An accurate power state is the basis of the normal functioning of the smart grid. However, false data injection attacks (FDIAs) take advantage of the vulnerability in the bad data detection mechanism of the power system to manipulate the process of state estimation. By attacking the measurements, then affecting the estimated state, FDIAs have become a serious hidden danger that affects the security and stable operation of the power system. To address the bad data detection vulnerability, in this paper, a false data attack detection method based on weighted least squares (WLS) and an adaptive interpolation extended Kalman filter (AIEKF) is proposed. On the basis of applying WLS and AIEKF, the Euclidean distance is used to calculate the deviation values of the two-state estimations to determine whether the current moment is subjected to a false data injection attack in the power system. Extensive experiments were conducted to simulate an IEEE-14-bus power system, showing that the adaptive interpolation extended Kalman filter can compensate for the deficiency in the bad data detection mechanism and successfully detect FDIAs.

This paper introduces a novel hybrid filtering algorithm that leverages the advantages of Phasor Measurement Units (PMU) to address state estimation challenges in power systems. The primary objective is to integrate the benefits of PMU measurements into the design of traditional power system dynamic estimators. It is noteworthy that PMUs and Supervisory Control and Data Acquisition (SCADA) systems typically operate at different sampling rates in power system estimation, necessitating synchronization during the filtering process. To address this issue, the paper employs a predictive interpolation method for SCADA measurements within the framework of the Extended Kalman Filter (EKF) algorithm. This approach achieves more accurate estimates, closer to real observation data, by averaging the KL distribution. The algorithm is particularly well-suited for state estimation tasks in power systems that combine traditional and PMU measurements. Extensive simulations were conducted on the IEEE-14 and IEEE-30 test systems, and the results demonstrate that the fused estimator outperforms individual estimators in terms of estimation accuracy.

State estimation plays a central role in ensuring the secure operation of the smart grid. However, deliberately designed false data injection attacks (FDIAs) can pass by conventional detections to manipulate the process of state estimation by injecting malicious data into measurements. Ultimately, FDIAs make the result of state estimation deviate from secure value and affect the security and stable operation of the power system. In this paper, we consider the different distribution characteristics between normal measurements and false measurements and build a Gaussian mixture model (GMM). Particularly, we focus on achieving joint detection and localization of FDIAs. To tackle these challenges, a model-based algorithm named Joint Maximum a Posteriori - Maximum Likelihood (JMAP-ML) is proposed to estimate the individual parameters of GMM and achieve joint detection and localization of FDIAs with high accuracy. Different testing scenarios in the IEEE-14-bus and IEEE-30-bus power systems are simulated to show the performance of the proposed algorithm on parameters estimation, FDIAs detection and localization. Numerical examples demonstrate the proposed algorithm achieves satisfactory results in detecting and localizing FDIAs compared to the other algorithms.

False Data Injection Attack (FDIA) is a prevalent cyber threat in modern smart grids, capable of altering measurement data and circumventing Bad Data Detection (BDD) mechanisms, which results in inaccurate state estimations. This paper proposes a combined filtering algorithm, the extended Kalman filter and smooth variable structure filter (EK-SVSF), to address state estimation issues under FDIA and to overcome the limitations of traditional bad data detection (BDD) algorithms in detecting sophisticated FDIA. The EKF introduces a smooth variable structure to force the estimates to stay within the region of the true state trajectory by switching the gains, keeping the state estimates close to the true values. This approach enhances the robustness and stability of the estimation, effectively addressing the accuracy challenges of EKF based estimation under FDIA. Second, a state estimation-based FDIA detection method is proposed by leveraging the different characteristics presented by static state estimation Weighted Least Squares (WLS) and dynamic state estimation EK-SVSF under attack conditions. The Euclidean distance between the dynamic state estimates from EK-SVSF and the static estimates from WLS is calculated and compared to a predefined detection threshold to identify FDIA effectively. Finally, simulations conducted on IEEE 14-bus and IEEE 30-bus systems demonstrate that the proposed method effectively detects FDIA.