基于统计学习的模糊暂态稳定事故筛选与排序

暂态稳定事故筛选与排序的目的是针对一组预想事故集合挑选出严重事故或滤除掉无害事故,以减少待分析的预想事故数目,满足在线动态安全分析的需要.文中提出一种基于统计学习的模糊暂态稳定事故筛选与排序方法,该方法采用了反映事故严重程度的10个性能指标,并通过样本学习,在每个指标集合上具体定义了系统稳定性的模糊隶属度函数;然后综合运用这10个性能指标的稳定性模糊隶属度得到的平均稳定性模糊隶属度,对预想事故集合按严重性进行了排序;接着根据排序结果,结合所设定的稳定阈值,将无害事故过滤掉.最后,用新英格兰10机39节点网络验证了该方法的有效性.     

Fast real power contingency ranking using counter propagation network: feature selection by neuro-fuzzy model

In deregulated operating regime power system security is an issue that needs due consideration from researchers in view of unbundling of generation and transmission. Real power contingency ranking is an integral part of security assessment. The objective of contingency screening and ranking is to quickly and accurately shortlist critical contingencies from a large list of credible contingencies and rank them according to their severity for further rigorous analysis. In the present work, modified counter propagation network (CPN) with neuro-fuzzy (NF) feature selector is used for real power contingency ranking of the transmission system. The CPN is trained to estimate the severity of a series of contingencies for given pre-contingencies line-flows. But for larger size system it becomes rather difficult to cope with the increased size of input pattern and network as well. And it adversely affected the performance of the network and computational overhead. The proposed NF feature selector prunes the size of input pattern by exploring the individual power of features to characterize/discriminate different clusters. The reduced set of discriminating inputs not only ensures saving in training time but also improves estimation accuracy and execution time and these are the deciding parameters in evaluating the performance of particular contingency ranking technique. The effectiveness of proposed approach is demonstrated on IEEE 30-bus test system and practical 75-bus Indian system.     

A comparison of MW ranking methods

A ranking method is a means of reducing the prohibitively large computational burden involved in the conventional exhaustive contingency analysis which is carried out for assessing the steady-state security of a power system. This paper summarizes the theory and compares the accuracies and efficiencies of various MW ranking methods which utilize the criteria of line flow problems for achieving a ranked list of all the proposed contingencies. The original forms of all the methods as well as the modified forms of a number of them are tested on a 275 kV power system assuming primarily single line contingencies. The effect of the higher values of the exponent(s) of the performance index on the accuracy and efficiency of several of the methods is also illustrated.     

Probabilistic steady-state security analysis including (sub)station generated contingencies

A method is suggested for modeling (sub)station generated common-cause outages for properly ranking contingencies affecting power system steady-state security. The method and the security indices proposed are based upon the Markov state transition model of the power system which makes it possible to include probability weighted impact factors for appropriate contingency ranking and selection. An illustrative real-life example is also included, demonstrating the relevance of (sub)station associated contingencies for a proper security analysis.     

A unified approach to transient stability contingency filtering,ranking and assessment

This paper proposes a unified approach to contingency filtering, ranking and assessment in power system transient stability studies. The approach consists of two-block techniques in which the first block selects from a list of contingencies the a priori “interesting” ones, that the second block ranks and assesses. This filtering, ranking and assessment (FILTRA) approach relies on SIME (for Single Machine Equivalent). SIME is a hybrid direct-time-domain stability method which combines accuracy and flexibility skills of time-domain methods with sound additional possibilities of direct methods. The FILTRA approach is fully general: it may adapt itself to the specifics of any power system (modeling, protection characteristics, etc.), any contingency scenario and mode of (in)stability (first- or multi-swing, local or inter-area mode) and any application context (planning, operation planning and real-time operation). The approach is illustrated on two EHV power systems, having different structures, control and protective devices. The adequacy of its filtering and ranking capabilities is illustrated in terms of performance criteria such as reliability (ability to consistently capture all dangerous contingencies), effectiveness (ability to avoid false alarms) and computational efficiency (ability to comply with real-time requirements). Further, the assessment task is shown to provide the operator with sound information and effective means of control     

N-2组合故障集的暂态功角稳定在线快速评估

大电网中N-2组合故障数目巨大,若对每个组合故障分别进行暂态功角稳定评估,难以满足在线评估的时间要求。基于N-2组合故障中第一个元件故障的暂态功角稳定在线评估结果(裕度和主导群发电机的参与因子),结合第一个元件故障的暂态功角稳定主导群中发电机的有功功率对第二个开断元件有功功率的灵敏度,计算出N-2组合故障的暂态功角稳定严重程度排序指标。通过优先对排序在前位的组合故障进行基于详细模型时域仿真的暂态功角稳定量化评估,直至排序号连续的多个组合故障的暂态功角稳定裕度都大于0,则直接判定排序在其后的所有组合故障均是暂态功角稳定的,从而满足N-2组合故障集暂态功角稳定在线评估对计算速度的要求。实际电网算例验证了该方法的有效性。     

电力系统在线偶然事故排序的一种新方法

采用间接法对电力系统中的偶然事故进行排序,同时将偶然事故排序中通常采用的系统行为指标同系统的可靠性与经济性指标相结合,形成新的偶然事故对系统影响的期望值,根据新的期望值对偶然事故进行排序,使排序更具有合理性.并对一给定的系统进行试算及分析,证明这种方法是合理的。     

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.     

一种新的故障筛选和排序的方法

为了避免在故障筛选与排序中忽略严重故障而导致大停电的发生,提出了一种比较精确的新的故障筛选和排序方法。以动态安全域的求解为基础,在故障筛选时运用解析法求动态安全域,进而求解失稳度。以故障的失稳度作为故障筛选的指标,快速选出严重的故障,形成故障筛选集。在故障排序时对于筛选集中的故障线路运用BP神经网络求解动态安全域,从而求解概率不安全指标,以故障的概率不安全指标作为故障排序的指标,得到精确的故障排序。通过IEEE10机39节点系统算例验证了该方法能够快速、全面、准确地实现故障的筛选与排序,同时通过IEEE4机11节点系统验证了BP神经网络求解动态安全域的可行性,误差为0.0608,满足要求。     

静态电压稳定分析的故障筛选和排序方法

提出一种新的故障筛选和排序方法。首先,定义一种负荷裕度阈值,用连续潮流法计算此裕度下的潮流工况,在此工况下,利用最优乘子法依次求解所有的开断潮流,将有解的大部分开断作为安全故障筛选掉,少量无解的则为不安全故障。然后,在基态下利用最优乘子法依次求解不安全故障的开断潮流。有解则为危险故障,并用连续潮流法求解负荷裕度,按裕度给出排序;无解则为失稳故障,结合最小二乘潮流解失配量中隐含的校正控制灵敏度信息,使用序列线性规划法给出使潮流恢复有解的最小切负荷代价,据此代价来排序失稳故障。新英格兰10机39节点系统的仿真结果验证了所提出的方法能快速、可靠、全面地实现电压稳定的故障筛选和排序。     

Multi-criteria contingency ranking method for voltage stability

The objective of this paper is to propose a fast, accurate, and robust method for ranking contingencies according to their severities as far as voltage stability is concerned. In general terms, the proposed procedure consists of obtaining the operating state and computing performance indices for each contingency of a predefined list. The contingencies are then ranked according to their performance indices. The proposed method can be used as a real time operation tool, since its computational effort is very small. The proposed method is able to pick most of the severe contingencies, resulting in very good capture ratios. Simulation results are shown for small test and large realistic power systems.     

An on-line contingency filtering scheme for dynamic securityassessment

This paper describes the philosophy and development of a contingency screening system for the selection and ranking of dynamic security assessment. The most severe cases are identified and ranked high on the contingency list for more exact analysis. The nonsevere cases are filtered out of the list. In the proposed system, a cascade of more restrictive filters is developed based on the sparse transient energy function method     

全局输配电网电压稳定故障筛选与排序的分布式计算方法

随着传统配电网向含大量分布式电源的主动配电网转变,输电网、配电网电压稳定评估已不再适宜各自独立计算。基于输电网、配电网分属于不同控制中心调控,提出一种全局输配电网电压稳定故障筛选与排序的分布式计算方法。该方法分为两阶段：阶段1中采用输配电网主从分裂分布式潮流工具在系统要求最小负荷裕度值的工况下进行各预想故障的潮流计算,采用最优乘子法筛选出潮流不可解的严重故障;阶段2中采用基于输配电网分布式连续潮流的步长加速二次曲线拟合方法计算严重故障的负荷裕度并进行排序。由1个IEEE 118节点输电网和2个IEEE 33节点配电网组成的全局输配系统的仿真算例表明所提方法能够快速可靠地实现全局输配电网电压稳定故障筛选与排序。     

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.     

基于图卷积网络的快速暂态安全评估方法

快速、可靠的电力系统动态安全评估能够显著提高电力系统运行方式优化调整的效率。针对电力系统暂态稳定预想事故扫描需要完成大量仿真、过于耗时的问题,提出了基于图卷积网络的快速动态安全分析方法。该方法基于电力系统的潮流特征和拓扑特征构建电力系统潮流特征图。利用图卷积方法对电力系统运行状态进行特征挖掘与特征学习,将动态安全评估问题建模为图上节点分类问题。所得模型在读取电网拓扑与潮流运行状态后,仅须完成一次前向计算即可同时给出预想事故集中多个预想事故的稳定性预测结果,无须依赖仿真波形或量测数据,实现快速暂态稳定预想事故扫描。IEEE39节点系统算例测试表明,算法设计正确、高效、准确率高,能够显著提高暂态稳定预想事故扫描的效率,实现快速动态安全评估。     

A methodology for probabilistic simultaneous transfer capabilityanalysis

This paper presents a methodology for simultaneous transfer capability analysis based on a probabilistic approach. All areas in a large scale power system are divided into three groups: (a) study area, (b) transfer participating areas, and (c) external areas which have no direct transactions or they have fixed transactions with the study area. A performance index based contingency selection procedure is applied within the study and transfer participating areas to rank those contingencies which will affect simultaneous transfer capability. The contingency ranking order is utilized by a variation of the Wind Chime diagram to selected contingencies which are then evaluated by an optimal power flow algorithm. Subsequently, the probability distribution of simultaneous transfer capability is computed based on the electric load, circuit and unit outage Markov models. The 24×3 bus IEEE RTS is utilized to evaluate the proposed method. The performance of the proposed method is also demonstrated on an actual large scale system (2182 bus, 8 area system)     

基于最优乘子潮流估计的故障筛选与排序

提出了一种用于电压稳定安全评估的快速故障筛选和排序方法。该方法从故障前的临界点出发,通过最优乘子潮流法计算得到的故障后边界点来估计故障后临界点,当初始点接近临界点,通过一次迭代即可满足所需的计算精度,从而快速地识别出非严重故障。对于严重故障和某些特殊情况的故障进行了分析并提出了解决办法,保证了对严重故障的估计精度。通过某一实际703节点系统算例验证了该方法的有效性。     

一种用于电力系统静态稳定性分析的故障筛选与排序方法

提出了一种用于电力系统静态稳定性分析的故障筛选和排序方法,该方法分为两个阶段:在第1阶段采用基于灵敏度的负荷裕度预估方法和通过两次潮流迭代预估系统电压降的方法从全部故障中筛选出严重故障集;在第2阶段采用改进的二次曲线预估方法对严重故障进行负荷裕度的预估和排序,同时应用故障连续潮流对失稳故障进行识别和排序.利用该方法开发的电力系统静态稳定分析与控制软件在国外某实际电力系统中的成功运行表明了该方法的有效性.     

Power system probabilistic security assessment using Bayes classifier

This paper presents a method for power system security assessment based on the Bayes classifier. This method can be applied to calculate probabilistic security indices as well as on-line security assessment. In general, the determination of security breach is a cumbersome and time-consuming process due to dynamic and steady-state effects. These effects can be incorporated by considering transient stability, satisfaction of system load without violation of constraints, and voltage stability studies. The variation of the system load, as well as contingencies, may cause system transition to a different operating state. It is impractical if not impossible to evaluate all these situations, such as contingencies resulting from load variation. The straightforward Monte Carlo simulation, one of the possible methods in power system reliability analysis, requires the evaluation of a system operating state for each sampled state and is computation-intensive. In the proposed method, first the joint probability density of feature vectors has been obtained by using some training data. Once this joint distribution is obtained, the Bayes classifier provides the assessment of system security without complicated contingency analyses and can reduce the computational burden. Security status of a given feature vector can be determined by a posteriori probability rule called Bayes rule, which can be implemented in on-line security assessment or power system reliability studies.     

Optimal location and parameter setting of UPFC for enhancing power system security based on Differential Evolution algorithm

This paper presents a new approach based on Differential Evolution (DE) technique to find out the optimal placement and parameter setting of Unified Power Flow Controller (UPFC) for enhancing power system security under single line contingencies. Firstly, we perform a contingency analysis and ranking process to determine the most severe line outage contingencies considering line overloads and bus voltage limit violations as a Performance Index. Secondly, we apply DE technique to find out the optimal location and parameter setting of UPFC under the determined contingency scenarios. To verify our proposed approach, we perform simulations on an IEEE 14-bus and an IEEE 30-bus power systems. The results we have obtained indicate that installing UPFC in the location optimized by DE can significantly enhance the security of power system by eliminating or minimizing the overloaded lines and the bus voltage limit violations.