Fault detection and classification in solar based distribution systems in the presence of deep learning and social spider method

Abstract

This research proposes an intelligent method for fault detection and classification (FDC) in solar based distribution systems using Generative Adversarial Networks (GANs) and Social Spider method. The suggested method is constructed using the combination of GANs and Social Spider method, which is a hybrid system to increase its capability for the classification purposes. The GANs model is used to detect fault signatures in the system, while the Improved Social Spider method is used to reinforce its training process. The proposed method is evaluated on the big data gathered using the digital twin of a solar based distribution system for different situations of operation. The results show that GANs can detect fault signatures with high accuracy and the Improved Social Spider method can classify the fault types with high accuracy. The proposed method compared with other existing methods and the results show that the suggested method outperforms the existing methods considering accuracy, recall, precision and speed. The proposed method can be used for FDC in solar based distribution systems, and can be developed to other distribution systems.