On-line Security Assessment Research Articles

Distributed Feature Selection for Power System Dynamic Security Region Based on Grid-Partition and Fuzzy-Rough Sets

In order to satisfy the requirements of modern online security assessment of power systems with continuously increasing complexity in terms of structure and scale, it is desirable to develop a power system dynamic security region (DSR) analysis. However, data-driven methods suffer from expensive model training costs and overfitting when determining DSR boundaries with high-dimensional grid features. Given this problem, a distributed feature selection method based on grid partition and fuzzy-rough sets is proposed in this paper. The method first employs the Louvain algorithm to partition the power grid and divide the original feature set so that high-dimensional features can be allocated to multiple computational units for distributed screening. At this point, the connections between features of different computational units are minimized to a relatively low level, thereby avoiding large errors in the distributed results. Then, an incremental search algorithm based on the fuzzy-rough set theory (FRST) is used for feature selection at each computational unit, which can effectively take into account the intrinsic connections between features. Finally, the results of all computational units are integrated in the coordination unit to complete the overall feature selection. The experimental results based on the IEEE-39 bus system show that the proposed method can help simplify the power system DSR analysis with high-dimensional features by screening the critical features. And compared with other commonly used filter methods, it has higher screening accuracy and lower time costs.

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Optimal Corrective Rescheduling of Real and Reactive Powers for Power System Security Enhancement.(Dept.E)

This paper presents an optimal technique for solving the corrective rescheduling problem in p ower system. A successive dual linear programming approach is used with linearized p ower flow model. The system real and reactive power generations and transformer tap ratios are optimized within their operating limits so that no limit voilations of the line flow and bus voltage magnitudes in either the base case and contengency cases. The rest results on IEEE-30 bus test system show that the proposed technique can be successfully employed in conjunction with any fast security assessment scheme for a fast on-line security assessment and control.

Online frequency security assessment based on an analytical model considering limiting module

Due to the increasing implementation of high voltage direct current (HVDC) and the integration of renewable resources, the power system frequency stability problems are drawing more attention in recent years. It has become more necessary to carry out online frequency security assessments to ensure the safe operation of power systems. Considering the low time-efficiency of simulation-based methods, analytical models, such as the frequency nadir prediction(FNP) model, are more suitable for online assessment which requires calculating the worst frequency deviation under contingencies. Based on the FNP model, the FNP-L model for online frequency security assessment is proposed in this paper. The proposed model implements security assessment by calculating and checking the frequency features, including the nadir time and frequency, followed by contingencies. The effect of the governor including the nonlinear constraints is approximated into polynomial functions so that results are given by solving multiple polynomial equations. Case studies are carried out over the New-England 39-bus system and a regional power grid, which show that the proposed model could achieve both high speed and high accuracy, and can therefore be applied in online security assessment.

Real-time dynamic security analysis of power systems using strategic PMU measurements and decision tree classification

Fast and accurate online dynamic security analysis (DSA) is the key enabler for secure operation of modern power systems. Real-time assessment of the current power system operating state and increased awareness about plausible future insecurity can enable necessary operational and control measures to ensure secure operation. This paper proposes an ensemble decision tree (DT)-based online DSA method for large-scale interconnected power system networks using wide area measurement (WAMS) with phasor measurement units (PMU). A novel attribute selection method has been demonstrated for optimizing PMU installation at strategic buses in large-scale power networks. Multi-stage screening of the initial measurements has been done to minimize the data acquisition cost and computation overhead, which are the key challenges in real-time DSA. The ensemble DT classifier was trained offline using data from the operational model of the power system under different system loading and contingency conditions. The trained classifier provides online security assessment and classifies the power system’s current operating state as secure or insecure based on real-time measurements of the key attributes by selective PMUs. The proposed scheme was tested on IEEE 118-bus system, and the results demonstrate that it has the potential to be used as a reliable online DSA method.

Security Assessment and Coordinated Emergency Control Strategy for Power Systems with Multi-Infeed HVDCs

Short-circuit faults in a receiving-end power system can lead to blocking events of the feed-in high-voltage direct-current (HVDC) systems, which may further result in system instability. However, security assessment methods based on the transient stability (TS) simulation can hardly catch the fault propagation phenomena between AC and DC subsystems. Moreover, effective emergency control strategies are needed to prevent such undesired cascading events. This paper focuses on power systems with multi-infeed HVDCs. An on-line security assessment method based on the electromagnetic transient (EMT)-TS hybrid simulation is proposed. DC and AC subsystems are modeled in EMTDC/PSCAD and PSS/E, respectively. In this way, interactions between AC and DC subsystems can be well reflected. Meanwhile, high computational efficiency is maintained for the on-line application. In addition, an emergency control strategy is developed, which coordinates multiple control resources, including HVDCs, pumped storages, and interruptible loads, to maintain the security and stability of the receiving-end system. The effectiveness of the proposed methods is verified by numerical simulations on two actual power systems in China. The simulation results indicate that the EMT-TS hybrid simulation can accurately reflect the fault propagation phenomena between AC and DC subsystems, and the coordinated emergency control strategy can work effectively to maintain the security and stability of systems.

Development of an Ensemble Decision Tree-Based Power System Dynamic Security State Predictor

Real-time assessment and prediction of dynamic security plays an important role in power systems operation and control. Analysis of the current operating state and prediction of plausible future insecurity is crucial for real-time decision-making and implementation of necessary control strategies to ensure secure operation of bulk power systems. On-line security assessment demands low complexity and computational time. The traditional methods of assessment of dynamic security of electric power systems often prove to be inadequate for online and real-time conditions due to their high complexity and long computation time. This article proposes a pattern recognition approach to dynamic security analysis for large and complex power systems using ensemble decision tree to form reliable decision rules for fast and accurate prediction of power system's operating states in a real-time and on-line environment. An ensemble security predictor (ENSP) was developed and trained to act as “dynamic security state predictor” to predict and classify power system's dynamic operating states into secure, insecure, and intermediate transitional classes. Two different case studies were performed on IEEE 118-bus and IEEE 300-bus systems to evaluate and compare the performance of the proposed method on systems of varying size and complexity. While prediction accuracy of some of the single-learner classifiers decreased considerably when implemented on a large sized system, the performance of ENSP declined marginally, and the results were acceptable for very large sized systems.

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.

Hybrid AC/DC Post-Contingency Power-Flow Algorithm Considering Control Interaction of Asynchronous Area

This paper presents an approach for the calculation and estimation of control interactions between asynchronous power systems coupled via multi-terminal high-voltage direct current grids using static analysis methods. This aims at possible application within online security assessment where, as of today, static analysis methods are still a common practice. Therefore, the postcontingency power flow is evaluated using an integrated approach for the solution of AC and DC systems in the power-flow algorithm. Thereby, equations for DC voltage control and AC frequency control of converter stations, generators, loads as well as wind turbines with frequency support are embedded into the extended system Jacobian. The results show that by using the proposed approach, the post-contingency power-flow situation can be accurately determined and the frequency changes in each subsystem can be sufficiently tracked. Using an application example of an offshore power system with frequency support by offshore wind turbines, all results have been validated against dynamic simulation, while simulation models and controllers are provided. It is shown that active power balancing controls of AC and DC systems could have a high impact on line loadings and need to be respected for security analyses.

Machine Learning Techniques for Power System Security Assessment

Abstract: Modern electricity grids continue to be vulnerable to large-scale blackouts. As all states leading to large-scale blackouts are unique, there is no algorithm to identify pre-emergency states. Moreover, numerical conventional methods are computationally expensive, which makes it difficult to use for the on-line security assessment. Machine learning techniques with their pattern recognition, learning capabilities and high speed of identifying the potential security boundaries can offer an alternative approach. The purpose of this paper is not to suggest that one particular kind of machine learning technique for security assessment would be more appropriate than others. We start from the premise that almost every method may be useful within some restricted context. Based on this idea, we developed an automated multi-model approach for on-line security assessment. The proposed method allows us to automatically test the different state-of-art techniques in order to find both the best algorithm and its top performance tuning for particular analyzed power system. A case study using the IEEE RTC-96 system demonstrates the effectiveness of the proposed approach.

English

In the area of power system planning and operation, contingency analysis plays an important role. In the present days, on-line security assessment is done by contingency ranking, with the help of various computer software, incorporating iterative method like Newton Raphson power flow for obtaining the magnitudes of different parameters. The motive of contingency ranking is to create a short list of potential contingencies quickly and rank them according to their severity in an accurate manner. In this paper sensitivity based wide performance index with respect to outage have been presented. The Newton-Raphson power flow results were used to construct two kinds of performance indices, namely the real power performance index and voltage performance index, which reflect the degree of severity of contingencies. Then full AC load flow was performed for each contingency case, to study the contingency effect. The effectiveness of the proposed method has been demonstrated by contingency screening and ranking on a standard 6-bus and IEEE 14-bus system.

On-line voltage security assessment and control

SUMMARY Voltage stability analysis and control are key applications for maintaining and enhancing the voltage security of bulk power systems. In today's control center, as power systems become more stressed and the penetration of renewable energies increases, system operators need to analyze voltage security of the systems based on actual operating conditions, contingency and power transactions of the system. In this paper, the model used to represent a power system losing stability is described. It is shown that the voltage collapse point is reached at saddle-node bifurcation or a structure-induced bifurcation. Computation of P–V curves showing system load margin to voltage collapse, system load margin to voltage-limit violation and system load margin to thermal-limit violation is presented. The architecture and example results of the Voltage Stability Analysis and Enhancement system at the California Independent System Operator implementing this research are shown. The continuation power flow engine at the core of this work is presented in detail, and study results are shown. Other key functions, contingency selection (insecure or critical) and ranking, preventive control to increase the load margin of insecure contingencies such that it is secure and enhancement control to increase the load margin of critical contingencies, are presented. Finally, a novel method to enhance the continuation power flow in handling uncertainty of renewable energy is shown. Copyright © 2014 John Wiley & Sons, Ltd.

Cutset-angle based wide area thermal security region and its application in China Southern Power Grid

SUMMARY This paper presents a novel methodology for constructing wide area thermal security region (WATSR) to monitor the wide area thermal security of bulk power system under “N−1” criterion by utilizing wide area measurement system. A general cutset angle for large-scale inter-area power system is defined and developed as a measurement to assess the system thermal security so as to construct WATSR in cutset-angle space. WATSR consists of all stable operating points under “N−1” criterion, while its boundary is a complete set of all the critical points. In this paper, the boundary of WATSR is represented via its piecewise approximation with a set of hyperplanes to enhance the calculating speed. Hyperplanes are described as a set of linear equalities, which are convenient to be determined. And, because coefficients of a hyperplane can indicate the sensitivities between various control parameters, it is beneficial to power system online security assessment and control strategy optimization. A two-step algorithm is given to apply WATSR to online monitoring for bulk power system. The first step is offline calculation. The aim of this step is to determine a minimum set of hyperplanes to properly describe the boundary of WATSR. A hyperplane is a sub-segment of the security region boundary and normally corresponds to a certain constraint. The second step is online matching. Data retrieved from wide area measurement system are used to determine the relative constraints given by the hyperplanes. And then, the stability of the system can be quickly and accurately determined by the assistance of WATSR result. Once the power system turns to critical, WATSR can help the operators to optimize their control strategy. The methodology of WATSR has been successfully implemented in the China Southern Power Grid, which is known as one of the largest alternating current/direct current parallel transmission system in the world. Feasibility and effectiveness of the presented method have been validated through intensive analyses in China Southern Power Grid. Copyright © 2014 John Wiley & Sons, Ltd.

On the possible use of probabilistic techniques for purposes of short-term load dispatching

The security assessment of the working point set by the energy markets is one of the main tasks of any System Operator (SO). The experience of vertically-integrated utilities in the use of probabilistic methods has proved to be very useful also in the new deregulated environment: an increasing number of SOs is presently enforcing with probabilistic tools the deterministic criteria traditionally employed to validate the ex-ante dispatching. Very challenging, promising and discussed, but not yet well-established, is conversely the use of probabilistic tools also for real-time decision-support and very short-term dispatching. The computational complexity of many time-consuming algorithms has constituted for years a crucial barrier; nowadays, the availability of cheap and fast computers discloses new opportunities for probabilistic techniques also for purposes of on-line security assessment. In this paper a probabilistic technique for real-time security assessment and operational decision-making is proposed and described. The use of a novel power system simulator, based on a Sequential Monte Carlo technique, is applied to very short-term dispatching, in order to alert the SO if the system reliability is about to decrease in the next few hours, thus anticipating critical contingencies and supporting the control room to find the most cost-effective corrective action. A case study relevant to the IEEE Reliability Test System RTS-96 is shown and discussed

Static Security Assessment in Power Systems Using Multi-Class SVM with Parameter Selection Methods

 Abstract—Security assessment is a major concern in real time operation of electric power systems. Traditional method of security assessment performed by continuous load flow analysis involves long computer time and generates voluminous results. This paper presents a practical and feasible Support Vector Machine Based Pattern Classification (SVMBPC) approach for static security assessment in power systems. The proposed approach classifies the security status of any given operating condition in one of the four classes - Secure, Critically Secure, Insecure and Highly Insecure based on the computation of a numeric value called security index. The feature selection stage uses a simple and straightforward forward sequential method to select the best feature set from a large set of variables. The static security classifier is designed by a multi-class SVM with different parameter tuning methods. The proposed approach is implemented in New England 39 bus and IEEE 118 bus systems and the results are validated. techniques and expert systems like fuzzy set theory has been proposed for security assessment problem, overcoming the pitfalls of traditional method. Literatures have reported the use of Artificial Neural Network techniques (4), (5), fuzzy logic combined with neural network (6), genetic based neural network (7) for static security assessment process. The performance of all these existing techniques are highly problem dependent and hence its suitability cannot be generalized. Nowadays, pattern classification is gaining more importance in solving many power system problems. In this approach, main bulk of work is done off-line to generate sufficient dataset. The classification function, designed based on the train set, helps to access the system security level in a short period of time. This paper addresses security assessment as a pattern classification problem with the classifier function designed by Support Vector Machine (SVM). SVM is a new and promising tool for learning separating functions in PR system with the capability of handling non-linear separability. The SVM classifier is designed for multi-classification based on the calculation of a term called Static Security Index (SSI), for each specified contingency. In this paper, four-class logic is used for the definition of system security viz., secure, critically secure, insecure, highly insecure. An operator likes to know exactly the severity level of disturbances for a given system operating condition. On-line security assessment allows the operator to know the security status and helps to determine the corrective actions. This paper also addresses different heuristic optimization techniques like Particle Swarm Optimization (8), Real Coded Genetic Algorithm (9) and Differential Evolution (10) used in the selection of SVM parameters globally. The classification approach is implemented in New England (NE) 39 bus system and IEEE 118 bus system and the results are compared.

PMU-Based Wide-Area Security Assessment: Concept, Method, and Implementation

This paper presents a concept, method, and implementation of utilizing phasor measurement unit (PMU) information to monitor the wide-area security of a power system. The close dependency of major transmission paths requires an approach that takes that interaction into account while establishing operational transfer capability, and evaluates grid reliability and security on a system-wide basis. Thus, the concept of wide-area security region, which considers all essential constraints, including thermal, voltage stability, transient stability, and small signal stability, is proposed. This approach expands the idea of traditional transmission system nomograms to a multidimensional case, involving multiple system limits and parameters such as transmission path constraints, zonal generation or load, etc., considered concurrently. In this paper, the security region boundary is represented using piecewise approximation with the help of linear inequalities (so called hyperplanes) in a multidimensional space, consisting of system parameters that are critical for security analysis. The goal of this approximation is to find a minimum set of hyperplanes that describe the boundary with a given accuracy. Offline computer simulations are conducted to build the security region and the hyperplanes can be applied in real time with phasor information for on-line security assessment. Numerical simulations have been performed for the full size Western Electricity Coordinating Council (WECC) system model, which comprises 15 126 buses and 3034 generators. Simulation results demonstrated the feasibility and effectiveness of this approach, and proved that the proposed approach can significantly enhance the wide-area situation awareness for a bulk power system like WECC.

Online voltage security assessment considering comfort-constrained demand response control of distributed heat pump systems

A resilient strategy for optimal demand response control based on the management of highly-distributed electric loads is presented to meet transmission-level control aimed at maintaining voltage stability. The proposed load control scheme balances device- and grid-level objectives simultaneously, and is demonstrated for a system comprising a distributed responsive population of 14,000 residential-sized buildings integrated in a transmission system network consisting of six buses. Air-source heat pumps are implemented as the primary heating source in the responsive building population, and are introduced as a dispatchable grid-side energy resource, where aggregated output can be objectively ramped up or down through the use of an optimal centralized control strategy. A two step multi-objective optimization procedure is implemented to simultaneously satisfy balancing of the power system and customer objectives across a multi-scalar system. At the power system-level, the optimal preventive control scheme is obtained based on steady-state voltage stability constraints. At the customer-level, an optimal demand response strategy is proposed, wherein the aggregate power demand from a population of heat pumps is controlled to follow load-shedding requirements. The customer comfort is continuously maintained by constrained regulation of the thermal set-point governing operation of the heat pump device.The proposed demand-side strategy achieves similar goals to conventional approaches to regulation and spinning reserve ancillary services, but with the significant benefit of higher efficiencies. Under the proposed scheme, ancillary services provided by the electric loads effectively become virtual generators that enhance the voltage stability in power systems operating under increased uncertainty, and replace severe or multiple contingency reserves typically supplied by conventional generators. The proposed demand response control scheme can be extended to other potentially responsive end-use appliances, and opens avenues for increased exploitation of intermittent renewable energy resources while reducing operating costs and emissions.

On-line voltage stability assessment using radial basis function network model with reduced input features

In recent years, voltage instability has become a major threat for the operation of many power systems. This paper presents an artificial neural network (ANN)-based approach for on-line voltage security assessment. The proposed approach uses radial basis function (RBF) networks to estimate the voltage stability level of the system under contingency state. Maximum L-index of the load buses in the system is taken as the indicator of voltage stability. Pre-contingency state power flows are taken as the input to the neural network. The key feature of the proposed method is the use of dimensionality reduction techniques to improve the performance of the developed network. Mutual information based technique for feature selection is proposed to enhance overall design of neural network. The effectiveness of the proposed approach is demonstrated through voltage security assessment in IEEE 30-bus system and Indian practical 76 bus system under various operating conditions considering single and double line contingencies and is found to predict voltage stability index more accurate than feedforward neural networks trained by back propagation algorithm and AC load flow. Experimental results show that the proposed method reduces the training time and improves the generalization capability of the network than the multilayer perceptron networks.

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 to determining the local boundaries of voltage stability region with wind farms in power injection space

A new approach to determining the local boundaries of voltage stability region in power injection space (IVSR) for power system with wind farms is presented. It can be used for power system voltage stability analysis and on-line security assessment with considering the output uncertainty of the wind farms. Firstly, power flow tracing and double dispatching are used to determine the generators that are closely related to the wind farms, in order to balance the power fluctuations caused by the wind speed variation. Then, modal analysis is used to get the key generators to achieve an effective dimension reduction for IVSR. Finally, the forecasting output power (or wind speed) of wind farms is divided into several intervals. For each interval, the corresponding local IVSR boundaries can be calculated by the method based on small disturbance. Moreover, parallel process is used to accelerate the computing speed. The presented approach is validated by several power systems. It can be revealed that the approach can give the local IVSR boundaries at different wind speeds and has a good engineering application prospect.