The increasing focus on cyber-physical security in Smart Grids (SGs) has catalyzed a surge in research over recent years. This paper comprehensively reviews SG cyber-physical security advancements, diverging from conventional studies that concentrate on specific attack types. It begins with a structured overview of SGs, delineating their cyber and physical layers and analyzing the key processes: generation, transmission, distribution, and consumption. Subsequent sections critique existing survey studies, identifying gaps and underscoring overlooked aspects in the current literature, particularly concerning the challenges faced. The review progresses to analyze current research trends in SG security, evaluating methodologies across both layers and categorizing them into Machine Learning-based, data-driven, and model-based approaches. The analysis includes a detailed classification of research focused on Control, Monitoring, and Protection across each component and stage of SGs. Additionally, the paper examines emerging cyberattack strategies in SGs that have not been extensively reviewed in existing literature. In conclusion, the paper reflects on significant gaps and challenges in SG cyber-physical security research, underscoring the need for further exploration and innovation in this domain. Thus, this review serves as a critical roadmap for future research, delineating the current state and potential directions in the rapidly evolving field of SG security.

With the rapid integration of electronically interfaced renewable energy resources and loads into smart grids, there is increasing interest in power quality disturbances (PQD) classification to enhance the security and efficiency of these grids. This paper introduces a new approach to PQD classification based on the Vision Transformer (ViT) model. When a PQD occurs, the proposed approach first converts the power quality signal into an image and then utilizes a pre-trained ViT to accurately determine the class of the PQD. Unlike most previous works, which were limited to a few disturbance classes or small datasets, the proposed method is trained and tested on a large dataset with 17 disturbance classes. Our experimental results show that the proposed ViT-based approach achieves PQD classification precision and recall of 98.28% and 97.98%, respectively, outperforming recently proposed techniques applied to the same dataset.

Line Current Differential Relays (LCDRs) are high-speed relays progressively used to protect critical transmission lines. However, LCDRs are vulnerable to cyberattacks. Fault-Masking Attacks (FMAs) are stealthy cyberattacks performed by manipulating the remote measurements of the targeted LCDR to disguise faults on the protected line. Hence, they remain undetected by this LCDR. In this paper, we propose a two-module framework to detect FMAs. The first module is a Mismatch Index (MI) developed from the protected transmission line's equivalent physical model. The MI is triggered only if there is a significant mismatch in the LCDR's local and remote measurements while the LCDR itself is untriggered, which indicates an FMA. After the MI is triggered, the second module, a neural networkbased classifier, promptly confirms that the triggering event is a physical fault that lies on the line protected by the LCDR before declaring the occurrence of an FMA. The proposed framework is tested using the IEEE 39-bus benchmark system. Our simulation results confirm that the proposed framework can accurately detect FMAs on LCDRs and is not affected by normal system disturbances, variations, or measurement noise. Our experimental results using OPAL-RT's real-time simulator confirm the proposed solution's real-time performance capability.