WO2020177706A1 - 配电网故障区段与故障时刻的判定方法 - Google Patents

配电网故障区段与故障时刻的判定方法 Download PDF

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WO2020177706A1
WO2020177706A1 PCT/CN2020/077732 CN2020077732W WO2020177706A1 WO 2020177706 A1 WO2020177706 A1 WO 2020177706A1 CN 2020077732 W CN2020077732 W CN 2020077732W WO 2020177706 A1 WO2020177706 A1 WO 2020177706A1
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fault
time
data
measurement
data set
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French (fr)
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刘洋
王蕾
张新慧
陈羽
徐丙银
张雪
谭培红
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山东理工大学
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/08—Locating faults in cables, transmission lines, or networks
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S10/00—Systems supporting electrical power generation, transmission or distribution
    • Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
    • Y04S10/52—Outage or fault management, e.g. fault detection or location

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  • the invention relates to the technical field of distribution network protection containing distributed power sources, in particular to a method for judging fault sections and moments of faults in a distribution network.
  • the access of distributed power sources has transformed the distribution network from a passive network to an active network, which has changed the power flow direction of the distribution network, resulting in changes and differences in the characteristics of the faults, and the existing relay protection of the existing distribution network is prone to errors.
  • Accurate identification of the fault section is one of the important measures to effectively reduce the fault location range and ensure the reliable and stable operation of the system.
  • the accurate determination of the time when the fault occurs provides a guarantee for subsequent fault location, line maintenance and reduces labor and other economic costs. Therefore, how to quickly realize the fault section of the distribution system containing distributed power sources and the accurate identification and determination of the time when the fault occurs are issues of great concern to the power grid operation and protection personnel.
  • the traditional method is usually based on the given load characteristics, network topology, line concentration parameters, estimation of distributed parameters or assumptions that can be calculated under the conditions of the fault point current analysis and computational. If the above conditions are unknown or incompletely known fault zone determination and fault occurrence time identification is not applicable.
  • the purpose of the present invention is to provide a method for determining the fault zone and the moment of the fault in the distribution network, which can not only solve the problem of load characteristics, network topology, line concentration parameters, and incomplete line distribution parameters.
  • the fault section is determined under known conditions and the problem is identified when the fault occurs, and it can meet the judgment results that meet the needs of actual applications.
  • the present invention provides a method for determining the fault section and the fault time of a distribution network, the method includes:
  • the process of constructing a data set includes:
  • the measurement data set at the k-th time includes the measurement data at the k-th time and the measurement data at the previous time M-1.
  • the evaluation index includes an evaluation index of a single type of measurement data and a comprehensive evaluation index of multiple types of measurement data, the evaluation index ⁇ of the single type of measurement data and multiple types of measurement data
  • the calculation formula of the comprehensive evaluation index ⁇ is shown in formula (1) and formula (2):
  • X is the historical data set
  • Y is the online measured data set
  • cov(X,Y) and cov(X,X) represent the covariance matrix of the online measured data set and the historical data set respectively
  • 2 is seeking 2 norm
  • ⁇ '1 and ⁇ 1 is the largest singular value were measured online historical data set and data set covariance matrix
  • L is a measurement of the actual data type, data type includes an actually measured voltage magnitude, Voltage phase angle, current amplitude, current phase angle, power flow at each measuring point, and on/off state of various switches at the measuring point
  • ⁇ i is the evaluation index of the i-th data type
  • ⁇ i is the weight of the i-th data evaluation index
  • the monitoring threshold range for the fluctuation of the evaluation index is set to be between 0.95 and 1.95, namely
  • ⁇ and ⁇ are respectively the evaluation index of single type measurement data and the comprehensive evaluation index of multiple types of measurement data.
  • the process of determining the failure time and the failure area based on the calculated evaluation index is:
  • the distance between the fault point and the end point is determined according to the magnitude of the end measurement point data fluctuation of the fault area, that is, the greater the fluctuation, the closer to the point.
  • the process of determining the faulty phase is to determine the faulty phase according to the measurement data of each phase at the measurement point at the fault area end, that is, to calculate the single-type quantity for the measurement data of each phase at the measurement point at the fault area end.
  • the determination method further includes:
  • the output result includes the time of occurrence of the fault, the range of the fault section, the distance of the fault point from the fault area and the fault phase.
  • the present invention provides a method for determining the fault zone and the fault moment of the distribution network, including:
  • the fault section of the distribution network and the fault time are judged.
  • the process of determining the fault section of the distribution network and the time of the fault according to the ratio of the maximum singular value includes:
  • the fault phase is judged using the data of any measurement point at the fault area.
  • the technical solution of the embodiment of the present invention provides a method for determining the fault section and the fault moment of a distribution network, the method includes: constructing a data set; calculating an evaluation index; setting a monitoring threshold for the fluctuation of the evaluation index; and an evaluation index based on the calculation Determine the fault time and fault area; determine the fault phase.
  • the measurement data comes from the measurement device installed in the existing power distribution system.
  • the judgment result includes the fault occurrence zone, the fault occurrence time and the fault phase.
  • Real-time detection after determining the time of failure, determine the degree of data mutation at each measuring point at the time of failure, determine the fault area according to the time of failure and ranking of the degree of mutation, and use the degree of abnormality to determine the fault point. The distance of the measuring point. Finally, the fault phase is judged using the data of any measurement point at the fault area.
  • This technical solution is suitable for any load level, does not need to obtain distributed power and load distribution rules, and has a small amount of calculation. It can provide theoretical and technical support for the fault diagnosis of the distribution system connected to the distributed power supply or the existing power distribution system. The method can be integrated into the software of the existing measurement equipment without replacing the equipment, and has strong practical industrial application value.
  • the technical scheme of the embodiment of the present invention provides a method for judging fault sections and fault moments of a distribution network.
  • historical measurement data and real-time online data are used to form historical data sets and online data sets respectively;
  • this embodiment proposed a two-stage determination method: the first stage is to calculate the relevant determination amount of historical data, and the second stage is the online evaluation stage. Under the condition of setting the threshold, the second stage is based on the historical data set covariance matrix and The maximum singular value ratio of the covariance matrix of the online data set is determined.
  • the measurement points of the entire network are detected in real time. After the fault occurrence time is determined, the degree of data mutation at the measurement points is determined according to the judgment results of the measurement points at the fault time.
  • the fault area is determined according to the time of occurrence of the fault and the ranking of the degree of abnormality, and the degree of abnormality can be used to determine the distance between the fault point and the measuring point.
  • the fault phase is judged using the data of any measurement point at the fault area. Sorting the data abnormality of each measuring point according to the degree of limit violation to determine the fault time and the fault section can meet the requirements for the relay protection of the distribution network under the access of distributed power sources.
  • This method is suitable for any load level, does not need to obtain distributed power and load distribution rules, and has a small amount of calculation. It can provide theoretical and technical support for the fault diagnosis of distributed power distribution systems or existing power distribution systems. This method can be integrated into the software of existing measurement equipment without changing equipment and has strong practical industrial applications
  • the judgment method provided by the technical solution of the embodiment of the present invention is simple to calculate, only uses the measurement data of the existing measurement equipment of the system, overcomes the large amount of calculation of directly using data and the reliability of the result, and can realize the load characteristics, network topology, line Judgment and identification of the fault section of the distribution network and the occurrence time of the fault under the condition that the centralized parameters and line distribution parameters are not completely known.
  • Fig. 1 is a flowchart showing a method for determining a fault section and a fault moment of a distribution network according to an exemplary embodiment
  • Fig. 2 is a flowchart showing a method for determining a fault section and a fault time of a distribution network according to another exemplary embodiment.
  • Fig. 3 is an evaluation flowchart of the judgment method proposed by the present invention.
  • Fig. 4 is a flowchart showing the determination result in Fig. 3.
  • the purpose of the present invention is to propose a two-stage determination method: the first stage is to calculate the relevant determination amount of historical data, the second stage is for online data, under the condition of setting the threshold, the data of each measuring point is changed by The degree of limitation is sorted to determine the failure time and the failure zone.
  • the present invention provides a simple method for determining the fault section and fault time of the distribution network to determine the fault time, the fault section and the fault phase.
  • the method can meet the requirements of the distribution network relay under the distributed power supply. Protection requirements.
  • Fig. 1 is a flowchart showing a method for determining a fault section and a fault time of a distribution network according to an exemplary embodiment. As shown in Figure 1, the embodiment of the present invention provides
  • a method for determining fault sections and moments of faults in a distribution network comprising:
  • the process of constructing a data set includes:
  • the measurement data set at the k-th time includes the measurement data at the k-th time and the measurement data at the previous time M-1.
  • the evaluation index includes an evaluation index of a single type of measurement data and a comprehensive evaluation index of multiple types of measurement data.
  • the evaluation index ⁇ of the single type of measurement data and the integration of multiple types of measurement data The calculation formula of the evaluation index ⁇ is shown in formula (1) and formula (2):
  • X is the historical data set
  • Y is the online measured data set
  • cov(X,Y) and cov(X,X) represent the covariance matrix of the online measured data set and the historical data set respectively
  • 2 is seeking 2 norm
  • ⁇ '1 and ⁇ 1 is the largest singular value were measured online historical data set and data set covariance matrix
  • L is a measurement of the actual data type, data type includes an actually measured voltage magnitude, Voltage phase angle, current amplitude, current phase angle, power flow at each measuring point, and on/off state of various switches at the measuring point
  • ⁇ i is the evaluation index of the i-th data type
  • ⁇ i is the weight of the i-th data evaluation index
  • the monitoring threshold range of the fluctuation of the evaluation index is set between 0.95 and 1.95, that is,
  • ⁇ and ⁇ are respectively the evaluation index of single type measurement data and the comprehensive evaluation index of multiple types of measurement data.
  • the process of determining the failure time and the failure area based on the calculated evaluation index is:
  • the distance between the fault point and the end point is determined according to the size of the end measurement point data fluctuation of the fault area, that is, the greater the fluctuation, the closer to the point.
  • the process of determining the faulty phase is to determine the faulty phase according to the measurement data of each phase at the end of the fault area, that is, to calculate the single-type measurement data for the measurement data of each phase at the end of the fault area. If
  • the determination method further includes:
  • Output results are not shown in Figure 1.
  • the output results include the time of occurrence of the fault, the range of the fault section, the distance of the fault point from the end of the fault area, and the fault phase.
  • This embodiment uses the previously measured data to form a historical data set, and the current online data forms an online data set, and judges based on the maximum singular value ratio of the covariance matrix of the historical data set and the covariance matrix of the online data set.
  • the measuring points are detected in real time. After the fault occurrence time is determined, the degree of data mutation at the measuring point is determined according to the judgment results of each measuring point at the time of the failure, and the fault area is determined according to the time of the failure and the ranking of the degree of mutation. The distance between the fault point and the measuring point. Finally, the fault phase is judged using the data of any measurement point at the fault area.
  • This technical solution is suitable for any load level, does not need to obtain distributed power and load distribution rules, and has a small amount of calculation. It can provide theoretical and technical support for the fault diagnosis of the distribution system connected to the distributed power supply or the existing power distribution system.
  • the method can be integrated into the software of the existing measurement equipment without replacing the equipment, and has strong practical industrial application value.
  • the judgment method provided in this embodiment is simple to calculate, only uses the measurement data of the existing measurement equipment of the system, overcomes the large amount of calculation of directly using data and the reliability of the results, and can realize the load characteristics, network topology, line concentration parameters, line Judgment and identification of the fault section of the distribution network and the time of the fault under the condition that the distribution parameters are not completely known.
  • Fig. 2 is a flowchart showing a method for determining a fault section and a fault time of a distribution network according to another exemplary embodiment. As shown in Figure 2, the embodiment of the present invention provides
  • a method for determining fault sections and moments of faults in a distribution network includes:
  • the fault section of the distribution network and the fault time are judged.
  • the process of judging the fault section of the distribution network and the time of the fault according to the ratio of the maximum singular value includes:
  • the fault phase is judged using the data of any measurement point at the fault area.
  • the evaluation index of single-type measurement data and the comprehensive evaluation index of multi-type measurement data are:
  • ⁇ and ⁇ are respectively the evaluation index of single type measurement data and the comprehensive evaluation index of multiple types of measurement data.
  • X is the historical data set
  • Y is the online measured data set
  • cov(X,Y) and cov(X,X) represent the covariance matrix of the online measured data set and historical data set respectively
  • ⁇ ′ 1 and ⁇ 1 are the largest singular values of the covariance matrix of the online measured data set and the historical data set, respectively.
  • the singular value can be obtained by the singular value decomposition method; l is the actual measured data type, data type Including voltage amplitude, voltage phase angle, current amplitude, current phase angle, power flow of each measuring point, and switching state of various switches at the measuring point; ⁇ i is the evaluation index of the i-th data type; ⁇ i is the i-th type The weight of the data evaluation index is satisfied
  • the monitoring threshold range during normal operation can be determined to be between 0.95 and 1.95, that is
  • the judgment method of Embodiment 2 includes two stages.
  • offline calculation is performed to obtain the largest singular value of the historical data set;
  • the steps for comparing the maximum singular value of the historical data set obtained in the first stage are as follows:
  • Phase 1 Use the data of the measurement equipment at each measuring point to recover from the most recent failure to construct a historical data set, calculate the maximum singular value ⁇ 1 through singular value decomposition, and report the data to the master station according to the requirements of system distribution automation Or separately stored in each measuring equipment;
  • the measurement data set at time k includes the measurement data at time k and the data at time M-1 before, then the online data set at time k can be expressed as
  • M is the number of sample values contained in the data set.
  • Phase 2 Use singular value decomposition to obtain the maximum singular value ⁇ ′ 1 for the online data at each moment, and report the data to the master station according to the requirements of system distribution automation or store it separately in each measuring device;
  • Phase 2 For the measurement point data of the whole network, calculate the ratio according to formula (1) and formula (2). If
  • Stage 2 Sorting the time sequence of the occurrence of abnormal data can determine the time when the fault occurs and the preliminary judgment of the fault area;
  • Stage 2 Sorting according to the degree of data violation of each measuring point, that is, sorting by the size of
  • Phase 2 Determine the phase of the fault based on the measurement data of each phase of the measurement point data at the end of the fault area according to the process of steps (4) to (6) of the phase two.
  • stage one reconstructs the historical data set based on the data after the system returns to normal, and calculates the maximum singular value.
  • the historical measurement data and real-time online data are used to form the historical data set and the online data set respectively; then the covariance matrix of the historical data set and the maximum singular value of the covariance matrix of the online data set are respectively solved; and according to the ratio of the maximum singular value Determine the fault zone and fault time of the distribution network.
  • This embodiment proposes a two-stage determination method: the first stage calculates the relevant determination amount of historical data, the second stage is the online assessment stage, and the second stage is based on the historical data set covariance matrix and The maximum singular value ratio of the covariance matrix of the online data set is determined. First, the measurement points of the entire network are detected in real time.
  • the degree of data mutation at the measurement points is determined according to the judgment results of the measurement points at the fault time.
  • the fault area is determined according to the time of occurrence of the fault and the ranking of the degree of abnormality, and the degree of abnormality can be used to determine the distance between the fault point and the measuring point.
  • the fault phase is judged using the data of any measurement point at the fault area. Sorting the data abnormality of each measuring point according to the degree of limit violation to determine the fault time and the fault section can meet the requirements for the relay protection of the distribution network under the access of distributed power sources.
  • This method is suitable for any load level, does not need to obtain distributed power sources and load distribution rules, has a small amount of calculation, and can provide theoretical and technical support for the fault diagnosis of distributed power distribution systems or existing distribution systems.
  • This method can be integrated into the software of existing measurement equipment without changing equipment, and has strong practical industrial applications
  • the judgment method provided in this embodiment is simple to calculate, only uses the measurement data of the existing measurement equipment of the system, overcomes the large amount of calculation of directly using data and the reliability of the results, and can realize the load characteristics, network topology, line concentration parameters, line Judgment and identification of the fault section of the distribution network and the time of the fault under the condition that the distribution parameters are not completely known

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Abstract

一种配电网故障区段与故障时刻的判定方法，包括：构建数据集；计算评价指标；设置评价指标波动的监测阈值；基于计算的评价指标确定故障时间和故障区域；判定故障相。根据历史数据集和在线数据集的协方差矩阵最大奇异值比值进行判定，对全网各量测点进行实时检测，确定出故障发生时刻后，根据故障时刻各测点的判定结果确定测点数据异变程度，根据故障发生时刻和异变程度排名确定故障区域，并利用异变程度确定故障点离测点的远近程度，利用故障区域端任意测点数据判断故障相。该方法适合于任何负荷水平，无需获取分布式电源和负荷的分布规律，计算量小，可为分布式电源接入的配电***或现有配电***的故障诊断提供理论和技术保障。

Description

配电网故障区段与故障时刻的判定方法 技术领域
本发明涉及含分布式电源的配电网保护技术领域,具体涉及一种配电网故障区段与故障时刻的判定方法。
背景技术
分布式电源的接入使得配电网由无源网络转变为有源网络，改变了配电网的功率流向，使得故障特征发生变化差异，导致现有配电网现有继电保护容易发生误动和拒动，进而对配电网的故障定位、隔离、恢复等产生不可忽视的影响。故障区段准确识别是有效缩小故障定位范围，保证***可靠稳定运行的重要措施之一，而故障发生时刻的准确确定为后续的故障测距、线路维修提供的保障并减少人工等经济费用。因此如何快速实现含分布式电源的配电***故障区段以及故障发生时刻的准确识别和确定是电网运行保护人员非常关心的问题。
关于故障区段确定以及故障发生时刻识别问题，传统的方法通常是以给定的负荷特性、网络拓扑、线路集中参数、对分布参数的估计或假设可以计算得到故障点电流的条件下进行分析与计算的。若对上述条件未知时或不完全已知的故障区段确定以及故障发生时刻识别不适用。
综上所述，为克服上述困难有必要给出一种快速、高效、准确可信的判定方法。
发明内容
为了解决上述现有技术存在的问题，本发明的目的是提供一种配电网故障区段与故障时刻的判定方法，不仅能够解决在负荷特性、网络拓扑、线路 集中参数、线路分布参数不完全已知条件下的故障区段确定以及故障发生时刻识别问题，而且能够满足符合实际应用需要的判别结果。
为实现上述目的，本发明采用以下技术方案：
一方面，本发明提供的一种配电网故障区段与故障时刻的判定方法，所述方法包括：
构建数据集；
计算评价指标；
设置评价指标波动的监测阈值；
基于计算的评价指标确定故障时间和故障区域；
判定故障相。
作为本实施例一种可能的实现方式，构建数据集的过程包括：
建历史数据集：利用各量测点的量测设备最近一次故障恢复后的数据构建历史数据集，通过奇异值分解计算得到历史数据集协方差矩阵的最大奇异值σ 1；
构建在线数据集：第k时刻的量测数据集包括第k时刻的量测数据以及之前M-1时刻的量测数据，则第k时刻在线数据集可表示为：Y＝[y k-M+1，…，y k]，其中M为数据集所包含采样值的个数，y k为第k时刻的量测数据，y k-M+1为第k时刻之前M-1时刻的量测数据；并对每一时刻的在线数据集利用奇异值分解求得在线数据集协方差矩阵的最大奇异值σ′ 1。
作为本实施例一种可能的实现方式，评价指标包括单类量测数据的评价指标和多类量测数据的综合评价指标，所述单类量测数据的评价指标ψ和多类量测数据的综合评价指标Ψ的计算公式如式(1)和式(2)所示：
Figure PCTCN2020077732-appb-000001
Figure PCTCN2020077732-appb-000002
其中，X为历史数据集，Y为在线实测数据集；cov(X,Y)和cov(X,X)分别表示在线实测数据集和历史数据集的协方差矩阵；||·|| 2为求2范数；σ′ 1和σ 1分别为在线实测数据集和历史数据集的协方差矩阵的最大奇异值；l为实际量测的数据种类，实际量测的数据种类包括电压幅值、电压相角、电流幅值，电流相角、各测点潮流、测点上各类开关的开关状态；ψ i为第i类数据类型的评价指标；α i为第i类数据评价指标的权重且满足
Figure PCTCN2020077732-appb-000003
作为本实施例一种可能的实现方式，设置评价指标波动的监测阈值范围在0.95～1.95之间，即
|ψ-1|≤0.05          (3)
|Ψ-1|≤0.05            (4)
ψ和Ψ分别为单类量测数据的评价指标和多类量测数据的综合评价指标。
作为本实施例一种可能的实现方式，基于计算的评价指标确定故障时间和故障区域的过程为：
对配电网全网测量点量测数据分别计算单类量测数据的评价指标和多类量测数据的综合评价指标，如果|ψ-1|＞0.05或|Ψ-1|＞0.05，则说明对应测量点有故障发生；
对发生故障测量点的异常量测数据出现时间进行排序，最早出现异常量测数据的时间为故障发生的时间；
将所有出现异常量测数据的测量点所在区域标记未疑似故障区域；
对疑似故障区域内各测点量测数据按照越限程度进行排序，即安装|ψ-1|或|Ψ-1|的大小排序，进行确定故障区域。
作为本实施例一种可能的实现方式，根据故障区域的端测点数据波动大 小确定故障点离端点的距离程度，即波动越大说明离该点越近。
作为本实施例一种可能的实现方式，判定故障相的过程为；根据故障区域端测点每相的测量数据确定故障相，即对故障区域端测点每相的测量数据分别计算单类量测数据的评价指标和多类量测数据的综合评价指标，如果|ψ-1|＞0.05或|Ψ-1|＞0.05，则确定该相为故障相。
作为本实施例一种可能的实现方式，所述判定方法还包括：
输出结果；输出的结果包括故障发生时间、故障区段范围、故障点离故障区域端远近程度和故障相。
另一方面，本发明提供的一种配电网故障区段与故障时刻的判定方法，包括：
利用历史测量数据和实时在线数据分别构成历史数据集和在线数据集；
分别求解历史数据集协方差矩阵和在线数据集协方差矩阵的最大奇异值；
根据最大奇异值的比值进行配电网故障区段与故障时刻判定。
作为本实施例一种可能的实现方式，根据最大奇异值的比值进行配电网故障区段与故障时刻判定的过程包括：
首先对全网各量测点进行实时检测，确定出故障发生时刻；
根据故障时刻各测点的判定结果确定测点数据异变程度；
根据故障发生时刻和异变程度排名确定故障区域，并可利用异变程度确定故障点离测点的远近程度；
最后利用故障区域端任意测点数据判断故障相。
本发明实施例的技术方案可以具有的有益效果如下：
本发明实施例的技术方案提供的一种配电网故障区段与故障时刻的判定方法，所述方法包括：构建数据集；计算评价指标；设置评价指标波动的监测阈值；基于计算的评价指标确定故障时间和故障区域；判定故障相。其中量测数据来自现有配电***中安装的量测装置。其判断结果包括故障发生区段，故障发生时刻以及故障相。使用之前所测量数据构成历史数据集，当前 在线数据构成在线数据集，并根据历史数据集协方差矩阵和在线数据集协方差矩阵的最大奇异值比值进行判定，首先对全网各量测点进行实时检测，确定出故障发生时刻后，根据故障时刻各测点的判定结果确定测点数据异变程度，根据故障发生时刻和异变程度排名确定故障区域，并可利用异变程度确定故障点离测点的远近程度。最后利用故障区域端任意测点数据判断故障相。该技术方案适合于任何负荷水平，无需获取分布式电源和负荷的分布规律，计算量小，可为分布式电源接入的配电***或现有配电***的故障诊断提供理论和技术保障。该方法可以集成到现有量测设备软件中，无需更换设备，具有较强的实际工业应用价值。
本发明实施例的技术方案提供的一种配电网故障区段与故障时刻的判定方法，首先利用历史测量数据和实时在线数据分别构成历史数据集和在线数据集；然后分别求解历史数据集协方差矩阵和在线数据集协方差矩阵的最大奇异值；并根据最大奇异值的比值进行配电网故障区段与故障时刻判定。为解决在负荷特性、网络拓扑、线路集中参数、线路分布参数不完全已知条件下的故障区段确定以及故障发生时刻识别问题，并最终给出满足符合实际应用需要的判别结果，该实施例的技术方案提出一种两阶段的判定方法：第一阶段计算历史数据的相关判定量，第二阶段为在线评定阶段，在设定阈值的条件下，第二阶段根据历史数据集协方差矩阵和在线数据集协方差矩阵的最大奇异值比值进行判定，首先对全网各量测点进行实时检测，确定出故障发生时刻后，根据故障时刻各测点的判定结果确定测点数据异变程度，根据故障发生时刻和异变程度排名确定故障区域，并可利用异变程度确定故障点离测点的远近程度。最后利用故障区域端任意测点数据判断故障相。将各测点数据异变按越限程度进行排序来确定故障时间和故障区段，能够满足对分布式电源接入下的配电网继电保护的要求。，本方法适合于任何负荷水平，无需获取分布式电源和负荷的分布规律，计算量小，可为分布式电源接入的配电***或现有配电***的故障诊断提供理论和技术保障。该方法可以集成到现 有量测设备软件中，无需更换设备，具有较强的实际工业应用
本发明实施例的技术方案提供的判断方法计算简便、只使用***现有量测设备的量测数据、克服直接使用数据的计算量大和结果可信问题，能够实现在负荷特性、网络拓扑、线路集中参数、线路分布参数不完全已知条件下对配电网故障区段和故障发生时间的判定和识别。
附图说明
图1是根据一示例性实施例示出的一种配电网故障区段与故障时刻的判定方法的流程图；
图2是根据另一示例性实施例示出的一种配电网故障区段与故障时刻的判定方法的流程图。
图3是本发明提出的判定方法的一种评定流程图；
图4是图3中给出判定结果的流程图。
具体实施方式
下面结合附图与实施例对本发明做进一步说明：
为能清楚说明本方案的技术特点，下面通过具体实施方式，并结合其附图，对本发明进行详细阐述。下文的公开提供了许多不同的实施例或例子用来实现本发明的不同结构。为了简化本发明的公开，下文中对特定例子的部件和设置进行描述。此外，本发明可以在不同例子中重复参考数字和/或字母。这种重复是为了简化和清楚的目的，其本身不指示所讨论各种实施例和/或设置之间的关系。应当注意，在附图中所图示的部件不一定按比例绘制。本发明省略了对公知组件和处理技术及工艺的描述以避免不必要地限制本发明。
利用现有网络中已有量测设备的量测数据，虽然可以直接求解故障区段以及故障发生时刻识别。但是，直接求解方法的计算量庞大。其求解难点在于：
(1)范数求解问题：量测设备的量测数据非常海量的，如果直接通过一般范数求解的方法计算，随着处理数据集的增大，计算量十分庞大。
(2)求解结果判定可信问题：如果直接从数据本身进行求解，由于实际运行中量测设备量测误差，分布式电源接入和出力不同的问题，使得直接依据数据本身进行判定结果可信性没法满足实际应用的要求。
因此，为解决在负荷特性、网络拓扑、线路集中参数、线路分布参数不完全已知条件下的故障区段确定以及故障发生时刻识别问题，并最终给出满足符合实际应用需要的判别结果。本发明的目的在于提出一种两阶段的判定方法：第一阶段计算历史数据的相关判定量，第二阶段为针对在线数据，在设定阈值的条件下，将各测点数据异变按越限程度进行排序来确定故障时间和故障区段。
本发明给出了一个简便的配电网故障区段与故障时刻的判定方法，来确定故障时间和故障区段以及故障相，该方法能够满足对分布式电源接入下的配电网继电保护的要求。
实施例1
图1是根据一示例性实施例示出的一种配电网故障区段与故障时刻的判定方法的流程图。如图1所示，本发明实施例提供的
一种配电网故障区段与故障时刻的判定方法，所述方法包括：
构建数据集；
计算评价指标；
设置评价指标波动的监测阈值；
基于计算的评价指标确定故障时间和故障区域；
判定故障相。
在一种可能的实现方式中，构建数据集的过程包括：
建历史数据集：利用各量测点的量测设备最近一次故障恢复后的数据构建历史数据集，通过奇异值分解计算得到历史数据集协方差矩阵的最大奇异 值σ 1；
构建在线数据集：第k时刻的量测数据集包括第k时刻的量测数据以及之前M-1时刻的量测数据，则第k时刻在线数据集可表示为：Y＝[y k-M+1，…，y k]，其中M为数据集所包含采样值的个数，y k为第k时刻的量测数据，y k-M+1为第k时刻之前M-1时刻的量测数据；并对每一时刻的在线数据集利用奇异值分解求得在线数据集协方差矩阵的最大奇异值σ′ 1。
在一种可能的实现方式中，评价指标包括单类量测数据的评价指标和多类量测数据的综合评价指标，所述单类量测数据的评价指标ψ和多类量测数据的综合评价指标Ψ的计算公式如式(1)和式(2)所示：
Figure PCTCN2020077732-appb-000004
Figure PCTCN2020077732-appb-000005
其中，X为历史数据集，Y为在线实测数据集；cov(X,Y)和cov(X,X)分别表示在线实测数据集和历史数据集的协方差矩阵；||·|| 2为求2范数；σ′ 1和σ 1分别为在线实测数据集和历史数据集的协方差矩阵的最大奇异值；l为实际量测的数据种类，实际量测的数据种类包括电压幅值、电压相角、电流幅值，电流相角、各测点潮流、测点上各类开关的开关状态；ψ i为第i类数据类型的评价指标；α i为第i类数据评价指标的权重且满足
Figure PCTCN2020077732-appb-000006
在一种可能的实现方式中，设置评价指标波动的监测阈值范围在0.95～1.95之间，即
|ψ-1|≤0.05         (3)
|Ψ-1|≤0.05         (4)
ψ和Ψ分别为单类量测数据的评价指标和多类量测数据的综合评价指标。
在一种可能的实现方式中，基于计算的评价指标确定故障时间和故障区域的过程为：
对配电网全网测量点量测数据分别计算单类量测数据的评价指标和多类量测数据的综合评价指标，如果|ψ-1|＞0.05或|Ψ-1|＞0.05，则说明对应测量点有故障发生；
对发生故障测量点的异常量测数据出现时间进行排序，最早出现异常量测数据的时间为故障发生的时间；
将所有出现异常量测数据的测量点所在区域标记未疑似故障区域；
对疑似故障区域内各测点量测数据按照越限程度进行排序，即安装|ψ-1|或|Ψ-1|的大小排序，进行确定故障区域。
在一种可能的实现方式中，根据故障区域的端测点数据波动大小确定故障点离端点的距离程度，即波动越大说明离该点越近。
在一种可能的实现方式中，判定故障相的过程为；根据故障区域端测点每相的测量数据确定故障相，即对故障区域端测点每相的测量数据分别计算单类量测数据的评价指标和多类量测数据的综合评价指标，如果|ψ-1|＞0.05或|Ψ-1|＞0.05，则确定该相为故障相。
在一种可能的实现方式中，所述判定方法还包括：
输出结果；输出结果步骤未在图1中示出，输出的结果包括故障发生时间、故障区段范围、故障点离故障区域端远近程度和故障相。
本实施例使用之前所测量数据构成历史数据集，当前在线数据构成在线数据集，并根据历史数据集协方差矩阵和在线数据集协方差矩阵的最大奇异值比值进行判定，首先对全网各量测点进行实时检测，确定出故障发生时刻后，根据故障时刻各测点的判定结果确定测点数据异变程度，根据故障发生时刻和异变程度排名确定故障区域，并可利用异变程度确定故障点离测点的远近程度。最后利用故障区域端任意测点数据判断故障相。
该技术方案适合于任何负荷水平，无需获取分布式电源和负荷的分布规律，计算量小，可为分布式电源接入的配电***或现有配电***的故障诊断提供理论和技术保障。该方法可以集成到现有量测设备软件中，无需更换设备，具有较强的实际工业应用价值。
本实施例提供的判断方法计算简便、只使用***现有量测设备的量测数据、克服直接使用数据的计算量大和结果可信问题，能够实现在负荷特性、网络拓扑、线路集中参数、线路分布参数不完全已知条件下对配电网故障区段和故障发生时间的判定和识别。
实施例2
图2是根据另一示例性实施例示出的一种配电网故障区段与故障时刻的判定方法的流程图。如图2所示，本发明实施例提供的
一种配电网故障区段与故障时刻的判定方法，包括：
利用历史测量数据和实时在线数据分别构成历史数据集和在线数据集；
分别求解历史数据集协方差矩阵和在线数据集协方差矩阵的最大奇异值；
根据最大奇异值的比值进行配电网故障区段与故障时刻判定。
在一种可能的实现方式中，根据最大奇异值的比值进行配电网故障区段与故障时刻判定的过程包括：
首先对全网各量测点进行实时检测，确定出故障发生时刻；
根据故障时刻各测点的判定结果确定测点数据异变程度；
根据故障发生时刻和异变程度排名确定故障区域，并可利用异变程度确定故障点离测点的远近程度；
最后利用故障区域端任意测点数据判断故障相。
本实施例需要计算单类型量测数据的评价指标和多类型量测数据的综合评价指标，考虑***正常运行中评价指标波动的监测阈值确定规则，考虑指标的故障时间确定、故障区域、故障相判定方法。
计算单类型量测数据的评价指标和多类型量测数据的综合评价指标如下：
在任意负荷水平下，单类量测数据的评价指标和多类量测数据的综合评价指标为：
Figure PCTCN2020077732-appb-000007
Figure PCTCN2020077732-appb-000008
其中，ψ和Ψ分别单类量测数据的评价指标和多类量测数据的综合评价指标。X为历史数据集，Y为在线实测数据集；cov(X,Y)和cov(X,X)分别表示在线实测数据集和历史数据集的协方差矩阵；||·|| 2求2范数；σ′ 1和σ 1分别为在线实测数据集和历史数据集的协方差矩阵的最大奇异值，该奇异值可以通过奇异值分解方法求得；l为实际量测的数据种类，数据种类包括电压幅值、电压相角、电流幅值，电流相角、各测点潮流、测点上各类开关的开关状态；ψ i为第i类数据类型的评价指标；α i为第i类数据评价指标的权重且满足
Figure PCTCN2020077732-appb-000009
考虑***正常运行中评价指标波动的监测阈值确定规则：
无论负荷或分布式电源，如果接入影响符合国家标准和国网标准，则表明该状态下***运行正常，可确定正常运行时的监测阈值范围在0.95～1.95之间，即
|ψ-1|≤0.05         (3)
或
|Ψ-1|≤0.05         (4)
考虑指标的故障时间确定、故障区域、故障相判定方法。
如图3和图4所示，实施例2的判定方法包括两个阶段，在第一阶段离线计算，获得历史数据集的最大奇异值；第二阶段根据在线数据集计算最大奇异值，与第一阶段所得历史数据集最大奇异值的比较结果进行其步骤如下：
(1)阶段一：利用各测点的量测设备最近一次故障恢复后的数据构建历史数据集，通过奇异值分解计算得到其最大奇异值σ 1，根据***配电自动化 要求该数据上报主站或单独存在各量测设备中；
(2)阶段二：根据在线评价要求，按如下实例方式构建在线数据集
第k时刻的量测数据集包括第k时刻的量测数据以及之前M-1时刻的数据，则第k时刻在线数据集可表示为
Y＝[y k-M+1，…，y k]
其中M为数据集所包含采样值的多少。
(3)阶段二：对每一时刻的在线数据利用奇异值分解求得最大奇异值σ′ 1,根据***配电自动化要求该数据上报主站或单独存在各量测设备中；
(4)阶段二：对全网测量点数据，按式(1)和式(2)计算比值，若|ψ-1|＞0.05或|Ψ-1|＞0.05，则判定有故障发生；
(5)阶段二：对于异常数据出现时间顺序进行排序可以确定出故障发生的时间和故障区域的初步判断；
(6)阶段二：根据各测点数据越限程度排序，即|ψ-1|或|Ψ-1|的大小排序，并结合第(5)步的初步判断结果，可以确定故障区域并根据故障区域端测点数据波动大小确定故障点离端点的距离程度，即波动越大说明离该点越近；
(7)阶段二：根据故障区域端测点数据每相的测量数据根据阶段二步骤(4)至(6)的过程确定故障发生相。
(8)输出结果，包括故障发生时间，故障区段范围，故障点离故障区域端远近程度，故障相。
至此，本发明中涉及的故障区段确定以及故障发生时刻识别过程完毕。
后续的故障区段确定以及故障发生时刻识别过程开始时，转入阶段一：根据***恢复正常后的数据重新构建历史数据集，并计算最大奇异值。
本实施例首先利用历史测量数据和实时在线数据分别构成历史数据集和在线数据集；然后分别求解历史数据集协方差矩阵和在线数据集协方差矩阵的最大奇异值；并根据最大奇异值的比值进行配电网故障区段与故障时刻判 定。该实施例提出一种两阶段的判定方法：第一阶段计算历史数据的相关判定量，第二阶段为在线评定阶段，在设定阈值的条件下，第二阶段根据历史数据集协方差矩阵和在线数据集协方差矩阵的最大奇异值比值进行判定，首先对全网各量测点进行实时检测，确定出故障发生时刻后，根据故障时刻各测点的判定结果确定测点数据异变程度，根据故障发生时刻和异变程度排名确定故障区域，并可利用异变程度确定故障点离测点的远近程度。最后利用故障区域端任意测点数据判断故障相。将各测点数据异变按越限程度进行排序来确定故障时间和故障区段，能够满足对分布式电源接入下的配电网继电保护的要求。本方法适合于任何负荷水平，无需获取分布式电源和负荷的分布规律，计算量小，可为分布式电源接入的配电***或现有配电***的故障诊断提供理论和技术保障。该方法可以集成到现有量测设备软件中，无需更换设备，具有较强的实际工业应用
本实施例提供的判断方法计算简便、只使用***现有量测设备的量测数据、克服直接使用数据的计算量大和结果可信问题，能够实现在负荷特性、网络拓扑、线路集中参数、线路分布参数不完全已知条件下对配电网故障区段和故障发生时间的判定和识别。
上述虽然结合附图对本发明的具体实施方式进行了描述，但并非对本发明保护范围的限制。对于所属领域的技术人员来说，在上述说明的基础上还可以做出其它不同形式的修改或变形。这里无需也无法对所有的实施方式予以穷举。在本发明的技术方案的基础上，本领域技术人员不需要付出创造性劳动即可做出的各种修改或变形仍在本发明的保护范围以内。

Claims (10)

  1. 一种配电网故障区段与故障时刻的判定方法，其特征在于，所述方法包括：
    构建数据集；
    计算评价指标；
    设置评价指标波动的监测阈值；
    基于计算的评价指标确定故障时间和故障区域；
    判定故障相。
  2. 根据权利要求1所述的配电网故障区段与故障时刻的判定方法，其特征在于，构建数据集的过程包括：
    建历史数据集：利用各量测点的量测设备最近一次故障恢复后的数据构建历史数据集，通过奇异值分解计算得到历史数据集协方差矩阵的最大奇异值σ 1；
    构建在线数据集：第k时刻的量测数据集包括第k时刻的量测数据以及之前M-1时刻的量测数据，则第k时刻在线数据集可表示为：Y＝[y k-M+1，…，y k]，其中M为数据集所包含采样值的个数，y k为第k时刻的量测数据，y k-M+1为第k时刻之前M-1时刻的量测数据；并对每一时刻的在线数据集利用奇异值分解求得在线数据集协方差矩阵的最大奇异值σ′ 1。
  3. 根据权利要求2所述的配电网故障区段与故障时刻的判定方法，其特征在于，评价指标包括单类量测数据的评价指标和多类量测数据的综合评价指标，所述单类量测数据的评价指标ψ和多类量测数据的综合评价指标Ψ的计算公式如式(1)和式(2)所示：
    Figure PCTCN2020077732-appb-100001
    Figure PCTCN2020077732-appb-100002
    其中，X为历史数据集，Y为在线实测数据集；cov(X,Y)和cov(X,X)分别表示在线实测数据集和历史数据集的协方差矩阵；||·|| 2为求2范数；σ′ 1和σ 1分别为在线实测数据集和历史数据集的协方差矩阵的最大奇异值；l为实际量测的数据种类，实际量测的数据种类包括电压幅值、电压相角、电流幅值，电流相角、各测点潮流、测点上各类开关的开关状态；ψ i为第i类数据类型的评价指标；α i为第i类数据评价指标的权重且满足
    Figure PCTCN2020077732-appb-100003
  4. 根据权利要求3所述的配电网故障区段与故障时刻的判定方法，其特征在于，设置评价指标波动的监测阈值范围在0.95～1.95之间，即
    |ψ-1|≤0.05  (3)
    |Ψ-1|≤0.05  (4)
    ψ和Ψ分别为单类量测数据的评价指标和多类量测数据的综合评价指标。
  5. 根据权利要求4所述的配电网故障区段与故障时刻的判定方法，其特征在于，基于计算的评价指标确定故障时间和故障区域的过程为：
    对配电网全网测量点量测数据分别计算单类量测数据的评价指标和多类量测数据的综合评价指标，如果|ψ-1|＞0.05或|Ψ-1|＞0.05，则说明对应测量点有故障发生；
    对发生故障测量点的异常量测数据出现时间进行排序，最早出现异常量测数据的时间为故障发生的时间；
    将所有出现异常量测数据的测量点所在区域标记未疑似故障区域；
    对疑似故障区域内各测点量测数据按照越限程度进行排序，即安装|ψ-1|或|Ψ-1|的大小排序，进行确定故障区域。
  6. 根据权利要求5所述的配电网故障区段与故障时刻的判定方法，其特征在于，根据故障区域的端测点数据波动大小确定故障点离端点的距离程度， 即波动越大说明离该点越近。
  7. 根据权利要求5所述的配电网故障区段与故障时刻的判定方法，其特征在于，判定故障相的过程为；根据故障区域端测点每相的测量数据确定故障相，即对故障区域端测点每相的测量数据分别计算单类量测数据的评价指标和多类量测数据的综合评价指标，如果|ψ-1|＞0.05或|Ψ-1|＞0.05，则确定该相为故障相。
  8. 根据权利要求1-7任意一项所述的配电网故障区段与故障时刻的判定方法，其特征在于，还包括：
    输出结果；输出的结果包括故障发生时间、故障区段范围、故障点离故障区域端远近程度和故障相。
  9. 一种配电网故障区段与故障时刻的判定方法，其特征在于，包括：
    利用历史测量数据和实时在线数据分别构成历史数据集和在线数据集；
    分别求解历史数据集协方差矩阵和在线数据集协方差矩阵的最大奇异值；
    根据最大奇异值的比值进行配电网故障区段与故障时刻判定。
  10. 根据权利要求9所述的配电网故障区段与故障时刻的判定方法，其特征在于，根据最大奇异值的比值进行配电网故障区段与故障时刻判定的过程包括：
    首先对全网各量测点进行实时检测，确定出故障发生时刻；
    根据故障时刻各测点的判定结果确定测点数据异变程度；
    根据故障发生时刻和异变程度排名确定故障区域，并可利用异变程度确定故障点离测点的远近程度；
    最后利用故障区域端任意测点数据判断故障相。
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