Power System Vulnerability Assessment Research Articles

A Preventive Control Approach for Power System Vulnerability Assessment and Predictive Stability Evaluation

Early detection of cascading failures phenomena is a vital process for the sustainable operation of power systems. Within the scope of this work, a preventive control approach implementing an algorithm for selecting critical contingencies by a dynamic vulnerability analysis and predictive stability evaluation is presented. The analysis was carried out using a decision tree with a multi-parameter knowledge base. After the occurrence of an initial contingency, probable future contingencies are foreseen according to several vulnerability perspectives created by an adaptive vulnerability search module. Then, for cases identified as critical, a secure operational system state is proposed through a vulnerability-based, security-constrained, optimal power flow algorithm. The modular structure of the proposed algorithm enables the evaluation of possible vulnerable scenarios and proposes a strategy to alleviate the technical and economic impacts due to prospective cascading failures. The presented optimization methodology was tested using the IEEE-39 bus test network and a benchmark was performed between the proposed approach and a time domain analysis software model (EMTP). The obtained results indicate the potential of analysis approach in evaluating low-risk but high-impact vulnerabilities in power systems.

Electrical DebtRank Algorithm–Based Identification of Vulnerable Transmission Lines in Power Systems

With the increase of complexity of the power system structure and operation mode, the risk of large-scale power outage accidents rises, which urgently need an accuracy algorithm for identifying vulnerabilities and mitigating risks. Aiming at this, the improved DebtRank (DR) algorithm is modified to adapt to the property of the power systems. The overloading state of the transmission lines plays a notable role of stable operation of the power systems. An electrical DR algorithm is proposed to incorporate the overloading state to the identification of vulnerable lines in the power systems in this article. First, a dual model of power system topology is established, the nodes of which represent the lines in the power systems. Then, besides the normal state and failure state having been considered, the definition of the overloading state is also added, and the line load and network topology are considered in the electrical DR algorithm to identify vulnerable lines. Finally, the correctness and reasonability of the vulnerable lines of the power systems identified by the electrical DR algorithm are proved by the comparative analysis of cascade failure simulation, showing its better advantages in vulnerability assessment of power systems.

Identifying and assessing power system vulnerabilities to transmission asset outages via cascading failure analysis

Power systems as critical infrastructure are an integral part of human society and are therefore of paramount importance to modern life. Vulnerabilities in the system, that are revealed either by accidental or deliberate events, can cause large losses of power supply with sever social and economic consequences. A tool that identifies the vulnerabilities in a power system can provide the operators the means to support reliable power system operations. This paper presents a methodology for power system vulnerability assessment that couples an AC based cascading failure simulation model and a meta-heuristic optimization procedure. The objectives of the assessment are to (1) rank the most important branches in the transmission grid, and (2) identify sets of branches if simultaneously tripped will cause the cascade with highest intensity. The first objective is achieved by ranking the criticality of the branches using two criteria (i) the impact that each branch failure has on the DNS and (ii) the frequency of line overload. The second objective is achieved by hard linking an AC based cascading failure simulation model and a meta-heuristic based optimization procedure. The methodology allows the generation and the identification of vulnerability scenarios, and therefore, provides insights that can be used by operators in developing strategies to minimize the effects of accidental and deliberate events. The algorithm developed for the purpose of this study is applied to the IEEE 118-bus test system and the Swiss power grid. The results demonstrate the capability of the proposed methodology for assessing power system vulnerability.

Vulnerability assessment of power system based on Theil entropy of branch potential energy

To achieve the safe operation of power system and prevent cascading failures, a vulnerability assessment method of power system is proposed based on the state correlation of branch fault. Firstly, a weighted correlation network based on the branch potential energy is established by using the state transition relationship of the two-level cascading failures. Secondly, the vulnerability assessment model of power system is constructed by applying the Theil entropy theory into the weighted correlation network. Finally, the simulation results on IEEE_39 bus system and an actual power system show that the model can not only evaluate the vulnerability of power system effectively but also find the vulnerable branches in power network more accurately.

A Graph Theoretic Approach to Power System Vulnerability Identification

During major power system disturbances, when multiple component outages occur in rapid succession, it becomes crucial to quickly identify the transmission interconnections that have limited power transfer capability. Understanding the impact of an outage on these critical interconnections (called saturated cut-sets) is important for enhancing situational awareness and taking correct actions. This paper proposes a new graph theoretic approach for analyzing whether a contingency will create a saturated cut-set in a meshed power network. A novel feature of the proposed algorithm is that it lowers the solution time significantly making the approach viable for real-time operations. It also indicates the minimum amount by which the power transfer through the critical interconnections should be reduced so that post-contingency saturation does not occur. Robustness of the proposed algorithm for enhanced situational awareness is demonstrated using the IEEE-118 bus system as well as a 17,000+ bus model of the Western Interconnection (WI). Comparisons made with different approaches for power system vulnerability assessment prove the utility of the proposed scheme for aiding power system operations during extreme exigencies.

Assessment of the Electric Grid Interdiction Problem using a nonlinear modeling approach

This paper addresses the vulnerability assessment of power systems under multiple outages, both unintentional and deliberate. In this context, a new model and solution approach for the Electric Grid Interdiction Problem (EGIP) are proposed. The EGIP is based on the interaction of two agents: a Disruptive Agent (DA) that aims at maximizing load shedding by performing attacks in the power system; and a System Operator (SO), who reacts to these attacks by modifying the generation dispatch in order to minimize load shedding. The interaction of these agents is modeled as a bilevel programming problem. Due to its non-convexity and non-linearity the EGIP has been traditionally approached by means of linearized equivalents of the network and recast as a single-level Mixed Integer Linear Programing (MILP) problem. In this paper the EGIP is formulated as a Mixed Integer Non-Linear Programming (MINLP) problem using an AC modeling of the network, providing more accurate results than traditional DC models. Another distinctive modeling feature is introduced: the possibility of attacks not only on branches but also on generators. The proposed model is solved by means of an Iterated Local Search (ILS) algorithm. Performance of the proposed ILS is compared with a conventional Genetic Algorithm (GA). Several tests were performed on two benchmark IEEE test systems showing the applicability and robustness of the proposed approach.

Vulnerability Assessment of Power Systems to Intentional Attacks using a Specialized Genetic Algorithm

<p class="Abstract"><span lang="EN-US">A specialized genetic algorithm applied to the solution of the electric grid interdiction problem is presented in this paper. This problem consists in the interaction of a disruptive agent who aims at maximizing damage of the power system (measured as load shed), and the system operator, who implements corrective actions to minimize system load shed. This problem, also known as “the terrorist threat problem”, is formulated in a bilevel programming structure and solved by means of a genetic algorithm. The solution identifies the most vulnerable links of the network in terms of a terrorist attack, providing signals for future reinforcement of the network or more strict surveillance of critical elements. The proposed approach has been tested on three case studies: a didactic five-bus power system, a prototype of the Colombian power system and the IEEE Reliability Test System. Results show the robustness and applicability of the proposed approach.</span></p>

Vulnerability Assessment of a Large Sized Power System Using Neural Network Considering Various Feature Extraction Methods

Vulnerability assessment of power systems is important so as to determine their ability to continue to provide service in case of any unforeseen catastrophic contingency such as power system component failures, communication system failures, human operator error, and natural calamity. An approach towards the development of on-line power system vulnerability assessment is by means of using an artificial neural network (ANN), which is being used successfully in many areas of power systems because of its ability to handle the fusion of multiple sources of data and information. An important consideration when applying ANN in power system vulnerability assessment is the proper selection and dimension reduction of training features. This paper aims to investigate the effect of using various feature extraction methods on the performance of ANN as well as to evaluate and compare the efficiency of the proposed feature extraction method named as neural network weight extraction. For assessing vulnerability of power systems, a vulnerability index based on power system loss is used and considered as the ANN output. To illustrate the effectiveness of ANN considering various feature extraction methods for vulnerability assessment on a large sized power system, it is verified on the IEEE 300-bus test system.

Vulnerability Assessment of Power System Using Radial Basis Function Neural Network and a New Feature Extraction Method

Vulnerability assessment in power systems is important so as to determine how vulnerable a power system in case of any unforeseen catastrophic events. This paper presents the application of Radial Basis Function Neural Network (RBFNN) for vulnerability assessment of power system incorporating a new proposed feature extraction method named as the Neural Network Weight Extraction (NNWE) for dimensionality reduction of input data. The performance of the RBFNN is compared with the Multi Layer Perceptron Neural Network (MLPNN) so as to evaluate the effectiveness of the RBFNN in assessing the vulnerability of a power system based on the indices, power system loss and possible loss of load. In this study, vulnerability analysis simulations were carried out on the IEEE 300 bus test system using the Power System Analysis Toolbox and the development of neural network models were implemented in MATLAB version 7. Test results prove that the RBFNN give better vulnerability assessment performance than the multilayer perceptron neural network in terms of accuracy and training time. The proposed feature extraction method decreases the training time drastically from hours to less than seconds, this bound to influence the vulnerability classification and increase the speed of convergence. It is also concluded that the reduction in error is achieved by using PSL as an output variable of ANN, in all the cases the error of RBFNN output by PSL is less than 4.87% which is well within tolerable limits.

New Method for Vulnerability Assessment of Power System

Vulnerability assessment in power systems is to determine a power system`s ability to continue to provide service in case of an unforeseen catastrophic contingency. It combines information on the level of system security as well as information on a wide range of scenarios, events and contingencies. To assess the level of system strength or weakness relative to the occurrence of an undesired event, a quantitative measure based on vulnerability index is often considered. In this study, a new vulnerability assessment method is proposed based on total power system loss which considers power generation loss due to generation outage, power line loss due to line outage, increase in total load and amount of load disconnected. The objective of this study is to investigate the effectiveness of the new proposed method in assessing the vulnerability of power system when subject to various contingencies. The performance of the proposed vulnerability assessment method is compared with other known vulnerability assessment methods based on anticipated loss of load as well as comprehensive system information of individual system components. In this study, vulnerability analysis was carried out on the IEEE 24 bus test system using the Power System Analysis and Toolbox (PSAT) and the vulnerability indices were calculated using the Matlab program. Obtained results, indicate the efficiency of proposed method.