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

•Introducing a novel deep learning based model for fault detection and classification (FDC) in solar-based distribution systems, which utilizes the generative adversarial networks (GANs) for accurate and precise classification of faults.•Utilizing GANs in FDC due to their ability to generate realistic data from a given input, detect and classify faults that may not be visible to the naked eye, and learn from the data they generate, allowing for continuous improvement in accuracy over time.•Suggesting the use of the digital twin of the distribution system for recording synchronous data, as it allows for more efficient and effective data collection.•Proposing the social spider optimization (SSO) algorithm to optimize the search space of the generator and discriminator networks in GANs, improving the accuracy and performance of the FDC system.•Demonstrating the effectiveness of the proposed method using real-world datasets, showcasing its potential as a reliable and efficient solution for FDC in solar-based distribution systems.