Biblio
This study proposes to apply an efficient formulation to solve the stochastic security-constrained generation capacity expansion planning (GCEP) problem using an improved method to directly compute the generalized generation distribution factors (GGDF) and the line outage distribution factors (LODF) in order to model the pre- and the post-contingency constraints based on the only application of the partial transmission distribution factors (PTDF). The classical DC-based formulation has been reformulated in order to include the security criteria solving both pre- and post-contingency constraints simultaneously. The methodology also takes into account the load uncertainty in the optimization problem using a two-stage multi-period model, and a clustering technique is used as well to reduce load scenarios (stochastic problem). The main advantage of this methodology is the feasibility to quickly compute the LODF especially with multiple-line outages (N-m). This idea could speed up contingency analyses and improve significantly the security-constrained analyses applied to GCEP problems. It is worth to mentioning that this approach is carried out without sacrificing optimality.
With the integration of computing, communication, and physical processes, the modern power grid is becoming a large and complex cyber physical power system (CPPS). This trend is intended to modernize and improve the efficiency of the power grid, yet it makes the CPPS vulnerable to potential cascading failures caused by cyber-attacks, e.g., the attacks that are originated by the cyber network of CPPS. To prevent these risks, it is essential to analyze how cyber-attacks can be conducted against the CPPS and how they can affect the power systems. In light of that General Packet Radio Service (GPRS) has been widely used in CPPS, this paper provides a case study by examining possible cyber-attacks against the cyber-physical power systems with GPRS-based SCADA system. We analyze the vulnerabilities of GPRS-based SCADA systems and focus on DoS attacks and message spoofing attacks. Furthermore, we show the consequence of these attacks against power systems by a simulation using the IEEE 9-node system, and the results show the validity of cascading failures propagated through the systems under our proposed attacks.
The previous consideration of power grid focuses on the power system itself, however, the recent work is aiming at both power grid and communication network, this coupling networks are firstly called as interdependent networks. Prior study on modeling interdependent networks always extracts main features from real networks, the model of network A and network B are completely symmetrical, both degree distribution in intranetwork and support pattern in inter-network, but in reality this circumstance is hard to attain. In this paper, we deliberately set both networks with same topology in order to specialized research the support pattern between networks. In terms of initial failure from power grid or communication network, we find the remaining survival fraction is greatly disparate, and the failure initially from power grid is more harmful than failure initially from communication network, which all show the vulnerability of interdependency and meantime guide us to pay more attention to the protection measures for power grid.
Reliable operation of electrical power systems in the presence of multiple critical N - k contingencies is an important challenge for the system operators. Identifying all the possible N - k critical contingencies to design effective mitigation strategies is computationally infeasible due to the combinatorial explosion of the search space. This paper describes two heuristic algorithms based on the iterative pruning of the candidate contingency set to effectively and efficiently identify all the critical N - k contingencies resulting in system failure. These algorithms are applied to the standard IEEE-14 bus system, IEEE-39 bus system, and IEEE-57 bus system to identify multiple critical N - k contingencies. The algorithms are able to capture all the possible critical N - k contingencies (where 1 ≤ k ≤ 9) without missing any dangerous contingency.
Enhancing the security and resilience of interdependent infrastructures is crucial. In this paper, we establish a theoretical framework based on Markov decision processes (MDPs) to design optimal resiliency mechanisms for interdependent infrastructures. We use MDPs to capture the dynamics of the failure of constituent components of an infrastructure and their cyber-physical dependencies. Factored MDPs and approximate linear programming are adopted for an exponentially growing dimension of both state and action spaces. Under our approximation scheme, the optimally distributed policy is equivalent to the centralized one. Finally, case studies in a large-scale interdependent system demonstrate the effectiveness of the control strategy to enhance the network resilience to cascading failures.
With the rapid development of DC transmission technology and High Voltage Direct Current (HVDC) programs, the reliability of AC/DC hybrid power grid draws more and more attentions. The paper takes both the system static and dynamic characteristics into account, and proposes a novel AC/DC hybrid system reliability evaluation method considering transient security constraints based on Monte-Carlo method and transient stability analytical method. The interaction of AC system and DC system after fault is considered in evaluation process. The transient stability analysis is performed firstly when fault occurs in the system and BPA software is applied to the analysis to improve the computational accuracy and speed. Then the new system state is generated according to the transient analysis results. Then a minimum load shedding model of AC/DC hybrid system with HVDC is proposed. And then adequacy analysis is taken to the new state. The proposed method can evaluate the reliability of AC/DC hybrid grid more comprehensively and reduce the complexity of problem which is tested by IEEE-RTS 96 system and an actual large-scale system.
As a consequence of the recent development of situational awareness technologies for smart grids, the gathering and analysis of data from multiple sources offer a significant opportunity for enhanced fault diagnosis. In order to achieve improved accuracy for both fault detection and classification, a novel combined data analytics technique is presented and demonstrated in this paper. The proposed technique is based on a segmented approach to Bayesian modelling that provides probabilistic graphical representations of both electrical power and data communication networks. In this manner, the reliability of both the data communication and electrical power networks are considered in order to improve overall power system transmission line fault diagnosis.
Situational awareness during sophisticated cyber attacks on the power grid is critical for the system operator to perform suitable attack response and recovery functions to ensure grid reliability. The overall theme of this paper is to identify existing practical issues and challenges that utilities face while monitoring substations, and to suggest potential approaches to enhance the situational awareness for the grid operators. In this paper, we provide a broad discussion about the various gaps that exist in the utility industry today in monitoring substations, and how those gaps could be addressed by identifying the various data sources and monitoring tools to improve situational awareness. The paper also briefly describes the advantages of contextualizing and correlating substation monitoring alerts using expert systems at the control center to obtain a holistic systems-level view of potentially malicious cyber activity at the substations before they cause impacts to grid operation.
As societies are becoming more dependent on the power grids, the security issues and blackout threats are more emphasized. This paper proposes a new graph model for online visualization and assessment of power grid security. The proposed model integrates topology and power flow information to estimate and visualize interdependencies between the lines in the form of line dependency graph (LDG) and immediate threats graph (ITG). These models enable the system operator to predict the impact of line outage and identify the most vulnerable and critical links in the power system. Line Vulnerability Index (LVI) and Line Criticality Index (LCI) are introduced as two indices extracted from LDG to aid the operator in decision making and contingency selection. This package can be useful in enhancing situational awareness in power grid operation by visualization and estimation of system threats. The proposed approach is tested for security analysis of IEEE 30-bus and IEEE 118-bus systems and the results are discussed.
This paper studies physical layer security in a wireless ad hoc network with numerous legitimate transmitter-receiver pairs and passive eavesdroppers. A hybrid full-/half-duplex receiver deployment strategy is proposed to secure legitimate transmissions, by letting a fraction of legitimate receivers work in the full-duplex (FD) mode sending jamming signals to confuse eavesdroppers upon their own information receptions, and other receivers work in the half-duplex mode just receiving desired signals. This paper aims to properly choose the fraction of the FD receivers to enhance network security. Tractable expressions for the connection outage probability and the secrecy outage probability of a typical legitimate link are first derived, based on which the network-wide secrecy throughput is maximized. Some insights into the optimal fraction are further developed. It is concluded that the fraction of the FD receivers triggers a non-trivial trade-off between reliability and secrecy, and the optimal fraction significantly improves the network security performance.
We consider an underlay cognitive network with secondary users that support full-duplex communication. In this context, we propose the application of antenna selection at the secondary destination node to improve the secondary user secrecy performance. Antenna selection rules for cases where exact and average knowledge of the eavesdropping channels are investigated. The secrecy outage probabilities for the secondary eavesdropping network are analyzed, and it is shown that the secrecy performance improvement due to antenna selection is due to coding gain rather than diversity gain. This is very different from classical antenna selection for data transmission, which usually leads to a higher diversity gain. Numerical simulations are included to verify the performance of the proposed scheme.
This paper investigates physical layer security of non-orthogonal multiple access (NOMA) in cognitive radio (CR) networks. The techniques of NOMA and CR have improved the spectrum efficiency greatly in the traditional networks. Because of the difference in principles of spectrum improving, NOMA and CR can be combined together, i.e. CR NOMA network, and have great potential to improving the spectrum efficiency. However the physical layer security in CR NOMA network is different from any single network of NOMA or CR. We will study the physical layer security in underlay CR NOMA network. Firstly, the wiretap network model is constructed according to the technical characteristics of NOMA and CR. In addition, new exact and asymptotic expressions of the security outage probability are derived and been confirmed by simulation. Ultimately, we have studied the effect of some critical factors on security outage probability after simulation.
Power networks can be modeled as networked structures with nodes representing the bus bars (connected to generator, loads and transformers) and links representing the transmission lines. In this manuscript we study cascaded failures in power networks. As network structures we consider IEEE 118 bus network and a random spatial model network with similar properties to IEEE 118 bus network. A maximum flow based model is used to find the central edges. We study cascaded failures triggered by both random and targeted attacks to the edges. In the targeted attack the edge with the maximum centrality value is disconnected from the network. A number of metrics including the size of the largest connected component, the number of failed edges, the average maximum flow and the global efficiency are studied as a function of capacity parameter (edge critical load is proportional to its capacity parameter and nominal centrality value). For each case we identify the critical capacity parameter by which the network shows resilient behavior against failures. The experiments show that one should further protect the network for a targeted attack as compared to a random failure.
The smart grid is an electrical grid that has a duplex communication. This communication is between the utility and the consumer. Digital system, automation system, computers and control are the various systems of Smart Grid. It finds applications in a wide variety of systems. Some of its applications have been designed to reduce the risk of power system blackout. Dynamic vulnerability assessment is done to identify, quantify, and prioritize the vulnerabilities in a system. This paper presents a novel approach for classifying the data into one of the two classes called vulnerable or non-vulnerable by carrying out Dynamic Vulnerability Assessment (DVA) based on some data mining techniques such as Multichannel Singular Spectrum Analysis (MSSA), and Principal Component Analysis (PCA), and a machine learning tool such as Support Vector Machine Classifier (SVM-C) with learning algorithms that can analyze data. The developed methodology is tested in the IEEE 57 bus, where the cause of vulnerability is transient instability. The results show that data mining tools can effectively analyze the patterns of the electric signals, and SVM-C can use those patterns for analyzing the system data as vulnerable or non-vulnerable and determines System Vulnerability Status.
This paper proposes a practical time-phased model to analyze the vulnerability of power systems over a time horizon, in which the scheduled maintenance of network facilities is considered. This model is deemed as an efficient tool that could be used by system operators to assess whether how their systems become vulnerable giving a set of scheduled facility outages. The final model is presented as a single level Mixed-Integer Linear Programming (MILP) problem solvable with commercially available software. Results attained based on the well-known IEEE 24-Bus Reliability Test System (RTS) appreciate the applicability of the model and highlight the necessity of considering the scheduled facility outages in assessing the vulnerability of a power system.
A novel approach is developed for analyzing power system vulnerability related to extraordinary events. Vulnerability analyses are necessary for identification of barriers to prevent such events and as a basis for the emergency preparedness. Identification of cause and effect relationships to reveal vulnerabilities related to extraordinary events is a complex and difficult task. In the proposed approach, the analysis starts by identifying the critical consequences. Then the critical contingencies and operating states, and which external threats and causes that may result in such severe consequences, are identified. This is opposed to the traditional risk and vulnerability analysis which starts by analyzing threats and what can happen as a chain of events. The vulnerability analysis methodology is tested and demonstrated on real systems.
Exhaustive enumeration of a S-select-k problem for hypothesized substations outages can be practically infeasible due to exponential growth of combinations as both S and k numbers increase. This enumeration of worst-case substations scenarios from the large set, however, can be improved based on the initial selection sets with the root nodes and segments. In this paper, the previous work of the reverse pyramid model (RPM) is enhanced with prioritization of root nodes and defined segmentations of substation list based on mean-time-to-compromise (MTTC) value that is associated with each substation. Root nodes are selected based on the threshold values of the substation ranking on MTTC values and are segmented accordingly from the root node set. Each segmentation is then being enumerated with S-select-k module to identify worst-case scenarios. The lowest threshold value on the list, e.g., a substation with no assignment of MTTC or extremely low number, is completely eliminated. Simulation shows that this approach demonstrates similar outcome of the risk indices among all randomly generated MTTC of the IEEE 30-bus system.
Cyber intrusions to substations of a power grid are a source of vulnerability since most substations are unmanned and with limited protection of the physical security. In the worst case, simultaneous intrusions into multiple substations can lead to severe cascading events, causing catastrophic power outages. In this paper, an integrated Anomaly Detection System (ADS) is proposed which contains host- and network-based anomaly detection systems for the substations, and simultaneous anomaly detection for multiple substations. Potential scenarios of simultaneous intrusions into the substations have been simulated using a substation automation testbed. The host-based anomaly detection considers temporal anomalies in the substation facilities, e.g., user-interfaces, Intelligent Electronic Devices (IEDs) and circuit breakers. The malicious behaviors of substation automation based on multicast messages, e.g., Generic Object Oriented Substation Event (GOOSE) and Sampled Measured Value (SMV), are incorporated in the proposed network-based anomaly detection. The proposed simultaneous intrusion detection method is able to identify the same type of attacks at multiple substations and their locations. The result is a new integrated tool for detection and mitigation of cyber intrusions at a single substation or multiple substations of a power grid.
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.
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.
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.
Smart grids, where cyber infrastructure is used to make power distribution more dependable and efficient, are prime examples of modern infrastructure systems. The cyber infrastructure provides monitoring and decision support intended to increase the dependability and efficiency of the system. This comes at the cost of vulnerability to accidental failures and malicious attacks, due to the greater extent of virtual and physical interconnection. Any failure can propagate more quickly and extensively, and as such, the net result could be lowered reliability. In this paper, we describe metrics for assessment of two phases of smart grid operation: the duration before a failure occurs, and the recovery phase after an inevitable failure. The former is characterized by reliability, which we determine based on information about cascading failures. The latter is quantified using resilience, which can in turn facilitate comparison of recovery strategies. We illustrate the application of these metrics to a smart grid based on the IEEE 9-bus test system.
The reliability theory used in the design of complex systems including electric grids assumes random component failures and is thus unsuited to analyzing security risks due to attackers that intentionally damage several components of the system. In this paper, a security risk analysis methodology is proposed consisting of vulnerability analysis and impact analysis. Vulnerability analysis is a method developed by security engineers to identify the attacks that are relevant for the system under study, and in this paper, the analysis is applied on the communications network topology of the electric grid automation system. Impact analysis is then performed through co-simulation of automation and the electric grid to assess the potential damage from the attacks. This paper makes an extensive review of vulnerability and impact analysis methods and relevant system modeling techniques from the fields of security and industrial automation engineering, with a focus on smart grid automation, and then applies and combines approaches to obtain a security risk analysis methodology. The methodology is demonstrated with a case study of fault location, isolation and supply restoration smart grid automation.