System Security Assessment Research Articles

Progressive entropy based contingency grouping for deriving decision trees for multiple contingencies

This paper deals with decision tree based power system security assessment for multiple contingencies. In this paper, we propose a contingency grouping technique to produce good, but reduced number of decision trees for multiple contingencies. The grouping is based on the degree of overlap in class boundary regions or post-contingency performance measures of every contingency, as captured by the graphical index progressive entropy. The proposed method has been demonstrated on French EHV system for a voltage collapse problem in west France, Brittany region.

A Reliable Intelligent System for Real-Time Dynamic Security Assessment of Power Systems

A new intelligent system (IS) is developed for real-time dynamic security assessment (DSA) of power systems. Taking an ensemble learning scheme, the IS structures a series of extreme learning machines (ELMs) and generalizes the randomness of single ELMs during the training. Benefiting from the unique properties of ELM and the strategically designed decision-making rules, the IS learns and works very fast and can estimate the credibility of its DSA results, allowing an accurate and reliable pre-fault DSA mechanism: credible results can be directly adopted while incredible results are decided by alternative tools such as time-domain simulation. This makes the IS promising for practical application since the potential unreliable results can be eliminated for use. Case studies considering classification and prediction are, respectively, conducted on an IEEE 50-machine system and a dynamic equivalent system of a real-world large power grid. The efficiency, robustness, accuracy, and reliability of the IS are demonstrated. In particular, it is observed that the IS could provide 100% classification accuracy and very low prediction error on its decided instances.

A Software Security Assessment System Based On Analysis of Vulnerabilities

In recent years, software security plays an important role in verifying system safety and avoiding the casualties and property losses, but it is difficult to assess system security in traditional software engineering and software test. Focusing on the software security assessment system, this paper based on the vulnerability analysis method, which takes the advantage of both qualitative analysis and quantitative analysis to assess the security of software. The major methods of domestic and foreign software security measurement are discussed. Through the comparative analysis of existing vulnerability rating system, especially the CVSS and VRSS, this paper discovers their respective advantages and proposes a more accurate rating system to obtain the final security score of the software system. An example application is provided in the paper, which shows that the assessment system we proposed is practical.

Research on Information Security Model

Security of computer information system more and more aroused people’s attention, and information security assessment and computer system security model is closely related to information security model which is patricularly important. This article describes the security model of the characteristics and uses, and several security models are compared, these models were from a different perspective considering system security, through comparison Analysis, Establishment of information security model obtained several important aspects to consider.

Supervised learning approach to online contingency screening and ranking in power systems

This paper proposes a supervised learning approach to fast and accurate power system security assessment and contingency analysis. The severity of the contingency is measured by two scalar performance indices (PIs): Voltage-reactive power Performance Index, PIVQ and line MVA Performance Index, PIMVA. In this paper, Feed-Forward Artificial Neural Network (FFNN) is employed that uses pattern recognition methodology for security assessment and contingency analysis. A feature selection technique based on the correlation coefficient has been employed to identify the inputs for the FFNN. The effectiveness of the proposed methodology is demonstrated on IEEE 39-bus New England system at different loading conditions corresponding to single line outage. The overall accuracy of the test results for unknown patterns highlights the suitability of the approach for online applications at Energy Management Center.

A comparative assessment of specification-based and risk-management-based approaches to the security assessment of complex technical systems

Specification-based organization of security of complex technical systems based on an assignment of margins for the main ways of reaching limit states is considered. We present a computational approach to the organization of security based on risk management and assuming a successive reduction of threats that the technical system is exposed to, and vulnerability towards active threats and damage in the case of failures in complex technical systems.

A Safety Assessment Method of Power Enterprises Based on Historical Case Knowledge

This paper provides a case-based approach for the management system security assessment decision making of power generation enterprises. A case matching method named CBR-Grey is introduced in which Delphi approach and Grey System theory are integrated. Based on this method, we implement a prototype of enterprise assessment information system for the panel of experts. The experimental results based on a real-world power generation enterprises safety assessment data set show that CBRSYS-TPP has high accuracy and systematically performance.

E-commerce System Security Assessment Based on Grey Relational Analysis Comprehensive Evaluation

E-commerce System Security Assessment Based on Grey Relational Analysis Comprehensive Evaluation

Classification and Assessment of Power System Security Using Multiclass SVM

Security assessment and classification are the major concerns in real-time operation of electric power systems. This paper proposes a multiclass support vector machine (SVM) classifier for static and transient security assessment and classification. A straightforward and quick procedure called the sequential forward selection method is used for a feature selection process. The security status of any given operating condition is classified into four modes, viz., secure, critically secure, insecure, and highly insecure, based on the computation of a security index. The proposed SVM-based pattern classifier system is implemented and tested on standard benchmark systems. The simulation results of the multiclass SVM classifier are compared with least-squares, probabilistic neural network, extreme learning machine, and extreme SVM classifiers. The feasibility of implementation of the proposed classifier system for online security evaluation is also discussed.

A New Method about Port Scan of Network Hosts

To resolve the question: accurately scanned the port of remote network host through firewall, researched deeply on the existing scan methods, found their weakness, provided a new method about port detection through firewall. Described the design thinking and scan process, design the first part of ETHERNET, send out and receive of the data packet was turned from the IP layer to data link layer. delivered the data packet directly to network hosts. At last, developed the network security assessment system based on this way. Test results show that this method can penetrate firewall and accurately scan the port of remote network host.

Design of pattern recognition system for static security assessment and classification

Static security analysis is an important study carried out in the control centers of electric utilities. Static security assessment (SSA) is the process of determining whether the current operational state is in a secure or emergency (insecure) state. Conventional method of security evaluation involves performing continuous load flow analysis, which is highly time consuming and infeasible for real-time applications. This led to the application of pattern recognition (PR) approach for static security analysis. This paper presents a more efficient design of a PR system suitable for on-line SSA. The feature selection stage in the PR system uses many algorithms to select the optimal feature set. This paper proposes the use of Support Vector Machine (SVM), a recently introduced machine learning tool, in the classifier design stage of PR system. The developed PR system is implemented in IEEE standard test systems for SSA and classification. The performance of SVM classifier is compared with the conventional K-nearest neighbor, method of least squares and neural network classifiers. Simulation results prove that the SVM-PR classifier outperforms other equivalent classifier algorithms, giving high classification accuracy and less misclassification rate. The feasibility of SVM-PR classifier for on-line security assessment process is also presented.

An Approach for Security Assessment of Transmission Grid Concerning the Influence of Indices Balance

Modern power system has been gradually developed into a complex system with the features of multi-level structure, multi-time scale, a variety of control parameters, wide-area, open, uncertainties, non-autonomous and it is difficult to indicate the security level of power system by a single index. Therefore, power system security assessment based on theory of comprehensive assessment is developed rapidly in recent years. However, traditional comprehensive assessment method considers few about the balance of the indices, when there are great differences between the value of the indices, it is easy to take place the phenomenon that the small indices has been “submerged”, which will influence the rationality of the comprehensive evaluation result. In this paper, a comprehensive assessment model which considers the influence of the index balance is established. By introducing the balance coefficient, the model can amend the original judgment matrix and the weight obtained by traditional AHP method. Since the coordinated and balanced situation of the indices have been reflected in the evaluation process, the results of the assessment model proposed in this paper has more scientific nature and more credibility. Finally, an example is provided to validate the model in this paper.

Transient stability evaluation of electrical power system using generalized regression neural networks

Transient stability evaluation (TSE) is part of dynamic security assessment of power systems, which involves the evaluation of the system's ability to remain in equilibrium under credible contingencies. Neural networks (NN) have been applied to the security assessment of power systems and have shown great potential for predicting the security of power systems. This paper proposes a generalized regression neural networks (GRNN) based classification for transient stability evaluation in power systems. In the proposed method, learning data sets have been generated using time domain simulation (TDS). The GRNN input nodes representing the voltage magnitude for all buses, real and reactive powers on transmission lines, the output node representing the transient stability index. The proposed GRNN was implemented and tested on IEEE 9-bus and 39-bus test systems. NN results show that the stability condition of the power system can be predicted with high accuracy and less misclassification rate.

Approach to security assessment of critical infrastructures’ information systems

This study presents an approach to the security assessment of the information systems of critical infrastructures. The approach is based on the faithful reconstruction of the evaluated information system in a computer security laboratory followed by simulations of possible threats against the system. The evidence collected during the experiments, stored and organised using a proprietary system InSAW, may later be used for the creation of trust cases which provide valuable information for the end users of the infrastructure. Another new proposal is MAlSim - mobile agent-based simulator of malicious software (viruses, worms, etc). To the best of the authors- knowledge, such a simulator has not been proposed before. The present approach was applied to the verification of the security of industrial control systems and power plants. In the study, one of the experiments related to the security study of an information system of a power plant, a simulation of zero-day worm attack, is described.

Assessment of wind farm models from a transmission system operator perspective using field measurements

To understand the impact of increasing penetration of wind energy on power system, dynamic models of wind turbine generator are being incorporated into power system simulation software by transmission system operators. This enables them to carry out security assessment during planning and operations. Unlike synchronous machine-based generators, majority of wind generator models have not been universally standardised and validated. The performance of different wind turbine models (i.e. generic and manufacturer-specific models) under grid disturbance and their cumulative impact as seen by the transmission will need to be completely understood to enable realistic system security assessment. Comparison of these models with actual field measurements is also an attractive option for opportunistic testing. This study will examine model development and its use from the transmission system operator perspective. The generic model will then be compared with detailed individual model for actual wind farm connection studies. The measurement data based on naturally occurring power system disturbance will be used for model validation.

Research on Security Assessment Model of Water Supply System Based on Leakage Control

Abstract Based on the characteristics of urban water supply system, the urban pipe network security assessment system was established, and then AHP method combined with fuzzy evaluation was used to carry out a comprehensive security assessment for the leakage of the urban pipe network. By calculating a real case and analyzing various leakage causes and their impact, the assessment model was verified reasonable.

Supervised fuzzy C-means clustering technique for security assessment and classification in power systems

Security assessment is an important concern in planning and operation studies of an electric power system. Conventional method of security evaluation is performed by simulation consisting of load flow program and transient stability analysis, consuming long computer time and generating voluminous results. This paper presents a practical Pattern Recognition (PR) approach for security assessment in power systems. The problem of security assessment is focused in two modes, viz., static and transient. Static security pertains to the study of violation of system components when subjected to contingencies like line/generator outages. Transient Security study deals with system dynamic behavior when subjected to severe perturbations like three phase faults. A Supervised Fuzzy C-Means (SFCM) algorithm is proposed in the classification phase of PR system for security assessment. The proposed algorithm is tested on 39 Bus New England and IEEE 57 Bus systems. The classification results of the proposed SFCM classifier is compared with the Method of Least Squares (MLS) and Multilayer Perceptron (MLP) classifiers. The results prove that the former gives high classification accuracy and less misclassification rate compared to the latter, enhancing the feasibility and applicability of SFCM algorithm for on-line security evaluation. Keywords: Security Assessment, Static Security, Transient Security, Pattern Recognition, Fuzzy C-Means.

Catastrophe Predictors From Ensemble Decision-Tree Learning of Wide-Area Severity Indices

Catastrophe precursors are essential prerequisites for response-based remedial action schemes, at both the protective and the operator levels. In this paper, wide-area-severity indices (WASI) derived from PMU measurements serve as the basis for building fast catastrophe predictors using random-forest (RF) learning. Given the randomness in the ensemble of decision trees (DTs) stacked in the RF model, it can provide at the recall stage not only an early assessment of the stable/unstable status of an ongoing contingency but also a probability outcome which quantifies the confidence level of the decision. This methodology, which to the best of our knowledge is new to the dynamic security assessment (DSA) of power systems, is also very effective in evaluating the importance of and interaction among the various WASI input features. Our research unexpectedly showed that the ensemble of trees in the RF is very robust in the presence of small changes in the training data and generalize across widely different network dynamics. Thus, the same RF performed very well on a large database with more than 60 000 instances from a test system (10%) and an actual (90%) system combined. One such a general RF (with 210 trees) boosted the reliability of a 9-cycle catastrophe predictor to 99.9%, compared to only 70% when a single conventionally trained DT is used.

Regional aquatic ecological security assessment in Jinan, China

Regional aquatic ecological security is an important indicator that presents the aquatic ecological state and can guide the aquatic environmental management. Here, an index system for the regional aquatic ecological security assessment was established based on the Pressure-State-Response Model and analytical hierarchy process. The system was applied in the city of Jinan in eastern China. The results indicated that the state of the aquatic ecological security is best in Changqing District which has relatively high vegetation coverage, a well-conserved ecological condition and a relatively smaller population density than other districts. Following were Lixia, Shizhong, Huaiyin, and Licheng Districts. The state of the aquatic ecological security is the poorest in Tianqiao District, mainly due to its over-exploitation of groundwater, declining landscape diversity and lower scientific and technical investment. The driving force test also demonstrated that economic development and population size are the dominant impact factors for the variation of aquatic ecological security. Environmental protection investment should be paid more attention as an important measure to improve the aquatic ecological security in Jinan. It was demonstrated that the index system can be effectively applied in an aquatic ecological security assessment to identify the primary impact factors that support decision-making on water resources management.

Voltage Stability Assessment Using Neural Networks in The Deregulated Market Environment

The project was done in Nanyang technological university, Singapore at Electric power research Lab during the period from Sep 2006 to Nov 2007. The liberalization of electric market has generated the unbundling of large utilities into separated generation, Transmission and Distribution companies with subsequent changes in the operating condition at electric power systems. Most of the breakdowns in the electric power systems are caused by unfavorable dynamic response of the networks following system disturbances. In addition, environmental and economic consideratioNS are forcing power systems to be operated closer to their limits of stability .Therefore dynamic security assessment of power systems is becoming increasingly important. Voltage collapse is one of the instability which occurs when the system is heavily loaded. In addition, the mechanism of voltage collapse is due to the load reacting to the voltage changed which leads to further voltage changes. In this project, indicators utilizing the generator-load mismatch and active/reactive power margins for on-line assessment of proximity of voltage instability of power system is to be developed. The approach utilizes an artificial neural networks(ANN) function approximator. In the area of Power systems, problems may be expressed in different ways depending upon their nature. The problem formulation may be expressed in terms of complex systems, say nonlinear, large scale, dynamical, discrete, stochastic, ransom like quasi-periodical, time-variant parameter systems etc. Among these factors, the nonlinear and the large scale systems make power system problems more hard to solve. Apart from linear systems, no good analytical system are available for the complicated problems. ANN is a good promising candidate for dealing with them.