CN109901022B - Power distribution network area positioning method based on synchronous measurement data - Google Patents

Power distribution network area positioning method based on synchronous measurement data Download PDF

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CN109901022B
CN109901022B CN201910277333.1A CN201910277333A CN109901022B CN 109901022 B CN109901022 B CN 109901022B CN 201910277333 A CN201910277333 A CN 201910277333A CN 109901022 B CN109901022 B CN 109901022B
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刘洋
赵艳雷
王蕾
凌平
陈东
刘劲松
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Shandong University of Technology
State Grid Shanghai Electric Power Co Ltd
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State Grid Shanghai Electric Power Co Ltd
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    • Y02E40/70—Smart grids as climate change mitigation technology in the energy generation sector
    • 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
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E60/00—Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
    • 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
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    • 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
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Abstract

The invention discloses a power distribution network area positioning method based on synchronous measurement data, which judges a fault area according to generalized characteristic values of a covariance matrix of a historical data set and a covariance matrix of an online data set and a set threshold confidence interval. The method specifically comprises the following steps: determining the fluctuation degree of the covariance ratio of monitoring point data according to the real-time mu PMU data configured in the whole network; judging whether a fault occurs according to whether the fluctuation degree exceeds a set threshold confidence interval; the failure zones are determined in order of varying degrees. The method can not only solve the problem of positioning the fault area with incompletely known load characteristics, network topology, line parameters and distributed power supply output, but also meet the requirement on relay protection of the power distribution network under the condition of distributed power supply access.

Description

Power distribution network area positioning method based on synchronous measurement data
Technical Field
The invention relates to the technical field of fault area positioning of active power distribution networks, in particular to a power distribution network area positioning method based on synchronous measurement data.
Background
The access of the distributed power supply enables the power distribution network to be changed from a passive network to an active network, changes the power flow direction of the power distribution network, changes the fault characteristics and causes the fault and the failure of the existing relay protection of the existing power distribution network, and further generates non-negligible influence on fault location, isolation, recovery and the like of the power distribution network.
Accurate identification of a fault section is one of important measures for effectively reducing a fault positioning range and ensuring reliable and stable operation of a system, and as a large number of Micro phasor measurement units (mu PMU) are connected to a power distribution system, how to quickly realize positioning of a fault area of the power distribution system containing a distributed power supply by using measurement data of the mu PMU becomes a problem which is very concerned by power grid operation protection personnel.
With respect to fault section determination, conventional methods typically analyze and calculate given load characteristics, network topology, line concentration parameters, estimates of distribution parameters, or assumptions that the fault point current can be calculated. However, the determination of the fault section and the identification of the time when the fault occurs are not suitable if the above conditions are unknown or not completely known.
Disclosure of Invention
In order to solve the problems in the prior art, the invention aims to provide a power distribution network area positioning method based on synchronous measurement data, which can not only solve the problem of positioning in a fault area with incompletely known load characteristics, network topology, line parameters and distributed power supply output, but also meet the requirement on relay protection of a power distribution network under the condition of distributed power supply access.
In order to achieve the purpose, the invention adopts the following technical scheme:
the invention provides a distribution network area positioning method based on synchronous measurement data, which is used for judging a fault area according to generalized eigenvalues of a covariance matrix of a historical data set and a covariance matrix of an online data set and a set threshold confidence interval.
As a possible implementation manner of this embodiment, the method for locating a distribution network area includes:
determining the fluctuation degree of the covariance ratio of monitoring point data according to the real-time mu PMU data configured in the whole network;
judging whether a fault occurs according to whether the fluctuation degree exceeds a set threshold confidence interval;
the failure zones are determined in order of varying degrees.
As a possible implementation manner of this embodiment, the process of determining the fluctuation degree of the covariance ratio of the monitoring point data according to the μ PMU real-time data configured in the whole network includes: and forming a historical data set by using the measured data, forming an online data set by using the current online data, and calculating the covariance ratio of the monitoring point data according to the covariance matrix of the historical data set and the covariance matrix of the online data set.
As a possible implementation manner of this embodiment, the method for locating a distribution network area includes:
calculating a comprehensive evaluation index of mu PMU measurement data;
determining an index threshold confidence interval;
setting a mu PMU monitoring point data abnormity judgment rule;
and positioning the fault area.
As a possible implementation manner of this embodiment, the process of calculating the comprehensive evaluation index of the PMU measurement data includes: under any load level, the evaluation index of the single-class measurement data and the comprehensive evaluation index of the multi-class measurement data are as follows:
Figure BDA0002020465370000021
Figure BDA0002020465370000022
Figure BDA0002020465370000023
wherein the content of the first and second substances,
Figure BDA0002020465370000031
and omega respectively represents the evaluation index of single-class measurement data and the comprehensive evaluation index of multi-class measurement data; x is a historical data set, and Y is an online measured data set; cov (X, Y) and cov (X, X) represent covariance matrices of online measured datasets and historical datasets, respectively; solving the characteristic value of the matrix; lambda [ alpha ]iIs the ith generalized eigenvalue; p is a generalized characteristicThe number of values;
Figure BDA0002020465370000032
is the product of p generalized eigenvalues; l is the actually measured data type, and the data type comprises a voltage amplitude, a voltage phase angle, a current amplitude and a current phase angle;
Figure BDA0002020465370000033
the evaluation index is the evaluation index of the ith type of data; kappaiIs the weight of the evaluation index of the ith class data and meets the requirement
Figure BDA0002020465370000034
As a possible implementation manner of this embodiment, the process of determining the confidence interval of the index threshold value is as follows:
the upper and lower limit sets of the generalized characteristic value, the monitoring index and the comprehensive evaluation index are respectively as follows:
Figure BDA0002020465370000035
Figure BDA0002020465370000036
Figure BDA0002020465370000037
wherein U isiAnd LiAre respectively the ith generalized eigenvalue lambdaiHas an upper and lower limit; u shapeθAnd LθRespectively an upper limit and a lower limit of an index theta confidence interval; u shapeΩAnd LΩRespectively the upper and lower limits of the confidence interval of the index omega;
Figure BDA0002020465370000038
is a (1-alpha)/2 critical value of standard normal distribution, and alpha is a confidence level, and alpha is selected according to actual detection requirements.
As a possible implementation manner of this embodiment, the process of setting the mu PMU monitoring point data abnormality determination rule is as follows:
rule one is as follows: if | omega-1 | is less than or equal to 0 or | omega-1 | is more than 0 and L is less than or equal toΩ≤1≤UΩData of the mu PMU monitoring point is not abnormal;
rule two: if | Ω -1| is > 0 and LΩIf the data is abnormal, the mu PMU monitors that the data is abnormal and a fault occurs.
As a possible implementation manner of this embodiment, the process of locating the fault area includes:
construction of historical data set Using data from the last recovery of a mu PMU failure, by | (cov (X, X))-1I, calculating a generalized characteristic value of historical data;
constructing an online data set: the PMU measurement data set at the kth time includes measurement data at the kth time and data at a previous time M-1, and the online data set at the kth time is expressed as:
Y＝[yk-M+1，…，yk]
wherein M is how many sampling values the data set contains;
obtaining the generalized characteristic value of the online data according to | cov (X, Y) | for the online data at each moment;
calculating an evaluation index value and determining a confidence interval;
judging whether a fault occurs according to a mu PMU monitoring point data abnormity judgment rule, and if so, sequencing the mu PMU monitoring points according to the overrun degree;
and sequencing and determining the fault area according to the data out-of-limit degree of each monitoring point.
As a possible implementation manner of this embodiment, the process of locating the fault area further includes:
reconstructing a historical data set according to the data after the system is recovered to be normal, and calculating the covariance matrix of the historical data set and the generalized eigenvalue of the covariance matrix of the online data set;
and outputting the result, namely outputting the fault area with the mu PMU monitoring point as a boundary.
The technical scheme of the embodiment of the invention has the following beneficial effects:
according to the distribution network area positioning method based on the synchronous measurement data, provided by the technical scheme of the embodiment of the invention, the fault area is judged according to the generalized characteristic values of the covariance matrix of the historical data set and the covariance matrix of the online data set and the set threshold confidence interval, so that the problem of positioning the fault area with incompletely known load characteristics, network topology, circuit parameters and distributed power supply output can be solved, and the requirement on relay protection of a distribution network under the condition of distributed power supply access can be met.
The power distribution network region positioning method based on the synchronous measurement data, provided by the technical scheme of the embodiment of the invention, is suitable for any load level, does not need to acquire the distribution rule of a distributed power supply and a load, is small in calculated amount, can provide theoretical and technical support for fault diagnosis of an active power distribution system or the existing power distribution system, and has a strong practical industrial application value.
The technical scheme provided by the embodiment of the invention provides the power distribution network area positioning method based on the synchronous measurement data, which is only used for judging by using the measurement data of the miniature phasor measurement unit without excessive system prior knowledge. The method comprises the following steps: in the first stage, off-line calculation is carried out, and characteristic values related to a historical data set are obtained; and in the second stage, calculating a characteristic value related to the online data set, multiplying the characteristic value obtained in the first stage by the characteristic value to obtain a product of generalized characteristic values, and then positioning by combining a confidence interval.
According to the power distribution network region positioning method based on the synchronous measurement data, the method is simple and convenient to calculate, only measurement data of the existing measurement equipment of the system are used, the problems that the calculated amount of the directly used data is large and the prior knowledge of the system is excessively relied on are solved, and the positioning of a power distribution network fault region can be realized under the condition that the load characteristics, the network topology, the line parameters and the output of the distributed power supply are not completely known.
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Fig. 1 is a flow chart illustrating a method for power distribution network area location based on synchronized metrology data in accordance with an exemplary embodiment;
fig. 2 is a flowchart illustrating a method for power distribution network area location based on synchronous measurement data according to another exemplary embodiment.
Detailed Description
The invention is further illustrated by the following examples in conjunction with the accompanying drawings:
in order to clearly explain the technical features of the present invention, the following detailed description of the present invention is provided with reference to the accompanying drawings. The following disclosure provides many different embodiments, or examples, for implementing different features of the invention. To simplify the disclosure of the present invention, the components and arrangements of specific examples are described below. Furthermore, the present invention may repeat reference numerals and/or letters in the various examples. This repetition is for the purpose of simplicity and clarity and does not in itself dictate a relationship between the various embodiments and/or configurations discussed. It should be noted that the components illustrated in the figures are not necessarily drawn to scale. Descriptions of well-known components and processing techniques and procedures are omitted so as to not unnecessarily limit the invention.
In order to solve the problem of fault area positioning under the condition that load characteristics, network topology, line parameters and distributed power supply output are not completely known and finally provide a judgment result meeting the actual application requirement, the invention provides a power distribution network fault area positioning method based on synchronous measurement data.
Example 1
Fig. 1 is a flowchart illustrating a method for positioning an area of a power distribution network based on synchronous measurement data according to an exemplary embodiment. As shown in fig. 1, a method for positioning a distribution network area based on synchronous measurement data according to an embodiment of the present invention includes:
determining the fluctuation degree of the covariance ratio of monitoring point data according to the real-time mu PMU data configured in the whole network;
judging whether a fault occurs according to whether the fluctuation degree exceeds a set threshold confidence interval;
the failure zones are determined in order of varying degrees.
In a possible implementation manner, the process of determining the fluctuation degree of the covariance ratio of the monitoring point data according to the μ PMU real-time data configured in the whole network includes: and forming a historical data set by using the measured data, forming an online data set by using the current online data, and calculating the covariance ratio of the monitoring point data according to the covariance matrix of the historical data set and the covariance matrix of the online data set.
In one possible implementation, the process of determining whether a fault occurs according to whether the fluctuation degree exceeds the set threshold confidence interval is as follows: and if the fluctuation degree exceeds the set threshold confidence interval, judging that the fault occurs, otherwise, judging that no fault occurs.
In one possible implementation, the process of determining the fault region in order of degree of variation is: and sequencing according to the size of the fluctuation degree exceeding the set threshold confidence interval so as to determine the fault area.
According to the method, the fault area is judged according to the generalized eigenvalues of the covariance matrix of the historical data set and the covariance matrix of the online data set and the set threshold confidence interval, the problem of fault area positioning which is not completely known in load characteristics, network topology, line parameters and distributed power supply output can be solved, the requirement for relay protection of a power distribution network under the condition of distributed power supply access can be met, the method is suitable for any load level, the distribution rule of the distributed power supply and the load does not need to be obtained, the calculated amount is small, theoretical and technical guarantees can be provided for fault diagnosis of an active power distribution system or an existing power distribution system, and the method has a high practical industrial application value.
Example 2
Fig. 2 is a flowchart illustrating a method for power distribution network area location based on synchronous measurement data according to another exemplary embodiment. As shown in fig. 2, a method for positioning a distribution network area based on synchronous measurement data according to an embodiment of the present invention includes:
calculating a comprehensive evaluation index of mu PMU measurement data;
determining an index threshold confidence interval;
setting a mu PMU monitoring point data abnormity judgment rule;
and positioning the fault area.
In a possible implementation manner, the process of calculating the comprehensive evaluation index of the PMU measurement data includes: under any load level, the evaluation index of the single-class measurement data and the comprehensive evaluation index of the multi-class measurement data are as follows:
Figure BDA0002020465370000071
Figure BDA0002020465370000072
Figure BDA0002020465370000075
wherein, theta and omega respectively represent the evaluation index of single-class measurement data and the comprehensive evaluation index of multi-class measurement data; x is a historical data set, and Y is an online measured data set; cov (X, Y) and cov (X, X) represent covariance matrices of online measured datasets and historical datasets, respectively; | is a characteristic value of the matrix (| is | cov (X, Y) |, | cov (X, X) | and | (cov (X, X))-1|)；λiIs the ith generalized eigenvalue; p is the number of generalized eigenvalues;
Figure BDA0002020465370000073
is the product of p generalized eigenvalues; l is the actually measured data type, and the data type comprises a voltage amplitude, a voltage phase angle, a current amplitude and a current phase angle; thetaiThe evaluation index is the evaluation index of the ith type of data; kappaiIs the weight of the evaluation index of the ith class data and meets the requirement
Figure BDA0002020465370000074
In one possible implementation, the process of determining the confidence interval of the indicator threshold is:
the upper and lower limit sets of the generalized characteristic value, the monitoring index and the comprehensive evaluation index are respectively as follows:
Figure BDA0002020465370000081
Figure BDA0002020465370000082
Figure BDA0002020465370000083
wherein U isiAnd LiAre respectively the ith generalized eigenvalue lambdaiHas an upper and lower limit; u shapeθAnd LθRespectively an upper limit and a lower limit of an index theta confidence interval; u shapeΩAnd LΩThe upper and lower limits of the confidence interval of the index omega are respectively.
Figure BDA0002020465370000084
Is the (1-alpha)/2 cutoff value of the standard normal distribution, and alpha is the confidence level, and is selected according to the actual detection requirement, and is usually selected to be 0.95 or 0.99.
In a possible implementation manner, the process of setting the mu PMU monitoring point data abnormality determination rule is as follows:
rule one is as follows: if | omega-1 | is less than or equal to 0 or | omega-1 | is more than 0 and L is less than or equal toΩ≤1≤UΩData of the mu PMU monitoring point is not abnormal;
rule two: if | Ω -1| is > 0 and LΩIf the data is abnormal, the mu PMU monitors that the data is abnormal and a fault occurs.
In one possible implementation, the process of locating the fault area includes:
construction of historical data set Using data from the last recovery of a mu PMU failure, by | (cov (X, X))-1I, calculating a generalized characteristic value of historical data;
constructing an online data set: the PMU measurement data set at the kth time includes measurement data at the kth time and data at a previous time M-1, and the online data set at the kth time is expressed as:
Y＝[yk-M+1，…，yk]
wherein M is how many sampling values the data set contains;
obtaining the generalized characteristic value of the online data according to | cov (X, Y) | for the online data at each moment;
calculating an evaluation index value and determining a confidence interval;
judging whether a fault occurs according to a mu PMU monitoring point data abnormity judgment rule, and if so, sequencing the mu PMU monitoring points according to the overrun degree;
and sequencing and determining the fault area according to the data out-of-limit degree of each monitoring point.
In one possible implementation, the process of locating the fault region further includes:
reconstructing a historical data set according to the data after the system is recovered to be normal, and calculating the covariance matrix of the historical data set and the generalized eigenvalue of the covariance matrix of the online data set;
and outputting the result, namely outputting the fault area with the mu PMU monitoring point as a boundary.
The specific process of locating the distribution network area by using the method of embodiment 2 includes: in the first stage, off-line calculation is carried out, and characteristic values related to a historical data set are obtained; and in the second stage, calculating a characteristic value related to the online data set, multiplying the characteristic value obtained in the first stage by the characteristic value to obtain a product of generalized characteristic values, and then positioning by combining a confidence interval. The specific positioning steps are as follows:
(1) stage one: construction of historical data set Using data from the last recovery of a mu PMU failure, by | (cov (X, X))-1And l, reporting the data to a master station or independently storing the data in each measuring device according to the system power distribution automation requirement.
(2) And a second stage: the online data set is constructed as an example as follows:
the mu PMU measurement data set at the k time comprises the measurement data at the k time and the data at the previous M-1 time, then the k timeThe time k online dataset may be represented as Y ═ Yk-M+1，…，yk]Where M is how many sample values the data set contains.
(3) And a second stage: and obtaining a characteristic value of the online data at each moment according to | cov (X, Y) |, and reporting the data to a master station or independently storing the data in each measuring device according to the system power distribution automation requirement.
(4) And a second stage: and calculating to obtain an evaluation index value according to the following formula:
Figure BDA0002020465370000091
Figure BDA0002020465370000092
Figure BDA0002020465370000093
wherein, theta and omega respectively represent the evaluation index of single-class measurement data and the comprehensive evaluation index of multi-class measurement data; x is a historical data set, and Y is an online measured data set; cov (X, Y) and cov (X, X) represent covariance matrices of online measured datasets and historical datasets, respectively; solving the characteristic value of the matrix; lambda [ alpha ]iIs the ith generalized eigenvalue; p is the number of generalized eigenvalues;
Figure BDA0002020465370000101
is the product of p generalized eigenvalues; l is the actually measured data type, and the data type comprises a voltage amplitude, a voltage phase angle, a current amplitude and a current phase angle; thetaiThe evaluation index is the evaluation index of the ith type of data; kappaiIs the weight of the evaluation index of the ith class data and meets the requirement
Figure BDA0002020465370000102
(5) And a second stage: the confidence interval is determined according to the following formula:
Figure BDA0002020465370000103
Figure BDA0002020465370000104
Figure BDA0002020465370000105
wherein U isiAnd LiAre respectively the ith generalized eigenvalue lambdaiHas an upper and lower limit; u shapeθAnd LθRespectively an upper limit and a lower limit of an index theta confidence interval; u shapeΩAnd LΩThe upper and lower limits of the confidence interval of the index omega are respectively.
Figure BDA0002020465370000106
Is the (1-alpha)/2 cutoff value of the standard normal distribution, and alpha is the confidence level, and is selected according to the actual detection requirement, and is usually selected to be 0.95 or 0.99.
(6) And a second stage: judging whether a fault occurs according to a mu PMU monitoring point data abnormity judgment rule, wherein the specific rule is as follows:
rule one is as follows: if | omega-1 | is less than or equal to 0 or | omega-1 | is more than 0 and L is less than or equal toΩ≤1≤UΩData of the mu PMU monitoring point is not abnormal;
rule two: if | Ω -1| is > 0 and LΩIf the data is abnormal, the mu PMU monitors that the data is abnormal and a fault occurs. If yes, mu PMU monitoring points are sorted according to the degree of overrun.
(7) And a second stage: and sequencing according to the data out-of-limit degree of each monitoring point, and determining a fault area.
(8) Stage one: and reconstructing a historical data set according to the data after the system is recovered to be normal, and calculating a characteristic value.
(9) And outputting the result, namely outputting the fault area with the mu PMU monitoring point as a boundary.
So far, the process of determining the fault section and identifying the fault occurrence time involved in the present invention is completed.
In the embodiment, the judgment is only carried out by using the measurement data of the micro phasor measurement unit, and excessive system prior knowledge is not needed. The method comprises the following steps: in the first stage, off-line calculation is carried out, and characteristic values related to a historical data set are obtained; and in the second stage, calculating a characteristic value related to the online data set, multiplying the characteristic value obtained in the first stage by the characteristic value to obtain a product of generalized characteristic values, and then positioning by combining a confidence interval.
The method of the embodiment is simple and convenient to calculate, only uses the measured data of the existing measuring equipment of the system, overcomes the problems of large calculated amount of directly used data and excessive dependence on system priori knowledge, and can realize the positioning of the fault area of the power distribution network under the condition that the load characteristics, the network topology, the line parameters and the output of the distributed power supply are not completely known.
The method can solve the problem of positioning the fault area with incompletely known load characteristics, network topology, line parameters and distributed power supply output, can meet the requirement on relay protection of the power distribution network under the condition of distributed power supply access, is suitable for any load level, does not need to obtain the distribution rule of the distributed power supply and the load, has small calculated amount, can provide theoretical and technical support for fault diagnosis of an active power distribution system or the conventional power distribution system, and has high practical industrial application value.
Although the embodiments of the present invention have been described with reference to the accompanying drawings, the scope of the present invention is not limited thereto. Various modifications and alterations will occur to those skilled in the art based on the foregoing description. And are neither required nor exhaustive of all embodiments. On the basis of the technical scheme of the invention, various modifications or changes which can be made by a person skilled in the art without creative efforts are still within the protection scope of the invention.

Claims (3)

1. A distribution network area positioning method based on synchronous measurement data is characterized in that fault area judgment is carried out according to generalized eigenvalues of a covariance matrix of a historical data set and a covariance matrix of an online data set and a set threshold confidence interval;
the power distribution network area positioning method comprises the following steps:
calculating a comprehensive evaluation index of mu PMU measurement data;
determining an index threshold confidence interval;
setting a mu PMU monitoring point data abnormity judgment rule;
positioning a fault area;
the process of locating the fault area comprises:
construction of historical data set Using data from the last recovery of a mu PMU failure, by | (cov (X, X))-1I, calculating a generalized characteristic value of historical data;
constructing an online data set: the PMU measurement data set at the kth time includes measurement data at the kth time and data at a previous time M-1, and the online data set at the kth time is expressed as:
Y＝[Yk-hir+i,...,Yk]
wherein M is how many sampling values the data set contains;
obtaining the generalized characteristic value of the online data according to | cov (X, Y) | for the online data at each moment;
calculating an evaluation index value and determining a confidence interval;
judging whether a fault occurs according to a mu PMU monitoring point data abnormity judgment rule, and if so, sequencing the mu PMU monitoring points according to the overrun degree;
sequencing and determining a fault area according to the data out-of-limit degree of each monitoring point;
reconstructing a historical data set according to the data after the system is recovered to be normal, and calculating the covariance matrix of the historical data set and the generalized eigenvalue of the covariance matrix of the online data set;
outputting a result, namely outputting a fault area with a mu PMU monitoring point as a boundary;
the process of calculating the comprehensive evaluation index of the mu PMU measurement data comprises the following steps: under any load level, the evaluation index of the single-class measurement data and the comprehensive evaluation index of the multi-class measurement data are as follows:
Figure FDA0003330935460000021
Figure FDA0003330935460000022
Figure FDA0003330935460000023
wherein, theta and omega respectively represent the evaluation index of single-class measurement data and the comprehensive evaluation index of multi-class measurement data; x is a historical data set, and Y is an online measured data set; cov (X, Y) and cov (X, X) represent covariance matrices of online measured datasets and historical datasets, respectively; solving the characteristic value of the matrix; lambda [ alpha ]iIs the ith generalized eigenvalue; p is the number of generalized eigenvalues;
Figure FDA0003330935460000024
is the product of p generalized eigenvalues; l is the actual measured data type; thetaiThe evaluation index is the evaluation index of the ith type of data; kappaiIs the weight of the evaluation index of the ith class data and meets the requirement
Figure FDA0003330935460000025
2. The method of claim 1, wherein determining the confidence interval of the indicator threshold comprises:
the upper and lower limit sets of the generalized characteristic value, the monitoring index and the comprehensive evaluation index are respectively calculated by the following formulas:
Figure FDA0003330935460000026
Figure FDA0003330935460000027
Figure FDA0003330935460000031
wherein U isiAnd LiAre respectively the ith generalized eigenvalue lambdaiHas an upper and lower limit; u shapeθAnd LθRespectively an upper limit and a lower limit of an index theta confidence interval; u shapeΩAnd LΩRespectively the upper and lower limits of the confidence interval of the index omega;
Figure FDA0003330935460000032
is the (1- α)/2 cutoff for the standard normal distribution, and α is the confidence level.
3. The method for locating distribution network regions based on synchronous measurement data according to claim 2, wherein the process of setting the mu PMU monitoring point data anomaly determination rule is as follows:
rule one is as follows: if | omega-1 | is less than or equal to 0 or | omega-1 | non-woven cells>0 and LΩ≤1≤UΩData of the mu PMU monitoring point is not abnormal;
rule two: if | omega-1->0 and LΩ>1, mu PMU monitors the abnormal point data and has fault.
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