Power system security assessment using neural networks: featureselection using Fisher discrimination

One of the most important considerations in applying neural networks to power system security assessment is the proper selection of training features. Modern interconnected power systems often consist of thousands of pieces of equipment each of which may have an effect on the security of the system. Neural networks have shown great promise for their ability to quickly and accurately predict the system security when trained with data collected from a small subset of system variables. This paper investigates the use of Fisher's linear discriminant function, coupled with feature selection techniques as a means for selecting neural network training features for power system security assessment. A case study is performed on the IEEE 50-generator system to illustrate the effectiveness of the proposed techniques     

Power system self-excitation online security assessment

Power system online security assessment is a significant new approach for power system operation. A new pattern-recognition-based application for power system security assessment is described. Here, the method is applied to the power system self-excitation operating problem. The security function used has the learning capacity to adapt to different network configurations and to new load and generation schedules. This paper relates to online security assessment and the corrective actions required. The system under study is the northern collector system for Manitoba Hydro, where self-excitation is a serious operating problem.     

Power system voltage stability and security assessment

The purpose of this investigation is to review the recent research progress in voltage stability and security assessment of power systems. This paper presents the fundamental concepts of voltage stability, gives a short recall of the classical voltage theory and different criteria currently available for predicting voltage collapse problems in power systems, which have been associated with multiple power flow solutions, bifurcation, dynamics of tap changers, stochastic loads, singular values, etc. The various methods for studying voltage collapse are compared. The application of expert system techniques to assist in the decision-making process of the reactive power/voltage control problem of power systems and future research directions towards voltage stability and security assessment will also be proposed in this paper.     

采用等分散抽样法的电力系统概率仿真

针对电力系统概率仿真中常规蒙特卡洛抽样容量太大、效率低的问题,采用等分散抽样技术减少抽样次数和计算时间,从而提高模拟精度,加快蒙特卡洛的收敛速度。由IEEE标准系统的计算结果表明该方法是可行有效的。     

Power system steady state security analysis using vector processingcomputers

The main difficulties encountered in applying a vector processor to the solution of a power flow is the sparsity of the power flow matrices which results in very short vectors when the nonzero terms are gathered into a packed vector format. The authors describe a steady-state security analysis code which overcomes the problem of very short vectors. The length of the list of outages is the major problem in security analysis since it usually includes at least each individual piece of equipment taken one at a time, and a large number of cases with multiple outages. The use of contingency selection and screening has opened the way to reducing this burden, but a very large number of individual cases must be run to satisfy the need to be sure that no cases have gone unchecked. The efforts described have extended the computational speed with which this can be accomplished, and the implementation of very high performance security analysis codes has been made possible     

动态安全评估系统在北京电网调度系统中的应用

确保大电网在各种运行方式、负荷水平下均能安全稳定运行,并有足够的安全稳定裕度是电网调度机构的工作重点之一.通过动态安全评估系统(Dynamic Security Assessment System,DSAS)对电网实时数据的分析、计算,从电网潮流和电压安全的角度,给出了电网的安全运行水平及稳定域度,对调度运行给予辅助参考,为电网稳定提供辅助决策.北京电力调度通信中心将DSAS运用到计划校核、方式分析及事故处理中,有效地提高了工作效率及电网安全稳定水平.     

ANN based integrated security assessment of power system using parallel computing

This paper presents the application of cascade neural network (CANN) based approach for integrated security (voltage and line flow security) assessment. The developed cascade neural network is a combination of one screening module and two ranking modules, which are Levenberg–Marquardt Algorithm based neural networks (LMANNs). All the single line outage contingency cases are applied to the screening module, which is 3-layered feed-forward ANN having two outputs. The screening module is trained to classify them either in critical contingency class or in non-critical contingency class from the viewpoint of voltage/line loading. The screened critical contingencies are passed to the corresponding ranking modules, which are developed simultaneously by using parallel computing. Parallel computing deals with the development of programs where multiple concurrent processes cooperate in the fulfillment of a common task. For contingency screening and ranking, two performance indices: one based on voltage security of power system (VPI) and other based on line flow (MWPI) are used. Effectiveness of the proposed cascade neural network based approach has been demonstrated by applying it for contingency selection and ranking at different loading conditions for IEEE 30-bus and a practical 75-bus Indian system. The results obtained clearly indicate the superiority of the proposed approach in terms of speedup in training time of neural networks as compared to the case when the two ranking neural networks were developed sequentially to estimate VPI and MWPI.     

基于选择性贝叶斯分类器的变压器故障诊断

电力变压器故障诊断中的测试数据信息不完备、有偏差,而贝叶斯网络处理不确定性问题能力强.提出了一种基于选择性贝叶斯分类器的、溶解气体分析结合其他电气试验结果的变压器故障诊断方法,并建立了变压器选择性贝叶斯故障诊断模型.详细阐述并验证了该方法解决信息不完备问题的优越性.该模型还可以通过不断积累完善训练样本,自动修正网络结构...     

含风电场的电力系统随机生产模拟

针对风电机组出力的间歇性和波动性,提出了用于电力系统随机模拟的时序多状态风电场模型。该模型把研究周期分成不同的阶段,在每个阶段内,风电场的输出功率状态以及各状态的概率都与时间间隔有关系,时间间隔越小,记录的风速数据越多,这样更详细地反映风电场输出功率的波动性;而且在不同的阶段,风速的情况有所差异,因此各阶段风电场输出功率的状态数、各状态值及各状态值对应的概率都可能不相同。该模型反映了风电场输出功率的时序性。基于等效电量函数法的含风电场的电力系统随机模拟计算表明,该模型比较客观地反映风电场输出功率的实际情况     

组合电力系统概率安全性与概率充裕性综合评估

概率安全性与概率充裕性评估是组合电力系统可靠性研究的两个方面。由于建模的复杂性和巨大的计算量,二者在过去一直是分开研究的,这样造成所得的两类指标缺乏可比性和统一性。本文将二者协调统一在同一框架内,提出了一整套综合评估风险指标,建立了完整的基于序贯蒙特卡罗仿真的发输电合成系统可靠性综合评估新体系。算法结合自组织映射网络和合并相同系统状态加速计算技术,并在安全性评估中考虑了不对称故障等多种故障模式对可靠性指标的影响。对IEEE标准可靠性测试系统进行计算分析,结果表明所提算法可行有效。     

Transient security assessment using multivariate polynomial approximation

This paper presents an efficient method for transient security assessment. The proposed method exploits the computational efficiency of linear estimation methods to determine an accurate nonlinear mapping function. The novelty of the proposed method is that a nonlinear transformation is applied to the original variables, voltage magnitudes and phase angles, so that the mapping function is approximately linear in terms of the transformed variables. The linear function obtained using the transformed variables is indeed nonlinear in terms of original variables. The attractiveness of this method is that the estimated function is not a linearized approximation although a linear estimation method is used. The potential of the proposed method is demonstrated using the New England 39-bus system and a larger power system with 470 buses.     

ANN for transmission system static security assessment

This paper presents computationally efficient artificial neural network technique for assessing the security of the power system against line outages. Performance index (PI), which accounts for various line limit violations per contingency, is defined. The basic purpose of ANN is to assess the severity of line outages in terms of PI, based on training examples from off-line analysis. The selection of input signals for ANN is influenced by the operating state of the system and the contingency in question which determines the extent of line power limit violations. In an attempt to attain perfection in PI prediction, suitable architecture and topology for the network is investigated. To expedite learning process, saturating linear coupled neuron model (sl-CONE) is also tried out. The effectiveness of proposed technique is demonstrated on 5-bus (7-line) and IEEE 14-bus (21-line) test systems. Computation efficiency of the method makes it potential candidate for inclusion in on-line comprehensive security analysis package.     

基于动态安全域的电力系统安全概率评估模型

针对电力系统安全性问题特性,提出一种基于动态安全域的安全概率评估模型。模型核心是不安全概率指标,并通过该指标来表征各电力元件以及整个电力系统的安全性状况。概率方法的引入,使得模型不仅计及系统中各种主要的不确定性因素,而且对系统的安全状况进行了定量的描述。动态安全域理论大大降低了安全概率评估的计算量,使得该模型具有实用性。评估结果可以用于指导运行人员在安全控制中制定正确的决策。用新英格兰10机39节点系统(New England 10-generator 39-bussystem)对该模型做了验证。     

考虑元件综合重要度的电网安全性风险评估方法

从系统学角度出发,综合考虑系统中元件的结构属性、状态属性及社会属性,提出一种考虑元件综合重要度的电网安全性风险评估方法。综合元件结构重要度因子模型与元件状态灵敏度因子模型构建元件综合重要度模型,该模型克服了传统风险评估方法中对于不同元件的重要度无法表征的不足;综合考虑电网结构与状态,建立表征电网潮流分布均衡性与负荷损失的严重度模型,完善安全性风险评估指标体系。仿真结果表明,相较于传统方法,该方法对电网N-1故障风险排序更为合理,对N-k故障序列分级风险辨识度更高。     

Classification and assessment of power system static security using decision tree and random forest classifiers

The power system static security classification and assessment is essential in order to identify the post‐contingency problems and take corrective measures and to protect the system from blackout. In this paper, application of two data mining classifiers have been proposed for the security classification and assessment of a multiclass security problem. To design the security problem, contingency analysis is carried out under N‐1 line outage, and static severity index (SSI) is computed, which is a function of the line overload and the voltage deviation using Newton–Raphson load flow method, considering the variable load and generating conditions. Corresponding to the computed values of SSI, the voltage, phase angle, Mega Volt Ampere line flow and so on, a 1 × 7 pattern vector is generated. The generated pattern vectors are used to design a multiclass security problem. The designed security pattern vectors are given as inputs to the decision tree (DT) and random forest (RF) model in order to classify the security status of the power system. The proposed classifiers are investigated on an IEEE 30‐bus test system. The classification accuracy of the DT and the RF are compared with state‐of‐the‐art classifier models, namely, multilayer perceptron (MLP), radial basis function (RBF), and support vector machine (SVM).The simulation results clearly indicate that the proposed DT and RF classifiers are more efficient, reliable, and out performs MLP, RBF, and SVM classifiers for the assessment of the security status of the power system. Hence, DT and RF classifiers are found to be suitable for online implementation. Copyright © 2015 John Wiley & Sons, Ltd.     

安全性评价管理系统及其辅助决策系统的应用研究

;结合实际的供电企业安全性评价管理工作的需要,利用.net Web服务技术、数据库技术,设计出一套供电企业安全性评价管理(SAM)系统,构建了专家辅助决策数据仓库,并建立星型模式的数据立方体,运用OLAP和数据挖掘技术能够为安评工作管理人员提供有效的决策信息.介绍了SAM系统的开发基础、功能模块,及其在重庆璧山供电局的实际应用情况.     

安全性评价管理系统及其辅助决策系统的应用研究

结合实际的供电企业安全性评价管理工作的需要,利用.net Web服务技术、数据库技术,设计出一套供电企业安全性评价管理（SAM）系统,构建了专家辅助决策数据仓库,并建立星型模式的数据立方体,运用OLAP和数据挖掘技术能够为安评工作管理人员提供有效的决策信息。介绍了SAM系统的开发基础、功能模块,及其在重庆璧山供电局的实际应用情况。     

AC-HVDC solution and security assessment using a diakoptical method

The paper describes a direct solution of an HVDC link within an AC loadflow solution and a direct security analysis using diakoptical techniques. The direct solution is faster than the more conventional boundary iterative method and therefore is more economical when used for regular calculation such as security assessment and transient stability analysis. It can easily be incorporated in existing AC loadflow algorithms.     

An online power system static security assessment module using multi-layer perceptron and radial basis function network

Efficient contingency screening and ranking method has gained importance in modern power systems for its secure operation. This paper proposes two artificial neural networks namely multi-layer feed forward neural network (MFNN) and radial basis function network (RBFN) to realize the online power system static security assessment (PSSSA) module. To assess the severity of the system, two indices have been used, namely active power performance index and voltage performance index, which are computed using Newton–Raphson load flow (NRLF) analysis for variable loading conditions under N − 1 line outage contingencies. The proposed MFNN and RBFN models based PSSSA module, are fed with power system operating states, load conditions and N − 1 line outage contingencies as input features to train the neural network models, to predict the performance indices for unseen network conditions and rank them in descending order based on performance indices for security assessment. The proposed approaches are tested on standard IEEE 30-bus test system, where the simulation results prove its performance and robustness for power system static security assessment. The comparison of severity obtained by the neural network models and the NRLF analysis in terms of time and accuracy, signifies that the proposed model is quick, accurate and robust for power system static security evaluation for unseen network conditions. Thus, the proposed PSSSA module implemented using MFNN and RBFN models are found to be feasible for online implementation.     

Power system operating state forecasting for security analysis applications

A methodology for power system operating state forecasting with applications in the study mode security analysis is described. Additional application areas are advance or dynamic dispatch and measurement replacement. The operating state is represented by the active and reactive nodal injections which are forecast using a four-step procedure: bus active and reactive load forecast, simulation of the economic dispatch and on-line load flow. The multivariate Box and Jenkins approach and the adaptive Kalman filter are used for the active and reactive bus load forecast, respectively. The results of numerical experiments with the proposed methodology indicate a performance with potential for practical applications.