Consensus-Based Power System State Estimation Algorithm Under Collaborative Attack.

ABSTRACTAs a result of the rapid growth of distributed state estimation in modern power systems, power networks are facing increasingly serious security problems, thus requiring the advancement of defense techniques against cyber attacks. This paper is devoted to investigating distributed consensus state estimation with a defense mechanism against false data injection (FDI) attacks for power systems. By introducing a transformation matrix, the local subsystem model associated with the mixed remote terminal unit and phasor measurement unit measurements is constructed. Taking into account historical estimation information on state variables without being attacked, a defense mechanism constructed by secure historical estimation value is presented to protect the estimator against FDI attacks while maintaining accuracy in estimation. Then a distributed Kalman consensus filter (DKCF) is hosted to estimate the dynamic states of power systems under FDI attack protector. Considering scalability in large‐scale power systems, a suboptimal DKCF with the designed protector is developed. By means of the Lyapunov‐based approach, a sufficient condition is provided to ensure that the proposed estimator equipped with the defense mechanism is stable. Finally, the proposed distributed state estimation algorithms are validated on an IEEE benchmark 14‐bus power system.

In the distributed load frequency control (DLFC) scheme, the compromised areas by attackers may endanger the stable and reliable operation of the entire power system. A credibility‐based secure DLFC strategy is in this work proposed to sustain the stable operation of the power system under false data injection attacks. In this secure DLFC method, the gradient‐variation‐based credibility metrics are defined to detect potential attacked measurements. Then, the communication isolation decisions are made based on the adaptive thresholds. To compensate the impact of the communication isolation of misbehaving areas, predictions of future measurements by using the average gradient variation of stored healthy data are feeded back to the area controller. Simulation results of a four‐area power system show the proposed secure DLFC can identify and cut off the communication of the misbehaving areas and still maintain the stability of the power system.

For target tracking in radar network, any anomaly in a part of the system can quickly spread over the network and lead to tracking failures. False data injection (FDI) attacks can damage the state estimation mechanism by modifying the radar measurements with unknown and time-varying attack variables, therefore making traditional filters inapplicable. To tackle this problem, we propose a novel consensus-based distributed state estimation (DSE) method for target tracking with FDI attacks, which is effective even when all radars are under FDI attacks. First, a real-time residual-based detector is introduced to the DSE framework, which can effectively detect FDI attacks by analyzing the statistical properties of the residual. Secondly, a simple yet effective attack parameter estimation method is proposed to provide attack parameter estimation based on a pseudo-measurement equation, which has the advantage of decoupled estimation of state and attack parameters compared with augmented state filters. Finally, for timely attack mitigation and global consistency achievement, a novel hybrid consensus method is proposed which can compensate for the estimation error caused by FDI attacks and provide estimation accuracy improvement. The simulation results show that the proposed solution is effective and superior to the traditional DSE method for target tracking in the presence of FDI attacks.

This paper studies the secure state estimation problem for multi-area power systems with unknown load deviation when the sensor measurements of power systems encounter false data injection (FDI) attacks. By modeling a new state-space model including the variables of load deviation and FDI attack signals, an intermediate observer-based method is developed to simultaneously estimate the load deviation and FDI attacks. Then, the secure state estimator is designed by compensating FDI attacks, while the unknown load deviation in power systems is compensated by its estimated values to eliminate system oscillations as much as possible. Simulations show the effectiveness of the proposed methods.

Event-triggered model-free adaptive predictive control for multi-area power systems under deception attacks

In this paper, we consider false data injection (FDI) attacks with limited information of transmission-line susceptances and new countermeasures in smart grids. First, we prove that the adversary could launch FDI attacks to modify the state variable on a bus or superbus only if he/she knows the susceptance of every transmission line that is incident to that bus or superbus. Based on this observation, we provide a new countermeasure against FDI attacks, i.e., to make the susceptances of n-1 interconnected transmission lines that cover all buses unknown to the adversary (e.g., by proactively perturbing transmission-line susceptances through distributed flexible AC transmission system (D-FACTS) devices), where n is the total number of buses. This new countermeasure can work alone or in conjunction with traditional ones to reduce the number of meter measurements/state variables that are to be secured against FDI attacks. The implementation of FDI attacks with limited susceptance information and the effectiveness of new countermeasures are demonstrated by using an illustrative 4-bus power system and the IEEE 9-bus, 14-bus, 30-bus, 118-bus, and 300-bus test power systems.

While acquiring precise and intelligent state sensing and control capabilities, the cyber physical power system is constantly exposed to the potential cyber-attack threat. False data injection (FDI) attack attempts to disrupt the normal operation of the power system through the coupling of cyber side and physical side. To deal with the situation that stealthy FDI attack can bypass the bad data detection and thus trigger false commands, a system feature extraction method in state estimation is proposed, and the corresponding FDI attack detection method is presented. Based on the principles of state estimation and stealthy FDI attack, we analyze the impacts of FDI attack on measurement residual. Gaussian fitting method is used to extract the characteristic parameters of residual distribution as the system feature, and attack detection is implemented in a sliding time window by comparison. Simulation results prove that the proposed attack detection method is effectiveness and efficiency.

The smart grid combines the classical power system with the information technology, leading to a cyber-physical system. In such an environment, the malicious injection of data has the potential to cause severe consequences. Classical residual-based methods for bad data detection are unable to detect well designed false data injection (FDI) attacks. Moreover, most of the works on FDI attack detection are based on the linearized DC model of the power system and fails to detect attacks based on the AC model. The aim of this paper is to address these problems by using the graph structure of the grid and the AC power flow model. We derive an attack detection method that is able to detect previously undetectable FDI attacks. This method is based on concepts originating from graph signal processing (GSP). The proposed detection scheme calculates the graph Fourier transform of an estimated grid state and filters the graph’s high-frequency components. By comparing the maximum norm of this outcome with a threshold, we can detect the presence of FDI attacks. Case studies on the IEEE 14-bus system demonstrate that the proposed method is able to detect a wide range of previously undetectable attacks, both on angles and on magnitudes of the voltages.

Load frequency control (LFC) is a crucial application in modern power systems as it ensures the system frequency remains within an acceptable range through demand control and active power generation. However, as information and communication technology (ICT) becomes more prevalent in power system, there are both opportunities for improved reliability and efficiency as well as potential security threats. This article proposes an LFC approach that takes into account the simultaneous occurrence of false data injection (FDI) attacks on the sensor-controller side, disturbances in the states of the system and time delays in the controller-actuator side. To tackle these challenges, a slide mode observer is utilized to estimate system states and detect cyber-attack signals as an extra virtual state. Subsequently, a cyber-attack-resilient predictor slide mode controller is designed to establish a robust control law capable of overcoming system challenges, when all system states are not within reach. Therefore, the robust control law is designed based on the estimated states, cyber signal and measured system output. By integrating these techniques, the proposed methodology offers a promising solution to enhance the resilience and performance of power system faced with cybersecurity threats. It improves the response speed of the system and minimizes the maximum overshoot compared to some published resilience control laws, thereby ensuring secure and reliable load frequency control. Additionally, reducing the number of sensors in the system helps to reduce overall costs. Finally, the performance of the controller is also verified in real-time using the OPAL-RT simulator testbed.

False data injection (FDI) attack causes a disadvantage to the safety of a power system. In the past, the adversary launching FDI attack mostly needed the complete network topology information of the power system and a large amount of measurements. However, the reality is that the adversary has limited resources and can hardly manipulate massive measurements without being detected. This paper proposes a new method to form attack vector in the condition of incomplete system information. The proposed method first maps the restricted data into a new Jacobian matrix by a kernel-independent component analysis, then constructs the undetectable attack model, and finally, designs a less costly attack vector to accomplish the FDI attack. The experimental results on different IEEE buses with Matpower tool illustrate the validity of the proposed method. Additionally, the proposed method shows a relatively high success rate in the cases of different degrees of incomplete information and various quantities of manipulated data in different power systems.

Under the background of the construction of “three types and two networks”, the introduction of a large number of heterogeneous intelligent terminals, ubiquitous open networks, interactive real-time control services and wide area interconnected power systems have transmitted the network security risks to all aspects of the power system. The security defense capability of power system is not only the superposition of security defense equipment, but also the excellent network security team. The continuous improvement of the level of network security is inseparable from the accumulation of network security attack and defense technology and practical experience, as well as practical tools and equipment with the characteristics of the power industry. In order to meet the increasingly severe security protection challenges, actively implement the security protection requirements of the power information system, ensure the safe and stable operation of the power system, build a network shooting range suited to the attributes of the power industry, and continuously improve the ability of network security protection, consolidate the foundation of digital transformation to ensure the safe and stable operation of power system and reliable power supply.The safe and stable operation of the information system needs to carry out comprehensive and in-depth vulnerability troubleshooting. In view of the lack of power business loophole mining and security event verification shooting range environment, this paper studies the power security shooting range construction technology.

According to the contradiction between the static power system state estimation and the actual operation of power system, based on the study of the classical algorithms of power system state estimation, apply the gray system theory of power system based on GM (1, 1) mode to the state estimation of power system, and create a new dynamic power system state estimation algorithm, for example to a four-node power system network test for State estimation simulation. Measurement of a known algorithm used to predict the gray system theory. On the other hand, measurement of unknown quantity, using the result that come from the known quantity, then get all parameters with character of power system state through statistical analysis of simulation results that the normal measurement system, the method is feasible and has a good filtering effect. The power system state estimation algorithm based on the gray system theory of Power System based on GM (1, 1) model has high estimating precision, fast calculation, use small memory and the adaptability of the measurement system is very strong, so it has good prospects.

False data injection attacks with complete stealthiness in cyber–physical systems: A self-generated approach

The pervasiveness of information and communications technologies as well as intelligent electronic devices leads to an expanded attack surface in smart grids, making it increasingly challenging to withstand the high-profile false data injection (FDI) attacks. In this paper, we propose a Proactive Approach to Mitigate FDI Attacks (PAMA) in smart grids. With PAMA scheme, the critical information - power grid connections and configurations as well as the original measurement data - used for constructing FDI attacks is well protected from leakage or theft, so that FDI attacks are effectively mitigated. Specifically, we transform the state estimation and FDI detection application into a distributed one equipped with converted information from the critical information provided by the control center. In addition, the original measurement data is also protected by using a secure hybrid Paillier cryptosystem. Our PAMA scheme is proved to be secure and effective in mitigating FDI attacks on smart grids. The computational complexity and the communication overhead are evaluated on the standard IEEE 14-bus test system. Keywords <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">__</sup> Smart grids, false data injection (FDI) attack, Paillier cryptosystem, state estimation.

The cybersecurity of the smart grid is becoming a critical issue to the power systems. Mitigating potential cyberattacks on the control of the power electronic devices is an important concern in the controller design to enhance the resilience of the overall power grid. In this paper, a novel model predictive control (MPC) is proposed for the multilevel cascaded H-bridge inverters considering the false data injection (FDI) attacks. The FDI attack is a vicious attack scenario when the attack signals are injected into the sensors in the cyberphysical systems (CPS). It is shown that FDI signals can effectively penetrate the commonly used Kalman Filter (KF) based state estimation and jeopardize the control of the inverters. The k-Nearest Neighbors (KNN) algorithm is employed in the proposed scheme to mitigate the FDI attacks. The proposed KNN-based MPC scheme was tested with various types of attack signals. The results show that the proposed scheme exhibits a robust performance in the face of the malicious FDI attacks.