Joint detection and localization of False Data Injection Attacks in smart grids: An enhanced state estimation approach

Abstract

The transition to smart grids introduces significant cybersecurity vulnerabilities, particularly with the rise of False Data Injection Attacks (FDIAs). These attacks allow malicious actors to manipulate sensor data, alter the internal state of the grid, and bypass traditional Bad Data Detection (BDD) systems. FDIAs pose a serious threat to grid security, potentially leading to incorrect state estimation and destabilization of the power system, which could result in system outages and economic losses. To address this challenge, this paper proposes a novel detection and localization method. First, false data and measurement errors are modeled as non-Gaussian noise. Recognizing the limitations of the traditional Extended Kalman Filter (EKF) under non-Gaussian conditions, the Maximum Correntropy Criterion (MCC) is integrated into the EKF to improve the robustness of state estimation. Additionally, the Maximum Correntropy Criterion Extended Kalman Filter (MCCEKF) is combined with Weighted Least Squares (WLS), and cosine similarity is introduced to quantify the differences between these two estimators for FDIA detection. A partition approach is then used to construct a logical localization matrix, with cosine similarity detection applied in each section to generate a detection matrix. By performing a logical AND operation on these matrices, the attacked bus is identified. Simulations on IEEE-14-bus and IEEE-30-bus systems validate the proposed approach, demonstrating its effectiveness in reliably detecting and localizing FDIAs in smart grids.