CN112345972A - Power failure event-based power distribution network line transformation relation abnormity diagnosis method, device and system - Google Patents

Power failure event-based power distribution network line transformation relation abnormity diagnosis method, device and system Download PDF

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CN112345972A
CN112345972A CN202011202022.8A CN202011202022A CN112345972A CN 112345972 A CN112345972 A CN 112345972A CN 202011202022 A CN202011202022 A CN 202011202022A CN 112345972 A CN112345972 A CN 112345972A
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power failure
distribution transformer
suspected
power
line
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CN112345972B (en
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陈烨
陈锦铭
刘伟
袁宇波
焦昊
叶迪卓然
于聪聪
宋伟伟
史曙光
郭雅娟
崔晋利
张超
李岩
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State Grid Corp of China SGCC
State Grid Jiangsu Electric Power Co Ltd
Electric Power Research Institute of State Grid Jiangsu Electric Power Co Ltd
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State Grid Corp of China SGCC
State Grid Jiangsu Electric Power Co Ltd
Electric Power Research Institute of State Grid Jiangsu Electric Power Co Ltd
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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
    • G01R31/50—Testing of electric apparatus, lines, cables or components for short-circuits, continuity, leakage current or incorrect line connections
    • G01R31/55—Testing for incorrect line connections
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/11—Complex mathematical operations for solving equations, e.g. nonlinear equations, general mathematical optimization problems
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J13/00—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—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
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
    • 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

Abstract

The invention discloses a power failure event-based power distribution network line transformation relation abnormity diagnosis method, device and system, wherein the method comprises the steps of generating a historical power failure distribution transformer set based on acquired power data of a distribution transformer; acquiring a bus and a distribution transformer nearby based on longitude and latitude information of a power failure distribution transformer in the power failure distribution transformer set; calculating the correlation coefficient indexes of the power failure distribution transformer and the adjacent buses to find out suspected corresponding buses; screening out all lines which are suspected to belong to a bus and correspond to the power failure distribution transformer; calculating the power failure distribution transformer number ratio index corresponding to all lines under the suspected corresponding bus, and seeking the power failure distribution transformer number ratio index threshold; and screening suspected similar lines of the power-off distribution transformer based on the threshold value, and finding the power-off distribution transformer with the wrong line transformation relation and the suspected similar lines thereof. The method is simple in calculation, and can help operators to find the abnormal line-to-line relation distribution transformer in time and recommend the line to which the abnormal line-to-line relation distribution transformer belongs.

Description

Power failure event-based power distribution network line transformation relation abnormity diagnosis method, device and system
Technical Field
The invention belongs to the field of medium voltage distribution network line-to-line relation abnormity diagnosis, and particularly relates to a power failure event-based power distribution network line-to-line relation abnormity diagnosis method, device and system, which are suitable for medium voltage distribution networks.
Background
The medium voltage distribution network line transformation relation is a mutual connection relation among the medium voltage bus, the distribution transformer load, the interconnection switch and other devices, and describes a power supply path from the medium voltage bus to the distribution transformer load. The manually maintained energy Management System (PMS) mainly depends on site investigation of basic level staff, and due to the factors of complicated equipment, long time consumption, high investigation difficulty and the like, the correct line variation relationship of the medium-voltage distribution network is difficult to obtain, so that the actual site is inconsistent with the line variation relationship of the PMS, and further service pain points such as high potential safety hazard of line maintenance, high calculation error of medium-voltage line loss rate, low reliability analysis accuracy and the like are caused, and the first-class construction process of the distribution network is severely restricted.
Therefore, the method for diagnosing the line-variable relation abnormity of the medium-voltage distribution network is an important research subject, and research results can help operators to find the distribution transformer with the line-variable relation error in time and recommend the suspected specific line to which the distribution transformer belongs to for the distribution transformer, so that the power grid operation and maintenance ledger basis can be effectively tamped.
Disclosure of Invention
Aiming at the problems, the invention provides a power failure event-based power distribution network line-variable relation abnormity diagnosis method, device and system, which can be used for identifying power distribution network line-variable relation connection errors of any scale and recommending correct connection lines.
In order to achieve the technical purpose and achieve the technical effects, the invention is realized by the following technical scheme:
in a first aspect, the invention provides a power failure event-based power distribution network line change relation abnormity diagnosis method, which includes:
generating a historical outage distribution transformer set based on the acquired power data of the distribution transformer;
acquiring nearby buses and distribution transformers and voltage measurement data of nearby buses based on longitude and latitude information of the power failure distribution transformers in the power failure distribution transformer set;
calculating the correlation coefficient index of the power failure distribution transformer and the adjacent bus, and finding out the suspected corresponding bus;
screening out all lines which are suspected to belong to a bus and correspond to the power failure distribution transformer;
calculating the power failure distribution transformer number ratio index corresponding to all lines under the suspected corresponding bus, and evaluating and seeking the power failure distribution transformer number ratio index threshold;
and screening suspected corresponding lines of the power failure distribution transformer based on the obtained threshold value, and finally finding the power failure distribution transformer with the wrong line transformation relation and the suspected corresponding lines thereof.
Optionally, the method for generating the historical outage distribution transformer set includes:
a certain distribution transformer MjConnected to line f, distribution transformer MjThe active power measurement data is Pk＝[pk,1,pk,2,...,pk,i,...,pk,n]N represents the number of sampling points of the measured data of the distribution transformer;
if distribution transformer MjActive power measurement data PkIn successive data sections pk,i,...,pk,j]0 or null, a distribution transformer M is generatedjData segment [ i, …, j ] of]For power failure debris, distribution transformer MjThen it is a power failure distribution transformer;
based on the rule for screening the power failure distribution transformers, historical power failure distribution transformers are screened from the acquired power data of the distribution transformers, and a power failure distribution transformer set M ═ M { M ═ is generated1,M2,...,MlL is the number of distribution transformers with power failure after screening.
Optionally, the obtaining, based on the longitude and latitude information of the blackout distribution transformer in the blackout distribution transformer set, the bus and the distribution transformer near the blackout distribution transformer, and the voltage measurement data of the bus near the blackout distribution transformer includes the following steps:
and calculating a bus set within a set threshold value from the geographic range of the power failure distribution transformer based on the longitude and latitude information of each distribution transformer in the power failure distribution transformer set, and acquiring voltage measurement data of the bus set and the lower line statistics.
Optionally, the method for finding an suspected corresponding bus comprises:
obtaining a correlation coefficient index calculation formula between a power failure distribution transformer and a nearby bus, wherein the correlation coefficient index calculation formula is as follows:
Figure BDA0002755594170000021
wherein, R represents a correlation coefficient index; xiRepresenting the voltage measurement data of the power failure distribution transformer;
Figure BDA0002755594170000022
the average value of the voltage measurement data of the power failure distribution transformer is represented; y isiRepresenting the measurement data of the bus voltage near the power failure distribution transformer;
Figure BDA0002755594170000023
the average value of the measured data of the bus voltage near the power failure distribution transformer is represented; n represents the length of the measured data of the voltage of the power failure distribution transformer and the nearby bus;
and screening out proper data from the obtained voltage measurement data of the power failure distribution transformer and the nearby buses, and introducing the proper data into the correlation coefficient index calculation formula, calculating to obtain correlation coefficient index values between all buses in the power failure distribution transformer and the nearby bus set, and screening out suspected corresponding buses and lower lines thereof based on the correlation coefficient index values.
Optionally, the calculating a power failure distribution transformer number ratio index corresponding to all lines under the suspected corresponding bus, and evaluating and seeking a power failure distribution transformer number ratio index threshold includes the following steps:
calculating the power failure distribution transformer number ratio index P of all linesjForming a historical power failure distribution transformer number ratio index data set P;
the method for calculating the power failure distribution transformer number ratio index threshold value by using the confusion matrix method for supervised learning in artificial intelligence specifically comprises the following steps:
: respectively calculating different values P in the data set PjThe precision ratio and the recall ratio of the corresponding distribution transformer to the route;
calculating a derived index F1 based on the precision ratio and the recall ratioj：
Figure BDA0002755594170000031
Wherein A isjFor accuracy, RjThe recall ratio is checked;
after the calculation is completed, an evaluation index set F1 ═ { F1 ═ is formed1,F12,…,F1j,…,F1kK is the number of bus lines under the suspected bus;
and searching for the maximum value in the evaluation index set F1, and taking the power failure distribution transformer number ratio index corresponding to the maximum value as the power failure distribution transformer number ratio index threshold.
Optionally, the blackout distribution transformer number fraction index PjThe calculation method comprises the following steps:
aiming at all lines under suspected to-be-owned buses in a certain area, the number T of distribution transformers with power failure of each line under suspected to-be-owned buses is calculatedjAnd total number of distribution transformers ZjThen, the power failure distribution transformer number ratio index P is calculatedj。
Optionally, screening out suspected candidate lines of the power failure distribution transformer based on the obtained threshold, and finally finding the power failure distribution transformer with the wrong line transformation relation and the suspected candidate lines thereof, including the following steps:
the method comprises the steps of carrying out preset judgment steps on all power-off distribution transformers and lines under adjacent buses, and finally obtaining a power-off distribution transformer set, power-off distribution transformer number proportion index data sets of all lines under adjacent buses of the power-off distribution transformers, power-off distribution transformer number proportion index thresholds of all lines under suspected to-be-attributed buses, all distribution transformers with abnormal suspected line transformation relations and suspected to-be-attributed lines under suspected to-be-attributed buses;
the preset judging step comprises the following steps: if the suspected lower line of the bus to which the power failure distribution transformer belongs has power failure in a typical time period, and the index value of the number of the power failure distribution transformers is higher than the percentage index threshold value of the number of the power failure distribution transformers, the power failure distribution transformer is judged to be the suspected abnormal distribution transformer with the line transformer relation, and otherwise, the power failure distribution transformer is judged to be the normal distribution transformer with the line transformer relation.
In a second aspect, the present invention provides a power outage event-based power distribution network line change relationship abnormality diagnosis apparatus, including:
the generating unit is used for generating a historical power failure distribution transformer set based on the acquired power data of the distribution transformer;
the first calculation unit is used for obtaining a bus and a distribution transformer nearby the first calculation unit and voltage measurement data of the bus nearby the first calculation unit based on the longitude and latitude information of the power failure distribution transformer in the power failure distribution transformer set;
the second calculation unit is used for calculating the correlation coefficient indexes of the power failure distribution transformer and the adjacent buses thereof and finding out suspected corresponding buses;
the first screening unit is used for screening all lines which are suspected to belong to a bus and correspond to the power failure distribution transformer;
the third calculation unit is used for calculating the power failure distribution transformer number ratio index corresponding to all lines under the suspected corresponding bus, evaluating and seeking the power failure distribution transformer number ratio index threshold;
and the judging unit is used for screening suspected similar lines of the power failure distribution transformer based on the obtained threshold value, and finally finding the power failure distribution transformer with the wrong line transformation relation and the suspected similar lines thereof.
Optionally, the method for generating the historical outage distribution transformer set includes:
a certain distribution transformer MjConnected to line f, distribution transformer MjThe active power measurement data is Pk＝[pk,1,pk,2,...,pk,i,...,pk,n]N represents the number of sampling points of the measured data of the distribution transformer;
if distribution transformer MjActive power measurement data PkIn successive data sections pk,i,...,pk,j]0 or null, a distribution transformer M is generatedjData segment [ i, …, j ] of]For power failure debris, distribution transformer MjThen it is a power failure distribution transformer;
based on the rule for screening the power failure distribution transformers, historical power failure distribution transformers are screened from the acquired power data of the distribution transformers, and a power failure distribution transformer set M ═ M { M ═ is generated1,M2,...,MlL is the number of distribution transformers with power failure after screening.
Optionally, the method for finding an suspected corresponding bus comprises:
obtaining a correlation coefficient index calculation formula between a power failure distribution transformer and a nearby bus, wherein the correlation coefficient index calculation formula is as follows:
Figure BDA0002755594170000041
wherein, R represents a correlation coefficient index; xiRepresenting the voltage measurement data of the power failure distribution transformer;
Figure BDA0002755594170000042
the average value of the voltage measurement data of the power failure distribution transformer is represented; y isiRepresenting the measurement data of the bus voltage near the power failure distribution transformer;
Figure BDA0002755594170000043
the average value of the measured data of the bus voltage near the power failure distribution transformer is represented; n represents the length of the measured data of the voltage of the power failure distribution transformer and the nearby bus;
and screening out proper data from the obtained voltage measurement data of the power failure distribution transformer and the nearby buses, and introducing the proper data into the correlation coefficient index calculation formula, calculating to obtain correlation coefficient index values between all buses in the power failure distribution transformer and the nearby bus set, and screening out suspected corresponding buses and lower lines thereof based on the correlation coefficient index values.
Optionally, the method for finding an suspected corresponding bus comprises:
obtaining a correlation coefficient index calculation formula between a power failure distribution transformer and a nearby bus, wherein the correlation coefficient index calculation formula is as follows:
Figure BDA0002755594170000051
wherein, R represents a correlation coefficient index; xiRepresenting the voltage measurement data of the power failure distribution transformer;
Figure BDA0002755594170000052
the average value of the voltage measurement data of the power failure distribution transformer is represented; y isiRepresenting the measurement data of the bus voltage near the power failure distribution transformer;
Figure BDA0002755594170000054
the average value of the measured data of the bus voltage near the power failure distribution transformer is represented; n represents the length of the measured data of the voltage of the power failure distribution transformer and the nearby bus;
and screening out proper data from the obtained voltage measurement data of the power failure distribution transformer and the nearby buses, and introducing the proper data into the correlation coefficient index calculation formula, calculating to obtain correlation coefficient index values between all buses in the power failure distribution transformer and the nearby bus set, and screening out suspected corresponding buses and lower lines thereof based on the correlation coefficient index values.
Optionally, the calculating a power failure distribution transformer number ratio index corresponding to all lines under the suspected corresponding bus, and evaluating and seeking a threshold of the power failure distribution transformer number ratio index includes the following steps:
calculating the power failure distribution transformer number ratio index P of all linesjForming a historical power failure distribution transformer number ratio index data set P;
the method for calculating the power failure distribution transformer number ratio index threshold value by using the confusion matrix method for supervised learning in artificial intelligence specifically comprises the following steps:
respectively calculating different values P in the data set PjThe precision ratio and the recall ratio of the corresponding distribution transformer to the route;
calculating a derived index F1 based on the precision ratio and the recall ratioj：
Figure BDA0002755594170000053
Wherein A isjFor accuracy, RjThe recall ratio is checked;
after the calculation is completed, an evaluation index set F1 ═ { F1 ═ is formed1,F12,…,F1j,…,F1kK is the number of bus lines under the suspected bus;
and searching for the maximum value in the evaluation index set F1, and taking the power failure distribution transformer number ratio index corresponding to the maximum value as the power failure distribution transformer number ratio index threshold.
Optionally, the blackout distribution transformer number fraction index PjThe calculation method comprises the following steps:
aiming at all lines under suspected to-be-owned buses in a certain area, the number T of distribution transformers with power failure of each line under suspected to-be-owned buses is calculatedjAnd total number of distribution transformers ZjThen, the power failure distribution transformer number ratio index P is calculatedj。
Optionally, screening out suspected candidate lines of the power failure distribution transformer based on the obtained threshold, and finally finding the power failure distribution transformer with the wrong line transformation relation and the suspected candidate lines thereof, including the following steps:
the method comprises the steps of carrying out preset judgment steps on all power-off distribution transformers and lines under adjacent buses, and finally obtaining a power-off distribution transformer set, power-off distribution transformer number proportion index data sets of all lines under adjacent buses of the power-off distribution transformers, power-off distribution transformer number proportion index thresholds of all lines under suspected to-be-attributed buses, all distribution transformers with abnormal suspected line transformation relations and suspected to-be-attributed lines under suspected to-be-attributed buses;
the preset judging step comprises the following steps: if the suspected lower line of the bus to which the power failure distribution transformer belongs has power failure in a typical time period, and the index value of the number of the power failure distribution transformers is higher than the percentage index threshold value of the number of the power failure distribution transformers, the power failure distribution transformer is judged to be the suspected abnormal distribution transformer with the line transformer relation, and otherwise, the power failure distribution transformer is judged to be the normal distribution transformer with the line transformer relation.
In a third aspect, the invention provides a power failure event-based power distribution network line change relation abnormity diagnosis system, which comprises a storage medium and a processor;
the storage medium is used for storing instructions;
the processor is configured to operate in accordance with the instructions to perform the steps of the method according to any one of the first aspects.
Compared with the prior art, the invention has the beneficial effects that:
(1) the invention learns the historical mass data change trend in a data driving mode, calculates the line power failure distribution transformer number ratio threshold value for judging whether the line variable relation is abnormal or not, and avoids manual designation.
(2) The method can directly determine the suspected specific line of the power failure distribution transformer with the wrong line transformation relation according to the longitude and latitude information, and avoids the blind line inspection of field operators.
(3) The method is simple in calculation and clear in principle, can help distribution network operators to find the distribution transformer with the wrong line-variable relation in time and recommend the suspected line to which the distribution transformer belongs so that the operators can adjust the line-variable relation in time, and has a good application prospect.
Drawings
In order that the present disclosure may be more readily and clearly understood, reference is now made to the following detailed description of the present disclosure taken in conjunction with the accompanying drawings, in which:
fig. 1 is a flowchart of a medium voltage distribution network line-change relationship abnormality diagnosis method based on a power failure event according to the present invention;
fig. 2 is a schematic diagram of line-to-line relationship of a medium voltage distribution network.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail with reference to the following embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and do not limit the scope of the invention.
The following detailed description of the principles of the invention is provided in connection with the accompanying drawings.
Example 1
The embodiment of the invention provides a power failure event-based power distribution network line change relation abnormity diagnosis method, which comprises the following steps of:
generating a historical power failure distribution transformer set based on the acquired power data of the distribution transformer;
in a specific implementation manner of the embodiment of the present invention, the step (a) is specifically implemented as follows:
in an energy management system (the system is a system existing in the prior art), a distribution transformer to be processed is screened out based on power information of the distribution transformer, and power measurement data and basic equipment information of the distribution transformer, which are stored in the corresponding prior system, are read. For a distribution transformer MjConnected to line f, distribution transformer MjThe active power measurement data is Pk＝[pk,1,pk,2,…,pk,i,…,pk,n]N represents the number of sampling points of the measured data of the distribution transformer; if distribution transformer MjActive power measurement data PkIn connection withSuccessive data segments [ p ]k,i,…,pk,j]If it is empty, a distribution transformer M is generatedjData segment [ i, …, j ] of]For power failure fragments of the distribution transformer, MjThen the power failure distribution transformer.
Repeating the steps until all the distribution transformers are judged, and generating a power failure distribution transformer information set M ═ M1,M2,…,MlL is the number of distribution transformers with power failure after screening.
Step (B) obtaining a bus and a distribution transformer nearby the power failure distribution transformer and voltage measurement data of the bus nearby the power failure distribution transformer based on the longitude and latitude information of the power failure distribution transformer in the power failure distribution transformer set;
in a specific implementation manner of the embodiment of the present invention, the step (B) specifically includes the following sub-steps:
step 1: distribution transformer M for power failurejCalculating a bus set F5 km away from the distribution transformer geographic range based on the longitude and latitude information;
step 2: selecting a medium-voltage distribution network to be processed in an energy management system (the system is a system existing in the prior art), reading a line-to-line variation relation of 'bus-distribution transformer load' stored in the prior system, and deriving statistical voltage data of a power failure distribution transformer and a nearby bus sampled every 15min, wherein the sampling frequency sampled every 15min can be modified according to actual conditions.
The method further comprises the step of performing completion processing on the voltage measurement data of the bus, specifically, performing completion processing on the originally acquired voltage data by adopting a linear interpolation method, and the basic idea is that an interpolation function can approximately replace an original function, the interpolation function is of a first-order polynomial class, and the interpolation error on each interpolation node is required to be 0. Let known raw data f (x)i) Wherein x isi(i is 0,1,2,3, …, n), where n is the length of the original data sampled, and linear interpolation constructs a function
Figure BDA0002755594170000071
So that the absolute value of the error | R (x) | is atThe whole original data interval is small, namely:
Figure BDA0002755594170000081
i＝0,1,2,3,…,n
Figure BDA0002755594170000082
now based on the constructed interpolation function
Figure BDA0002755594170000083
If the original data has data missing at the position where i is m, namely f (m) is null, then
Figure BDA0002755594170000084
And completing the missing condition of the originally sampled voltage data.
Step (C) calculating the correlation coefficient index of the power failure distribution transformer and the adjacent bus, and finding out the suspected corresponding bus;
in a specific implementation manner of the embodiment of the present invention, the step (C) specifically includes the following steps:
a calculation formula of correlation indexes between each blackout distribution transformer and a nearby bus is obtained, the correlation indexes are used for measuring the connection relation between the distribution transformer and the bus, the similarity between the voltage data of the distribution transformer and the voltage data of the bus is obtained by comparing, and the similarity is given through calculation of a pearson correlation coefficient, as shown in fig. 2. When the bus voltage fluctuates, the voltage of the distribution transformer on the line is driven to fluctuate, namely, the voltage curves of the bus voltage and the distribution transformer have similarity. If a certain distribution transformer is connected on the line by mistake, the voltage fluctuation of the distribution transformer does not have similarity with the voltage fluctuation of the bus, and the Pearson correlation coefficient is lower. The correlation coefficient index calculation formula is as follows:
Figure BDA0002755594170000085
wherein, R represents a correlation coefficient index; xiRepresenting the voltage measurement data of the power failure distribution transformer;
Figure BDA0002755594170000086
the average value of the voltage measurement data of the power failure distribution transformer is represented; y isiRepresenting nearby bus voltage measurement data;
Figure BDA0002755594170000087
representing the average value of the measured data of the voltage of the nearby bus; n represents the length of the measured data of the voltage of the power failure distribution transformer and the nearby bus;
and screening out proper data from the obtained voltage measurement data of the power failure distribution transformer and the nearby buses, bringing the proper data into the correlation index calculation formula, calculating to obtain correlation coefficient index values between each power failure distribution transformer and the nearby buses to form a correlation coefficient index data set, and screening out the nearby buses and the lower lines of the power failure distribution transformer suspected to be similar based on the correlation coefficient index data set.
Screening out all lines suspected to belong to the bus corresponding to the power failure distribution transformer;
calculating the number ratio index of the power-off distribution transformers corresponding to all lines under the suspected corresponding bus, evaluating and seeking the number ratio index threshold of the power-off distribution transformers;
in a specific implementation manner of the embodiment of the present invention, the step (E) specifically includes the following sub-steps:
step 1: taking a certain 1 day in history as a typical historical time period, reading all power failure distribution transformers in the city as T ═ T { (T)1,T2…TkK is the number of the power-off distribution transformers in the city;
step 2: selecting M in the set of blackout distribution transformers according to the step (D)jTaking a power failure fragment of a certain 1 day as a typical time period, and calculating the number T of distribution transformers with power failure of each line under suspected to-be-owned buses aiming at all lines under the suspected to-be-owned buses in a certain cityjAnd total number of distribution transformers ZjThen, the power failure distribution transformer number ratio index P is calculatedj：
Figure BDA0002755594170000091
Repeating the steps to calculate the power failure distribution transformer number ratio index P of all linesjForming a historical outage distribution transformer number ratio index data set P ═ P1,P2,…,Pl}。
And step 3: the method for calculating the power failure distribution transformer number ratio index threshold value by using the confusion matrix method for supervised learning in artificial intelligence specifically comprises the following steps:
respectively calculating different values P in the data set PjThe precision ratio and the recall ratio of the corresponding distribution transformer to the route;
calculating a derived index F1 based on the precision ratio and the recall ratioj：
Figure BDA0002755594170000092
Wherein A isjFor accuracy, RjThe recall ratio is checked;
. After the calculation is completed, an evaluation index set F1 ═ { F1 ═ is formed1,F12,…,F1j,…,F1kK is the number of bus lines under the suspected bus;
and searching for the maximum value in the evaluation index set F1, and taking the power failure distribution transformer number ratio index corresponding to the maximum value as the power failure distribution transformer number ratio index threshold.
Screening suspected similar lines of the power-off distribution transformer based on the obtained threshold value, and finally finding the power-off distribution transformer with the wrong line transformation relation and the suspected similar lines of the power-off distribution transformer;
in a specific implementation manner of the embodiment of the present invention, the step (F) specifically includes the following sub-steps:
if the number ratio index value of the power failure distribution transformer and the power failure distribution transformer of the line in the adjacent bus is lower than the number ratio index threshold value of the power failure distribution transformer, the distribution transformer is judged to be a normal distribution transformer with the line transformation relation, otherwise, the distribution transformer is judged to be a suspected abnormal distribution transformer with the line transformation relation, and a suspected abnormal distribution transformer set with the line transformation relation is formed, wherein the set N is { N ═ N { (N) } N1,N2,…,NlAnd recording suspected line information under the corresponding adjacent bus, wherein l is the number of power failure distribution transformers suspected of abnormal line change relationship after the city is judged.
And repeating the steps until all the power failure distribution transformers in the set M are judged completely, and finding out suspected corresponding lines corresponding to the power failure distribution transformers with the line-to-line relation errors.
Example 2
Based on the same inventive concept as embodiment 1, an embodiment of the present invention provides a power outage event-based power distribution network line transformation relation abnormality diagnosis apparatus, including:
the generating unit is used for generating a historical power failure distribution transformer set based on the acquired power data of the distribution transformer;
the first calculation unit is used for obtaining a bus and a distribution transformer nearby the first calculation unit and voltage measurement data of the bus nearby the first calculation unit based on the longitude and latitude information of the power failure distribution transformer in the power failure distribution transformer set;
the second calculation unit is used for calculating the correlation coefficient indexes of the power failure distribution transformer and the adjacent buses thereof and finding out suspected corresponding buses;
the first screening unit is used for screening all lines which are suspected to belong to a bus and correspond to the power failure distribution transformer;
the third calculation unit is used for calculating the power failure distribution transformer number ratio index corresponding to all lines under the suspected corresponding bus, evaluating and seeking the power failure distribution transformer number ratio index threshold;
and the judging unit is used for screening suspected similar lines of the power failure distribution transformer based on the obtained threshold value, and finally finding the power failure distribution transformer with the wrong line transformation relation and the suspected similar lines thereof.
In a specific implementation manner of the embodiment of the present invention, the method for generating the historical outage distribution transformer set includes:
a certain distribution transformer MjConnected to line f, distribution transformer MjThe active power measurement data is Pk＝[pk,1,pk,2,...,pk,i,...,pk,n]N represents the number of sampling points of the measured data of the distribution transformer;
if distribution transformer MjActive power measurement data PkIn successive data sections pk,i,...,pk,j]0 or null, a distribution transformer M is generatedjData segment [ i, …, j ] of]For power failure debris, distribution transformer MjThen it is a power failure distribution transformer;
based on the rule for screening the power failure distribution transformers, historical power failure distribution transformers are screened from the acquired power data of the distribution transformers, and a power failure distribution transformer set M ═ M { M ═ is generated1,M2,...,MlL is the number of distribution transformers with power failure after screening.
In a specific implementation manner of the embodiment of the present invention, the method for finding an suspected corresponding bus includes:
obtaining a correlation coefficient index calculation formula between a power failure distribution transformer and a nearby bus, wherein the correlation coefficient index calculation formula is as follows:
Figure BDA0002755594170000101
wherein, R represents a correlation coefficient index; xiRepresenting the voltage measurement data of the power failure distribution transformer;
Figure BDA0002755594170000102
the average value of the voltage measurement data of the power failure distribution transformer is represented; y isiRepresenting the measurement data of the bus voltage near the power failure distribution transformer;
Figure BDA0002755594170000103
the average value of the measured data of the bus voltage near the power failure distribution transformer is represented; n represents the length of the measured data of the voltage of the power failure distribution transformer and the nearby bus;
and screening out proper data from the obtained voltage measurement data of the power failure distribution transformer and the nearby buses, and introducing the proper data into the correlation coefficient index calculation formula, calculating to obtain correlation coefficient index values between all buses in the power failure distribution transformer and the nearby bus set, and screening out suspected corresponding buses and lower lines thereof based on the correlation coefficient index values.
In a specific implementation manner of the embodiment of the present invention, the method for finding an suspected corresponding bus includes:
obtaining a correlation coefficient index calculation formula between a power failure distribution transformer and a nearby bus, wherein the correlation coefficient index calculation formula is as follows:
Figure BDA0002755594170000111
wherein, R represents a correlation coefficient index; xiRepresenting the voltage measurement data of the power failure distribution transformer;
Figure BDA0002755594170000112
the average value of the voltage measurement data of the power failure distribution transformer is represented; y isiRepresenting the measurement data of the bus voltage near the power failure distribution transformer;
Figure BDA0002755594170000113
the average value of the measured data of the bus voltage near the power failure distribution transformer is represented; n represents the length of the measured data of the voltage of the power failure distribution transformer and the nearby bus;
and screening out proper data from the obtained voltage measurement data of the power failure distribution transformer and the nearby buses, and introducing the proper data into the correlation coefficient index calculation formula, calculating to obtain correlation coefficient index values between all buses in the power failure distribution transformer and the nearby bus set, and screening out suspected corresponding buses and lower lines thereof based on the correlation coefficient index values.
In a specific implementation manner of the embodiment of the present invention, the calculating a power failure distribution transformer number ratio index corresponding to all lines under suspected corresponding buses, and evaluating and seeking a power failure distribution transformer number ratio index threshold includes the following steps:
calculating the power failure distribution transformer number ratio index P of all linesjForming a historical power failure distribution transformer number ratio index data set P;
the method for calculating the power failure distribution transformer number ratio index threshold value by using the confusion matrix method for supervised learning in artificial intelligence specifically comprises the following steps:
: respectively calculating different values P in the data set PjThe precision ratio and the recall ratio of the corresponding distribution transformer to the route;
calculating a derived index F1 based on the precision ratio and the recall ratioj：
Figure BDA0002755594170000114
Wherein A isjFor accuracy, RjThe recall ratio is checked;
after the calculation is completed, an evaluation index set F1 ═ { F1 ═ is formed1,F12,…,F1j,…,F1kK is the number of bus lines under the suspected bus;
and searching for the maximum value in the evaluation index set F1, and taking the power failure distribution transformer number ratio index corresponding to the maximum value as the power failure distribution transformer number ratio index threshold.
In a specific implementation manner of the embodiment of the invention, the power failure distribution transformer number ratio index PjThe calculation method comprises the following steps:
aiming at all lines under suspected to-be-owned buses in a certain area, the number T of distribution transformers with power failure of each line under suspected to-be-owned buses is calculatedjAnd total number of distribution transformers ZjThen calculates the power failureDistribution transformer number ratio index Pj。
In a specific implementation manner of the embodiment of the present invention, the screening out suspected candidate lines of the power failure distribution transformer based on the obtained threshold, and finally finding the power failure distribution transformer with the wrong line transformation relationship and the suspected candidate lines thereof, includes the following steps:
the method comprises the steps of carrying out preset judgment steps on all power-off distribution transformers and lines under adjacent buses, and finally obtaining a power-off distribution transformer set, power-off distribution transformer number proportion index data sets of all lines under adjacent buses of the power-off distribution transformers, power-off distribution transformer number proportion index thresholds of all lines under suspected to-be-attributed buses, all distribution transformers with abnormal suspected line transformation relations and suspected to-be-attributed lines under suspected to-be-attributed buses;
the preset judging step comprises the following steps: if the suspected lower line of the bus to which the power failure distribution transformer belongs has power failure in a typical time period, and the index value of the number of the power failure distribution transformers is higher than the percentage index threshold value of the number of the power failure distribution transformers, the power failure distribution transformer is judged to be the suspected abnormal distribution transformer with the line transformer relation, and otherwise, the power failure distribution transformer is judged to be the normal distribution transformer with the line transformer relation.
Example 3
Based on the same inventive concept as embodiment 1, the embodiment of the invention provides a power failure event-based power distribution network line change relation abnormity diagnosis system, which comprises a storage medium and a processor, wherein the storage medium is used for storing a power failure event;
the storage medium is used for storing instructions;
the processor is configured to operate in accordance with the instructions to perform the steps of the method according to any of embodiment 1.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While the present invention has been described with reference to the embodiments shown in the drawings, the present invention is not limited to the embodiments, which are illustrative and not restrictive, and it will be apparent to those skilled in the art that various changes and modifications can be made therein without departing from the spirit and scope of the invention as defined in the appended claims.
The foregoing shows and describes the general principles and broad features of the present invention and advantages thereof. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are described in the specification and illustrated only to illustrate the principle of the present invention, but that various changes and modifications may be made therein without departing from the spirit and scope of the present invention, which fall within the scope of the invention as claimed. The scope of the invention is defined by the appended claims and equivalents thereof.

Claims (14)

1. A power failure event-based power distribution network line change relation abnormity diagnosis method is characterized by comprising the following steps:
generating a historical outage distribution transformer set based on the acquired power data of the distribution transformer;
acquiring nearby buses and distribution transformers and voltage measurement data of nearby buses based on longitude and latitude information of the power failure distribution transformers in the power failure distribution transformer set;
calculating the correlation coefficient index of the power failure distribution transformer and the adjacent bus, and finding out the suspected corresponding bus;
screening out all lines which are suspected to belong to a bus and correspond to the power failure distribution transformer;
calculating the power failure distribution transformer number ratio index corresponding to all lines under the suspected corresponding bus, and evaluating and seeking the power failure distribution transformer number ratio index threshold;
and screening suspected corresponding lines of the power failure distribution transformer based on the obtained threshold value, and finally finding the power failure distribution transformer with the wrong line transformation relation and the suspected corresponding lines thereof.
2. The method for diagnosing abnormal relation of line-to-line variation of power distribution network based on power failure event as claimed in claim 1, wherein the method for generating the historical power failure distribution transformer set comprises:
distribution transformer MjConnected to line fDistribution transformer MjThe active power measurement data is Pk＝[pk,1,pk,2,...,pk,i,...,pk,n]N represents the number of sampling points of the measured data of the distribution transformer;
if distribution transformer MjActive power measurement data PkIn successive data sections pk,i,...,pk,j]0 or null, a distribution transformer M is generatedjData segment [ i, …, j ] of]For power failure debris, distribution transformer MjA distribution transformer for power failure;
based on the rule for screening the power failure distribution transformers, historical power failure distribution transformers are screened from the acquired power data of the distribution transformers, and a power failure distribution transformer set M ═ M { M ═ is generated1,M2,...,MlL is the number of distribution transformers with power failure after screening.
3. The method for diagnosing the abnormal relation of the line-to-line variation of the power distribution network based on the power failure event according to claim 1, characterized in that: the method for acquiring the nearby bus and distribution transformer and the voltage measurement data of the nearby bus based on the longitude and latitude information of the power failure distribution transformer in the power failure distribution transformer set comprises the following steps:
and calculating a bus set within a set threshold value from the geographic range of the power failure distribution transformer based on the longitude and latitude information of each distribution transformer in the power failure distribution transformer set, and acquiring voltage measurement data of the bus set and the bus.
4. The method for diagnosing the abnormal relation of the line-to-line variation of the power distribution network based on the power failure event according to claim 1, characterized in that: the searching method of the suspected corresponding bus comprises the following steps:
obtaining a correlation coefficient index calculation formula between a power failure distribution transformer and a nearby bus, wherein the correlation coefficient index calculation formula is as follows:
Figure FDA0002755594160000021
wherein, R represents a correlation coefficient index; xiRepresenting the voltage measurement data of the power failure distribution transformer;
Figure FDA0002755594160000022
the average value of the voltage measurement data of the power failure distribution transformer is represented; y isiRepresenting the measurement data of the bus voltage near the power failure distribution transformer;
Figure FDA0002755594160000023
the average value of the measured data of the bus voltage near the power failure distribution transformer is represented; n represents the length of the measured data of the voltage of the power failure distribution transformer and the nearby bus;
and screening out proper data from the obtained voltage measurement data of the power failure distribution transformer and the nearby buses, and introducing the proper data into the correlation coefficient index calculation formula, calculating to obtain correlation coefficient index values between all buses in the power failure distribution transformer and the nearby bus set, and screening out suspected corresponding buses and lower lines thereof based on the correlation coefficient index values.
5. The method for diagnosing the abnormal relation of the line-to-line variation of the power distribution network based on the power failure event according to claim 1, characterized in that: the method comprises the following steps of calculating the power failure distribution transformer number ratio index corresponding to all lines under the suspected corresponding bus, evaluating and seeking the power failure distribution transformer number ratio index threshold value:
calculating the power failure distribution transformer number ratio index P of all linesjForming a historical power failure distribution transformer number ratio index data set P;
the method for calculating the power failure distribution transformer number ratio index threshold value by using the confusion matrix method for supervised learning in artificial intelligence specifically comprises the following steps:
respectively calculating different values P in the data set PjThe precision ratio and the recall ratio of the corresponding distribution transformer to the route;
based on the searchCalculating derived index F1 by standard rate and recall ratioj：
Figure FDA0002755594160000024
Wherein A isjFor accuracy, RjThe recall ratio is checked;
after the calculation is completed, an evaluation index set F1 ═ { F1 ═ is formed1,F12,…,F1j,…,F1kK is the number of bus lines under the suspected bus;
and searching for the maximum value in the evaluation index set F1, and taking the power failure distribution transformer number ratio index corresponding to the maximum value as the power failure distribution transformer number ratio index threshold.
6. The method for diagnosing the abnormal line-to-line relationship of the power distribution network based on the power failure event as claimed in claim 5, wherein the power failure distribution transformer number ratio index PjThe calculation method comprises the following steps:
aiming at all lines under suspected to-be-owned buses in a certain area, the number T of distribution transformers with power failure of each line under suspected to-be-owned buses is calculatedjAnd total number of distribution transformers ZjThen, the power failure distribution transformer number ratio index P is calculatedj。
7. The method for diagnosing the abnormal relation of the line-to-line transformation of the power distribution network based on the power failure event as claimed in claim 1, wherein the suspected corresponding line of the power failure distribution transformer is screened out based on the obtained threshold, and finally the power failure distribution transformer with the wrong line-to-line transformation relation and the suspected corresponding line thereof are found out, comprising the following steps:
the method comprises the steps of carrying out preset judgment steps on all power-off distribution transformers and lines under adjacent buses, and finally obtaining a power-off distribution transformer set, power-off distribution transformer number proportion index data sets of all lines under adjacent buses of the power-off distribution transformers, power-off distribution transformer number proportion index thresholds of all lines under suspected to-be-attributed buses, all distribution transformers with abnormal suspected line transformation relations and suspected to-be-attributed lines under suspected to-be-attributed buses;
the preset judging step comprises the following steps: if the suspected lower line of the bus to which the power failure distribution transformer belongs has power failure in a typical time period, and the index value of the number of the power failure distribution transformers is higher than the percentage index threshold value of the number of the power failure distribution transformers, the power failure distribution transformer is judged to be the suspected abnormal distribution transformer with the line transformer relation, and otherwise, the power failure distribution transformer is judged to be the normal distribution transformer with the line transformer relation.
8. A distribution network line change relation abnormity diagnosis device based on power failure events is characterized by comprising:
the generating unit is used for generating a historical power failure distribution transformer set based on the acquired power data of the distribution transformer;
the first calculation unit is used for obtaining a bus and a distribution transformer nearby the first calculation unit and voltage measurement data of the bus nearby the first calculation unit based on the longitude and latitude information of the power failure distribution transformer in the power failure distribution transformer set;
the second calculation unit is used for calculating the correlation coefficient indexes of the power failure distribution transformer and the adjacent buses thereof and finding out suspected corresponding buses;
the first screening unit is used for screening all lines which are suspected to belong to a bus and correspond to the power failure distribution transformer;
the third calculation unit is used for calculating the power failure distribution transformer number ratio index corresponding to all lines under the suspected corresponding bus, evaluating and seeking the power failure distribution transformer number ratio index threshold;
and the judging unit is used for screening suspected similar lines of the power failure distribution transformer based on the obtained threshold value, and finally finding the power failure distribution transformer with the wrong line transformation relation and the suspected similar lines thereof.
9. The apparatus according to claim 8, wherein the method for generating the historical outage distribution transformer set comprises:
a certain distribution transformer MjConnected to line f, distribution transformer MjThe active power measurement data is Pk＝[pk,1,pk,2,...,pk,i,...,pk,n]N represents the number of sampling points of the measured data of the distribution transformer;
if distribution transformer MjActive power measurement data PkIn successive data sections pk,i,...,pk,j]0 or null, a distribution transformer M is generatedjData segment [ i, …, j ] of]For power failure debris, distribution transformer MjThen it is a power failure distribution transformer;
based on the rule for screening the power failure distribution transformers, historical power failure distribution transformers are screened from the acquired power data of the distribution transformers, and a power failure distribution transformer set M ═ M { M ═ is generated1,M2,...,MlL is the number of distribution transformers with power failure after screening.
10. The power failure event-based power distribution network line variation relation abnormity diagnosis device according to claim 8, wherein the suspected corresponding bus searching method comprises the following steps:
obtaining a correlation coefficient index calculation formula between a power failure distribution transformer and a nearby bus, wherein the correlation coefficient index calculation formula is as follows:
Figure FDA0002755594160000041
wherein, R represents a correlation coefficient index; xiRepresenting the voltage measurement data of the power failure distribution transformer;
Figure FDA0002755594160000042
the average value of the voltage measurement data of the power failure distribution transformer is represented; y isiRepresenting the measurement data of the bus voltage near the power failure distribution transformer;
Figure FDA0002755594160000043
the average value of the measured data of the bus voltage near the power failure distribution transformer is represented; n represents the length of the measured data of the voltage of the power failure distribution transformer and the nearby bus;
and screening out proper data from the obtained voltage measurement data of the power failure distribution transformer and the nearby buses, and introducing the proper data into the correlation coefficient index calculation formula, calculating to obtain correlation coefficient index values between all buses in the power failure distribution transformer and the nearby bus set, and screening out suspected corresponding buses and lower lines thereof based on the correlation coefficient index values.
11. The power failure event-based power distribution network line transformation relation abnormity diagnosis device according to claim 8, wherein the power failure distribution transformer number proportion index corresponding to all lines under suspected corresponding buses is calculated, and the threshold value of the power failure distribution transformer number proportion index is evaluated and searched, and the method comprises the following steps:
calculating the power failure distribution transformer number ratio index P of all linesjForming a historical power failure distribution transformer number ratio index data set P;
the method for calculating the power failure distribution transformer number ratio index threshold value by using the confusion matrix method for supervised learning in artificial intelligence specifically comprises the following steps:
respectively calculating different values P in the data set PjThe precision ratio and the recall ratio of the corresponding distribution transformer to the route;
calculating a derived index F1 based on the precision ratio and the recall ratioj：
Figure FDA0002755594160000044
Wherein A isjFor accuracy, RjThe recall ratio is checked;
after the calculation is completed, an evaluation index set F1 ═ { F1 ═ is formed1,F12,…,F1j,…,F1kWhere k is the number of bus lines under the suspected busAn amount;
and searching for the maximum value in the evaluation index set F1, and taking the power failure distribution transformer number ratio index corresponding to the maximum value as the power failure distribution transformer number ratio index threshold.
12. The apparatus according to claim 8, wherein the power failure distribution transformer count ratio indicator P is a power failure event-based power distribution network line transformation relation abnormality diagnosis apparatusjThe calculation method comprises the following steps:
aiming at all lines under suspected to-be-owned buses in a certain area, the number T of distribution transformers with power failure of each line under suspected to-be-owned buses is calculatedjAnd total number of distribution transformers ZjThen, the power failure distribution transformer number ratio index P is calculatedj。
13. The power failure event-based power distribution network line-change relation abnormity diagnosis device according to claim 8, wherein the power failure distribution transformer suspected to be similar to the corresponding line is screened out based on the obtained threshold, and finally the power failure distribution transformer with the wrong line-change relation and the suspected similar corresponding line are found out, comprising the following steps:
the method comprises the steps of carrying out preset judgment steps on all power-off distribution transformers and lines under adjacent buses, and finally obtaining a power-off distribution transformer set, power-off distribution transformer number proportion index data sets of all lines under adjacent buses of the power-off distribution transformers, power-off distribution transformer number proportion index thresholds of all lines under suspected to-be-attributed buses, all distribution transformers with abnormal suspected line transformation relations and suspected to-be-attributed lines under suspected to-be-attributed buses;
the preset judging step comprises the following steps: if the suspected lower line of the bus to which the power failure distribution transformer belongs has power failure in a typical time period, and the index value of the number of the power failure distribution transformers is higher than the percentage index threshold value of the number of the power failure distribution transformers, the power failure distribution transformer is judged to be the suspected abnormal distribution transformer with the line transformer relation, and otherwise, the power failure distribution transformer is judged to be the normal distribution transformer with the line transformer relation.
14. The utility model provides a distribution network line becomes abnormal relation diagnostic system based on power failure incident which characterized in that: comprising a storage medium and a processor;
the storage medium is used for storing instructions;
the processor is configured to operate in accordance with the instructions to perform the steps of the method according to any one of claims 1 to 7.
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