WO2017016019A1 - 一种电力系统广域同调辨识方法及其装置 - Google Patents

一种电力系统广域同调辨识方法及其装置 Download PDF

Info

Publication number
WO2017016019A1
WO2017016019A1 PCT/CN2015/088385 CN2015088385W WO2017016019A1 WO 2017016019 A1 WO2017016019 A1 WO 2017016019A1 CN 2015088385 W CN2015088385 W CN 2015088385W WO 2017016019 A1 WO2017016019 A1 WO 2017016019A1
Authority
WO
WIPO (PCT)
Prior art keywords
wide
power system
area measurement
measurement time
time series
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Ceased
Application number
PCT/CN2015/088385
Other languages
English (en)
French (fr)
Inventor
贾宏杰
姜涛
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Tianjin University
Original Assignee
Tianjin University
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Tianjin University filed Critical Tianjin University
Publication of WO2017016019A1 publication Critical patent/WO2017016019A1/zh
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

Links

Images

Classifications

    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—ELECTRIC POWER NETWORKS; CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for AC mains or AC distribution networks

Definitions

  • the present invention relates to the field of power systems, and in particular, to a method and apparatus for wide-area coherent identification of a power system, and more particularly to a method for coherent identification of a power system using wide-area measurement information of a power system.
  • the methods for identifying coherent clusters are mainly divided into two categories: coherent cluster identification based on power system model and coherent cluster identification based on power system wide-area measurement information.
  • the coherent group identification method based on power system model has the following methods: 1 based on graph segmentation, applying slow coherence theory to power system homology grouping; 2 calculating rotor rotor angle in right eigenvector matrix of power system differential equation The cosine between the corresponding row vectors to determine the coherence group in the power system; 3 to construct the weighted Laplacian matrix of the dynamic power network, to realize the coherent grouping of the power system by means of the complex network community structure theory; The balance point is linearized, and the homology grouping is performed according to the characteristic values of the power system motion trajectory.
  • the above-mentioned coherent group identification method based on power system model relies on the detailed model and accurate parameters of the power system, and it is difficult to consider the change of the operation mode of the power system and the influence of the fault type on the coherent grouping. Therefore, the coherent grouping method based on the power system model is mostly used for offline safety and stability analysis of power systems.
  • the method of coherent group identification based on wide-area measurement information of power system mainly includes: 1 based on least squares support vector machine to realize on-line identification of power system coherence group; 2 based on independent component method for coherent grouping of units in power system; 3 Based on the principal components of the wide-area measured signals, the clustering analysis method is used to homogenize the generators in the power system; 4 to identify the homology group in the power system by means of the wavelet phase difference; 5 to perform Kuppen through the wide-area measured signals The mode decomposition realizes the homology grouping of the power system; 6 on the basis of the method 5, the coherent group of the power system is identified by decoupling the dynamic oscillation mode of the wide-area measured information.
  • the above method can realize the accurate identification of the power system coherence group based on wide-area measurement information, but the above method will face the processing of massive data in the identification process. How to mine its key information and improve the recognition efficiency is to use the wide-area Test information to solve the problem of power system coherence identification.
  • the invention provides a wide-area coherent identification method for power system and a device thereof, and the invention combines power field wide-area measurement information with principal component analysis method in multivariate statistics to realize power system based on wide-area measurement information.
  • On-line identification of coherent clusters mentioning the identification efficiency, as described below:
  • a wide-area coherent identification method for a power system comprising the following steps:
  • Principal component analysis is performed on the standardized wide-area measurement time series, and the number of principal components of the wide-area measurement time series and corresponding feature vectors are determined according to the cumulative contribution rate of the principal components;
  • Coherent grouping of power system units is achieved based on the positive and negative signs of the elements in the selected feature vector.
  • the wide-area measurement time series is normalized by the variance and the mean.
  • the step of performing principal component analysis on the standardized wide-area measurement time series is specifically as follows:
  • the step of determining the number of principal components of the wide-area measurement time series and the corresponding feature vector according to the cumulative contribution rate of the principal components is specifically:
  • the cumulative contribution rate reaches the threshold, it is determined that the number of principal components of the wide-area measurement time series is k, and the feature vectors corresponding to the k principal components are retained.
  • the step of realizing the homology grouping of the power system unit according to the positive and negative signs of the selected feature vector element is specifically:
  • the generator with the same combination of positive and negative elements in the selected feature vector is divided into a group of coherent groups, and then multiple sets of coherent groups are obtained to realize the homology identification of the power system based on the wide-area measurement information.
  • a wide-area coherent identification device for a power system comprising:
  • a principal component analysis module for performing principal component analysis on the standardized wide-area measurement time series
  • An obtaining module configured to determine a number of principal components of the wide-area measurement time series and a corresponding feature vector according to the cumulative contribution rate of the principal component
  • the dividing module is configured to implement the coherent grouping of the power system unit according to the positive and negative signs of the elements in the selected feature vector.
  • the standardization module comprises:
  • the variance and mean modules are used to obtain the variance and mean of the wide-area measurement time series under the same time section and the same measurement length;
  • a normalization sub-module for normalizing the wide-area measurement time series by the variance and the mean is
  • the principal component analysis module includes:
  • the principal component analysis sub-module is configured to obtain the feature values and feature vectors of the standardized wide-area measurement time series by principal component analysis.
  • the obtaining module includes:
  • a first obtaining submodule configured to obtain, according to the feature value, a contribution rate of each principal component and a cumulative contribution rate of the first k principal components
  • the second obtaining submodule is configured to determine that the number of principal components of the wide-area measurement time series is k after the cumulative contribution rate reaches the threshold, and retain the feature vector corresponding to the k principal components.
  • the dividing module includes:
  • a first dividing sub-module configured to divide the generators with the same combination of positive and negative elements in the selected feature vector into a group of coherent groups
  • the second sub-module is configured to acquire a plurality of sets of coherent groups, and realize co-ordinate identification of the power system based on the wide-area measurement information.
  • the technical solution provided by the present invention has the beneficial effects that the present invention determines the number of principal components of the wide-area measurement time series and the corresponding feature value vector by calculating the cumulative contribution rate of the principal component, and sequentially extracts the retained features of each generator.
  • the positive and negative signs of the corresponding elements in the vector are divided into a group of coherent groups by the generators with the same order and negative sequence, so that multiple sets of coherent groups are obtained.
  • the present invention completely based on the wide-area measurement information for the power system.
  • the homology group division avoids the influence of the model accuracy and parameter accuracy of the power system on the homology grouping result, realizes the online identification of the power system coherence group based on the wide-area measurement information, reduces the computational complexity, and mentions the recognition efficiency.
  • 1 is a flow chart of a method for wide-area coherent identification of a power system
  • FIG. 2 is a schematic diagram of a wide-area coherent identification device of a power system
  • Figure 3 is a schematic diagram of a standardized module
  • Figure 4 is a schematic diagram of a main component analysis module
  • Figure 5 is a schematic diagram of an acquisition module
  • Figure 6 is a schematic diagram of a division module
  • Figure 7 is a schematic diagram of a typical 4-machine 11-node test system
  • Figure 8 is a schematic illustration of a rotor angle rocking curve.
  • the invention combines the need of improving the safety and stability online monitoring of the power system, and improves the risk of the power grid to prevent the blackout accident, and combines the wide-area measurement information of the power system with the principal component analysis method in the multi-statistics to propose a utilization of the power system.
  • the technical framework of the field measurement information online identification system coherent cluster the following describes the method in detail with reference to specific embodiments:
  • FIG. 1 A method for wide-area coherent identification of a power system, see FIG. 1, the method comprising the following steps:
  • the step 101 is specifically: obtaining the variance and the mean of the wide-area measurement time series under the same time section and the same measurement length; and normalizing the wide-area measurement time series by the variance and the mean.
  • the step 102 is specifically: obtaining the eigenvalues and eigenvectors of the standardized wide-area measurement time series by principal component analysis; obtaining the contribution rate of each principal component and the cumulative contribution rate of the first k principal components according to the eigenvalues. After the cumulative contribution rate reaches the threshold, it is determined that the number of principal components of the wide-area measurement time series is k, and the feature vectors corresponding to the k principal components are retained.
  • the step 103 is specifically: sequentially extracting the positive and negative signs of the elements corresponding to the respective generators in the reserved feature vectors; dividing the generators in which the positive and negative numbers are arranged in the same order into a group of coherent groups, thereby obtaining Multiple sets of coherent clusters are used to realize power system coherence group identification based on wide-area measurement information.
  • the method realizes on-line identification of the power system coherent cluster based on wide-area measurement information through steps 101-103, which improves the recognition efficiency and satisfies various needs in practical applications.
  • the wide-area measurement time series X of the generator in the power system is:
  • n is the length of the wide-area measurement sequence
  • x i is the rotor angular measurement time series of the i-th generator
  • x ij is the j-th rotor angle measurement information of the i-th generator.
  • X * is a standardized wide-area measurement time series.
  • the step 202 is specifically: solving the X* covariance matrix ⁇ , the covariance matrix ⁇ eigenvalues ⁇ 1 , ⁇ 2 , . . . , ⁇ p , and the corresponding unit orthogonalization eigenvector A(1) , A(2), ..., A(p).
  • the specific solution steps are well known to those skilled in the art, and will not be described in detail in the embodiments of the present invention.
  • the cumulative contribution rate of the first k principal components is calculated according to the eigenvalues ⁇ 1 , ⁇ 2 , ..., ⁇ p of the covariance matrix ⁇ according to equation (4)
  • the threshold for the cumulative contribution rate is 80%, if And Then, the number of principal components of the wide-area measurement time series X is k, and it can be considered that the generator homology information reflected by the first k principal components is basically consistent with the generator homology information that can be reflected by the wide-area measurement time series X. Further, the unit orthogonalized feature vectors A(1), A(2), ..., A(k) corresponding to the first k eigenvalues ⁇ 1 , ⁇ 2 , ..., ⁇ k are retained.
  • the threshold of the cumulative contribution rate is set according to the requirements in the actual application, which is not limited by the embodiment of the present invention.
  • SGN(i) represents the ith column of matrix SGN, corresponding to the ith generator; sign(.) is the symbolic operation, sign(A(i,j)) Indicates the positive and negative signs of the element A(i,j); the element A(i,j) represents the jth element of the i-th unit orthogonalized feature vector A(i).
  • the generators with the same positive and negative signs in the symbol matrix SGN are divided into a group, which is regarded as a group of coherent groups, and then according to different combinations of positive and negative signs of each line, multiple sets of coherent groups are divided to realize wide-based
  • the power measurement system of the domain measurement information is identified online.
  • the embodiment of the present invention implements on-line identification of the power system coherence group based on the wide-area measurement information through steps 201-205, which satisfies various needs in practical applications.
  • a wide-area coherent identification device for a power system see FIG. 2, the device includes:
  • Standardization module 1 for normalizing a wide-area measurement time series of generator rotor angles in a power system
  • the principal component analysis module 2 is configured to perform principal component analysis on the standardized wide-area measurement time series
  • the obtaining module 3 is configured to determine the number of principal components of the wide-area measurement time series and the corresponding feature vector according to the cumulative contribution rate of the principal components;
  • the dividing module 4 is configured to implement the homology grouping of the power system unit according to the positive and negative signs of the elements in the selected feature vector.
  • the standardization module 1 includes:
  • the variance and mean sub-module 11 is configured to obtain the variance and the mean of the wide-area measurement time series under the same time section and the same measurement length;
  • the normalization sub-module 12 is configured to normalize the wide-area measurement time series by the variance and the mean.
  • the principal component analysis module 2 includes:
  • the principal component analysis sub-module 21 is configured to obtain the feature values and feature vectors of the normalized wide-area measurement time series by principal component analysis.
  • the obtaining module 3 includes:
  • a first obtaining sub-module 31 configured to acquire, according to the feature value, a contribution rate of each principal component and a cumulative contribution rate of the first k principal components;
  • the second obtaining sub-module 32 is configured to determine that the number of principal components of the wide-area measurement time series is k after the cumulative contribution rate reaches the threshold, and retain the feature vector corresponding to the k principal components.
  • the dividing module 4 includes:
  • a first dividing sub-module 41 configured to divide the generators whose element combination of the positive and negative elements in the selected feature vector are identical into a group of coherent groups
  • the second dividing sub-module 42 is configured to acquire a plurality of sets of coherent clusters, and realize power system coherent identification based on wide-area measurement information.
  • the device realizes the coherent identification of the power system based on the wide-area measurement information through the above-mentioned standardization module 1, principal component analysis module 2, acquisition module 3 and division module 4, which satisfies the needs in practical applications.
  • modules and sub-modules in the embodiments of the present invention may be implemented by using a single-chip microcomputer, a PC, or the like.
  • the embodiment of the present invention does not limit the main device corresponding to the foregoing modules and sub-modules.
  • the implementation method and practical effects of the present invention will be described below with reference to an example.
  • This example is tested on a typical 4-machine 11-node test system.
  • the network topology is shown in Figure 7.
  • the test system includes 11 nodes (1, 2, ..., 11), 4 generator sets (G1, G2, G3, G4) and 2 load nodes (L7, L9).
  • This example verifies the efficiency and accuracy of the method by comparing the method with the simulation curve.
  • the characteristic values, feature vectors and cumulative contribution rate indicators of the test system can be calculated, as shown in Table 1.
  • the cumulative contribution rate can be seen from Table 1. It can be considered that the number of principal components of the generator rotor angle wide-area measurement time series X in FIG. 8 is 1, and the corresponding generator homology information can be reflected by the first principal component, thereby preserving the corresponding first eigenvalue ⁇ 1 .
  • the unit orthogonalizes the feature vector A(1).
  • the unit orthogonalized feature vector A(1) corresponding to the first eigenvalue ⁇ 1 is [0.7300, 0.6823, -0.0393], and it can be seen that the generators G1 and G2 take a positive value in the A(1) direction. G4 is negative, indicating that G1 is the same as G2 and opposite to G4. Therefore, the homology information reflected by the first principal component is: a group of generators G1 and G2, and a group of G4, which is completely consistent with FIG.
  • the homology information reflected by the second principal component can be further studied.
  • the unit orthogonalized eigenvector A(2) corresponding to the second eigenvalue ⁇ 2 in Table 1 is [-0.1863, 0.2540, -0.9491], which can be seen.
  • Generators G1 and G4 take a negative value on A(2) and G2 is positive, indicating that G1 is the same as G4 and opposite to G2. Therefore, the homology information reflected by the second principal component is: a group of generators G1 and G4. A separate set of G2, this principal component reflects the local coherence mode of the test system.
  • the above-mentioned homology grouping method based on principal component analysis can be explained as follows: after three-phase permanent faults between any of the bus bars 8 and 9, the main excitation region oscillation mode is adopted. At this time, G1 and G2 are coherent, and G4 is coherent.
  • the test system is mainly coherent mode; in addition, the fault also induces local oscillation of the test system, that is, relative motion between G1 and G2, and the present invention can also accurately reflect the feature vector corresponding to the second eigenvalue in Table 1. .
  • the oscillation between G1 and G2 is not the main oscillation mode of the test system, in Fig. 8, the rotor rocking curves of G1 and G2 exhibit the same trend motion.

Landscapes

  • Engineering & Computer Science (AREA)
  • Power Engineering (AREA)
  • Supply And Distribution Of Alternating Current (AREA)

Abstract

一种电力系统广域同调辨识方法及其装置，方法包括：对电力系统中发电机转子角的广域量测时间序列进行标准化；对标准化后的广域量测时间序列进行主成分分析，根据主成分的累计贡献率，确定广域量测时间序列的主成分个数及对应的特征向量；根据所选定特征向量中元素的正、负号实现电力系统机组的同调分群。装置包括：标准化模块(1)、主成分分析模块(2)、获取模块(3)和划分模块(4)，通过上述模块实现了基于广域量测信息的电力系统同调机群在线辨识。上述完全基于广域量测信息进行电力系统同调机群划分，避免了因电力系统的模型精度和参数准确性对同调分群结果的影响。

Description

一种电力系统广域同调辨识方法及其装置 技术领域
本发明涉及电力系统领域，尤其涉及一种电力系统广域同调辨识方法及其装置，特别是涉及利用电力系统的广域量测信息进行电力系统同调辨识的方法。
背景技术
区域间电网互联规模不断扩大，大容量远距离输电不断增加，间隙性新能源大规模接入，使得区域电网振荡已成为威胁电网安全稳定运行的主要因素之一。为抑制区域间功率振荡，改善整个互联系统的动态稳定，电力系统研究和运行人员提出多种措施来改善电力系统的阻尼特性，例如：加装电力系统稳定器、直流功率调制、改变电网运行方式等，并在紧急情况下，通过主动解列以防止长时间区域间功率振荡所诱发的电网大停电事故。而在主动解列过程中，快速搜索和确定电力系统的同调机群是其关键。
目前，识别同调机群的方法主要分为两类：基于电力系统模型的同调机群识别方法和基于电力系统广域量测信息的同调机群识别方法。其中，基于电力系统模型的同调机群识别方法又有如下方法：①基于图分割，将慢同调理论应用于电力系统同调分群中；②通过计算电力系统微分方程的右特征向量矩阵中发电机转子角所对应行向量之间的方向余弦来确定电力系统中的同调机群；③构造动态电力网络的加权拉普拉斯矩阵，借助复杂网络社团结构理论实现电力系统的同调分群；④将电力系统在非平衡点处线性化，根据电力系统运动轨迹的特征值进行同调分群。上述基于电力系统模型的同调机群识别方法均依赖于电力系统的详细模型和准确参数，难以考虑电力系统运行方式变化及故障类型对同调分群的影响。因而，基于电力系统模型的同调分群方法，多用于电力系统离线安全稳定分析。
广域量测系统在电力系统中的广泛应用，为电力系统在线实现同调分群提供了可能。当前，基于电力系统广域量测信息的同调机群识别方法主要有：①基于最小二乘支持向量机实现电力系统同调机群的在线辨识；②基于独立分量法对电力系统中的机组进行同调分群；③基于广域实测信号的主成分，采用聚类分析方法对电力系统中发电机进行同调分群；④借助小波相位差来辨识电力系统中的同调机群；⑤通过对广域实测信号进行库普曼模式分解实现电力系统的同调分群；⑥在方法⑤的基础上，通过对广域实测信息进行动态振荡模式解耦来识别电力系统的同调机群。
采用上述方法均可实现基于广域量测信息的电力系统同调机群准确辨识，但上述方法在辨识过程中都会面临对海量数据的处理，如何挖掘其关键信息，提高识别效率是利用广域量 测信息进行电力系统同调辨识亟需解决的问题。
发明内容
本发明提供了一种电力系统广域同调辨识方法及其装置，本发明将电力系统广域量测信息与多元统计中的主成分分析方法相结合，实现了基于广域量测信息的电力系统同调机群在线辨识，提到了识别效率，详见下文描述：
一种电力系统广域同调辨识方法，所述方法包括以下步骤：
对电力系统中发电机转子角的广域量测时间序列进行标准化；
对标准化后的广域量测时间序列进行主成分分析，根据主成分的累计贡献率，确定广域量测时间序列的主成分个数及对应的特征向量；
根据所选定特征向量中元素的正、负号实现电力系统机组的同调分群。
所述对电力系统中发电机转子角的广域量测时间序列进行标准化的步骤具体为：
在同一时间断面，同一量测长度的条件下，获取广域量测时间序列的方差和均值；
通过所述方差和所述均值，对广域量测时间序列标准化。
所述对标准化后的广域量测时间序列进行主成分分析的步骤具体为：
通过主成分分析，获取标准化后的广域量测时间序列的特征值及特征向量。
所述根据主成分的累计贡献率，确定广域量测时间序列的主成分个数及对应的特征向量的步骤具体为：
根据所述特征值，获取各主成分的贡献率及前k个主成分的累计贡献率；
当累计贡献率达到阈值后，确定广域量测时间序列的主成分个数为k，并保留这k个主成分所对应的特征向量。
所述根据所选定特征向量元素的正、负号实现电力系统机组的同调分群的步骤具体为：
将所选定特征向量中元素正、负号组合完全相同的发电机划分为一组同调机群，进而获取多组同调机群，实现基于广域量测信息的电力系统同调辨识。
一种电力系统广域同调辨识装置，所述装置包括：
标准化模块，用于对电力系统中发电机转子角的广域量测时间序列进行标准化；
主成分分析模块，用于对标准化后的广域量测时间序列进行主成分分析；
获取模块，用于根据主成分的累计贡献率，确定广域量测时间序列的主成分个数及对应的特征向量；
划分模块，用于根据所选定特征向量中元素的正、负号实现电力系统机组的同调分群。
其中，所述标准化模块包括：
方差和均值模块，用于在同一时间断面，同一量测长度的条件下，得到广域量测时间序列的方差和均值；
标准化子模块，用于通过所述方差和所述均值，对广域量测时间序列标准化。
其中，所述主成分分析模块包括：
主成分分析子模块，用于通过主成分分析，获取标准化后的广域量测时间序列的特征值及特征向量。
进一步地，所述获取模块包括：
第一获取子模块，用于根据所述特征值，获取各主成分的贡献率及前k个主成分的累计贡献率；
第二获取子模块，用于当累计贡献率达到阈值后，确定广域量测时间序列的主成分个数为k，并保留这k个主成分所对应的特征向量。
进一步地，所述划分模块包括：
第一划分子模块，用于将所选定特征向量中元素正、负号组合完全相同的发电机划分为一组同调机群；
第二划分子模块，用于获取多组同调机群，实现基于广域量测信息的电力系统同调辨识。
本发明提供的技术方案的有益效果是：本发明通过计算主成分的累计贡献率，确定广域量测时间序列的主成分个数及对应的特征值向量，顺序提取各发电机在各保留特征向量中所对应元素的正、负号，将正、负号顺序排列组合完全一致的发电机划分为一组同调机群，从而得到多组同调机群，本发明完全基于广域量测信息进行电力系统同调机群划分，避免了电力系统的模型精度和参数准确性对同调分群结果的影响，实现了基于广域量测信息的电力系统同调机群在线辨识，降低了计算复杂度，提到了识别效率。
附图说明
图1为一种电力系统广域同调辨识方法的流程图；
图2为一种电力系统广域同调辨识装置的示意图；
图3为标准化模块的示意图；
图4为主成分分析模块的示意图；
图5为获取模块的示意图；
图6为划分模块的示意图；
图7为典型4机11节点测试系统的示意图；
图8为转子角摇摆曲线的示意图。
附图中，各标号所代表的部件列表如下：
1：标准化模块；                   2：主成分分析模块；
3：获取模块；                     4：划分模块；
11：方差和均值子模块；            12：标准化子模块；
21：主成分分析子模块；            31：第一获取子模块；
32：第二获取子模块；              41：第一划分子模块；
42：第二划分子模块。
具体实施方式
为使本发明的目的、技术方案和优点更加清楚，下面对本发明实施方式作进一步地详细描述。
本发明根据完善电力系统安全稳定在线监测的需要、提高电网防御大停电事故风险的需求，将电力系统广域量测信息与多元统计中的主成分分析方法相结合，提出一种利用电力系统广域量测信息在线辨识系统同调机群的技术框架，下面结合具体的实施例对本方法进行详细的描述：
实施例1
一种电力系统广域同调辨识方法，参见图1，该方法包括以下步骤：
101：对电力系统中发电机转子角的广域量测时间序列进行标准化；
102：对标准化后的广域量测时间序列进行主成分分析，根据主成分的累计贡献率，确定广域量测时间序列的主成分个数及对应的特征向量；
103：根据所选定特征向量元素的正、负号实现电力系统机组的同调分群。
其中，步骤101具体为：在同一时间断面，同一量测长度的条件下，获取广域量测时间序列的方差和均值；通过方差和所述均值，对广域量测时间序列标准化。
其中，步骤102具体为：通过主成分分析，获取标准化后的广域量测时间序列的特征值及特征向量；根据特征值，获取各主成分的贡献率及前k个主成分的累计贡献率；当累计贡献率达到阈值后，确定广域量测时间序列的主成分个数为k，并保留这k个主成分所对应的特征向量。
该步骤103具体为：顺序提取出各发电机在各保留特征向量中所对应的元素的正、负号；将正、负号顺序排列组合完全一致的发电机划分为一组同调机群，从而得到多组同调机群，实现基于广域量测信息的电力系统同调机群辨识。
本方法通过步骤101-步骤103实现了基于广域量测信息的电力系统同调机群在线辨识，提高了识别效率，满足了实际应用中的多种需要。
实施例2
下面结合具体的计算公式，对实施例1中的方案进行详细描述：
201：从电网广域量测系统中读取发电机转子角的广域量测时间序列，将广域量测时间序列标准化；
假设电力系统中发电机数量为p，发电机转子角量测信息可通过广域量测系统获取，则电力系统中发电机的广域量测时间序列X为：
Figure PCTCN2015088385-appb-000001
式中，n为广域量测序列的长度；xi为第i个发电机的转子角量测时间序列；xij为第i个发电机的第j次的转子角量测信息。
按在同一时间断面，同一量测长度的条件下，计算式(1)中广域量测时间序列X的均值向量
Figure PCTCN2015088385-appb-000002
及方差var(X)：
Figure PCTCN2015088385-appb-000003
式中，结合式(2)，
Figure PCTCN2015088385-appb-000004
为第i台发电机的广域量测时间序列的均值；var(xi)为第i台发电机的广域量测时间序列的方差；xki为第k个发电机的第i次的转子角量测信息；对式(1)的时间序列X进行标准化得：
Figure PCTCN2015088385-appb-000005
式中，X*为标准化后的广域量测时间序列。
202：对标准化后的广域量测时间序列进行主成分分析；
其中，该步骤202具体为：求解X*的协方差矩阵Σ、协方差矩阵Σ的特征值λ1、λ2、...、λp，以及相应的单位正交化特征向量A(1)、A(2)、...、A(p)。具体的求解步骤为本领域技术人员所公知，本发明实施例对此不做赘述。
203：根据主成分分析结果，计算主成分的累计贡献率；
即，依据协方差矩阵Σ的特征值λ1、λ2、...、λp按式(4)计算前k个主成分的累计贡献率
Figure PCTCN2015088385-appb-000006
Figure PCTCN2015088385-appb-000007
204：根据累计贡献率，确定主成分的个数，进而确定所保留的特征向量个数；
例如：设定累计贡献率的阈值为80％，若
Figure PCTCN2015088385-appb-000008
且
Figure PCTCN2015088385-appb-000009
则广域量测时间序列X的主成分个数为k，可认为前k个主成分所能反映的发电机同调信息与广域量测时间序列X所能反映的发电机同调信息基本一致，进而保留前k个特征值λ1、λ2、...、λk对应的单位正交化特征向量A(1)、A(2)、...、A(k)。
其中，累计贡献率的阈值根据实际应用中的需要进行设定，本发明实施例对此不做限制。
205：根据所选定特征向量元素的正、负号实现电力系统机组的同调分群。
即，提取p台发电机在前k个单位正交化特征向量A(1)、A(2)、...、A(k)中对应元素的正、负号，构成下列符号矩阵SGN：
Figure PCTCN2015088385-appb-000010
式中，SGN(i)表示矩阵SGN的第i列，对应于第i台发电机；sign(.)为符号运算，sign(A(i,j)) 表示取元素A(i,j)的正、负号；元素A(i,j)表示为第i个单位正交化特征向量A(i)的第j个元素。
将符号矩阵SGN中各行正、负号完全相同的发电机划分为一组，将其视为一组同调机群，进而根据各行正、负号的不同组合，划分出多组同调机群，实现基于广域量测信息的电力系统同调机群在线辨识。
本发明实施例通过步骤201-步骤205实现了基于广域量测信息的电力系统同调机群在线辨识，满足了实际应用中的多种需要。
实施例3
一种电力系统广域同调辨识装置，参见图2，装置包括：
标准化模块1，用于对电力系统中发电机转子角的广域量测时间序列进行标准化；
主成分分析模块2，用于对标准化后的广域量测时间序列进行主成分分析；
获取模块3，用于根据主成分的累计贡献率，确定广域量测时间序列的主成分个数及对应的特征向量；
划分模块4，用于根据所选定特征向量中元素的正、负号实现电力系统机组的同调分群。
其中，参见图3，标准化模块1包括：
方差和均值子模块11，用于在同一时间断面，同一量测长度的条件下，得到广域量测时间序列的方差和均值；
标准化子模块12，用于通过所述方差和所述均值，对广域量测时间序列标准化。
其中，参见图4，主成分分析模块2包括：
主成分分析子模块21，用于通过主成分分析，获取标准化后的广域量测时间序列的特征值及特征向量。
其中，参见图5，获取模块3包括：
第一获取子模块31，用于根据特征值，获取各主成分的贡献率及前k个主成分的累计贡献率；
第二获取子模块32，用于当累计贡献率达到阈值后，确定广域量测时间序列的主成分个数为k，并保留这k个主成分所对应的特征向量。
其中，参见图6，划分模块4包括：
第一划分子模块41，用于将所选定特征向量中元素正、负号组合完全相同的发电机划分为一组同调机群；
第二划分子模块42，用于获取多组同调机群，实现基于广域量测信息的电力系统同调辨识。
本装置通过上述的标准化模块1、主成分分析模块2、获取模块3和划分模块4实现了基于广域量测信息的电力系统同调辨识，满足了实际应用中的需要。
本发明实施例中的上述模块、子模块可以采用单片机、PC机等实现，具体实现时，本发明实施例对上述模块、子模块对应的主体器件不做限制。
实施例4
下面结合一个实例来介绍本发明的实施方法和实际效果。本实例在典型4机11节点测试系统上进行测试，其网络拓扑示意图如图7所示。该测试系统包括11个节点(1、2、...、11)、4台发电机组(G1、G2、G3、G4)、2个负荷节点(L7、L9)。本实例通过将本方法与仿真曲线进行对比，验证本方法的高效性和准确性。
以发电机G3为参考机，在母线8、9之间的任一支路设置三相永久性故障，故障持续时间0.1s，0.1s后故障线路切除，仿真时间为20s，发电机转子角摇摆曲线如图8所示。由图8不难看出，测试系统中存在两组同调机群，其中G1和G2为一组，G4为一组。采用图8所示的发电机转子角信息作为本发明中同调辨识方法的输入信号X。其余的操作步骤参见实施例1和2，本发明实施例对此不做赘述。
根据以上步骤可计算测试系统的特征值、特征向量及累计贡献率指标，如表1所示。
表1同调辨识的主成分计算结果
Figure PCTCN2015088385-appb-000011
从表1可看出累计贡献率
Figure PCTCN2015088385-appb-000012
即可认为图8中发电机转子角广域量测时间序列X的主成分个数为1，对应的发电机同调信息可由第一个主成分反映，进而保留第一个特征值λ1对应的单位正交化特征向量A(1)。
由表1可知第一个特征值λ1对应的单位正交化特征向量A(1)为[0.7300,0.6823,-0.0393]，可见发电机G1和G2在A(1)方向上取值为正，G4为负，表明G1与G2同调而与G4相反，因此在第一主成分所反映的同调信息为：发电机G1与G2一组，G4单独一组，该结论与图8完全一致。
类似的，可进一步深入研究第二主成分反映的同调信息，表1中第二个特征值λ2对应的 单位正交化特征向量A(2)为[-0.1863,0.2540,-0.9491]，可见发电机G1和G4在A(2)上取值为负，G2为正，表明G1与G4同调而与G2相反，因此第二主成分所反映的同调信息为：发电机G1与G4一组，G2单独一组，此主成分所反映的是测试系统的局部同调模式。
上述基于主成分分析的同调分群方法可解释为：母线8和9之间的任一支路三相永久性故障后，主要激发区域间振荡模式，此时G1与G2同调，G4同调，此为测试系统主同调模式；此外，该故障还诱发测试系统的局部振荡，即G1、G2间的相对运动，本发明同样可对其进行准确反映如表1中的第二特征值所对应的特征向量。但由于G1与G2间的振荡不是测试系统的主振荡模式，因而在图8中，G1与G2的转子摇摆曲线呈相同趋势运动。
因此可以得出结论，本方法相对与传统的同调辨识方法具有更高的精度和计算效率。
本领域技术人员可以理解附图只是一个优选实施例的示意图，上述本发明实施例序号仅仅为了描述，不代表实施例的优劣。
以上所述仅为本发明的较佳实施例，并不用以限制本发明，凡在本发明的精神和原则之内，所作的任何修改、等同替换、改进等，均应包含在本发明的保护范围之内。

Claims (10)

  1. 一种电力系统广域同调辨识方法，其特征在于，所述方法包括以下步骤：
    对电力系统中发电机转子角的广域量测时间序列进行标准化；
    对标准化后的广域量测时间序列进行主成分分析，根据主成分的累计贡献率，确定广域量测时间序列的主成分个数及对应的特征向量；
    根据所选定特征向量中元素的正、负号实现电力系统机组的同调分群。
  2. 根据权利要求1所述的一种电力系统广域同调辨识方法，其特征在于，所述对电力系统中发电机转子角的广域量测时间序列进行标准化的步骤具体为：
    在同一时间断面，同一量测长度的条件下，获取广域量测时间序列的方差和均值；
    通过所述方差和所述均值，对广域量测时间序列标准化。
  3. 根据权利要求1所述的一种电力系统广域同调辨识方法，其特征在于，所述对标准化后的广域量测时间序列进行主成分分析的步骤具体为：
    通过主成分分析，获取标准化后的广域量测时间序列的特征值及特征向量。
  4. 根据权利要求3所述的一种电力系统广域同调辨识方法，其特征在于，所述根据主成分的累计贡献率，确定广域量测时间序列的主成分个数及对应的特征向量的步骤具体为：
    根据所述特征值，获取各主成分的贡献率及前k个主成分的累计贡献率；
    当累计贡献率达到阈值后，确定广域量测时间序列的主成分个数为k，并保留这k个主成分所对应的特征向量。
  5. 根据权利要求1所述的一种电力系统广域同调辨识方法，其特征在于，所述根据所选定特征向量元素的正、负号实现电力系统机组的同调分群的步骤具体为：
    将所选定特征向量中元素正、负号组合完全相同的发电机划分为一组同调机群，进而获取多组同调机群，实现基于广域量测信息的电力系统同调辨识。
  6. 一种电力系统广域同调辨识装置，其特征在于，所述装置包括：
    标准化模块，用于对电力系统中发电机转子角的广域量测时间序列进行标准化；
    主成分分析模块，用于对标准化后的广域量测时间序列进行主成分分析；
    获取模块，用于根据主成分的累计贡献率，确定广域量测时间序列的主成分个数及对应 的特征向量；
    划分模块，用于根据所选定特征向量中元素的正、负号实现电力系统机组的同调分群。
  7. 根据权利要求6所述的一种电力系统广域同调辨识装置，其特征在于，所述标准化模块包括：
    方差和均值子模块，用于在同一时间断面，同一量测长度的条件下，得到广域量测时间序列的方差和均值；
    标准化子模块，用于通过所述方差和所述均值，对广域量测时间序列标准化。
  8. 根据权利要求6所述的一种电力系统广域同调辨识装置，其特征在于，所述主成分分析模块包括：
    主成分分析子模块，用于通过主成分分析，获取标准化后的广域量测时间序列的特征值及特征向量。
  9. 根据权利要求8所述的一种电力系统广域同调辨识装置，其特征在于，所述获取模块包括：
    第一获取子模块，用于根据所述特征值，获取各主成分的贡献率及前k个主成分的累计贡献率；
    第二获取子模块，用于当累计贡献率达到阈值后，确定广域量测时间序列的主成分个数为k，并保留这k个主成分所对应的特征向量。
  10. 根据权利要求6所述的一种电力系统广域同调辨识装置，其特征在于，所述划分模块包括：
    第一划分子模块，用于将所选定特征向量中元素正、负号组合完全相同的发电机划分为一组同调机群；
    第二划分子模块，用于获取多组同调机群，实现基于广域量测信息的电力系统同调辨识。
PCT/CN2015/088385 2015-07-28 2015-08-28 一种电力系统广域同调辨识方法及其装置 Ceased WO2017016019A1 (zh)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CN201510454081.7A CN105071380A (zh) 2015-07-28 2015-07-28 一种电力系统广域同调辨识方法及其装置
CN201510454081.7 2015-07-28

Publications (1)

Publication Number Publication Date
WO2017016019A1 true WO2017016019A1 (zh) 2017-02-02

Family

ID=54500693

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2015/088385 Ceased WO2017016019A1 (zh) 2015-07-28 2015-08-28 一种电力系统广域同调辨识方法及其装置

Country Status (2)

Country Link
CN (1) CN105071380A (zh)
WO (1) WO2017016019A1 (zh)

Cited By (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107590604A (zh) * 2017-09-13 2018-01-16 国网福建省电力有限公司 一种结合s变换和2dpca的同调机组分群方法及系统
CN111293685A (zh) * 2020-02-28 2020-06-16 华北电力大学 一种基于同调识别的系统分区惯量的评估方法
CN112668888A (zh) * 2020-12-30 2021-04-16 中国电力科学研究院有限公司 一种用于评价继电保护装置综合状态的方法及系统
CN114065600A (zh) * 2021-11-11 2022-02-18 国网天津市电力公司 基于小波变换的时间序列聚类分析的机组分群方法及装置
CN114418269A (zh) * 2021-11-30 2022-04-29 哈尔滨工业大学 一种工业机器人安全评价指标构建方法
CN115001043A (zh) * 2022-07-13 2022-09-02 广东电网有限责任公司 一种电力系统同调机组分群方法及系统
CN115618178A (zh) * 2022-09-07 2023-01-17 国家电网有限公司华东分部 电网振荡中心断面的识别方法

Families Citing this family (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107749621B (zh) * 2017-10-25 2020-08-11 东北电力大学 一种电力系统动态稳定协同辨识方法
CN109510245A (zh) * 2019-01-03 2019-03-22 东北电力大学 一种基于图分割的电力系统同调机群辨识方法

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20090099798A1 (en) * 2007-10-09 2009-04-16 Yanfeng Gong Real-Time Power System Oscillation Detection Using Modal Analysis
CN103178518A (zh) * 2013-03-01 2013-06-26 东北电力大学 根据轨迹及轨迹灵敏度定量评价电力系统暂态稳定性方法
CN103425883A (zh) * 2013-08-09 2013-12-04 中国南方电网有限责任公司 一种基于投影寻踪的跨区互联大电网同调辨识方法
CN104319762A (zh) * 2014-09-30 2015-01-28 华北电力大学（保定） 一种在线识别电力系统同调机群的新方法

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20090099798A1 (en) * 2007-10-09 2009-04-16 Yanfeng Gong Real-Time Power System Oscillation Detection Using Modal Analysis
CN103178518A (zh) * 2013-03-01 2013-06-26 东北电力大学 根据轨迹及轨迹灵敏度定量评价电力系统暂态稳定性方法
CN103425883A (zh) * 2013-08-09 2013-12-04 中国南方电网有限责任公司 一种基于投影寻踪的跨区互联大电网同调辨识方法
CN104319762A (zh) * 2014-09-30 2015-01-28 华北电力大学（保定） 一种在线识别电力系统同调机群的新方法

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
AN, JUN ET AL.: "Recognition of Power System Coherent Generators Based on Principal Component Analysis", POWER SYSTEM TECHNOLOGY, vol. 33, no. 3, 28 February 2009 (2009-02-28), ISSN: 1000-3673 *
FENG, KANGHENG ET AL.: "A Wide Area Information Based Online Recognition of Coherent Generators in Power System", POWER SYSTEM TECHNOLOGY, vol. 38, no. 8, 31 August 2014 (2014-08-31), XP055350024, ISSN: 1000-3673 *

Cited By (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107590604A (zh) * 2017-09-13 2018-01-16 国网福建省电力有限公司 一种结合s变换和2dpca的同调机组分群方法及系统
CN111293685A (zh) * 2020-02-28 2020-06-16 华北电力大学 一种基于同调识别的系统分区惯量的评估方法
CN112668888A (zh) * 2020-12-30 2021-04-16 中国电力科学研究院有限公司 一种用于评价继电保护装置综合状态的方法及系统
CN114065600A (zh) * 2021-11-11 2022-02-18 国网天津市电力公司 基于小波变换的时间序列聚类分析的机组分群方法及装置
CN114418269A (zh) * 2021-11-30 2022-04-29 哈尔滨工业大学 一种工业机器人安全评价指标构建方法
CN115001043A (zh) * 2022-07-13 2022-09-02 广东电网有限责任公司 一种电力系统同调机组分群方法及系统
CN115618178A (zh) * 2022-09-07 2023-01-17 国家电网有限公司华东分部 电网振荡中心断面的识别方法

Also Published As

Publication number Publication date
CN105071380A (zh) 2015-11-18

Similar Documents

Publication Publication Date Title
WO2017016019A1 (zh) 一种电力系统广域同调辨识方法及其装置
Gao et al. A physically inspired data-driven model for electricity theft detection with smart meter data
Jiang et al. Spatial-temporal synchrophasor data characterization and analytics in smart grid fault detection, identification, and impact causal analysis
CN108667005B (zh) 一种计及新能源影响的电网静动态结合脆弱性评估方法
CN105911334A (zh) 一种谐波阻抗未知条件下的谐波电流估计方法
Campaña Evaluación de la vulnerabilidad del sistema eléctrico de potencia en tiempo real usando tecnología de medición sincrofasorial
CN110162746A (zh) 一种多参数融合相似度的健康预警及故障诊断方法
CN106682407B (zh) 基于戴维南等值和支路传输功率极限的电压稳定评估方法
CN112564090B (zh) 基于mblda的交直流系统暂态电压稳定监测方法
Wang et al. Online analysis of voltage security in a microgrid using convolutional neural networks
CN112909939A (zh) 一种基于故障前后潮流特征的电力系统暂态稳定评估方法
Sun et al. Micro PMU based monitoring system for active distribution networks
Znidi et al. Coherency detection and network partitioning based on hierarchical DBSCAN
Nagi et al. Bayesian inference of parameters in power system dynamic models using trajectory sensitivities
Göl A decentralization method for hybrid state estimators
Ma et al. Robust dynamic state estimation for DFIG via the generalized maximum correntropy criterion ensemble Kalman filter
Konakalla et al. Feature based grid event classification from synchrophasor data
Hogan et al. Towards effective clustering techniques for the analysis of electric power grids
Wang et al. A data-driven method to detect and localize the single-phase grounding fault in distribution network based on synchronized phasor measurement
Gao et al. Short-Term Voltage Stability Prediction for Power Systems Based on a Dominant Koopman Operator-Enhanced MLE
CN120065059A (zh) 配电网短路故障类型的识别方法、装置、设备及介质
Jiang et al. Distributed state estimation method of distribution networks based on LSTM
CN111368933A (zh) 一种基于Softmax回归的配电网暂态过程故障分类方法及系统
CN106021191A (zh) 一种基于分解滤波的网络等值算法
Khazraj et al. Addressing single and multiple bad data in the modern PMU-based power system state estimation

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 15899371

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 15899371

Country of ref document: EP

Kind code of ref document: A1

32PN Ep: public notification in the ep bulletin as address of the adressee cannot be established

Free format text: NOTING OF LOSS OF RIGHTS PURSUANT TO RULE 112(1) EPC (EPO FORM 1205A DATED 01.08.2018)

122 Ep: pct application non-entry in european phase

Ref document number: 15899371

Country of ref document: EP

Kind code of ref document: A1