Biblio

Power system security is one of the key issues in the operation of smart grid system. Evaluation of power system security is a big challenge considering all the contingencies, due to huge computational efforts involved. Phasor measurement unit plays a vital role in real time power system monitoring and control. This paper presents static security assessment scheme for large scale inter connected power system with Phasor measurement unit using Artificial Neural Network. Voltage magnitude and phase angle are used as input variables of the ANN. The optimal location of PMU under base case and critical contingency cases are determined using Genetic algorithm. The performance of the proposed optimization model was tested with standard IEEE 30 bus system incorporating zero injection buses and successful results have been obtained.

Coming days are becoming a much challenging task for the power system researchers due to the anomalous increase in the load demand with the existing system. As a result there exists a discordant between the transmission and generation framework which is severely pressurizing the power utilities. In this paper a quick and efficient methodology has been proposed to identify the most sensitive or susceptible regions in any power system network. The technique used in this paper comprises of correlation of a multi-bus power system network to an equivalent two-bus network along with the application of Artificial neural network(ANN) Architecture with training algorithm for online monitoring of voltage security of the system under all multiple exigencies which makes it more flexible. A fast voltage stability indicator has been proposed known as Unified Voltage Stability Indicator (UVSI) which is used as a substratal apparatus for the assessment of the voltage collapse point in a IEEE 30-bus power system in combination with the Feed Forward Neural Network (FFNN) to establish the accuracy of the status of the system for different contingency configurations.

In view of the high demand for the security of visiting data in power system, a network data security analysis method based on DPI technology was put forward in this paper, to solve the problem of security gateway judge the legality of the network data. Considering the legitimacy of the data involves data protocol and data contents, this article will filters the data from protocol matching and content detection. Using deep packet inspection (DPI) technology to screen the protocol. Using protocol analysis to detect the contents of data. This paper implements the function that allowing secure data through the gateway and blocking threat data. The example proves that the method is more effective guarantee the safety of visiting data.

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, the design of an event-driven middleware for general purpose services in smart grid (SG) is presented. The main purpose is to provide a peer-to-peer distributed software infrastructure to allow the access of new multiple and authorized actors to SGs information in order to provide new services. To achieve this, the proposed middleware has been designed to be: 1) event-based; 2) reliable; 3) secure from malicious information and communication technology attacks; and 4) to enable hardware independent interoperability between heterogeneous technologies. To demonstrate practical deployment, a numerical case study applied to the whole U.K. distribution network is presented, and the capabilities of the proposed infrastructure are discussed.

This paper focuses on the issues of secure key management for smart grid. With the present key management schemes, it will not yield security for deployment in smart grid. A novel key management scheme is proposed in this paper which merges elliptic curve public key technique and symmetric key technique. Based on the Needham-Schroeder authentication protocol, symmetric key scheme works. Well known threats like replay attack and man-in-the-middle attack can be successfully abolished using Smart Grid. The benefits of the proposed system are fault-tolerance, accessibility, Strong security, scalability and Efficiency.

This paper presents a contextual anomaly detection method and its use in the discovery of malicious voltage control actions in the low voltage distribution grid. The model-based anomaly detection uses an artificial neural network model to identify a distributed energy resource's behaviour under control. An intrusion detection system observes distributed energy resource's behaviour, control actions and the power system impact, and is tested together with an ongoing voltage control attack in a co-simulation set-up. The simulation results obtained with a real photovoltaic rooftop power plant data show that the contextual anomaly detection performs on average 55% better in the control detection and over 56% better in the malicious control detection over the point anomaly detection.

Honeypot is a common method of attack capture, can maximize the reduction of cyber-attacks. However, its limited application layer simulation makes it impossible to use effectively in power system. Through research on sandboxing technology, this article implements the simulated power manager applications by packaging real power manager applications, in order to expand the honeypot applied range.

The study of the characteristics of disturbance propagation in the interconnected power networks is of great importance to control the spreading of disturbance and improve the security level of power systems. In this paper, the characteristics of disturbance propagation in a one-dimensional chained power network are studied from the electromechanical wave point of view. The electromechanical wave equation is built based on the discrete inertia model of power networks. The wave transfer function which can describe the variations of amplitude and the phase is derived. Then, the propagation characteristics of different frequency disturbances are analyzed. The corner frequency of the discrete inertia model is proposed. Furthermore, the frequency dispersion and local oscillation are considered and their relationships with the corner frequency are revealed as well. Computer simulations for a 50 generators chained network are carried out to verify the propagation characteristics of disturbances with different frequencies.

Wireless Mesh Networks (WMNs) are being considered as most adequate for deployment in the Neighborhood Area Network (NAN) domain of the smart grid infrastructure because their features such as self-organizing, scalability and cost-efficiency complement the NAN requirements. To enhance the security of the WMNs, the key refreshment strategy for the Simultaneous Authentication of Equals (SAE) or the Efficient Mesh Security Association (EMSA) protocols is an efficient way to make the network more resilient against the cyber-attacks. However, a security vulnerability is discovered in the EMSA protocol when using the key refreshment strategy. The first message of the Mesh Key Holder Security Handshake (MKHSH) can be forged and replayed back in the next cycles of the key refreshment leading to a Denial of Service (DoS) attack. In this paper, a simple one-way hash function based scheme is proposed to prevent the unprotected message from being replayed together with an enhancement to the key refreshment scheme to improve the resilience of the MKHSH. The Protocol Composition Logic (PCL) is used to verify the logical correctness of the proposed scheme, while the Process Analysis Toolkit (PAT) is used to evaluate the security functionality against the malicious 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.

The power system forms the backbone of a modern society, and its security is of paramount importance to nation's economy. However, the power system is vulnerable to intelligent attacks by attackers who have enough knowledge of how the power system is operated, monitored and controlled. This paper proposes a game theoretic approach to explore and evaluate strategies for the defender to protect the power systems against such intelligent attacks. First, a risk assessment is presented to quantify the physical impacts inflicted by attacks. Based upon the results of the risk assessment, this paper represents the interactions between the attacker and the defender by extending the current zero-sum game model to more generalized game models for diverse assumptions concerning the attacker's motivation. The attacker and defender's equilibrium strategies are attained by solving these game models. In addition, a numerical illustration is demonstrated to warrant the theoretical outcomes.

The use of multi-terminal HVDC to integrate wind power coming from the North Sea opens de door for a new transmission system model, the DC-Independent System Operator (DC-ISO). DC-ISO will face highly stressed and varying conditions that requires new risk assessment tools to ensure security of supply. This paper proposes a novel risk-based static security assessment methodology named risk-based DC security assessment (RB-DCSA). It combines a probabilistic approach to include uncertainties and a fuzzy inference system to quantify the systemic and individual component risk associated with operational scenarios considering uncertainties. The proposed methodology is illustrated using a multi-terminal HVDC system where the variability of wind speed at the offshore wind is included.

Risk-control optimization has great significance for security of power system. Usually the probabilistic uncertainties of parameters are considered in the research of risk optimization of power system. However, the method of probabilistic uncertainty description will be insufficient in the case of lack of sample data. Thus non-probabilistic uncertainties of parameters should be considered, and will impose a significant influence on the results of optimization. To solve this problem, a robust optimization operation method of power system risk-control is presented in this paper, considering the non-probabilistic uncertainty of parameters based on information gap decision theory (IGDT). In the method, loads are modeled as the non-probabilistic uncertainty parameters, and the model of robust optimization operation of risk-control is presented. By solving the model, the maximum fluctuation of the pre-specified target can be obtained, and the strategy of this situation can be obtained at the same time. The proposed model is applied to the IEEE-30 system of risk-control by simulation. The results can provide the valuable information for operating department to risk management.

The Polish Power System is becoming increasingly more dependent on Information and Communication Technologies which results in its exposure to cyberattacks, including the evolved and highly sophisticated threats such as Advanced Persistent Threats or Distributed Denial of Service attacks. The most exposed components are SCADA systems in substations and Distributed Control Systems in power plants. When addressing this situation the usual cyber security technologies are prerequisite, but not sufficient. With the rapidly evolving cyber threat landscape the use of partnerships and information sharing has become critical. However due to several anonymity concerns the relevant stakeholders may become reluctant to exchange sensitive information about security incidents. In the paper a multi-agent architecture is presented for the Polish Power System which addresses the anonymity concerns.

Cyber-physical systems (CPS) can potentially benefit a wide array of applications and areas. Here, the authors look at some of the challenges surrounding CPS, and consider a feasible solution for creating a robust, secure, and cost-effective architecture.

This paper proposes a methodology to assess cyber-related risks and to identify critical assets both at power grid and substation levels. The methodology is based on a two-pass engine model. The first pass engine is developed to identify the most critical substation(s) in a power grid. A mixture of Analytical hierarchy process (AHP) and (N-1) contingent analysis is used to calculate risks. The second pass engine is developed to identify risky assets within a substation and improve the vulnerability of a substation against the intrusion and malicious acts of cyber hackers. The risk methodology uniquely combines asset reliability, vulnerability and costs of attack into a risk index. A methodology is also presented to improve the overall security of a substation by optimally placing security agent(s) on the automation system.
 

Electrical Distribution Networks face new challenges by the Smart Grid deployment. The required metering infrastructures add new vulnerabilities that need to be taken into account in order to achieve Smart Grid functionalities without considerable reliability trade-off. In this paper, a qualitative assessment of the cyber attack impact on the Advanced Metering Infrastructure (AMI) is initially attempted. Attack simulations have been conducted on a realistic Grid topology. The simulated network consisted of Smart Meters, routers and utility servers. Finally, the impact of Denial-of-Service and Distributed Denial-of-Service (DoS/DDoS) attacks on distribution system reliability is discussed through a qualitative analysis of reliability indices.
 

To protect complex power-grid control networks, power operators need efficient security assessment techniques that take into account both cyber side and the power side of the cyber-physical critical infrastructures. In this paper, we present CPINDEX, a security-oriented stochastic risk management technique that calculates cyber-physical security indices to measure the security level of the underlying cyber-physical setting. CPINDEX installs appropriate cyber-side instrumentation probes on individual host systems to dynamically capture and profile low-level system activities such as interprocess communications among operating system assets. CPINDEX uses the generated logs along with the topological information about the power network configuration to build stochastic Bayesian network models of the whole cyber-physical infrastructure and update them dynamically based on the current state of the underlying power system. Finally, CPINDEX implements belief propagation algorithms on the created stochastic models combined with a novel graph-theoretic power system indexing algorithm to calculate the cyber-physical index, i.e., to measure the security-level of the system's current cyber-physical state. The results of our experiments with actual attacks against a real-world power control network shows that CPINDEX, within few seconds, can efficiently compute the numerical indices during the attack that indicate the progressing malicious attack correctly.
 

Wide-area monitoring and control (WAMC) systems are the next-generation operational-management systems for electric power systems. The main purpose of such systems is to provide high resolution real-time situational awareness in order to improve the operation of the power system by detecting and responding to fast evolving phenomenon in power systems. From an information and communication technology (ICT) perspective, the nonfunctional qualities of these systems are increasingly becoming important and there is a need to evaluate and analyze the factors that impact these nonfunctional qualities. Enterprise architecture methods, which capture properties of ICT systems in architecture models and use these models as a basis for analysis and decision making, are a promising approach to meet these challenges. This paper presents a quantitative architecture analysis method for the study of WAMC ICT architectures focusing primarily on the interoperability and cybersecurity aspects.
 

Wide area monitoring, protection and control for power network systems are one of the fundamental components of the smart grid concept. Synchronized measurement technology such as the Phasor Measurement Units (PMUs) will play a major role in implementing these components and they have the potential to provide reliable and secure full system observability. The problem of Optimal Placement of PMUs (OPP) consists of locating a minimal set of power buses where the PMUs must be placed in order to provide full system observability. In this paper a novel solution to the OPP problem using a Memetic Algorithm (MA) is proposed. The implemented MA combines the global optimization power of genetic algorithms with local solution tuning using the hill-climbing method. The performance of the proposed approach was demonstrated on IEEE benchmark power networks as well as on a segment of the Idaho region power network. It was shown that the proposed solution using a MA features significantly faster convergence rate towards the optimum solution.
 

Power grids are monitored by gathering data through remote sensors and estimating the state of the grid. Bad data detection schemes detect and remove poor data. False data is a special type of data injection designed to evade typical bad data detection schemes and compromise state estimates, possibly leading to improper control of the grid. Topology perturbation is a situational awareness method that implements the use of distributed flexible AC transmission system devices to alter impedance on optimally chosen lines, updating the grid topology and exposing the presence of false data. The success of the topology perturbation for improving grid control and exposing false data in AC state estimation is demonstrated. A technique is developed for identifying the false data injection attack vector and quantifying the compromised measurements. The proposed method provides successful false data detection and identification in IEEE 14, 24, and 39-bus test systems using AC state estimation.
 

Contingency analysis is a critical activity in the context of the power infrastructure because it provides a guide for resiliency and enables the grid to continue operating even in the case of failure. In this paper, we augment this concept by introducing SOCCA, a cyber-physical security evaluation technique to plan not only for accidental contingencies but also for malicious compromises. SOCCA presents a new unified formalism to model the cyber-physical system including interconnections among cyber and physical components. The cyber-physical contingency ranking technique employed by SOCCA assesses the potential impacts of events. Contingencies are ranked according to their impact as well as attack complexity. The results are valuable in both cyber and physical domains. From a physical perspective, SOCCA scores power system contingencies based on cyber network configuration, whereas from a cyber perspective, control network vulnerabilities are ranked according to the underlying power system topology.
 

This paper presents an overview of the research project “High-Performance Hybrid Simulation/Measurement-Based Tools for Proactive Operator Decision-Support”, performed under the auspices of the U.S. Department of Energy grant DE-OE0000628. The objective of this project is to develop software tools to provide enhanced real-time situational awareness to support the decision making and system control actions of transmission operators. The integrated tool will combine high-performance dynamic simulation with synchrophasor measurement data to assess in real time system dynamic performance and operation security risk. The project includes: (i) The development of high-performance dynamic simulation software; (ii) the development of new computationally effective measurement-based tools to estimate operating margins of a power system in real time using measurement data from synchrophasors and SCADA; (iii) the development a hybrid framework integrating measurement-based and simulation-based approaches, and (iv) the use of cutting-edge visualization technology to display various system quantities and to visually process the results of the hybrid measurement-base/simulation-based security-assessment tool. Parallelization and high performance computing are utilized to enable ultrafast transient stability analysis that can be used in a real-time environment to quickly perform “what-if” simulations involving system dynamics phenomena. EPRI's Extended Transient Midterm Simulation Program (ETMSP) is modified and enhanced for this work. The contingency analysis is scaled for large-scale contingency analysis using MPI-based parallelization. Simulations of thousands of contingencies on a high performance computing machine are performed, and results show that parallelization over contingencies with MPI provides good scalability and computational gains. Different ways to reduce the I/O bottleneck have been also exprored. Thread-parallelization of the sparse linear solve is explored also through use of the SuperLU_MT library. Based on performance profiling results for the implicit method, the majority of CPU time is spent on the integration steps. Hence, in order to further improve the ETMSP performance, a variable time step control scheme for the original trapezoidal integration method has been developed and implemented. The Adams-Bashforth-Moulton predictor-corrector method was introduced and designed for ETMSP. Test results show superior performance with this method.
 

When the system is in normal state, actual SCADA measurements of power transfers across critical interfaces are continuously compared with limits determined offline and stored in look-up tables or nomograms in order to assess whether the network is secure or insecure and inform the dispatcher to take preventive action in the latter case. However, synchrophasors could change this paradigm by enabling new features, the phase-angle differences, which are well-known measures of system stress, with the added potential to increase system visibility. The paper develops a systematic approach to baseline the phase-angles versus actual transfer limits across system interfaces and enable synchrophasor-based situational awareness (SBSA). Statistical methods are first used to determine seasonal exceedance levels of angle shifts that can allow real-time scoring and detection of atypical conditions. Next, key buses suitable for SBSA are identified using correlation and partitioning around medoid (PAM) clustering. It is shown that angle shifts of this subset of 15% of the network backbone buses can be effectively used as features in ensemble decision tree-based forecasting of seasonal security margins across critical interfaces.