Power System Security Assessment Research Articles

An advanced platform for power system security assessment accounting for forecast uncertainties

Accounting for the increasing uncertainties related to forecast of renewables is becoming an essential requirement while assessing the security of future power system scenarios. Project iTesla in the Seventh Framework Program (FP7) of the European Union (EU) tackles these needs and reaches several major objectives, including the development of a security platform architecture. In particular, the platform implements a stochastic dependence model to simulate a reasonable cloud of plausible 'future' states - due to renewable forecast - around the expected state, and evaluates the security on relevant states after sampling the cloud of uncertainty. The paper focuses on the proposed model for the uncertainty and its exploitation in power system security assessment process and it reports the relevant validation results.

Security Assessment for Cyber Physical Distribution Power System Under Intrusion Attacks

A cyber physical distribution power system (CPDS) is a large and complex infrastructure that coordinates the cyber communication system and the physical distribution power system. Because of the increasingly advanced information communication technology, the development of cyber physical distribution power system has caused key cyber security issues related to system operation. This paper is focused on realizing a unified system attack modeling and security assessment of an active distribution power system. In this paper, first we present an overview of the system operation from the fusion system perspective. The significant effects of network intrusion attacks on operational security are evaluated. A new unified cyber physical network model is established using a limited stochastic Petri net graph theory that considers refined firewalls and password components. Then, a security effectiveness evaluation method is proposed to analyze channel throughput variation and system robustness. Overall CPDS security risk values are determined based on physical influence coefficients. Finally, simulations of an improved IEEE-33 bus distribution power system and security assessment under intrusion attacks are described. The research work could raise awareness of the cyber intrusion threats and provide the basis for security defense.

An Online Dynamic Security Assessment in Power Systems Using RBF-R Neural Networks

ABSTRACT In this paper, the dynamic security of a large power system against any critical contingency is predicted by a new type of radial basis function neural network, RBF-R NN, as it classifies the system’s transient stability status online. In order to keep the number of measurements limited, as well as to reduce the complexity of the NNs used, the minimum redundancy maximum relevance is adopted as a feature selection method. Moreover, the classification performance of the RBF-R NNs is improved by eliminating the training set instances that are close to the security boundary. The proposed method is applied on a 16-generator-68-bus test system and the performance of the adopted RBF-R NNs is compared with RBF NNs, as well as with multilayer perceptrons. The simulation results show that a significant improvement in prediction accuracy is obtained by the RBF-R NNs together with the feature selection and the elimination of boundary instances.

Multilabel learning for the online transient stability assessment of electric power systems

Dynamic security assessment of a large power system operating over a wide range of conditions requires an intensive computation for evaluating the system's transient stability against a large number of contingencies. In this study, we investigate the application of multilabel learning for improving training and prediction time, along with the prediction accuracy, of neural networks for online transient stability assessment of power systems. We introduce a new multilabel learning method, which uses a contingency clustering step to learn similar contingencies together in the same multilabel multilayer perceptron. Experimental results on two different power systems demonstrate improved accuracy, as well as significant reduction in both training and testing time.

Online Static Security Assessment of Power Systems Based on Lasso Algorithm

As one important means of ensuring secure operation in a power system, the contingency selection and ranking methods need to be more rapid and accurate. A novel method-based least absolute shrinkage and selection operator (Lasso) algorithm is proposed in this paper to apply to online static security assessment (OSSA). The assessment is based on a security index, which is applied to select and screen contingencies. Firstly, the multi-step adaptive Lasso (MSA-Lasso) regression algorithm is introduced based on the regression algorithm, whose predictive performance has an advantage. Then, an OSSA module is proposed to evaluate and select contingencies in different load conditions. In addition, the Lasso algorithm is employed to predict the security index of each power system operation state with the consideration of bus voltages and power flows, according to Newton–Raphson load flow (NRLF) analysis in post-contingency states. Finally, the numerical results of applying the proposed approach to the IEEE 14-bus, 118-bus, and 300-bus test systems demonstrate the accuracy and rapidity of OSSA.

An Accurate Online Dynamic Security Assessment Scheme Based on Random Forest

With the increasing integration of renewable energy resources and other forms of dispersed generation, more and more variances and uncertainties are brought to modern power systems. The dynamic security assessment (DSA) of modern power systems is facing challenges in ensuring its accuracy for unpredictable operating conditions (OC). This paper proposes a novel approach that uses random forest (RF) for online DSA. Hourly scenarios are generated for the database according to the forecast errors of renewable energy resources, which are calculated from historical data. Fed with online measurement data, it is able to not only predict the security states of current OC with high accuracy, but also indicate the confidence level of the security states one minute ahead of the real time by an outlier identification method. The results of RF together with outlier identification show high accuracy in the presence of variances and uncertainties due to wind power generation. The performance of this approach is verified on the operational model of western Danish power system with around 200 transmission lines and 400 buses.

Network Topological Notions for Power Systems Security Assessment

The identification of topological vulnerabilities is a prerequisite for the study of security analysis. This paper presents a graph-theoretic framework to detect the minimum set of transmission lines interconnecting subnetworks inside of a power network. Moreover, the framework is used to develop a method to classify the criticality of substations. The approach can be used with power transfer distribution factors information to gain an insight about the power system security. Sometimes the power network exhibits high vulnerability related to critical transmission lines interconnecting critical substations from a physical point of view. The quantification of structural properties can provide meaningful information needed to assess and enhance the reliability and security of power system networks. The capabilities for the topological approach are illustrated on two large-scale networks. The proposed approach provides an effective tool for both real-time and offline environments for security analysis and control.

Probabilistic dynamic security assessment of large power systems using machine learning algorithms

Probabilistic dynamic security assessment of large power systems using machine learning algorithms

Synchronized measurements-based wide-area static security assessment and classification of power systems using case based reasoning classifiers

This paper proposes a human intuition-based concept of Case-Based Reasoning (CBR) for Static Security Assessment (SSA) of power systems. Prior to security assessment, a composite Security Index (SI) defined in terms of line overload and voltage violation, is computed for each training pattern to label them into either secure or insecure class. Out of all phasor measurements obtained using Phasor Measurement Units (PMUs), only the real and reactive power flows, which are identified as relevant features are given as inputs to the proposed CBR system. The proposed approach has been implemented on IEEE 14 bus, IEEE 30 bus and Indian 246 bus networks and the results have been compared with those obtained using Support Vector Machine (SVM) and Artificial Neural Network (ANN) based classifiers. The results indicate that the proposed approach is more efficient, reliable and outperforms the above said classifiers in terms of classification accuracy at a comparable computation cost.

Power system security assessment with high wind penetration using the farms models based on their correlation

Increasing the wind energy penetration in a power system presents some technical challenges to the transmission expansion planning (TEP). In the first stage of TEP, the transmission bottlenecks should be determined and ranked via security assessment. The high penetration of wind energy means a large number of wind farms exist in various geographic areas. It is found that there is a correlation between the wind speed series of different farms. Therefore, integration of the correlated wind farms into security assessment is addressed in this study. Hence, a new model of wind speed prediction is presented based on the correlated autoregressive moving average time series. The copula functions are used to make the predefined correlation among wind speed series. Here a linear correlation using a Gaussian copula is implemented. Then, the risk-based security assessment is performed using the proposed sequential time simulation. The proposed process is applied to the modified IEEE 39-bus test system which comprises 15 wind farms. Then, the transmission bottlenecks are identified and ranked regarding their overload risks. Finally, the impacts of the wind farms correlation on the risk index are investigated and those contingencies which impose the highest influence on this risk are identified.

Locating all real solutions of power flow equations: a convex optimisation‐based method

This paper proposes a convex optimization based method that either locates all real roots of a set of power flow equations or declares no real solution exists in the given area. In the proposed method, solving the power flow equations is reformulated as a global optimization problem (GPF for short) that minimizes the sum of slack variables. All the global minima of GPF with a zero objective value have a one-to-one correspondence to the real roots of power flow equations. By solving a relaxed version of GPF over a hypercube, if the optimal value is strictly positive, there is no solution in this area and the hypercube is discarded. Otherwise the hypercube is further divided into smaller ones. This procedure repeats recursively until all the real roots are located in small enough hypercubes through the successive refinement of the feasible region embedded in a bisection paradigm. This method is desired in a number of power system security assessment applications, for instance, the transient stability analysis as well as voltage stability analysis, where the closest unstable equilibrium and all Type-I unstable equilibrium is required, respectively. The effectiveness of the proposed method is verified by analyzing several test systems.

A Novel Feature Selection Method for the Dynamic Security Assessment of Power Systems Based on Multi-Layer Perceptrons

In this study, the effect of feature selection methods on the performance of multi-layer perceptrons used for the dynamic security assessment of electric power systems is investigated. The existence of many measurable parameters (features) characterizing the power system security status complicates the use of multi-layer perceptron both in terms of prediction accuracy and training time. In this paper, the dynamic security of a power system subject to a number of critical contingencies is assessed as the critical clearing time of any credible fault is predicted by a multi-layer perceptron. In addition to the study of two different feature selection methods, which are Minimum Redundancy Maximum Relevance (mRMR), and Regressional ReliefF (RReliefF), a novel multi-layer perceptron based feature selection method is proposed to be applied in the prediction of security indices. The performance of the feature selection methods on the dynamic security assessment is investigated on a 16-generator, 68-bus test system.

AdaBoost classifiers for phasor measurements‐based security assessment of power systems

Power system security is a major concern in real-time operation. It is essential to protect the system from blackout by taking proper control actions. This study proposes a boosting algorithm for the precise and accurate prediction of static security assessment of power systems using synchronised measurements. In addition to security status, the proposed approach also predicts the type of violations which may be either line overload/voltage violation or both of the insecure operating conditions. To overcome the computational complexity, the number of input phasor measurements is reduced by a statistical approach based on class separability and correlation coefficient indices. In the classification stage, support vector machines (SVMs) are used as weak classifiers and a strong classifier is constructed as the linear combination of many weak SVM classifiers. The performance of the Adaptive Boosting (AdaBoost) algorithm is further improved by a new weight updation strategy using fuzzy clustering thresholding technique. The efficiency of the proposed approach is demonstrated on IEEE 14-bus, IEEE 30-bus, and Indian 246-bus systems. Further, the test results reveal that the proposed method of security assessment performs better than the other traditional classifiers viz. SVM, feed forward neural network and k-nearest neighbour classifier.

Network Structure-Based Critical Bus Identification for Power System Considering Line Voltage Stability Margin

Voltage security assessment of power system is an important and all-inclusive aspect of power system operation and preventive control actions. Fast and accurate detection of critical components of the power system is one essential approach for preventing the occurrence of voltage collapse phenomenon. Over the years, several approaches for voltage collapse point identification and prevention have been widely studied using the continuous power flow approach, minimum singular value of eigenvalues, Jacobian matrices, and power transfer concept. In this work, critical node (bus) identification based on power system network structure is proposed. In this approach, the power system is treated as a multidimensional graph with several nodes (buses) linked together by the transmission lines. An improved line voltage stability margin estimator which is based on active and reactive power changes in a power system is used as the weight of each transmission line and an adaptation of the degree of centrality approach is used to determine the criticality of the system buses. A comparative analysis with other bus voltage stability indices is presented to test the suitability of the proposed approach using the IEEE 14, 30, 57 and 118 bus test systems.

A Risk-Based Methodology and Tool Combining Threat Analysis and Power System Security Assessment

A thorough investigation of power system security requires the analysis of the vulnerabilities to natural and man-related threats which potentially trigger multiple contingencies. In particular, extreme weather events are becoming more and more frequent due to climate changes and often cause large load disruptions on the system, thus the support for security enhancement gets tricky. Exploiting data coming from forecasting systems in a security assessment environment can help assess the risk of operating power systems subject to the disturbances provoked by the weather event itself. In this context, the paper proposes a security assessment methodology, based on an updated definition of risk suitable for power system risk evaluations. Big data analytics can be useful to get an accurate model for weather-related threats. The relevant software (SW) platform integrates the security assessment methodology with prediction systems which provide short term forecasts of the threats affecting the system. The application results on a real wet snow threat scenario in the Italian High Voltage grid demonstrate the effectiveness of the proposed approach with respect to conventional security approaches, by complementing the conventional “N − 1” security criterion and exploiting big data to link the security assessment phase to the analysis of incumbent threats.

An Analytical Approach to Probabilistic Dynamic Security Assessment of Power Systems Incorporating Wind Farms

An analytical approach to probabilistic dynamic security assessment of power systems incorporating wind farms is proposed. As the most important and complex step of the evaluation process, the probability of transient stability given a specific fault and uncertainties of output power of wind farm and load is calculated analytically based on the practical dynamic security region of power system with double fed induction generator and Cornish-Fisher expansion. The proposed method can provide meaningful and reliable evaluation results with high accuracy and much less computing time compared with Monte Carlo simulation.

Ensemble methods of classification for power systems security assessment

One of the most promising approaches for complex technical systems analysis employs ensemble methods of classification. Ensemble methods enable a reliable decision rules construction for feature space classification in the presence of many possible states of the system. In this paper the novel techniques based on decision trees are used to evaluate power system reliability. In this work a hybrid approach based on random forests models and boosting model is proposed. Such techniques can be applied to predict the interaction of increasing renewable power, storage devices and intelligent switching of smart loads from intelligent domestic appliances, storage heaters and air-conditioning units and electric vehicles with grid to enhance decision making. This ensemble classification method was tested on the modified 118-bus IEEE power system to examine whether the power system is secured under steady-state operating conditions.

Damping Nomogram Method for Small-Signal Security Assessment of Power Systems

This paper reviews the main concepts about power systems security assessment and describes the voltage security assessment (VSA), the transient security assessment (TSA) and the small-signal security assessment (SSA) of electrical power systems. Most methods of SSA are based on the calculation of oscillation modes of power systems and the evaluation of their damping factors. The objective of the power system security assessment is to determine the distance that these systems are to certain security boundary. In small-signal security assessment case, the security limits are, in general, associated to a minimum secure damping factor of the oscillation modes. This paper proposes a nomogram method based on the analysis of oscillation damping factors to be used for small-signal security assessment of power systems and describes its computational development.

Power system security assessment for multiple contingencies using multiway decision tree

The search for effective ways to promote the secure operation of power systems and to increase its understanding by operators has encouraged continuous research for new techniques and methods that can help in this task. In this paper, it is proposed an approach to assess power system operation security for multiple contingencies using a multiway decision tree (MDT). The MDT differs from other decision tree techniques for establishing, in the training step, one value of the categorical attributes by branch. This approach proposes the use of topologies (contingencies) as categorical attributes. In this way, it improves interpretability regarding the power system operational state, as the operator can see clearly the critical variables for each topology, such that the MDT rules can be used in aiding the decision-making. This proposal was used for security assessment of the Northern part of the Brazilian Interconnected Power System (BIPS), and it was tested with real data from one day operation, demonstrating good performance, with a set of simple and clear rules. It was also performed a comparison of the obtained results with other techniques based on DT and it turned out that MDT has resulted in a simpler procedure for power system security classification with good accuracy.

Situational Awareness Applied to Dynamic Security in Power Systems

The progressive development in power systems and their operational complexity has aroused interest in the power system dynamic security. Network operators cannot determine the exact state of the system in high-risk situations causing the spread of failures in the system and breakdowns in the worst cases. According to this, the current paper presents the development of a user interface made for the dynamic security assessment of power systems using the theory of situational awareness (SA) to improve the visualization and analysis of the network operator’s information in control centers. Likewise, this paper proposes a methodology, based on measurable information in a WAMS environment, to identify and classify the disturbance or phenomenon affecting the safety of the power system by using online identification of coherent groups, SA indexes and fuzzy logic. Significantly, the purpose of this document does not include control actions when a contingency occurs. It is noteworthy that the validation of this methodology is performed by simulations in the power system New England Test System (39 nodes) by using PSAT toolbox of Matlab®.