False data injection attacks (FDIA) are a main category of cyber-attacks threatening the security of power systems. Contrary to the detection of these attacks, less attention has been paid to identifying the attacked units of the grid. To this end, this work jointly studies detecting and localizing the stealth FDIA in power grids. Exploiting the inherent graph topology of power systems as well as the spatial correlations of measurement data, this paper proposes an approach based on the graph neural network (GNN) to identify the presence and location of the FDIA. The proposed approach leverages the auto-regressive moving average (ARMA) type graph filters (GFs) which can better adapt to sharp changes in the spectral domain due to their rational type filter composition compared to the polynomial type GFs such as Chebyshev. To the best of our knowledge, this is the first work based on GNN that automatically detects and localizes FDIA in power systems. Extensive simulations and visualizations show that the proposed approach outperforms the available methods in both detection and localization of FDIA for different IEEE test systems. Thus, the targeted areas can be identified and preventive actions can be taken before the attack impacts the grid.

False data injection attacks (FDIAs) represent a major class of attacks that aim to break the integrity of measurements by injecting false data into the smart metering devices in power grids. To the best of authors’ knowledge, no study has attempted to design a detector that automatically models the underlying graph topology and spatially correlated measurement data of the smart grids to better detect cyber attacks. The contributions of this article to detect and mitigate FDIAs are twofold. First, we present a generic, localized, and stealth (unobservable) attack generation methodology and publicly accessible datasets for researchers to develop and test their algorithms. Second, we propose a graph neural network (GNN) based, scalable and real-time detector of FDIAs that efficiently combines model-driven and data-driven approaches by incorporating the inherent physical connections of modern ac power grids and exploiting the spatial correlations of the measurement. It is experimentally verified by comparing the proposed GNN-based detector with the currently available FDIA detectors in the literature that our algorithm outperforms the best available solutions by 3.14%, 4.25%, and 4.41% in $F$ 1 score for standard IEEE testbeds with 14, 118, and 300 buses, respectively.

This paper introduces a novel approach for optimal operation of distribution networks at the presence of distributed generation resources and battery energy storage system. Modern power distribution networks must operate not only at the most economical way but also at a reasonable level of system reliability. Toward this end a special attention has been paid to reliability assessment in which turns the proposed problem into a multiobjective energy management (MOEM). Energy not supplied, a prominent reliability index, is considered as an objective function beside the operation cost. A hybrid evolutionary algorithm, hybrid grey wolf optimizer-particle swarm optimization algorithm, is also proposed to solve the proposed MOEM. Accuracy of the proposed algorithm is evaluated on IEEE 84-bus standard distribution system. Obtained results are compared with those available in literatures to prove the supremacy of the proposed algorithm. Furthermore, a benefit analysis has been done to assess the associated cost of the photovoltaic installment.