Smart Electrical grids Under the Lens of Adversarial Attacks

The detection of faults in smart electrical grids is a crucial task as it can have significant economic and societal impacts. In recent years, data-driven approaches have been adopted for various smart grid applications, including fault detection and load forecasting. This study aims to explore the challenges associated with ensuring the security of machine learning (ML) applications in the smart grid context. Despite the widespread use of data-driven algorithms, their robustness and security have not been thoroughly examined in all power grid applications. Our research demonstrates that deep neural network methods used in smart grids are vulnerable to adversarial perturbations. Additionally, we highlight the weaknesses of current ML algorithms in smart grids to various adversarial attacks by examining fault localization and type classification problems.