Biblio

Infrastructural systems such as the electricity grid, healthcare, and transportation networks today rely increasingly on the joint functioning of networked information systems and physical components, in short, on cyber-physical architectures. Despite tremendous advances in cryptography, physical-layer security and authentication, information attacks, both passive such as eavesdropping, and active such as unauthorized data injection, continue to thwart the reliable functioning of networked systems. In systems with joint cyber-physical functionality, the ability of an adversary to monitor transmitted information or introduce false information can lead to sensitive user data being leaked or result in critical damages to the underlying physical system. This paper investigates two broad challenges in information security in cyber-physical systems (CPSs): preventing retrieval of internal physical system information through monitored external cyber flows, and limiting the modification of physical system functioning through compromised cyber flows. A rigorous analytical framework grounded on information-theoretic security is developed to study these challenges in a general stochastic control system abstraction-a theoretical building block for CPSs-with the objectives of quantifying the fundamental tradeoffs between information security and physical system performance, and through the process, designing provably secure controller policies. Recent results are presented that establish the theoretical basis for the framework, in addition to practical applications in timing analysis of anonymous systems, and demand response systems in a smart electricity grid.

Reliable and secure grid operations become more and more challenging in context of increasing IT/OT convergence and decreasing dynamic margins in today's power systems. To ensure the correct operation of monitoring and control functions in control centres, an intelligent assessment of the different information sources is necessary to provide a robust data source in case of critical physical events as well as cyber-attacks. Within this paper, a holistic data stream assessment methodology is proposed using an expert knowledge based cyber-physical situational awareness for different steady and transient system states. This approach goes beyond existing techniques by combining high-resolution PMU data with SCADA information as well as Digital Twin and AI based anomaly detection functionalities.

A time-delay switch (TDS) cyber attack is a deliberate attempt by malicious adversaries aiming at destabilizing a power system by impeding the communication signals to/from the centralized controller from/to the network sensors and generating stations that participate in the load frequency control (LFC). A TDS cyber attack can be targeting the sensing loops (transmitting network measurements to the centralized controller) or the control signals dispatched from the centralized controller to the governor valves of the generating stations. A resilient TDS control strategy is proposed and developed in this work that thwarts network instabilities that are caused by delays in the sensing loops, and control commands, and guarantees normal operation of the LFC mechanism. This will be achieved by augmenting the telemetered control commands with locally generated feedback control laws (i.e., “decentralized” control commands) taking measurements that are available and accessible at the power generating stations (locally) independent from all the telemetered signals to/from the centralized controller. Our objective is to devise a controller that is capable of circumventing all types of TDS and DoS (Denial of Service) cyber attacks as well as a broad class of False Data Injection (FDI) cyber attacks.

This paper proposes new concepts for detecting and mitigating cyber attacks on substation automation systems by domain-based cyber-physical security solutions. The proposed methods form the basis of a distributed security domain layer that enables protection devices to collaboratively defend against cyber attacks at substations. The methods utilize protection coordination principles to cross check protection setting changes and can run real-time power system analysis to evaluate the impact of the control commands. The transient fault signature (TFS)-based cross-correlation coefficient algorithm has been proposed to detect the false sampled values data injection attack. The proposed functions were verified in a hardware-in-the-loop (HIL) simulation using commercial relays and a real-time digital simulator (RTDS). Various types of cyber intrusions are tested using this test bed to evaluate the consequences and impacts of cyber attacks to power grid as well as to validate the performance of the proposed research-grade cyber attack mitigation functions.

With the tighter integration of power system and Information and Communication Technology (ICT), power grid is becoming a typical cyber physical system (CPS). It is important to analyze the impact of the cyber event on power system, so that it is necessary to build a co-simulation system for studying the interaction between power system and ICT. In this paper, a cyber physical power system (CPPS) co-simulation platform is proposed, which includes the hardware-in-the-loop (HIL) simulation function. By using flexible interface, various simulation software for power system and ICT can be interconnected into the platform to build co-simulation tools for various simulation purposes. To demonstrate it as a proof, one simulation framework for real life cyber-attack on power system control is introduced. In this case, the real life denial-of-service attack on a router in automatic voltage control (AVC) is simulated to demonstrate impact of cyber-attack on power system.

The CPS standard can be more objective to evaluate the effect of control behavior in each control area on the interconnected power grid. The CPS standard is derived from statistical methods emphasizing the long-term control performance of AGC, which is beneficial to the frequency control of the power grid by mutual support between the various power grids in the case of an accident. Moreover, CPS standard reduces the wear of the equipment caused by the frequent adjustment of the AGC unit. The key is to adjust the AGC control strategy to meet the performance of CPS standard. This paper proposed a dynamic optimal CPS control methodology for interconnected power systems based on model predictive control which can achieve optimal control under the premise of meeting the CPS standard. The effectiveness of the control strategy is verified by simulation examples.

Real-time supervisory control and data acquisition (SCADA) systems use remote terminal units (RTUs) to monitor and manage the flow of power at electrical substations. As their connectivity to different utility and private networks increases, RTUs are becoming more vulnerable to cyber-attacks. Some attacks seek to access RTUs to directly control power system devices with the intent to shed load or cause equipment damage. Other attacks (such as denial-of-service) target network availability and seek to block, delay, or corrupt communications between the RTU and the control center. In the most severe case, when communications are entirely blocked, the loss of an RTU can cause the power system to become unobservable. It is important to understand how losing an RTU impacts the system state (bus voltage magnitudes and angles). The system state is determined by the state estimator and serves as the input to other critical EMS applications. There is currently no systematic approach for assessing the cyber-physical impact of losing RTUs. This paper proposes a methodology for N-1 RTU cyber-physical security assessment that could benefit power system control and operation. We demonstrate our approach on the IEEE 14-bus system as well as on a synthetic 200-bus system.

In this paper we report preliminary results from the novel coupling of cyber-physical emulation and interdiction optimization to better understand the impact of a CrashOverride malware attack on a notional electric system. We conduct cyber experiments where CrashOverride issues commands to remote terminal units (RTUs) that are controlling substations within a power control area. We identify worst-case loss of load outcomes with cyber interdiction optimization; the proposed approach is a bilevel formulation that incorporates RTU mappings to controllable loads, transmission lines, and generators in the upper-level (attacker model), and a DC optimal power flow (DCOPF) in the lower-level (defender model). Overall, our preliminary results indicate that the interdiction optimization can guide the design of experiments instead of performing a “full factorial” approach. Likewise, for systems where there are important dependencies between SCADA/ICS controls and power grid operations, the cyber-physical emulations should drive improved parameterization and surrogate models that are applied in scalable optimization techniques.

Large-scale blackouts that have occurred in the past few decades have necessitated the need to do extensive research in the field of grid security assessment. With the aid of synchrophasor technology, which uses phasor measurement unit (PMU) data, dynamic security assessment (DSA) can be performed online. However, existing applications of DSA are challenged by variability in system conditions and unaccounted for measurement errors. To overcome these challenges, this research develops a DSA scheme to provide security prediction in real-time for load profiles of different seasons in presence of realistic errors in the PMU measurements. The major contributions of this paper are: (1) develop a DSA scheme based on PMU data, (2) consider seasonal load profiles, (3) account for varying penetrations of renewable generation, and (4) compare the accuracy of different machine learning (ML) algorithms for DSA with and without erroneous measurements. The performance of this approach is tested on the IEEE-118 bus system. Comparative analysis of the accuracies of the ML algorithms under different operating scenarios highlights the importance of considering realistic errors and variability in system conditions while creating a DSA scheme.

This paper presents a methodology for utilizing Phasor Measurement units (PMUs) for procuring real time synchronized measurements for assessing the security of the power system dynamically. The concept of wide-area dynamic security assessment considers transient instability in the proposed methodology. Intelligent framework based approach for online dynamic security assessment has been suggested wherein the database consisting of critical features associated with the system is generated for a wide range of contingencies, which is utilized to build the data mining model. This data mining model along with the synchronized phasor measurements is expected to assist the system operator in assessing the security of the system pertaining to a particular contingency, thereby also creating possibility of incorporating control and preventive measures in order to avoid any unforeseen instability in the system. The proposed technique has been implemented on IEEE 39 bus system for accurately indicating the security of the system and is found to be quite robust in the case of noise in the measurement data obtained from the PMUs.

Analyzing performance of security and stability control system is of great importance for the safe and stable operation of the power grid. Digital dynamic experimental model is built by real time digital simulation (RTDS) in order to research security and stability system of Inner Mongolia in northern 500kV transmission channel. The whole process is closed-loop dynamic real-time simulation. According to power grid network testing technology standard, all kinds of stability control devices need to be tested in a comprehensive system. Focus on the following items: security and stability control strategy, tripping criterion as well as power system low frequency oscillations. Results of the trial indicated that the simulation test platform based on RTDS have the ability of detecting the safe and stable device. It can reflect the action behavior and control characteristics of the safe and stable device accurately. The device can be used in the case of low frequency oscillation of the system.

The smart grid consists of two parts, one is the physical power grid, the other is the information network. In order to study the cascading failure, the vulnerability analysis of the smart grid is done under a kind of community attack style in this paper. Two types of information networks are considered, i.e. topology consistency and scale-free cyber networks, respectively. The concept of control center is presented and the controllable power nodes and observable power lines are defined. Minimum load reduction model(MLRM) is given and described as a linear programming problem. A index is introduced to assess the vulnerability. New England 39 nodes system is applied to simulate the cascading failure process to demonstrate the effectiveness of the proposed MLRM where community the attack methods include attack the power lines among and in power communities.

Industrial control systems (ICS) are becoming more integral to modern life as they are being integrated into critical infrastructure. These systems typically lack application layer encryption and the placement of common network intrusion services have large blind spots. We propose the novel architecture, Cloud Based Intrusion Detection and Prevention System (CB-IDPS), to detect and prevent threats in ICS networks by using software defined networking (SDN) to route traffic to the cloud for inspection using network function virtualization (NFV) and service function chaining. CB-IDPS uses Amazon Web Services to create a virtual private cloud for packet inspection. The CB-IDPS framework is designed with considerations to the ICS delay constraints, dynamic traffic routing, scalability, resilience, and visibility. CB-IDPS is presented in the context of a micro grid energy management system as the test case to prove that the latency of CB-IDPS is within acceptable delay thresholds. The implementation of CB-IDPS uses the OpenDaylight software for the SDN controller and commonly used network security tools such as Zeek and Snort. To our knowledge, this is the first attempt at using NFV in an ICS context for network security.

This paper presents necessary modeling and control enhancements for Modular Multilevel Converters (MMC) to provide Fault-Ride-Through capability and fast fault current injection as required by the new German Technical Connection Rules for HVDC. HVDC converters have to be able to detect and control the grid voltage and grid currents accurately during all fault conditions. That applies to the positive as well as negative sequence components, hence a Decoupled Double Synchronous Reference Frame - Phase-Locked-Loop (DDSRF-PLL) and Current Control (DDSRF-CC) are implemented. In addition, an enhanced current limitation and an extension of the horizontal balancing control are proposed to complement the control structure for safe operation.

Distributed Generation Photovoltaic Systems -DGPV - connected to the power distribution grid through electronic inverters can contribute, in an aggregate scenario, to the performance of several power system control functions, notably in self-healing and voltage regulation along a distribution feeder. This paper proposes the use of an optimization method for voltage regulation, focused on reactive power injection control, based on a comprehensive architecture model that coordinates multiple photovoltaic distributed sources to support grid reconfiguration after self-healing action. A sensitivity analysis regarding the performance of voltage regulation, based on a co-simulation of PSCAD and MatLab, shows the effectiveness of using dispersed generation sources to assist grid reconfiguration after disturbances caused by severe faults.

The intelligent power grid is composed of a large number of industrial control equipment, and most of the industrial control equipment has security holes, which are vulnerable to malicious attacks and affect the normal operation of the power grid. By analyzing the security vulnerability of the firmware of industrial control equipment, the vulnerability can be detected in advance and the power grid's ability to resist attack can be improved. In this paper, a kind of industrial control device firmware protocol vulnerabilities associated technology, through the technology of information extraction from the mass grid device firmware device attributes and extract the industrial control system, the characteristics of the construction of industrial control system device firmware and published vulnerability information correlation, faster in the industrial control equipment safety inspection found vulnerabilities.

In recent years, there has been a significant increase in wind power penetration into the power system. As a result, the behavior of the power system has become more dependent on wind power behavior. Supervisory control and data acquisition (SCADA) systems responsible for monitoring and controlling wind farms often have vulnerabilities that make them susceptible to cyberattacks. These vulnerabilities allow attackers to exploit and intrude in the wind farm SCADA system. In this paper, a cyber-physical system (CPS) model for the information and communication technology (ICT) model of the wind farm SCADA system integrated with SCADA of the power system is proposed. Cybersecurity of this wind farm SCADA system is discussed. Proposed cyberattack scenarios on the system are modeled and the impact of these cyberattacks on the behavior of the power systems on the IEEE 9-bus modified system is investigated. Finally, an anomaly attack detection algorithm is proposed to stop the attack of tripping of all wind farms. Case studies validate the performance of the proposed CPS model of the test system and the attack detection algorithm.

In this work, the unknown cyber-attacks on cyber-physical systems are reconstructed using sliding mode differentiation techniques in concert with the sparse recovery algorithm, when only several unknown attacks out of a long list of possible attacks are considered non-zero. The approach is applied to a model of the electric power system, and finally, the efficacy of the proposed techniques is illustrated via simulations of a real electric power system.

The eleven papers in this special section focus on power electronics-enabled autonomous systems. Power systems are going through a paradigm change from centralized generation to distributed generation and further onto smart grid. Millions of relatively small distributed energy resources (DER), including wind turbines, solar panels, electric vehicles and energy storage systems, and flexible loads are being integrated into power systems through power electronic converters. This imposes great challenges to the stability, scalability, reliability, security, and resiliency of future power systems. This section joins the forces of the communities of control/systems theory, power electronics, and power systems to address various emerging issues of power-electronics-enabled autonomous power systems, paving the way for large-scale deployment of DERs and flexible loads.

Recent years, the issue of cyber security has become ever more prevalent in the analysis and design of electrical cyber-physical systems (ECPSs). In this paper, we present the TrueTime Network Library for modeling the framework of ECPSs and focuses on the vulnerability analysis of ECPSs under DoS attacks. Model predictive control algorithm is used to control the ECPS under disturbance or attacks. The performance of decentralized and distributed control strategies are compared on the simulation platform. It has been proved that DoS attacks happen at dada collecting sensors or control instructions actuators will influence the system differently.

Power system simulation environments with appropriate time-fidelity are needed to enable rapid testing of new smart grid technologies and for coupled simulations of the underlying cyber infrastructure. This paper presents such an environment which operates with power system models in the PMU time frame, including data visualization and interactive control action capabilities. The flexible and extensible capabilities are demonstrated by interfacing with a cyber infrastructure simulation.

The modern electric power grid is a complex cyber-physical system whose reliable operation is enabled by a wide-area monitoring and control infrastructure. Recent events have shown that vulnerabilities in this infrastructure may be exploited to manipulate the data being exchanged. Such a scenario could cause the associated control applications to mis-operate, potentially causing system-wide instabilities. There is a growing emphasis on looking beyond traditional cybersecurity solutions to mitigate such threats. In this paper we perform a testbed-based validation of one such solution - Attack Resilient Control (ARC) - on Iowa State University's PowerCyber testbed. ARC is a cyber-physical security solution that combines domain-specific anomaly detection and model-based mitigation to detect stealthy attacks on Automatic Generation Control (AGC). In this paper, we first describe the implementation architecture of the experiment on the testbed. Next, we demonstrate the capability of stealthy attack templates to cause forced under-frequency load shedding in a 3-area test system. We then validate the performance of ARC by measuring its ability to detect and mitigate these attacks. Our results reveal that ARC is efficient in detecting stealthy attacks and enables AGC to maintain system operating frequency close to its nominal value during an attack. Our studies also highlight the importance of testbed-based experimentation for evaluating the performance of cyber-physical security and control applications.

The power grid is a prime target of cyber criminals and warrants special attention as it forms the backbone of major infrastructures that drive the nation's defense and economy. Developing security measures for the power grid is challenging since it is physically dispersed and interacts dynamically with associated cyber infrastructures that control its operation. This paper presents a mathematical framework to investigate stability of two area systems due to data attacks on Automatic Generation Control (AGC) system. Analytical and simulation results are presented to identify attack levels that could drive the AGC system to potentially become unstable.

This paper formulates a power system related optimal control problem, motivated by potential cyber-attacks on grid control systems, and ensuing defensive response to such attacks. The problem is formulated as a standard nonlinear program in the GAMS optimization environment, with system dynamics discretized over a short time horizon providing constraint equations, which are then treated via waveform relaxation. Selection of objective function and additional decision variables is explored first for identifying grid vulnerability to cyber-attacks that act by modifying feedback control system parameters. The resulting decisions for the attacker are then fixed, and the optimization problem is modified with a new objective function and decision variables, to explore a defender's possible response to such attacks.

In this paper, we study the problem of privacy information leakage in a smart grid. The privacy risk is assumed to be caused by an unauthorized binary hypothesis testing of the consumer's behaviour based on the smart meter readings of energy supplies from the energy provider. Another energy supplies are produced by an alternative energy source. A controller equipped with an energy storage device manages the energy inflows to satisfy the energy demand of the consumer. We study the optimal energy control strategy which minimizes the asymptotic exponential decay rate of the minimum Type II error probability in the unauthorized hypothesis testing to suppress the privacy risk. Our study shows that the cardinality of the energy supplies from the energy provider for the optimal control strategy is no more than two. This result implies a simple objective of the optimal energy control strategy. When additional side information is available for the adversary, the optimal control strategy and privacy risk are compared with the case of leaking smart meter readings to the adversary only.