Sensor attacks on grid-tie photovoltaic (PV) inverters can cause severe damage. Considering uncertain environments and unknown model mismatches, real-time estimation and defense for sensor attacks on actual PV inverters are challenging. In this article, we propose an optimization-driven robust estimator within the attack frequency range using the H_∞ index, while the model mismatch effect on estimation is also minimized. To improve the real-time response under varying environments, an analytical solution from a convex quadratic programming reformulation is constructed. Guided by the estimation, we further develop a closed-loop compensation strategy with a tracking controller and a low-pass filter. Through code porting, our proposed defense strategy has been implemented in a microcommercial PV inverter. Hardware implementations show that our defense approach can effectively mitigate sensor attacks and maintain stable inverter operation.

The main objective of this article is to develop scalable dynamic anomaly detectors with high-fidelity simulators of power systems. On the one hand, models in high-fidelity simulators are typically 'intractable' if one opts to describe them in a mathematical formulation in order to apply existing model-based approaches from the anomaly detection literature. On the other hand, pure data-driven methods developed primarily in the machine learning literature neglect our knowledge about the underlying dynamics of power systems. In this study, we combine tools from these two mainstream approaches to develop a data-assisted model-based diagnosis filter utilizing both the knowledge from a picked abstract model and also the data of simulation results from high-fidelity simulators. The proposed diagnosis filter aims to achieve two desired features: (i) performance robustness with respect to model mismatch; (ii) high scalability. To this end, we propose a tractable (convex) optimization-based reformulation in which decisions are the filter parameters, the model-based information introduces feasible sets, and the data from the simulator forms the objective function to-be-minimized regarding the effect of model mismatch on the filter performance. To validate the theoretical results, we implement the developed diagnosis filter in DIgSILENT PowerFactory to detect false data injection attacks on the Automatic Generation Control measurements in the three-area IEEE 39-bus system.

This paper proposes a novel application for the optimal Linear Quadratic Gaussian (LQG) servo controller to enable a proper coordination of the AC/HVDC interconnected system with Virtual Synchronous Power (VSP) based inertia emulation. Particularly, the proposed control design takes the process disturbances and measurement noise of the studied VSP-HVDC system into account, while few studies have focused on this perspective. The proposed LQG controller with modifications is designed by means of a combination of Kalman Filter (state estimator) and an added Linear Quadratic Integrator (LQI) to observe the system model's states and track the reference commands while rejecting the effects of system noise. Besides, we utilize a swarm-based optimization algorithm to operate as the search process for the tuning of the elements in the weighting matrices involved in the controller design. The role of the proposed optimal LQG controller is to stabilize such AC/DC interconnected system with VSP-based inertia emulator while minimizing the associated performance index. According to the obtained simulation results, in addition to the advancement from the VSP-based approach for damping frequency oscillations excited by faults, application of the proposed LQG servo controller can achieve the targets on both estimating the state variables and tracking the reference signals with satisfactory performance, comparing with the conventional LQG regulator.

The high penetration of renewable energy resources and power electronic-based components has led to a low-inertia power grid which would bring challenges to system operations. The new model of load frequency control (LFC) must be able to handle the modern scenario where controlled areas are interconnected by parallel AC/HVDC links and storage devices are added to provide virtual inertia. Notably, vulnerabilities within the communication channels for wide-area data exchange in LFC loops may make them exposed to various cyber attacks, while it still remains largely unexplored how the new LFC in the AC/HVDC interconnected system with emulated inertia would be affected under malicious intrusions. Thus, in this article, we are motivated to explore possible effects of the major types of data availability and integrity attacks—Denial of Service (DoS) and false data injection (FDI) attacks—on such a new LFC system. By using a system-theoretic approach, we explore the optimal strategies that attackers can exploit to launch DoS or FDI attacks to corrupt the system stability. Besides, a comparison study is performed to learn the impact of these two types of attacks on LFC models of power systems with or without HVDC link and emulated inertia. The simulation results on the the exemplary two-area system illustrate that both DoS and FDI attacks can cause large frequency deviations or even make the system unstable; moreover, the LFC system with AC/HVDC interconnections and emulated inertia could be more vulnerable to these two types of attacks in many adversarial scenarios.

Power systems are moving towards hybrid AC/DC grids with the integration of HVDC links, renewable resources and energy storage modules. The load frequency control (LFC) of tomorrow has to consider the complex interactions between these components. Meanwhile, more attention should be paid to cyber security concerns as the LFC loop highly depends on data communications which may be exposed to cyber attacks. In this regard, this article aims to analyze the false data injection (FDI) attacks on the AC/DC interconnected LFC system with inertia emulation and develop advanced diagnosis tools to reveal their occurrence. We build an optimization-based framework for the purpose of vulnerability analysis. Considering the attack impact on frequency stability, it is shown that the multi-area LFC system with parallel AC/DC links and emulated inertia by storage devices is more vulnerable to FDI attacks, compared to the one without inertia emulation and the normal AC system. We then propose a detection approach to detect and isolate each FDI intrusion with a sufficient fast response, and even recover the attack value. In addition to theoretical results, the effectiveness of the proposed method is validated through simulations on the two-area AC/DC interconnected LFC system with inertia emulation capabilities.

The evolved smart grid has become a cyber physical energy system that could be exposed to a massive amount of cyber threats. Vulnerabilities within the cyber part can be used to launch multiple types of attacks that corrupt the physical system. The complexity of cyber physical energy system, the existing of different kinds of attacks, require an appropriate tool to aid in modeling and simulation for cyber security analysis. In this paper, we introduce a modeling language - Modelica to the security community of cyber physical system. We show the capability of Modelica in modeling complex systems and attacks by building up a power grid model with frequency control loop (i.e., automatic generation control), as well as data integrity attack and data availability attack models. The simulation results show how different types of attacks or even combined attacks can affect the system frequency stability.

Understanding smart grid cyber attacks is key for developing appropriate protection and recovery measures. Advanced attacks pursue maximized impact at minimized costs and detectability. This paper conducts risk analysis of combined data integrity and availability attacks against the power system state estimation. We compare the combined attacks with pure integrity attacks -false data injection (FDI) attacks. A security index for vulnerability assessment to these two kinds of attacks is proposed and formulated as a mixed integer linear programming problem. We show that such combined attacks can succeed with fewer resources than FDI attacks. The combined attacks with limited knowledge of the system model also expose advantages in keeping stealth against the bad data detection. Finally, the risk of combined attacks to reliable system operation is evaluated using the results from vulnerability assessment and attack impact analysis. The findings in this paper are validated and supported by a detailed case study.