On addressing the security and stability issues due to false data injection attacks in DC microgrids an adaptive observer approach

© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksThis paper proposes an observer-based methodology to detect and mitigate false data injection attacks in collaborative DC microgrids. The ability of observers to effectively detect such attacks is complicated by the presence of unknown non-linear constant power loads. This work determines that, in the presence of unknown constant power loads, the considered attack detection and mitigation problem involves non linearities, locally unobservable states, unknown parameters, uncertainty and noise. Taking into account these limitations, a distributed non linear adaptive observer is proposed to overcome these limitations and solve the concerned observation problem. The necessary conditions for the stability of the distributed scheme are found out. Moreover, numerical simulations are performed and then validated in a real experimental prototype, where communication delay, uncertainty and noise are considered.Peer ReviewedPostprint (author's final draft

Low Latency Anomaly Detection with Imperfect Models

The problem of anomaly detection deals with detecting abrupt changes/anomalies in the distribution of sequentially observed data in a stochastic system. This problem applies to many applications, such as signal processing, intrusion detection, quality control, medical diagnosis, etc. A low latency anomaly detection algorithm, which is based on the framework of quickest change detection (QCD), aims at minimizing the detection delay of anomalies in the sequentially observed data while ensuring satisfactory detection accuracy. Moreover, in many practical applications, complete knowledge of the post-change distribution model might not be available due to the unexpected nature of the change. Hence, the objective of this dissertation is to study low latency anomaly detection or QCD algorithms for systems with imperfect models such that any type of abnormality in the system can be detected as quickly as possible for reliable and secured system operations. This dissertation includes the theoretical foundations behind these low latency anomaly detection algorithms along with real-world applications. First, QCD algorithms are designed for detecting changes in systems with multiple post-change models under both Bayesian and non-Bayesian settings. Next, a QCD algorithm is studied for real-time detection of false data injection attacks in smart grids with dynamic models. Finally, a QCD algorithm for detecting wind turbine bearing faults is developed by analyzing the statistical behaviors of stator currents generated by the turbines. For all the proposed algorithms, analytical bounds of the system performance metrics are derived using asymptotic analysis and the simulation results show that the proposed algorithms outperform existing algorithms

False data injection attack detection in smart grid

Smart grid is a distributed and autonomous energy delivery infrastructure that constantly monitors the operational state of its overall network using smart techniques and state estimation. State estimation is a powerful technique that is used to determine the overall operational state of the system based on a limited set of measurements collected through metering systems. Cyber-attacks pose serious risks to a smart grid state estimation that can cause disruptions and power outages resulting in huge economical losses and are therefore a big concern to a reliable national grid operation. False data injection attacks (FDIAs), engineered on the basis of the knowledge of the network configuration, are difficult to detect using the traditional data detection mechanisms. These detection schemes have been found vulnerable and failed to detect these FDIAs. FDIAs specifically target the state data and can manipulate the state measurements in such a way that these false measurements appear real to the main control systems. This research work explores the possibility of FDIA detection using state estimation in a distributed and partitioned smart grid. In order to detect FDIAs we use measurements for residual-based testing which creates an objective function; and the probability of erroneous data is determined from this residual test. In this test, a preset threshold is determined based on the prior history of the state data. FDIA cases are simulated within a smart grid considering that the Chi-square detection state estimator fails in identifying such attacks. We compute the objective function using the standard weighted least problem and then test the objective function against the value in the Chi-square table. The gain matrix and the Jacobian matrix are computed. The state variables are computed in the form of a voltage magnitude. The state variables are computed after the inception of an attack to assess these state magnitude results. Different sizes of partitioning are used to improve the overall sensitivity of the Chi-square results. Our additional estimator is based on a Kalman estimation that consists of the state prediction and state correction steps. In the first step, it obtains the state and matrix covariance prediction, and in the second step, it calculates the Kalman gain and the state and matrix covariance update steps. The set of points is created for the state vector x at a time instant t. The initial vector and covariance matrix are based on a priori knowledge of the historical estimates. A set of sigma points is estimated by the state update function. Sigma points refer to the minimal set of sampling points that are selected and transformed using nonlinear function, and the new mean and the covariance are formed out of these transformed points. The idea behind this is that it is easier to compute a Gaussian distribution than an arbitrary nonlinear function. The filter gain, the mean and the covariance are used to estimate the next state. Our simulation results show that the combination of Kalman estimation and distributed state estimation improves the overall stability index and vulnerability assessment score of the smart grid. We built a stability index table for a smart grid based on the state estimates value after the inception of an FDIA. The vulnerability assessment score of the smart grid is based on common vulnerability scoring system (CVSS) and state estimates under the influence of an FDIA. The simulations are conducted in the MATPOWER program and different electrical bus systems such as IEEE 14, 30, 39, 118 and 300 are tested. All the contributions have been published in reputable journals and conferences.Doctor of Philosoph