Topology identification for a class of complex dynamical networks using output variables

A problem of topology identification for complex dynamical networks is investigated in this paper. An adaptive observer is proposed to identify the topology of a complex dynamical networks based on the Lyapunov stability theory. Here the output of the network and the states of the observer are used to construct the updating law of the topology such that the communication resources from the network to its observer are saved. Some convergent criteria of the adaptive observer are derived in the form of linear inequality matrices. Several numerical examples are shown to demonstrate the effectiveness of the proposed observer.

Optimization-based topology identification of complex networks

In many cases, the topological structures of a complex network are unknown or uncertain, and it is of significance to identify the exact topological structure. An optimization-based method of identifying the topological structure of a complex network is proposed in this paper. Identification of the exact network topological structure is converted into a minimal optimization problem by using the estimated network. Then, an improved quantum-behaved particle swarm optimization algorithm is used to solve the optimization problem. Compared with the previous adaptive synchronization- based method, the proposed method is simple and effective and is particularly valid to identify the topological structure of synchronization complex networks. In some cases where the states of a complex network are only partially observable, the exact topological structure of a network can also be identified by using the proposed method. Finally, numerical simulations are provided to show the effectiveness of the proposed method.

Identification Method for Users-Transformer Relationship in Station Area Based on Local Selective Combination in Parallel Outlier Ensembles Algorithm

In the power distribution system,the missing or incorrect file of users-transformer relationship(UTR)in lowvoltage station area(LVSA)will affect the leanmanagement of the LVSA,and the operation andmaintenance of the distribution network.To effectively improve the lean management of LVSA,the paper proposes an identification method for the UTR based on Local Selective Combination in ParallelOutlier Ensembles algorithm(LSCP).Firstly,the voltage data is reconstructed based on the information entropy to highlight the differences in between.Then,the LSCP algorithmcombines four base outlier detection algorithms,namely Isolation Forest(I-Forest),One-Class Support VectorMachine(OC-SVM),Copula-Based Outlier Detection(COPOD)and Local Outlier Factor(LOF),to construct the identification model of UTR.This model can accurately detect users’differences in voltage data,and identify users with wrong UTR.Meanwhile,the key input parameter of the LSCP algorithm is determined automatically through the line loss rate,and the influence of artificial settings on recognition accuracy can be reduced.Finally,thismethod is verified in the actual LVSA where the recall and precision rates are 100%compared with othermethods.Furthermore,the applicability to the LVSAs with difficult data acquisition and the voltage data error in transmission are analyzed.The proposed method adopts the ensemble learning framework and does not need to set the detection threshold manually.And it is applicable to the LVSAs with difficult data acquisition and high voltage similarity,which improves the stability and accuracy of UTR identification in LVSA.

Identification of Topological Surface State in PdTe2 Superconductor by Angle-Resolved Photoemission Spectroscopy

High-resolution angle-resolved photoemission measurements are carried out on transition metal dichalcogenide PdTe2 that is a superconductor with a Tc at 1.7K. Combined with theoretical calculations, we discover for the first time the existence of topologically nontrivial surface state with Dirac cone in PbTe2 superconductor. It is located at the Brillouin zone center and possesses helical spin texture. Distinct from the usual three-dimensional topological insulators where the Dirac cone of the surface state lies at the Fermi level, the Dirac point of the surface state in PdTe2 lies deeply below the Fermi level at - 1.75 eV binding energy and is well separated from the bulk states. The identification of topological surface state in PdTe2 superconductor deeply below the Fermi level provides a unique system to explore new phenomena and properties and opens a door for finding new topological materials in transition metal ehalcogenides.

Partial Correlation Analysis Based Identification of Distribution Network Topology

Accurately identifying distribution network topol-ogy,which tends to be a mesh configuration with increasing penetration rate of distributed energy resources(DERs),is critical for reliable operation of a smart distribution network.Multicollinearity among node voltages makes existing topology identification methods unstable and inaccurate.Considering partial correlation analysis can reveal the intrinsic correlation of two variables by eliminating the influence of other variables,this paper develops a novel data-driven method based on partial correlation analysis to identify distribution network topology(radial,mesh,or including DERs)using only historical voltage amplitude data.First,maximum spanning tree of network is generated through Prim algorithm.Then,the loops of network are identified by taking tree neighbors as controlling variables in partial correlation analysis.Finally,a new topology verification mechanism based on partial correlation analysis is developed to correct wrong connections caused by multicollinearity.Test results on IEEE 33-node system,IEEE 123-node system and practical distribution network demonstrate that our method outperforms common data-driven methods,and can robustly identify both radial and mesh distribution network with DERs.IndexTerms-Data-driven,linear correlation,partial correlation,smart meter,topology identification.

Phase Identification of Low-voltage Distribution Network Based on Stepwise Regression Method

Accurate information for consumer phase connectivity in a low-voltage distribution network(LVDN)is critical for the management of line losses and the quality of customer service.The wide application of smart meters provides the data basis for the phase identification of LVDN.However,the measurement errors,poor communication,and data distortion have significant impacts on the accuracy of phase identification.In order to solve this problem,this paper proposes a phase identification method of LVDN based on stepwise regression(SR)method.First,a multiple linear regression model based on the principle of energy conservation is established for phase identification of LVDN.Second,the SR algorithm is used to identify the consumer phase connectivity.Third,by defining a significance correction factor,the results from the SR algorithm are updated to improve the accuracy of phase identification.Finally,an LVDN test system with 63 consumers is constructed based on the real load.The simulation results prove that the identification accuracy achieved by the proposed method is higher than other phase identification methods under the influence of various errors.

Partial Topology Identification of Stochastic Multi-Weighted Complex Networks Based on Graph-Theoretic Method and Adaptive Synchronization

This article aims to identify the partial topological structures of delayed complex network.Based on the drive-response concept,a more universal model,which includes nonlinear couplings,stochastic perturbations and multi-weights,is considered into drive-response networks.Different from previous methods,we obtain identification criteria by combining graph-theoretic method and adaptive synchronization.After that,the partial topological structures of stochastic multi-weighted complex networks with or without time delays can be identified successfully.Moreover,response network can reach synchronization with drive network.Ultimately,the effectiveness of the proposed theoretical results is validated through numerical simulations.

基于CatBoost算法的配电网分区拓扑辨识

含多分布式电源配电网的拓扑结构具有多样性与多变性,影响拓扑辨识的实时性和准确性。提出一种基于CatBoost算法的配电网分区拓扑辨识方法。构建结合拓扑分区的配电网拓扑辨识框架,采用区域开关状态矩阵描述拓扑结构,以进行物理上的辨识降维;提出基于CatBoost算法的特征选择与拓扑辨识方法,通过分区并行离线训练得到历史拓扑和未知拓扑的区域拓扑辨识CatBoost模型,通过在线应用得到实时的区域开关状态矩阵标签,形成配电网开关状态矩阵,实现系统拓扑辨识。配电网算例系统测试结果验证了所提方法的有效性。

基于运行扰动数据分析的低压台区拓扑辨识方法研究

传统的低压台区拓扑识别方法具有精确度低、抗干扰能力差、效率低等一系列问题。针对上述问题,提出一种基于电流运行扰动数据的台区拓扑辨识方法。利用改进的灰色关联分析法对测量终端采集的低压出线柜、分支箱、光力柜、表箱的电流扰动数据进行分析,计算低压台区不同部分数据的相关性,实现台区供电隶属关系识别。通过电流确定台区中各部分的进出线连接关系,实现对台区拓扑的识别。算例结果表明,利用电流扰动数据进行台区拓扑辨识具有准确度高、效率高的特点,因此在实际应用中更具价值与潜力。

基于多特征符号聚合近似和层次聚类的户变关系识别方法

针对低压配电台区拓扑档案中可能存在的户变关系异常问题,文中提出了一种基于多特征符号聚合近似(MF-SAX)和层次聚类的户变关系识别方法。首先,运用符号聚合近似表达方法将用户电压时间序列转化为字符串序列,并引入电压波动系数和电压变化趋势两个附加参数对其特征表达进行强化。然后,基于编辑距离生成用户电压曲线相似性矩阵,并结合层次聚类算法实现户变关系的识别。最后,实际算例结果表明,提出的方法相比于现有方法准确率更高,误报更少,能直接应对数据缺失的情况,且具有更高的效率。

一种基于拓扑结构变化的主动配电网自适应保护方法

随着逆变型分布式电源大量接入配电网,配电网运行方式灵活多变,保护方案的设计与保护装置的动作范围受拓扑变化的影响。在此背景下并结合工程实际,提出一种基于拓扑结构变化的主动配电网自适应保护方法。通过多点量测信息感知配电网的运行状态,利用电压量和电流量之间的相关性,对拓扑结构的变化进行识别。然后,根据拓扑结构的变化,形成新的关联矩阵,自适应调整系统的保护方案与参数。最后,通过仿真验证了所提出的方法有效性。

基于智能电表数据的低压配电网拓扑与线路参数联合辨识

在低压配电网中,用户频繁地扩建和改接导致台账存在信息更新滞后、数据缺失等问题,难以获取当前运行状态下的拓扑及线路参数。针对无相角量测信息且含未知数量零注入功率“隐节点”的低压配电网,提出一种基于智能电表数据的低压配电网拓扑与线路参数联合辨识方法。基于低压配电网的电气特性以及辐射状网络的结构特点,推导出一种功率-电压比形式的线性逆潮流模型。通过线性回归求解得到阻抗距离矩阵,再利用无判定阈值形式的改进分组递归算法实现拓扑与线路参数的联合辨识。最后,在IEEE欧洲低压测试馈线和中国南京市某地区的实际低压配电网中对所提算法进行数值仿真,验证了所提算法的有效性。

基于加权最小绝对值状态估计的有源配电网拓扑辨识方法

在配电系统状态估计中,一方面众多网络设备难以完全监控导致量测数据不足,另一方面,频繁的拓扑切换亟须开发高效的算法。因此,提出了一种配电网拓扑辨识的加权最小绝对值状态估计方法。首先,对传统基于线性规划的加权最小绝对值状态估计方法进行改进,以提高计算效率;其次,在重新制定的加权最小绝对值状态估计方法中加入补充变量和约束,以适应拓扑辨识问题,形成了一个基于混合整数线性规划的加权最小绝对值状态估计方法;最后,4个算例系统的仿真结果验证了所提方法在不同数量的实时测量、高伪测量误差、被坏数据破坏的测量以及未知支路状态场景下的优异性能。

一种可用于航天Boost拓扑电源参数辨识的数字孪生方法

电力电子变换器中元器件随时间退化会影响到航天电源的稳定性与可靠性。为确保航天电源的可靠性,并准确识别零部件状态,数字孪生依靠其非侵入性、数字化、高性价比、低风险、可视化与实时性的特点,可以解决电力电子变换器的故障趋势判断与状态检测的问题,从而保证航天电源的稳定性与可靠性。通过验证Boost拓扑电路的数字孪生模型,同时采用禁忌搜索算法以比较结果最小二乘误差为目标函数得到全局最优解,进行Boost拓扑电路参数辨识、实时监控。并且对比了100、500、1000算法迭代次数对结果的影响,随着迭代次数的增加,最终结果与实际值仅有最大0.96%的误差。验证该项技术可应用到航天电源的实时状态监控,以保证其可靠性。

基于多源数据融合的配电网拓扑识别方法研究

针对目前配电网拓扑识别时多源融合数据利用率低的问题,提出了一种基于多源数据融合的配电网拓扑识别方法。首先,设计了一种多源数据时间尺度对齐方法。其次,提出了一种基于线性外推的伪测量数据生成方法,从而提高拓扑识别方法的数据精度。最后,建立了多源数据融合配电网离散化和线性化最优拓扑识别方法。在试验阶段,以IEEE 33馈线系统为例验证所提方法。试验结果表明,当节点注入功率数据的实时测量误差为30%、伪测量误差为5%时,所提方法拓扑识别准确率为70.98%,较无融合方法提升了5.86%。所提方法对提高配电网拓扑识别准确率具有一定指导作用。

基于虚拟阻抗的低压配电网拓扑识别方法

当前拓扑识别技术难以反映潮流特性对拓扑识别的影响,基于配电网现有量测数据,通过分析节点间的电气距离,提出了虚拟阻抗的概念。将节点间具备电气意义的且与电气距离成正相关的连续变量定义为虚拟阻抗,并提出了一种基于虚拟阻抗的低压配电网拓扑识别方法。首先,构建以节点间虚拟阻抗为因变量的多元线性回归方程。然后,通过岭回归计算每一个单相电表与关口电表构成的回归方程的虚拟阻抗,根据计算结果快速判别出拓扑关系异常的电气设备。最后,建立基于导数动态时间弯曲(derivative dynamic time warping,DDTW)距离的校验模型,重新构建得到电气设备的正确拓扑关系,实现低压配电网拓扑关系的修正。以实际的低压配电网台区样本数据为依据,验证了所提方法的有效性。

基于有限关键节点及Wasserstein距离的配网拓扑识别

明确配电网结构是配电网最优潮流、安全评估、网络重建、故障定位的基础。针对现有配电网拓扑识别方法缺乏结合现有网络结构参数和潮流信息,仅通过量测数据来进行拓扑识别效率低的问题,提出一种基于有限关键节点及Wasserstein距离的配电网拓扑识别方法。首先,利用子空间扰动模型证明配网拓扑变化时,可以利用有限的关键节点来进行拓扑识别,基于熵值法的混合K-Shell算法引入影响度概念,通过影响度与节点电气距离得出节点的重要度,确定配电网拓扑结构中的关键节点。其次,基于密度的噪声应用聚类算法通过电压、电流、有功、无功等4个特征来进行节点的聚类,将其他节点与关键节点进行类别归属,再结合Wasserstein距离得出节点间的连接关系从而得出配电网的拓扑结构。最后,通过IEEE 33节点算例和某小区实例,验证该方法的有效性。该方法极大地提高配电网拓扑识别效率与正确率,实现了配网拓扑结构的动态识别。

高压用户电气系统智能终端拓扑识别方法研究

随着电网环境越来越复杂,运行过程中的不确定性持续增大、拓扑结构不断发生改变导致难以准确识别故障。为了提高高压用户供用电系统的拓扑结构识别准确性,提出了一种基于智能终端测量数据的高压用户电气系统智能终端拓扑识别方法。利用电气智能终端的通信地址对各级交换机子节点进行关联,采集电气智能终端节点数据。根据聚类算法,确定各分支电能数据的父子关系特征。创新性地通过数据聚类分析方法得到系统中各级馈线节点的隶属关系,实现高压用户电气系统拓扑识别。在三种情境下进行拓扑识别效果测试。测试结果表明,该方法准确识别出了同一台区不同情况下的三种拓扑结构,能够有效利用测量数据准确识别配电系统的拓扑结构。

高比例户用光伏接入下低压配电网户变关系识别

近年来,随着分布式光伏的发展,用户可参与的户用光伏系统在低压配电网中的并网数量快速增长。针对高比例户用光伏下的低压配电网具备的基本特征,提出一种基于台区配变及用户功率平衡关系的光伏时序功率卷积模型,并在此基础上,采用基于高斯混合模型的电压聚类优化目标函数进行户变关系识别。首先,基于用户-配变功率平衡关系,建立了高比例户用光伏的低压户变关系的多元线性回归模型;其次,提出一种基于Bi LSTM算法的光伏时序识别方法,筛选出光伏瞬时渗透率最高的时序片段;根据已识别的光伏时序片段建立光伏权重矩阵,并引入该矩阵将多元线性回归模型进行加权回归后转化为光伏时序功率卷积模型求解,在平抑光伏波动影响的同时初步识别户变从属关系;然后,提出一种基于高斯混合模型进行电压聚类的优化目标函数作为验证步骤,能够进一步排除验证错误的用户,提高户变关系的准确率;最后,以某地区实际低压配电网为例,分别使用该地区推广户用光伏前后不同时期的历史数据集进行识别,通过识别结果对比,验证了所提方法的有效性和实用性。

基于功率阶跃特征序列匹配的户变关系识别算法

随着低压配电网的快速扩展和分布式能源、新型电力负荷的普遍接入,老旧城区配电网中普遍存在的私拉乱接、台账不清等问题,使低压配电网的运营管理和数字化转型面临前所未有的挑战。近年来,随着用电信息采集系统的普及和提升,通过用采数据实现台区拓扑物理问题的数字化解决成为可能。但限于用采系统实际采集能力和上线率,现有成果多数集中在对日负荷数据整体分析,而未能提取曲线数据中包含的用电行为特征并加以研究利用。文章提出一种基于功率阶跃特征序列匹配的户变关系识别算法,首先建立低压台区树状拓扑模型,接着从功率曲线中提取功率阶跃特征并组成序列,减除功率曲线中相似的峰谷趋势和噪声,随后在序列匹配后形成了低压台区的户变关系识别,并利用多种手段对户变关系识别结果进行校验和排异。最后通过对62个表箱和4812组采集数据的户变关系识别实例分析,验证所提方法的正确性和高效性。