在线识别同调机群的优化支持向量机算法

对大型电力系统采用动态等值可显著降低计算量,并能突出主要特征。同调等值法作为动态等值的一种,其核心是同调机群的自动识别。为此,提出将最小二乘支持向量机(least square support vector machine,LS-SVM)应用于同调机群的在线识别,通过离线学习建立表征发电机同调性的特征参数与发电机之间同调性指标的非线性映射关系,并对比2组不同输入特征找出更能表现发电机同调性的特征参数。为进一步提高最小二乘支持向量机的学习、预测能力,提出采用多层动态自适应优化算法对其参数进行优化。最后通过对中国电力科学研究院(EPRI)36节点系统的仿真计算,结果表明该方法具有准确、快速的优点,并且适用于系统不同运行方式,能有效解决同调机群的识别问题。     

A new approach for online coherency identification in power systems based on correlation characteristics of generators rotor oscillations

In this paper, a new approach for identifying coherent groups of generators in power systems based on the correlation coefficients between rotor angle/speed oscillations of generators is presented. The method uses a newly proposed clustering index based on the correlation coefficients of generators oscillations which is able to classify any number of generators into coherent groups. The proposed approach uses real time data of generators oscillations via VAMS/PMUs, so it is able to easily take into account the effect of system detailed modeling, generators and system controllers and type of events. The proposed correlation index evaluated from the real time behavior of generators in time-domain following disturbances are used to evaluate the degree of coherency between any pair of generators. The generators’ rotor angles and speeds can be obtained from synchronized measurements of system quantities using PMUs. Hence, the proposed method could be integrated into a wide-area measurement system enabling fast identification of coherent groups of generators. It is shown that by using COI of the aggregated in-phase coherent groups, the frequency of the inter-area mode can be evaluated. The proposed method is tested on the IEEE 39-bus with 10 generators and realistic power system of Iran with 405 generators.     

Coherency identification and equivalents for transient stability studies

This paper presents techniques for identifying coherent generators without conducting a transient stability study and for developing stability equivalents of a group of coherent generators. Identification is based on the property of equal acceleration for coherent machines and it requires only examination of the elements of the Jacobian matrix of the linearized power system model. After the coherent groups have been identified, the stability equivalents are developed on the basis of the power invariance condition at the tie buses and at the internal buses of the coherent generators. These stability equivalents are used in a transient stability study. The techniques are illustrated with a 12-machine system.These techniques greatly improve the efficiency of calculation of coherency based equivalents. They have potential applications in planning studies of large power systems.     

基于Epsilon分解的发电机同调识别新方法

提出了一种基于同调矩阵Epsilon分解的发电机同调识别新方法。为了扩大基于Epsilon分解的发电机同调识别算法的应用范围,并减小其计算量,文中对该方法进行了详细的分析与推导,得出反映发电机同调性的指标,并由此形成同调矩阵,对该矩阵进行一次Epsilon分解即可识别同调机群。该方法原理简单、计算量小、应用范围广。对新英格兰39节点系统的仿真计算验证了该方法的有效性。     

Coherency and model reduction: state space point of view

Analytical connections are determined that relate coherent stability behavior of generators to redundancy in the controllability and observability gramians of the linear model of the power system. This redundancy is removed from the gramians of the reduced-order model generated by aggregating the coherent machines. Model reduction in large power systems is shown to be better if the order estimation information in the controllability and observability gramians is weighted properly by balancing the system. Perfect coherency of generators is related to the parameters of the linear model of the power system. Waveforms for generating the RMS coherency measures are compared. An algorithm for order reduction based on balancing is outlined and justified by testing on a 240-generator, 2500-bus system     

Application of coherency and reduced order models to transient stability studies

Study of the transient stability of a large and interconnected power system requires a great deal of computational time. To reduce that time, power system equivalents are employed. A simplified transient stability method, based on the values of the ‘stability measures’, is presented in which generators with small swing are replaced by a new equivalent model, and appropriate simplified models are used for the rest of the generators. Results of this study are used to identify coherent groups of generators. Replacing coherent sets by their equivalent generators will further reduce the size of the system. The validity and accuracy of the method is demonstrated by stimulated tests on a sample power system.     

考虑同调约束的电力系统主动解列断面的搜索方法

电力系统主动解列是避免发生大停电事故和系统崩溃的有效保护措施,需要及时、准确地确定解列方案。文中提出一种考虑发电机同调约束的最佳解列断面的搜索方法。首先基于故障后广域测量系统(WAMS)提供的实时动态响应信息,利用改进拉普拉斯特征映射算法在线识别同调机群,利用发电机同调虚拟节点聚合同调机群,减小解列断面的搜索空间,然后利用加权拉普拉斯分群策略得到初始的解列断面。最后考虑断面有功功率的方向,利用启发式变邻域搜索算法确定最佳的解列断面,使得解列后各孤岛的净不平衡功率最小。文中方法能充分考虑不同故障模式所带来的同调行为的差异性,使得主动解列控制具有选择性和鲁棒性。通过新英格兰39节点系统验证了所述方法的有效性和适应性。     

Aggregation of exciter models for constructing power system dynamicequivalents

Constructing a dynamic equivalent for a power system involves several steps: the partition of the system into coherent areas, the coherent area aggregation, and the aggregation of the coherent generators and their control devices. In this paper we investigate the aggregation of exciter models. A trajectory sensitivity method is used to tune the aggregate exciter parameters of the reduced model. The optimal results are used to evaluate the aggregation from the DYNRED program and a weighted MVA method. A three-machine system with one coherent area satisfying the theoretical coherency conditions is used to investigate the impact of the variations of the individual generator, network, and exciter parameters on the aggregate exciter model parameters. The results are then applied to the exciter aggregation of a larger 48-machine system     

利用S变换局部奇异值的同调机组识别方法

文中提出一种利用S变换局部奇异值的同调机组识别方法.将广域测量系统(Wide Area MeasurementSystem,WAMS)采集到的系统发电机功角信息进行S变换,得到每台发电机的时频信息模值矩阵,将矩阵分割成块,计算各个子块的最大奇异值,利用时频信息模值矩阵中各个子块最大奇异值构造机组特征矩阵,采用分布聚类法对特征矩阵进行聚类分群.IEEE-39节点系统算例表明,该方法能够有效提取功角信息特征,具有很强的抗噪性,能够在不同故障类型下准确识别同调机组.     

基于相似度同调的双馈风电场聚合研究

文章提出了一种基于相似度同调的双馈风电场聚合方法。首先利用Prony算法提取各风电场中双馈风机故障下功率响应曲线的动态特征,基于相似理论确定双馈风机间振荡模式中相似部分,应用相似振荡模式中的频率、阻尼、幅值等信息定义了双馈风机同调判别的相似度量化指标并确定同调机群。然后,根据双馈风机的外特性,提出功率聚合算法,即以双馈风机输出有功功率为权重求取等值机的暂态内电势并确定等值母线及等值机参数。该方法从同调机群识别、同调母线聚合、同调机群参数聚合等方面,系统地解决了不同双馈风电场的同调聚合问题。参数聚合方法不仅解决了聚类分群的复杂性和局限性,而且有效保留原系统中电气与结构特征。最后以实际云南电网中某地区风电场为例进行仿真验证。     

A method for bulk power system reliability evaluation based on local coherency

The problem of bulk power system reliability evaluation is considered and a new solution approach is proposed. Bulk power system models are generally not coherent in the sense of reliability theory. Conditions are derived on network topology to guarantee coherency. Only parallel structure systems satisfy these conditions. Noncoherency of a power system may be the effect of only a subset of its transmission lines. Thus the concept of local coherency is introduced. A decomposition approach to bulk power system reliability evaluation based on the concept of local coherency is developed. Sufficient conditions for local coherency are derived.     

基于主成分分析法的电力系统同调机群识别

提出了一种基于主成分分析(principal component analysis,PCA)的电力系统同调机群分群识别方法。利用PCA可以保留源数据中的主要信息,采用发电机角速度作为源数据,可以获取协方差矩阵及协方差矩阵的特征根和特征相量,由此确定发电机角速度的主成分,然后通过比较各发电机对主成分的载荷系数实现对发电机的同调分群。中国电力科学研究院36节点纯交流系统算例表明,该方法计算简单,易于实现,避免了模型参数对分群的影响。     

Coherency recognition using epsilon decomposition

This paper proposes a new algorithm for the identification of coherent generators, which is based on epsilon decompositions of the Jacobian. By using the Jacobian, the algorithm overcomes some major drawbacks of other methods for coherency recognition; in addition, it can be directly integrated into programs for transient stability analysis. Test results on a 48 machine system are presented to evaluate the method     

基于小波模糊熵GG聚类的同调机群识别

针对同调机群识别面临的特征提取片面及计算过程复杂相互制约的问题,提出一种基于小波模糊熵-GG(Gath-Geva)聚类的同调机组识别新方法。根据广域测量系统获取的故障后系统机组功角信息,利用多尺度小波分解将功角摇摆曲线分解为整体趋势和细节信息,计算各个尺度小波系数的模糊熵,并作为发电机的特征向量,再通过GG模糊聚类算法对其进行同调聚类。对IEEE 39节点系统和某实际电网进行仿真,算例分析结果表明所提方法能够根据故障后系统机组功角信息的变化更新功角数据库,实现快速准确的同调机群识别。     

基于两步聚类法的受扰机群同调性分析

提出了一种基于两步聚类法的电力系统受扰机群同调性分析方法。首先通过BIRCH算法处理广域量测系统提供的受扰机群电角速度数据,构建受扰机群的聚类特征（clustering feature,CF）树来实现预聚类;然后采用层次聚类法处理预聚类结果并自动确定聚类数量来实现正式聚类;最后根据聚类结果进行受扰机群的同调性分析。通过对新英格兰10机39节点系统的算例仿真,对比不同的同调机组识别方法,验证了该方法的有效性以及能够实现自动确定同调机组数的特点。     

Robust principal component analysis‐based coherency identification of generators with missing PMU measurements

This study provides a new algorithm for grouping coherent generators in power systems using robust principal component analysis. In coherency identification of generators based on measurements by a phasor measurement unit (PMU), PMU measurements can become unavailable because of unexpected failure of data acquisition or communication links. However, the proposed algorithm is robust to missing PMU measurements and is demonstrated on an IEEE 16‐generator 68‐bus system. This effective identification of coherent clusters with missing PMU measurements is validated and compared with results obtained using the principal component analysis and independent component analysis methods. © 2015 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

Inertial and slow coherency aggregation algorithms for power systemdynamic model reduction

This paper presents new aggregation algorithms for obtaining reduced order power networks when coherent generators are aggregated. The generation terminal bus aggregation algorithm in the EPRI DYNRED software tends to stiffen the reduced order network during the aggregation process, thus increasing the frequencies of inter-area modes. The inertial and slow coherency aggregations will decrease the stiffening effect and produce, for the same coherent machine groups, aggregate networks with improved inter-area mode approximations. This paper contains new procedures to construct these aggregate networks and demonstrates the benefits of these new aggregate networks on a 48-machine power system using eigenvalues and nonlinear simulations     

Controlled islanding using transmission switching and load shedding for enhancing power grid resilience

A controlled splitting strategy is proposed as the last resort to determine the splitting points of an interconnected power system before occurring a critical transition. The proposed strategy is expressed as a mixed-integer formulation with considering the slow coherency of synchronous generators. In the proposed integer programming formulation, each coherent group of generators is located in an individual island. This grouping constraint may assure the synchronism of generators after islanding. Each island contains a coherent group of generators and its boundary is determined with the aim of achieving minimum load shedding. Two artificial DC load flow algorithms are proposed to model grouping and connectivity constraints. In addition to operational limits, a switching constraint and a frequency stability constraint are proposed to limit the number of line switchings and assure the stability of resulted islands, respectively. The proposed mixed integer model is solved using Benders Decomposition (BD) technique. Using BD technique, the CPU time of computation is reduced significantly. The proposed splitting strategy is simulated over the IEEE 30-Bus and IEEE 118-Bus test grids. Transient stability simulations are done to validate the accuracy of the proposed method.     

DETERMINATION,BY KOHONEN NETWORK,OF THE GENERATOR COHERENCY IN DYNAMIC STUDIES

ABSTRACT

For on-line security assessment, mere is a need for a tool which, during a disturbance, will quickly recognize impacted system components. A Kohonen artificial neural network is described which can be used for coherency identification among generators. Additional information provided by this method makes it better than the conventional approach used to identify coherency for dynamic equivalencing. The method is tested on two representative power systems.     

基于WAMS和曲线相似的同调机群识别方法*

提出了一种基于曲线相似的电力系统同调机群辨识新方法。首先获取WAMS实时监测得到的功角轨迹曲线作为基础数据,根据离散曲线相似性的定义,将各发电机的轨迹曲线分段,借助遗传算法确定分段轨迹之间的最优相似距离,保存了轨迹段的局部特性。然后整合各段相似距离得到各发电机之间的整体相似度并以此作为聚类指标,再利用层次聚类法实现多机系统同调机组分群。该方法简单可行,不受系统模型参数和故障类型的限制。最后对IEEE39节点系统进行分析计算,该仿真实验佐证了所提算法的有效性。