In recent years, the methods of machine learning are widely investigated to resolve the series arc fault (SAF) diagnosis problem in photovoltaic (PV) arrays. However, owing to the factors such as weak signal characteristics, long algorithm execution time, and sample imbalance in practical applications, these methods may have difficulties of detecting the SAF. To address these problems, a method based on the Gramian angular summation field (GASF) combined with the squeeze and excitation-deep convolution generative adversarial network (SE-DCGAN) is proposed. Firstly, the absolute difference of margin factor (ADMF) of the current signal is calculated to accurately extract the transient current data when the SAF occurs. Thereafter, the GASF is used to convert transient current data into two-dimensional images to amplify the universal characteristics of the SAF. Subsequently, the SE-DCGAN is adopted to augment the GASF images of the SAF to solve the problem of limited SAF samples. Finally, a convolutional neural network (CNN) is trained to identify the SAF. Also, a fusion sample training method is proposed in this research, that is, normal samples of different PV systems are added to the training set to enhance the generalization ability of CNN. The advantages of the proposed method are that the identification of SAF is improved by converting one-dimensional signals into two-dimensional images, and the generalization ability of the detection model is improved by exploiting the common features of SAFs and fusion training. The validity and generalization ability of the proposed method are verified by three datasets under different PV systems. Experimental results reveal that the proposed method can achieve high recognition accuracy for the measured data; moreover, no misjudgments occurred in identifying the interference events such as maximum power point tracking (MPPT) adjustment and irradiance mutation (IM). In addition, the experiments confirm that the fusion training method enables the model more universal and applicable.