Dynamic Security Assessment Research Articles

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.

A Suite of Intelligent Tools for Early Detection and Prevention of Blackouts in Power Interconnections

We propose a suite of intelligent tools based on the integration of methods of agent modeling and machine learning for the improvement of protection systems and emergency automatics. We propose an online approach to the assessment and management of dynamic security of electric power systems (EPS) with the use of a streaming modification of the random forest algorithm. The suite allows to recognize dangerous modes of complex closed-loop EPS, preventing the risk of emergencies on early stages. We show results of experimental tests on IEEE test systems.

A Method to Estimate Power System Voltage Stability Margins Using Time-Series From Dynamic Simulations With Sequential Load Perturbations

In this paper, a methodology to assess power system voltage stability margins from time-series data is presented. The proposed method obtains time-series from dynamic simulations subject to sequential load changes that are then used as an input to a voltage stability estimation method. After a contingency is applied, load perturbations aim to move the system through different loading levels. This approach allows estimating voltage stability margins from time-series without any information or numerical quantities of the mathematical model of the system other than the simulation’s output. This is useful for dynamic security assessment systems that only have transient simulators for their workflows and still require to estimate the voltage stability margins. The proposed method is used to quantify the severity of the contingencies using a three-layer index. The proposed approach is illustrated using a simple two-bus system and then tested using time-domain simulations of the Nordic-44 Bus test system.

Dynamic Power System Security Analysis Using a Hybrid PSO-APO Algorithm

In this paper, a novel hybrid particle swarm optimization and artificial physics optimization (HPSO-APO) algorithm is proposed to solve the dynamic security constrained optimal power flow (DSCOPF) problem for enhancing system security. The dynamic security assessment deals with contingency analysis which is carried out using a performance index. DSCOPF recommends preventive control actions like generator rescheduling to alleviate an existing credible contingency in the system while ensuring minimal operating cost. The OPF problem is a highly nonlinear differential one and becomes more complex when considering the rotor dynamics of the system. The APO algorithm has the capability to reach a near global optimum value. However, it suffers from convergence problem. On the other hand, PSO exhibits premature convergence characteristics, but it may get trapped at a local optima value. The proposed HPSO-APO algorithm combines both individual algorithm strengths, to get balance between global and local search capability. The proposed method has been evaluated on a standard IEEE six-generator, 30-bus system and a New England ten-generator, 39-bus test system. The proposed HPSO-APO algorithm gives an efficient and robust optimal solution of DSCOPF problem compared to standard PSO and APO methods.

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.

Intelligent Early Warning of Power System Dynamic Insecurity Risk: Toward Optimal Accuracy-Earliness Tradeoff

Dynamic insecurity risk of a power system has been increasingly concerned due to the integration of stochastic renewable power sources (such as wind and solar power) and complicated demand response. In this paper, an intelligent early-warning system to achieve reliable online detection of risky operating conditions is proposed. The proposed intelligent system (IS) consists of an ensemble learning model based on extreme learning machine (ELM) and a decision-making process under a multiobjective programming framework. Taking an ensemble form, the randomness existing in individual ELM training is generalized and reliable classification results can be obtained. The decision making is designed for ELM ensemble whose parameters are optimized to search for the optimal tradeoff between the warning accuracy and the warning earliness of the proposed IS. The compromise solution turns out to significantly speed up the overall computation with an acceptable sacrifice in the accuracy (e.g., from 100% to 99.9%). More importantly, the proposed IS can provide multiple and switchable performances to the operators in order to satisfy different local dynamic security assessment requirements.

Power System Event Ranking Using a New Linear Parameter-Varying Modeling with a Wide Area Measurement System-Based Approach

Detecting critical power system events for Dynamic Security Assessment (DSA) is required for reliability improvement. The approach proposed in this paper investigates the effects of events on dynamic behavior during nonlinear system response while common approaches use steady-state conditions after events. This paper presents some new and enhanced indices for event ranking based on time-domain simulation and polytopic linear parameter-varying (LPV) modeling of a power system. In the proposed approach, a polytopic LPV representation is generated via linearization about some points of the nonlinear dynamic behavior of power system using wide-area measurement system (WAMS) concepts and then event ranking is done based on the frequency response of the system models on the vertices. Therefore, the nonlinear behaviors of the system in the time of fault occurrence are considered for events ranking. The proposed algorithm is applied to a power system using nonlinear simulation. The comparison of the results especially in different fault conditions shows the advantages of the proposed approach and indices.

Robust classification model for PMU‐based on‐line power system DSA with missing data

Wide-area measurement system (WAMS) has been widely deployed in modern power systems to achieve data-driven dynamic security assessment (DSA). Considering the unavailability of measurement data during unintentional events, a robust classification model for phasor measurement unit (PMU)-based DSA is proposed. The proposed approach identifies the observability of different PMU combinations in the system and an ensemble of random vector functional link networks is trained with observability-constrained feature subsets to ensure its viability under any PMU missing condition. Meanwhile, the available operating features are comprehensively utilised by the proposed classification model to support accurate DSA under both normal and abnormal data collection conditions. The proposed classification model is tested on New England 39-bus system with high accuracy and strong robustness.

Implementation of a Massively Parallel Dynamic Security Assessment Platform for Large-Scale Grids

This paper presents a computational platform for dynamic security assessment (DSA) of large electricity grids, developed as part of the iTesla project. It leverages high performance computing to analyze large power systems, with many scenarios and possible contingencies, thus paving the way for pan-European operational stability analysis. The results of the DSA are summarized by decision trees of 11 stability indicators. The platform’s workflow and parallel implementation architecture is described in detail, including the way commercial tools are integrated into a plug-in architecture. A case study of the French grid is presented, with over 8000 scenarios and 1980 contingencies. Performance data of the case study (using 10 000 parallel cores) is analyzed, including task timings and data flows. Finally, the generated decision trees are compared with test data to quantify the functional performance of the DSA platform.

Risk-Based Dynamic Security Assessment for Power System Operation and Operational Planning

Assessment of dynamic stability in a modern power system (PS) is becoming a stringent requirement both in operational planning and in on-line operation, due to the increasingly complex dynamics of a PS. Further, growing uncertainties in forecast state and in the response to disturbances suggests the adoption of risk-based approaches in Dynamic Security Assessment (DSA). The present paper describes a probabilistic risk-based DSA, which provides instability risk indicators by combining an innovative probabilistic hazard/vulnerability analysis with the assessment of contingency impacts via time domain simulation. The tool implementing the method can be applied to both current and forecast PS states, the latter characterized in terms of renewable and load forecast uncertainties, providing valuable results for operation and operational planning contexts. Some results from a real PS model are discussed.

Accuracy analysis of electromechanical mode estimation under ambient conditions

Electromechanical modes estimated from ambient wide area measurement systems measurements are often used as a basis for system dynamic security assessment. It is therefore important to know the accuracy of the mode estimates. There are two interesting phenomena widely reported in ambient mode estimation: (i) the accuracy of the mode parameter estimates improves under the lighter damping conditions and (ii) the frequency estimates are more accurate than the damping ratio estimates. This study aims to systematically investigate the estimation accuracy of ambient mode analysis. An analytical differentiation-based perturbation method is presented to examine the variance propagation along the mode estimation procedure. Monte Carlo simulations in the two-area system are carried out to analyse the influences of varying the mode parameters on the estimation accuracy of the inter-area mode, in which the estimated variances are served as quantitative measures of the accuracy analysis. In addition, theoretical explanations for the observations in ambient mode estimation are also provided as a supplementary. The investigations and discussion of the estimation accuracy are of value for yielding insight into the variability of the mode estimates under ambient conditions.

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®.

Integrated Approach for Online Dynamic Security Assessment With Credibility and Visualization Based on Exploring Connotative Associations in Massive Data

An integrated approach for online dynamic security assessment based on exploring connotative associations in massive data is proposed; this approach consists of three stages and can give visual and credible dynamic security assessment results. The relationships of operation variables and transient stability margin are assigned scores by the maximal information coefficient and the Pearson-correlation coefficient. The connotative nonlinear functional relationships and linear relationships are explored by ranking the scores, and some highly ranked relationships are shown and curve fitted. In this approach, a processing unit is designed for dynamic security assessment based on the explored highly ranked relationships. The approach considers classification and prediction. This approach is tested in a 21-bus test system and a larger 1648-bus system. The robustness with respect to topology changes, variation of power distribution among generators/loads, and variation of peak load/minimum load are analyzed. The test results indicate that compared with the traditional methods, the proposed approach provides a relatively fast and accurate dynamic security assessment and is suitable for online use.

Transient stability based dynamic security assessment indices

AbstractAs power systems become increasingly stability constrained, the need for dynamic security assessment (DSA) becomes important. However, the resources and technology required to achieve on-line DSA at reasonable cost for utility companies are not easily come by. Therefore, an offline method is needed to examine and screen all likely contingencies to determine those that may lead to instability. Some indices and techniques have been proposed to tackle this problem but are mostly for small disturbances which are not really threats when timely cleared. Some methods are based on evolutionary computation which comes with a lot of drawbacks as there is no guarantee of finding optimal solutions within a finite time and parameter tuning are mostly by trial-and-error. In this paper, a simpler approach to power system DSA indices is proposed using time domain transient stability analysis simulations. A conventional power system which synchronous machines are modeled with two-axis dynamic model, and modeled by...

Dynamic security enhancement of power systems using mean-variance mapping optimization

In this paper, a preventive control action that involves both generation rescheduling and load curtailment is proposed for enhancing the dynamic security of large interconnected power systems. The control action is formulated as a security-constrained optimization problem that is solved by mean-variance mapping optimization (MVMO) integrated with a self-adaptive penalization technique and artificial neural networks to develop a fast and effective methodology. The proposed methodology is applied to a 16-generator 68-bus test system to solve the security-constrained optimization problem with both continuous and discrete decision variables. To find a proper and cost-effective solution for the control actions within an acceptable time, dynamic security assessment methodology based on artificial neural networks is integrated into the optimization process for predicting the violations of security constraints brought about by the candidate solutions. The proposed method effectively integrates a variety of popular heuristic optimization algorithms, including MVMO, differential evolution, particle swarm optimization, genetic algorithms, big bang-big crunch, and artificial bee colony. MVMO outperforms all the others in various aspects such as reliability and robustness.

Application of Energy Function as a Measure of Error in the Numerical Solution for Online Transient Stability Assessment

Abstract Online dynamic security assessment (DSA) is a computationally intensive task. In order to reduce the amount of computation, screening of contingencies is performed. Screening involves analyzing the contingencies with the system described by a simpler model so that computation requirement is reduced. Screening identifies those contingencies which are sure to not cause instability and hence can be eliminated from further scrutiny. The numerical method and the step size used for screening should be chosen with a compromise between speed and accuracy. This paper proposes use of energy function as a measure of error in the numerical solution used for screening contingencies. The proposed measure of error can be used to determine the most accurate numerical method satisfying the time constraint of online DSA. Case studies on 17 generator system are reported.

Effects of Power System Models on Angle Stability Margin in Transient Stability Analysis

Transient stability is the study of synchronism when a power system is subjected to a severe disturbance such as a short circuit fault. This study involves determining the rotor angles of the machines in a power system before, during and after a disturbance through dynamic simulations. Various power system instabilities that had occurred in the past could be linked to the use of too simplified model of power system for stability assessments. The accuracy of rotor angle dynamic responses determines how well transient stability of a multi-machine power system can be examined in dynamic security assessment. This accuracy is predetermined by the model of the power system used in dynamic simulations because the rotor angle stability margin can be compromised by using a simplified model for dynamic assessment. In this work, a more detailed model of synchronous machine, two-axis model, was used and compared with classical model which is popularly used for power system transient stability studies. Differential and Algebraic Equations (DAE) model of a multi-machine power system derived using two-axis and classical models of synchronous machines were used; with machine control dynamics (excitation and governor) added for both models. These equations were solved in MATLAB and taking a full advantage of time-domain simulation. IEEE 30-Bus test system was used for the analysis to compare the rotor angle responses for two-axis model with responses obtained for classical model. The stability margin of the system was found compromised with the use of the classical model, while the two-axis model gave a response that is more dependable for dynamic security assessment.

Impact of Overexcitation Limiters on the Power System Stability Margin Under Stressed Conditions

This paper investigates the impact of the overexcitation limiters (OELs) on the stability margin of a power system which is operating under stressed conditions. Several OEL modeling approaches are presented and the effect of their action has been examined in model power systems. It is realized that, more often than not, OEL operating status goes undetected by existing dynamic security assessment tools commonly used in the industry. It is found that the identification and accurate representation of OELs lead to significantly different transient stability margins. Unscented Kalman filtering is used to detect the OEL activation events. In the context of stressed system operation, such quantitative assessment is very useful for system control. This understanding is further reinforced through detailed studies in two model power systems.

Robust Stability Assessment in the Presence of Load Dynamics Uncertainty

Dynamic response of loads has a significant effect on system stability and directly determines the stability margin of the operating point. Inherent uncertainty and natural variability of load models make the stability assessment especially difficult and may compromise the security of the system. We propose a novel mathematical “robust stability” criterion for the assessment of small-signal stability of operating points. Whenever the criterion is satisfied for a given operating point, it provides mathematical guarantees that the operating point will be stable with respect to small disturbances for any dynamic response of the loads. The criterion can be naturally used for identification of operating regions secure from the occurrence of Hopf bifurcation. Several possible applications of the criterion are discussed, most importantly the concept of robust stability assessment (RSA), that could be integrated in dynamic security assessment packages and used in contingency screening and other planning and operational studies.

A Systematic Approach to -1-1 Analysis for Power System Security Assessment

Cascading outages can have a catastrophic impact on power systems. One such recent incident was the September 8, 2011 blackout that affected San Diego and large parts of the southwestern United States. To prepare for such events, the NERC transmission planning standards require utilities to plan for ${n}$ -1-1 outages. However, such analyses can be computationally burdensome for any realistic power system owing to the staggering number of possible ${n}$ -1-1 contingencies. This paper proposes a systematic approach to analyze ${n}$ -1-1 contingencies in a computationally tractable manner for power system security assessment. The proposed approach addresses both static and dynamic security assessment. The proposed methods have been tested on the Western Electricity Coordinating Council (WECC) system. The test results show that a substantial reduction in computational effort is achieved by implementing these methods for an ${n}$ -1-1 contingency analysis. In addition, the reliability of the proposed methods is evaluated by an exhaustive contingency analysis.