CN113239132B - Online out-of-tolerance identification method for voltage transformer - Google Patents

Online out-of-tolerance identification method for voltage transformer Download PDF

Info

Publication number
CN113239132B
CN113239132B CN202110785972.6A CN202110785972A CN113239132B CN 113239132 B CN113239132 B CN 113239132B CN 202110785972 A CN202110785972 A CN 202110785972A CN 113239132 B CN113239132 B CN 113239132B
Authority
CN
China
Prior art keywords
error
limit
value
data
statistic
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
CN202110785972.6A
Other languages
Chinese (zh)
Other versions
CN113239132A (en
Inventor
窦峭奇
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Wuhan Gelanruo Intelligent Technology Co ltd
Original Assignee
Wuhan Glory Road Intelligent Technology Co ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Wuhan Glory Road Intelligent Technology Co ltd filed Critical Wuhan Glory Road Intelligent Technology Co ltd
Priority to CN202110785972.6A priority Critical patent/CN113239132B/en
Publication of CN113239132A publication Critical patent/CN113239132A/en
Application granted granted Critical
Publication of CN113239132B publication Critical patent/CN113239132B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/28—Databases characterised by their database models, e.g. relational or object models
    • G06F16/284—Relational databases
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/24—Querying
    • G06F16/245—Query processing
    • 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
    • G06F17/15—Correlation function computation including computation of convolution operations
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/20—Administration of product repair or maintenance
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Electricity, gas or water supply
    • 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 provides an out-of-tolerance online identification method for a voltage transformer, which is characterized in that a mapping relation between monitoring statistics and each VT error variation is established on the basis of converting secondary measurement output values of a plurality of VTs in a transformer substation into the monitoring statistics. And taking the difference value between the VT first inspection report error value and the VT archive information error regulation limit value as an error change limit value for judging the VT error state, converting the error change limit value into monitoring statistics by using a mapping relation, dividing a plurality of control limits according to the contribution rate of each VT to the monitoring statistics, regarding the out-of-limit VT as suspected out-of-limit, and further determining whether the out-of-limit VT is out-of-limit or not for the suspected out-of-limit VT. The application of the invention can enable the detection of the out-of-tolerance VT in the long-term operation process to get rid of the dual dependence on planned power failure and a real object standard device, is beneficial to timely finding out the out-of-tolerance VT, guides the operation and maintenance work of a power company, promotes the accurate maintenance of power transmission and transformation equipment, and provides guarantee for the fair electric energy trade and the safe operation of a power grid.

Description

Online out-of-tolerance identification method for voltage transformer
Technical Field
The invention relates to the field of power distribution equipment state evaluation and fault diagnosis, in particular to an out-of-tolerance online identification method for a voltage transformer.
Background
A Voltage Transformer (VT) is a general-purpose device that converts a high voltage to a low voltage and thus enables measurement. During long-term operation, VT metering errors may degrade until the profile information error specification limit is exceeded, resulting in an out-of-tolerance fault. The super-difference may cause deviation of electric energy calculated from voltage and current data, possibly causing huge electric energy trade disputes, and may also cause intervention misappropriation and abnormal actions of a power grid automation system, possibly causing instability and cracking of a power system. Therefore, it is important to find and replace the out-of-tolerance VT in time.
To identify out-of-tolerance VT, current certification protocols require the use of off-line detection methods. The method forcibly carries out first verification (first inspection) and periodic verification (weekly inspection) in a period of 4 years before the VT is accessed to the network and in the subsequent operation process, evaluates the error value of the detected VT by comparing with a high-precision calibrator, and provides a detection report. The off-line detection method has the advantages that the method not only can detect the change of the error value, but also can find out the out-of-tolerance, so that the off-line detection result is the basis for making the out-of-tolerance VT maintenance plan. However, the method has the defects that power failure is required to be carried out, and due to the fact that planned power failure windows are few and time is short, off-line detection is difficult to complete on schedule, and a basis cannot be provided for making a maintenance plan. In order to get rid of the dependence of the out-of-tolerance fault detection on the planned power failure, a non-power-failure VT error online evaluation method is provided and quickly becomes a research hotspot.
The existing research results are divided into three categories, namely methods based on accurate modeling, signal processing and information physical fusion. The accurate modeling method is based on a known accurate equivalent model of the power system, and an equation set is established to solve unknown transformer errors. The high-precision equivalent parameters of the power grid required by modeling of the method are difficult to obtain, and the engineering application difficulty is high; the signal processing method analyzes the output signal identification error change of a single mutual inductor by using methods such as wavelet transformation, frequency shift algorithm, integrated filtering and the like. The method does not depend on accurate equivalent parameters, but only can find the error mutation of VT, can not find the error gradual change, and has weak engineering applicability.
The assessment method based on the information physical fusion aims to detect the measurement error drift, a VT group with electrical physical connection in the same transformer substation is used as an assessment object, physical correlation in the group is used as a constraint condition, information contained in secondary measurement voltage real-time large-scale data output by the VT group is cooperatively analyzed through a multivariate statistical method, the real-time state of the constraint relation is mined and tracked, and the information physical fusion method is used for realizing the online assessment of the VT individual operation error change. However, currently, this method can only determine whether the VT error changes, but cannot determine whether the VT exceeds the allowable error range for the following reasons: 1. this method does not allow to obtain the absolute value of the error. Since only the measured value of VT is used for evaluation, the initial value of error is not known, and it can only be determined from the change of the statistic whether the error state has changed relatively, but the absolute value of the error cannot be obtained, and therefore it cannot be determined whether the error after abnormal change exceeds the allowable error range. 2. The magnitude of the variation in error cannot be determined. The current method is to convert the measured data into monitoring statistic, judge whether error change occurs according to the monitoring statistic change of the modeling data and the monitoring data, not establish the quantitative relation between the monitoring statistic and the error variation, and only judge whether the error changes from the statistic change, so the error variation can not be obtained. In summary, the above method cannot evaluate the error value, and therefore, the method does not have the capability of identifying the out-of-tolerance VT, and cannot guide the making of the maintenance plan.
Disclosure of Invention
The invention provides an out-of-tolerance online identification method of a Voltage Transformer (VT) aiming at the technical problems in the prior art, which comprises the following steps: collecting secondary output signal data of a plurality of same-phase voltage transformers VT of a newly-accessed network transformer substation, and constructing a modeling data set
Figure 198950DEST_PATH_IMAGE001
And monitoring the data set
Figure 692117DEST_PATH_IMAGE002
(ii) a Modeling-based data set
Figure 334320DEST_PATH_IMAGE001
Calculating the Q statistic in the modeling stage and the corresponding contribution rate index Q of each VT to the Q statistic by taking the Q statistic as the monitoring statisticxiAnd establishing the error variation delta e between the Q statistic and each VTiMapping relation Δ Q = f (Δ e) therebetweeni) (ii) a Taking the collected error value of the first inspection report of each VT as an initial error change value, and taking the difference value between the initial error change value of each VT and the specified error limit value of each VT in the archive information as an error change limit value; calculating corresponding statistic according to the error change limit value and the mapping relation, and defining the control limit t of each VT based on the statistic contribution rate indexi(ii) a For monitoring data sets
Figure 670361DEST_PATH_IMAGE002
Calculating its real-time contribution rate index Q from the single-point data of VT to be detectedxiWhen Q isxiGreater than the control limit tiTime, judgeSuspected out-of-tolerance VT; and calculating the evaluation error mean value of all monitoring data in the overrun time segment in which the suspected overrun VT is located, and if the evaluation error mean value exceeds the error specified limit value in the archive information, judging that the overrun of the VT to be detected in the overrun time segment occurs.
The online identification method for the out-of-tolerance of the voltage transformer provided by the invention is characterized in that a mapping relation between monitoring statistics and each VT error variation is established on the basis of converting secondary measurement output values of a plurality of VTs in a transformer substation into the monitoring statistics. The difference value between the VT first inspection report error value and the VT archive information error regulation limit value is used as an error change limit value for judging the VT error state, then the error change limit value is converted into monitoring statistic by utilizing the mapping relation, a plurality of control limits are defined according to the contribution rate of each VT to the monitoring statistic, and the over-limit VT is regarded as suspected over-limit. In order to eliminate the influence of secondary output signal fluctuation on out-of-tolerance judgment and reduce erroneous judgment, for VT suspected to be out-of-tolerance, the mapping relation is utilized to convert the out-of-tolerance time period online monitoring data into an error variation mean value isomorphic with an error initial value, an evaluation error mean value is obtained by adding the variation mean value to the initial value, and the evaluation error mean value is compared with a file information error regulation limit value to further determine whether the VT is out-of-tolerance. The application of the invention can enable the detection of the out-of-tolerance VT in the long-term operation process to get rid of the dual dependence on planned power failure and a real object standard device, is beneficial to timely finding out the out-of-tolerance VT, guides the operation and maintenance work of a power company, promotes the accurate maintenance of power transmission and transformation equipment, and provides guarantee for the fair electric energy trade and the safe operation of a power grid.
Drawings
FIG. 1 is a flow chart of an online voltage transformer over-tolerance identification method provided by the present invention;
FIG. 2 is a schematic diagram of a typical wiring and voltage transformer VT configuration of a substation;
FIG. 3 is an overall flowchart of an online voltage transformer over-tolerance identification method according to the present invention;
fig. 4 is a graph of control limit out-of-tolerance risk identification results of the voltage transformer.
Detailed Description
The following detailed description of embodiments of the present invention is provided in connection with the accompanying drawings and examples. The following examples are intended to illustrate the invention but are not intended to limit the scope of the invention.
Fig. 1 is a flowchart of an online voltage transformer tolerance identification method provided by the present invention, and as shown in fig. 1, the method includes: s1, collecting secondary output signal data of a plurality of same-phase voltage transformers VT of the newly-connected substation, and constructing a modeling data set
Figure 846259DEST_PATH_IMAGE001
And monitoring the data set
Figure 465371DEST_PATH_IMAGE002
。
It can be understood that, for a newly-put-into-operation or a recently-completed transformer substation, in the case that at least 3 (including) VT are redundantly configured on a bus and an outgoing line corresponding to the same voltage in the substation, an online over-tolerance identification method for the voltage transformer can be provided. Firstly, collecting secondary output signal data X of each in-phase voltage transformer VT, and constructing a modeling data set based on the collected secondary output signal data of each voltage transformer VT
Figure 153841DEST_PATH_IMAGE001
And monitoring the data set
Figure 806539DEST_PATH_IMAGE002
. Wherein the data set is modeled
Figure 192652DEST_PATH_IMAGE001
Monitoring a data set to model usage
Figure 207882DEST_PATH_IMAGE002
I.e. the data to be monitored, the monitoring data set of each VT is identified to identify whether the VT is out of tolerance.
Wherein, step S1 specifically includes the following steps:
step S11, collecting the voltage corresponding to the same voltage at the newly-connected or recently-inspected substationIn-phase VT secondary output to generate continuously updated time sequence output signal
Figure 847679DEST_PATH_IMAGE003
，
Figure 331882DEST_PATH_IMAGE004
N is VT number, m is current sampling point number, output signal
Figure 957904DEST_PATH_IMAGE005
The method comprises the preferred normal data and the subsequent real-time data sampled after the network access operation or the weekly check is completed.
In step S12, considering that the switching of the line load generally has a significant periodicity per day, the unit time slices of 24 hours per day are counted
Figure 873645DEST_PATH_IMAGE006
A point (i.e. t sampling points in 24 hours per day) for outputting signals from a time sequence
Figure 451388DEST_PATH_IMAGE007
Selecting data k multiplied by t points of integral multiple unit time segments at head end of time sequence to establish normal data set
Figure 853245DEST_PATH_IMAGE008
Subsequent time series data sets are established as real-time data sets
Figure 705795DEST_PATH_IMAGE009
。
Step S13, respectively aligning the normal data sets X0And a real-time data set X1Standardized processing is carried out to obtain a modeling data set
Figure 898879DEST_PATH_IMAGE010
And monitoring the data set
Figure 83741DEST_PATH_IMAGE011
Wherein
Figure 604852DEST_PATH_IMAGE012
Is a normal data set X0The mean vector of (a), wherein,
Figure 166152DEST_PATH_IMAGE013
respectively is the average value and standard deviation matrix of normal data of each voltage transformer VT in different time sequences
Figure 26792DEST_PATH_IMAGE014
，
Figure 930026DEST_PATH_IMAGE015
Is X0To (1) a
Figure 843493DEST_PATH_IMAGE016
Standard deviation of the individual measured variables. For a normal data set X0And a real-time data set X1Standardized processing is carried out to obtain a modeling data set
Figure 965076DEST_PATH_IMAGE001
And monitoring the data set
Figure 444337DEST_PATH_IMAGE002
Then, the monitoring data is collected in unit time slices
Figure 65942DEST_PATH_IMAGE002
The data are divided into different segments, and the monitoring data of the i +1 th segment are as follows:
Figure 76492DEST_PATH_IMAGE017
and the monitoring data of each time slice comprises t monitoring data points of n voltage transformers.
It should be noted that, in step S1, the collected secondary output signal data of the multiple in-phase voltage transformers VT are preprocessed to obtain modeling data sets respectively
Figure 527065DEST_PATH_IMAGE001
And monitoring the data set
Figure 1778DEST_PATH_IMAGE002
For subsequent use in modeling and online identification of VT out-of-tolerance.
S2, based on the modeling data set
Figure 715656DEST_PATH_IMAGE001
Calculating the Q statistic in the modeling stage and the corresponding contribution rate index Q of each VT to the Q statistic by taking the Q statistic as the monitoring statisticxiAnd establishing Q statistic and error variation delta eiMapping relation Δ Q = f (Δ e) therebetweeni)
Figure 308442DEST_PATH_IMAGE018
The mapping relationship can be understood as the variation of Q statistic and the error variation Δ e of each VTiThe mapping relationship between them.
It will be appreciated that step S2 is primarily based on modeling the data set
Figure 31548DEST_PATH_IMAGE001
Establishing a contribution rate index Q of each VTxiAnd error variation Δ eiSpecifically, step S2 includes the following steps:
s21, modeling the data set
Figure 111499DEST_PATH_IMAGE001
Covariance of
Figure 263124DEST_PATH_IMAGE019
Singular value decomposition:
Figure 139945DEST_PATH_IMAGE020
；(1)
in the formula
Figure 932320DEST_PATH_IMAGE021
Is a unitary matrix of the first phase,
Figure 912783DEST_PATH_IMAGE022
is a diagonal matrix and satisfies
Figure 968464DEST_PATH_IMAGE023
。
Figure 270263DEST_PATH_IMAGE024
Is RnA set of criteria bases for the space,
Figure 335171DEST_PATH_IMAGE025
front of
Figure 779928DEST_PATH_IMAGE026
(k < n) linearly independent vectors
Figure 740931DEST_PATH_IMAGE027
Form a principal component space
Figure 90879DEST_PATH_IMAGE028
The last (n-k) linearly independent vectors
Figure 693898DEST_PATH_IMAGE029
Forming a residual space
Figure 884839DEST_PATH_IMAGE030
Number of principal elements
Figure 282323DEST_PATH_IMAGE026
Typically determined from variance accumulation and percentage.
Step S22, using Principal Component Analysis (PCA) to measure samples of single sampling points in the data set
Figure 594267DEST_PATH_IMAGE031
Decomposition into principal component
Figure 548448DEST_PATH_IMAGE032
And residual components
Figure 358010DEST_PATH_IMAGE033
：
Figure 5023DEST_PATH_IMAGE034
；（2）
In the formula
Figure 17978DEST_PATH_IMAGE035
，
Figure 8806DEST_PATH_IMAGE036
，
Figure 423607DEST_PATH_IMAGE032
And
Figure 179204DEST_PATH_IMAGE033
are respectively as
Figure 413876DEST_PATH_IMAGE037
In that
Figure 739553DEST_PATH_IMAGE028
And
Figure 759593DEST_PATH_IMAGE030
projection of (2).
Step S23, calculating Q statistic Q for monitoring residual error component in modeling stagenorAnd its expectation over that period is E (Q)nor)：
Figure 669780DEST_PATH_IMAGE038
；(3)
Figure 900736DEST_PATH_IMAGE039
；(4)
Calculating a contribution rate index Q of each voltage transformer VT in the modeling stage group to the corresponding contribution of Q statisticxiSize Qxi norAnd itThe expectation in this period is E (Q)xi nor)：
Figure 452940DEST_PATH_IMAGE040
；(5)
Figure 796328DEST_PATH_IMAGE041
；(6)
In the formula
Figure 142995DEST_PATH_IMAGE042
Is composed of
Figure 821101DEST_PATH_IMAGE033
To (1) a
Figure 160685DEST_PATH_IMAGE043
Component, PeiIs PeTo (1) a
Figure 670164DEST_PATH_IMAGE043
A row vector.
It should be noted that, the formula (3) and the formula (4) are the modeling and monitoring phase data sets respectively
Figure 141727DEST_PATH_IMAGE001
Q of residual component of medium monitoring datanorStatistics and their expectation E (Q)nor) The calculation formula of (2); the formula (5) and the formula (6) are the contribution rate indexes Q corresponding to the Q statistic of each voltage transformer VTxi norAnd expectation of E (Q)xi nor) The calculation formula of (2).
Step S24, based on the modeling data set
Figure 103867DEST_PATH_IMAGE001
Establishing a mapping relation delta Q = f (delta e) of Q statistic and error variationi)。
It should be noted that the secondary output signal data of each voltage transformer VT includes amplitude signal data and phase signal data, and the error of the amplitude signal dataThe difference drift is also called ratio error drift, and when the secondary output signal data of each voltage transformer VT is amplitude signal data, the ratio error drift delta ei=△εiThe mapping relation Δ Q = f (Δ ∈)i) Comprises the following steps:
Figure 981562DEST_PATH_IMAGE044
；(7)
in the formula
Figure 548810DEST_PATH_IMAGE045
，vi m(t) is the secondary output amplitude signal data of the ith station VT, ∈i norError value, K, reported for first inspection of amplitude signal data of ith station VTrRated ratio of VT, DσIs a matrix of standard deviations of the normal data set.
When the secondary output signal data of each voltage transformer VT is phase signal data, the phase error drifts delta ei=△（△φi) In time, the mapping Δ Q = f (Δ (#Φ)i) ) is:
Figure 502859DEST_PATH_IMAGE046
；(8)
in the formula
Figure 437448DEST_PATH_IMAGE047
。
It is noted that the modeling data set is based on
Figure 72829DEST_PATH_IMAGE048
Equations (7) and (8) provide a mapping of the Q statistic to different types of error variations.
S3, taking the collected error value of the first inspection report of each VT as the initial error change value, and taking the difference between the initial error change value of each VT and the error regulation limit value of each VT in the archive information as the error change limit value; calculating the error change limit value according to the mapping relationCorresponding statistic, and defining control limit t of each VT based on the size of statistic contribution rate indexi。
It is understood that step S3 specifically includes the following steps:
step S31, recording the first detection report error value e of each same-phase VTi norWherein the first inspection report error value ei norIncluding the first reported error value of the amplitude and phase signal data. Limiting the accuracy level VT file information error
Figure 806168DEST_PATH_IMAGE049
elimError value e from the first inspection reporti norMaking difference, and determining positive error change limit and negative error change limit, respectively delta epi=elim-ei norAnd
△eni=-elim-ei nor。
step S32, converting Δ epiAnd Δ eniInput mapping relation Δ Q = f (Δ e)i) Then dividing the corresponding delta Q into Q statistic contribution rate indexes Q corresponding to all VTsxiCalculating the variation quantity delta Q of the corresponding contribution rate index of each VT theoretically caused when the VT in the population has single error driftxi。
Wherein, in step S32, the limit value Δ e is changed according to the positive errorpiAnd negative error regulation limit value delta eniAnd a mapping relation for calculating corresponding delta QpAnd Δ QnWill be Δ QpAnd Δ QnDividing the data into Q statistic contribution rate indexes corresponding to all VTs
Figure 868801DEST_PATH_IMAGE050
And
Figure 805533DEST_PATH_IMAGE051
。
for the splitting process, namely splitting the delta Q into the Q statistic contribution rate index Q which has corresponding relation with each VTxiTo (2)The process is divided into a splitting process in the case of a ratio error drift and a splitting process in the case of a phase error drift, where the ratio error drift Δ ei=△εiTime, delta QxiComprises the following steps:
Figure 729758DEST_PATH_IMAGE052
；(9)
phase error drift Δ ei=△（△φi) Time, delta QxiComprises the following steps:
Figure 68336DEST_PATH_IMAGE053
。(10)
note that the forward error variation limit Δ epiAnd negative error regulation limit value delta eniCorresponding delta Q can be obtainedpAnd Δ QnFor Δ QpAnd Δ QnCan be respectively obtained by the formula (9)
Figure 353736DEST_PATH_IMAGE054
And
Figure 512184DEST_PATH_IMAGE055
or corresponding according to formula (10)
Figure 489368DEST_PATH_IMAGE056
And
Figure 433184DEST_PATH_IMAGE057
the formula (9) and the formula (10) are general formulas.
Based on
Figure 837621DEST_PATH_IMAGE058
、
Figure 998212DEST_PATH_IMAGE059
And expected value size E (Q) of the modeling phasexi nor) Determining a forward control limit t for each VTpiAnd a negative control limit tniThe calculation formula is asThe following:
Figure 575824DEST_PATH_IMAGE060
；(11)
Figure 561098DEST_PATH_IMAGE061
。(12)
similarly, for ratio error drift or phase error drift, equation (11) and equation (12) can be used to obtain the forward control limit t of each VTpiAnd a negative control limit tni。
It is noted that the modeling data set is based on
Figure 683906DEST_PATH_IMAGE048
After the analytical modeling process of steps S1, S2, and S3, the positive control limit and the negative control limit of each VT are determined, and it is determined whether the VT is suspected to be out of tolerance based on the control limits.
S4, for the monitoring data set
Figure 82526DEST_PATH_IMAGE002
Calculating its real-time contribution rate index Q from the single-point data of VT to be detectedxiWhen Q isxiGreater than the control limit tiIf so, the VT is judged to be out of tolerance.
It is understood that, in step S1, the monitoring data set is divided into time segments, and for the single-point monitoring data in a certain time segment, the real-time contribution rate index Q is calculated according to the above formula (5)xiTo do so by
Figure 447517DEST_PATH_IMAGE062
The sign of (2) determines the error drift direction, if the sign is positive, it is determined as positive drift, and if the real-time contribution rate index Q is the same as the positivexiGreater than the forward control limit tpiAnd judging the suspected positive direction out-of-tolerance of the voltage transformer VT to be detected. Otherwise, if the sign is negative, the negative drift is determined, and at this time, if Q isxiGreater than a negative control limit tniThen, it is judgedThe suspected negative direction out-of-tolerance exists in the voltage transformer VT to be detected.
S5, calculating the mean value of the evaluation errors of all the monitoring data of the overrun time segment where the suspected overrun VT is located, and if the mean value of the evaluation errors exceeds the error specified limit value in the archive information, judging that the overrun time segment is out of tolerance.
It should be noted that the out-of-tolerance condition of the voltage transformer VT to be detected is determined according to the control limit, and only the suspected out-of-tolerance of the voltage transformer VT can be determined, and whether the out-of-tolerance condition is determined in the corresponding time slice, which needs further verification.
Specifically, step S5 includes:
s51, when the suspected out-of-tolerance voltage transformer VT exists in the time segment j of the monitoring data set, the monitoring data set in the time segment j is processed
Figure 552876DEST_PATH_IMAGE063
Substituting the Q statistic calculation model shown in formula (4) to calculate the mean value of the Q statistic of the time period
Figure 846586DEST_PATH_IMAGE064
(Q)。
S52, in order
Figure 732502DEST_PATH_IMAGE064
(Q) and E (Q)nor) Magnitude of variation of (a) inverse extrapolation error variation Δ eiAnd then reporting error value with the suspected out-of-tolerance first inspection report of the power transformer VT
Figure 589600DEST_PATH_IMAGE065
i norThe mean value e of the evaluation errors is obtained by superpositioni est. Wherein the ratio estimates the mean of the errors
Figure 595471DEST_PATH_IMAGE066
See, e.g., equation (12), mean phase estimation error
Figure 840507DEST_PATH_IMAGE067
See, for exampleExpression of formula (13).
Figure 433294DEST_PATH_IMAGE068
；(12)
Figure 94082DEST_PATH_IMAGE069
；(13)
In the formula,. DELTA.ei=f-1(. DELTA.Q) is. DELTA.Q = f (. DELTA.e)i) Of the same sign as
Figure 237617DEST_PATH_IMAGE070
The sign of (1) is judged.
Step S53, when the mean error e is evaluatedi estExceeding file information error regulation limit
Figure 122396DEST_PATH_IMAGE049
elimAnd judging that the voltage transformer VT suspected to be out of tolerance is out of tolerance in the time slice.
The online identification method for the out-of-tolerance of the voltage transformer, provided by the invention, is based on the in-phase VT measurement data, the first inspection record error value and the file information error regulation limit value, and can identify the out-of-tolerance VT in time on the premise of getting rid of dual dependence of a physical standard device and power failure, so that the accurate overhaul of power transmission and transformation equipment is promoted; the error value of the VT with the out-of-tolerance risk can be evaluated, the abnormal change of the error can be quantified, and the out-of-tolerance degree of the current VT can be determined.
The online over-tolerance identification method for the voltage transformer provided by the invention is described in detail by using a specific embodiment.
Fig. 2 shows a typical topological structure of a double-bus connection of a high-voltage substation, wherein a bus-tie switch for connecting buses (including two buses W1 and W2) is closed, each bus is provided with one VT, each outgoing line is selectively provided with VT, and any same-phase VT of the same voltage class corresponds to the same voltage. The online identification method for the out-of-tolerance voltage transformer in the embodiment has the steps as shown in fig. 3, and comprises the following steps:
step 1: collecting cophase VT secondary output signal of newly-network-accessed transformer substation
Figure 468058DEST_PATH_IMAGE005
Constructing a modeling dataset
Figure 932538DEST_PATH_IMAGE001
And monitoring the data set
Figure 663733DEST_PATH_IMAGE002
。
In the embodiment, a new network-connected substation as shown in fig. 2 is adopted, the substation adopts double-bus connection, and one 0.2-level VT is configured on each phase of two buses and four outgoing lines. In order to reduce the influence of errors introduced by the acquisition device, the accuracy grade of the selected data acquisition device is higher than the VT accuracy grade by two grades, the acquisition device with the accuracy grade not lower than 0.05 grade is selected, and the output frequency of the acquired characteristic quantity is 1 Hz. From 0: 01 separately collecting six A-phase VT secondary amplitude values and phases, respectively generating continuously updated time sequence output signals XεAnd X△QWherein
Figure 234261DEST_PATH_IMAGE071
，
Figure 254169DEST_PATH_IMAGE072
VT number is 6, m is the current accumulated sampling point number, output signal
Figure 69810DEST_PATH_IMAGE073
Including normal data after recent weekly inspections and real-time data after a period of operation. The ratio of 0: 01-24: 1440 points are counted in 00 hours as unit time slices, and when the collected data meet 2880 points, the data matrix is obtained
Figure 655512DEST_PATH_IMAGE073
In selecting the timeOrder-head data creation normal data set
Figure 350935DEST_PATH_IMAGE074
Subsequent continuously updated time series data establishes a real-time data set
Figure 904145DEST_PATH_IMAGE075
。
Respectively align with a constant data set X0And a real-time data set X1The updated single point is subjected to standardization processing to obtain a modeling data set
Figure 507165DEST_PATH_IMAGE076
And monitoring data points in real time
Figure 166948DEST_PATH_IMAGE077
Wherein
Figure 564431DEST_PATH_IMAGE078
Is X0Mean vector, standard deviation matrix
Figure 604937DEST_PATH_IMAGE079
，
Figure 214910DEST_PATH_IMAGE080
Is X0To (1) a
Figure 775204DEST_PATH_IMAGE043
Standard deviation of the individual measured variables. Monitoring data set in unit time slice
Figure 359900DEST_PATH_IMAGE002
The data are divided into different segments, and the monitoring data of the i +1 th segment are as follows:
Figure 638435DEST_PATH_IMAGE081
。
step 2: establishing a calculation model by taking the Q statistic as a monitoring statistic, and calculating a modeling period
Figure 838384DEST_PATH_IMAGE082
Statistics and corresponding contribution rate index Q of each VT to Q statisticsxiEstablishing a mapping relation between the Q statistic and the error variation quantity delta Q = f (delta e)i)。
From a modeling dataset
Figure 456447DEST_PATH_IMAGE001
Establishing a Q statistic calculation model, and calculating the expected size E (Q) of the Q statistic of the amplitude and the phase at the modeling stage according to the formulas (3) and (4)nor)。
Figure 477624DEST_PATH_IMAGE083
；(3)
Figure 977876DEST_PATH_IMAGE084
；(4)
In the formula
Figure 710077DEST_PATH_IMAGE085
When E (Q) is E (Q)nor)。
And E (Q) is determined according to the formulas (5) and (6)nor) Decomposed into a contribution rate index E (Q) corresponding to six VTsxi nor)(i=1,2，...，6)。
Figure 510543DEST_PATH_IMAGE086
;(5)
Figure 702621DEST_PATH_IMAGE087
; (6)
In the formula
Figure 893431DEST_PATH_IMAGE085
When, E (Q)xi) Is E (Q)xi nor)。
Establishing a mapping relation delta Q = f (delta e) of Q statistic and error variationi)。
Ratio error drift Δ ei=△εiThe mapping relation Δ Q = f (Δ ∈)i) Comprises the following steps:
Figure 429323DEST_PATH_IMAGE088
;(7)
in the formula
Figure 818716DEST_PATH_IMAGE089
，vi m(t) is the secondary output amplitude of the ith station VT.
Phase error drift Δ ei=△（△φi) In time, the mapping Δ Q = f (Δ (#Φ)i) ) is:
Figure 368646DEST_PATH_IMAGE046
; (8)
in the formula
Figure 859802DEST_PATH_IMAGE047
。
And step 3: collecting VT first inspection report error value as error change initial value, using the difference value of every VT initial value and positive and negative file information error regulation limit value as error change limit value delta epiAnd Δ eniBased on the mapping relation Δ Q = f (Δ e)i) According to the index QxiDimensioning different VT control limits tpiAnd tni。
Recording the error values of the first inspection reports of 6A-phase VT and calculating the boundary of the error caused by out-of-tolerance due to positive and negative drift, and the ratio difference delta ei=△εiPhase difference Δ ei=△（△φi) The results of the ratio difference and phase difference are shown in table 1:
TABLE 1 VT first inspection report error value and error drift limit
Figure 153380DEST_PATH_IMAGE090
Will showInjection relationship Δ Q = f (Δ e)i) Decomposition of Delta Q in (1) into Delta QxiThe index is shown in formulas (9) and (10).
Will delta epsilonpiAnd Δ εniDelta epsilon with formula (9)iIn calculating the error drift of each ratio
Figure 646547DEST_PATH_IMAGE091
Size. When Δ εiIs Δ εpiWhen the temperature of the water is higher than the set temperature,
Figure 164116DEST_PATH_IMAGE091
is composed of
Figure 329518DEST_PATH_IMAGE092
When Δ εiIs Δ εniWhen the temperature of the water is higher than the set temperature,
Figure 708678DEST_PATH_IMAGE091
is composed of
Figure 338242DEST_PATH_IMAGE093
。
Figure 207804DEST_PATH_IMAGE094
。(9)
Will be provided with
Figure 657240DEST_PATH_IMAGE095
And
Figure 261845DEST_PATH_IMAGE097
Δ (Δ φ) substituted into equation (10)i) In which the error drift of each phase is calculated
Figure 121216DEST_PATH_IMAGE098
Size. When delta (Δ φ)i) Is Δ (Δ φ)pi) When the temperature of the water is higher than the set temperature,
Figure 307216DEST_PATH_IMAGE098
is composed of
Figure 543025DEST_PATH_IMAGE099
When delta (Δ φ)i) Is Δ (Δ φ)ni) When the temperature of the water is higher than the set temperature,
Figure 570018DEST_PATH_IMAGE098
is composed of
Figure 865870DEST_PATH_IMAGE100
。
Figure 227581DEST_PATH_IMAGE101
。(10)
Finally each delta QxiSubstituting into equation (11) to obtain the ratio error control limit of positive and negative directions of each VT
Figure 516349DEST_PATH_IMAGE102
、
Figure 443854DEST_PATH_IMAGE103
And a phase error control limit tpi △φ、tni △φ. When in use
Figure 599023DEST_PATH_IMAGE104
Is composed of
Figure 510347DEST_PATH_IMAGE105
And
Figure 25642DEST_PATH_IMAGE106
when the temperature of the water is higher than the set temperature,
Figure 322500DEST_PATH_IMAGE107
are respectively as
Figure 960155DEST_PATH_IMAGE102
And
Figure 47190DEST_PATH_IMAGE103
. When Δ QxiIs composed of
Figure 428493DEST_PATH_IMAGE099
And
Figure 585717DEST_PATH_IMAGE100
when t isiAre each tpi △φAnd tni △φ。
Figure 331956DEST_PATH_IMAGE108
。(11)
And 4, step 4: calculating a real-time contribution rate index Q of a monitoring data setxiWhen Q isxiGreater than the control limit tiIf so, the VT is judged to be out of tolerance.
Respectively inputting the monitored amplitude data and phase data into formulas (5) and (6) to calculate real-time QxiSize of
Figure 686714DEST_PATH_IMAGE109
The sign judges the error drift direction, the sign judges the forward drift, the real-time contribution rate index QxiAnd the control limit tpiComparison, QxiGreater than tpiJudging that the VT to be detected has the risk of positive out-of-tolerance, otherwise, QxiGreater than tniAnd judging that the negative over-tolerance risk exists, wherein the control limit identification effect is shown in fig. 4, wherein the X axis represents different voltage transformers VT, the Y axis represents the number of sampling points, and the Z axis represents the statistic Qxi.
And 5: and calculating the evaluation error mean value of the over-limit time segment VT, and judging that the VT to be detected is over-error when the evaluation error mean value exceeds the specified limit value of the file information error.
If the amplitude data of the VT to be detected in the step 4 exceeds the condition of the i +1 th monitoring data segment, the data segment is processed
Figure 888020DEST_PATH_IMAGE110
After normalization, the values are substituted into formulas (3) and (4), and the expected size of the statistic of the time interval Q is calculated
Figure 159470DEST_PATH_IMAGE111
Then, againWill be provided with
Figure 607769DEST_PATH_IMAGE112
And
Figure 466134DEST_PATH_IMAGE113
substituting the estimated ratio difference of VT calculated by equation (12)
Figure 720398DEST_PATH_IMAGE114
If there is a risk of forward out-of-tolerance and
Figure 111934DEST_PATH_IMAGE115
is greater than
Figure 747446DEST_PATH_IMAGE116
At the risk of a negative over-tolerance and
Figure 76796DEST_PATH_IMAGE114
is less than
Figure 118439DEST_PATH_IMAGE117
And judging that the VT to be detected is out of tolerance in the (i + 1) th monitoring data segment, and sending out-of-tolerance alarm and evaluation ratio difference.
The online identification method for the voltage transformer out-of-tolerance provided by the embodiment of the invention fuses three types of heterogeneous data, namely, voltage transformer VT secondary output signal data, a first inspection report error value and a file information error regulation limit value, so as to realize online identification of out-of-tolerance VT. The identification method establishes a mapping relation between monitoring statistics and each VT error variation on the basis of converting secondary measurement output values of a plurality of VTs in a transformer substation into the monitoring statistics. Then, the VT first inspection report error value is taken as an initial value, the difference value between the initial value and the VT archive information error regulation limit value is taken as an error change limit value for judging the VT error state, the error change limit value is converted into monitoring statistics by utilizing a mapping relation, a plurality of control limits are defined according to the contribution rate of each VT to the monitoring statistics, and the out-of-limit VT is taken as suspected out-of-limit, so that the problem of obtaining the absolute value of the VT error is solved. In order to eliminate the influence of secondary output signal fluctuation on out-of-tolerance judgment and reduce erroneous judgment, aiming at out-of-tolerance VT, the mapping relation is further utilized to convert the online monitoring data of the out-of-tolerance time segment into an error variation mean value which is isomorphic with an error initial value, the variation mean value is added to the initial value to obtain an evaluation error mean value, and the evaluation error mean value is compared with a file information error regulation limit value to judge whether the VT is out-of-tolerance. The application of the invention can enable the detection of the out-of-tolerance VT in the long-term operation process to get rid of the dual dependence on planned power failure and a real object standard device, is beneficial to timely finding out the out-of-tolerance VT, guides the operation and maintenance work of a power company, promotes the accurate maintenance of power transmission and transformation equipment, and provides guarantee for the fair electric energy trade and the safe operation of a power grid.
It should be noted that, in the foregoing embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to relevant descriptions of other embodiments for parts that are not described in detail in a certain embodiment.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention 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 invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. 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 computer, 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 preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.

Claims (5)

1. An out-of-tolerance online identification method for a voltage transformer is characterized by comprising the following steps:
collecting secondary output signal data of a plurality of same-phase voltage transformers VT of a newly-accessed network transformer substation, and constructing a modeling data set
Figure 132107DEST_PATH_IMAGE001
And monitoring the data set
Figure 783668DEST_PATH_IMAGE002
；
Modeling-based data set
Figure 297826DEST_PATH_IMAGE001
Calculating the Q statistic in the modeling stage and the corresponding contribution rate index Q of each VT to the Q statistic by taking the Q statistic as the monitoring statisticxiAnd establishing the error variation delta e between the Q statistic and each VTiMapping relation Δ Q = f (Δ e) therebetweeni)；
Taking the collected error value of the first inspection report of each VT as an initial error change value, and taking the difference value between the initial error change value of each VT and the specified error limit value of each VT in the archive information as an error change limit value; calculating corresponding statistic according to the error change limit value and the mapping relation, and defining the control limit t of each VT based on the statistic contribution rate indexi；
For monitoring data sets
Figure 37243DEST_PATH_IMAGE002
Calculating its real-time contribution rate index Q from the single-point data of VT to be detectedxiWhen Q isxiGreater than the control limit tiJudging that VT is suspected to be out of tolerance;
calculating the evaluation error mean value of all monitoring data in the overrun time segment in which the suspected overrun VT is located, and if the evaluation error mean value exceeds the error specified limit value in the archive information, judging that the to-be-detected VT is overrun in the overrun time segment;
collecting secondary output signal data of a plurality of same-phase voltage transformers VT of a newly-accessed network transformer substation, and constructing a modeling data set
Figure 320457DEST_PATH_IMAGE001
And monitoring the data set
Figure 877340DEST_PATH_IMAGE002
The method comprises the following steps:
acquisition of new network access power transformationSecondary output signal data of station in-phase voltage transformer
Figure 3428DEST_PATH_IMAGE003
，
Figure 671170DEST_PATH_IMAGE004
N is the number of voltage transformers, m is the number of sampling points, X is a measurement time sequence output signal containing a plurality of columns which sequentially change along with time, and the time sequence output signal X comprises normal data sampled after first inspection and real-time data of subsequent operation;
selecting data at the head end of the time sequence from the time sequence output signal X to establish a normal data set, and establishing a real-time data set by using subsequent time sequence data;
respectively carrying out standardization processing on the normal data set and the real-time data set to obtain a modeling data set
Figure 74469DEST_PATH_IMAGE001
And monitoring the data set
Figure 176155DEST_PATH_IMAGE002
And monitoring the data set
Figure 664905DEST_PATH_IMAGE005
Dividing the time slices into different time slices;
the modeling-based data set
Figure 136338DEST_PATH_IMAGE001
Calculating the Q statistic in the modeling stage and the corresponding contribution rate index Q of each VT to the Q statistic by taking the Q statistic as the monitoring statisticxiThe method comprises the following steps:
modeling-based data set
Figure 394144DEST_PATH_IMAGE001
Calculating the Q statistic of the modeling stage and the expected value E (Q) of the modeling stagenor) The Q statistic calculation formula is as follows:
Figure 683043DEST_PATH_IMAGE006
；
in the formula (I), the compound is shown in the specification,
Figure 659089DEST_PATH_IMAGE007
emodeling a data set
Figure 668633DEST_PATH_IMAGE001
The last n-k (k < n) load matrixes of the unitary matrix obtained by the singular value decomposition of the covariance matrix,
Figure 46525DEST_PATH_IMAGE008
modeling a data set
Figure 991479DEST_PATH_IMAGE001
The single sample point data of (1);
converting the Q statistic into a contribution rate index Q corresponding to each VTxiCalculate each QxiExpected value magnitude E (Q) at modeling stagexi nor)，QxiThe calculation formula of (a) is as follows:
Figure 454821DEST_PATH_IMAGE009
；
Figure 533635DEST_PATH_IMAGE010
；
in the formula, PeiIs PeTo (1) a
Figure 766034DEST_PATH_IMAGE011
A row vector, kt being the data volume in the modeling dataset;
the mapping relation Δ Q = f (Δ e)i) Error variation Δ e for each set of VTiQuantitative relation between Q statistic variation quantity delta QFurther establishing the error variation delta e of each VTiWith corresponding contribution rate index QxiAmount of change of (A) Δ QxiWherein the data set is modeled
Figure 272101DEST_PATH_IMAGE001
Including amplitude signal data and phase signal data;
when modeling a data set
Figure 81794DEST_PATH_IMAGE001
When the data in (1) is amplitude signal data, the ratio error variation value
△ei=△εiThe mapping relation of (1) is as follows:
Figure 698720DEST_PATH_IMAGE012
；
in the formula
Figure 785625DEST_PATH_IMAGE013
，KrIs the rated transformation ratio of VT, vi m(t) is the secondary output amplitude of the ith station VT,. epsiloni norError value, D, reported for first check of VTσA standard deviation matrix for the normal data set;
when modeling a data set
Figure 462594DEST_PATH_IMAGE001
When the data in (1) is phase signal data, the phase error change value
△ei=△（△φi) The mapping relation of (1) is as follows:
Figure 20570DEST_PATH_IMAGE014
；
in the formula
Figure 175607DEST_PATH_IMAGE015
。
2. The method of claim 1, wherein the step of collecting error values of the first inspection report of each VT as initial error variation values and the step of determining the difference between the initial error variation values of each VT and the predetermined error limit of each VT in the profile information as the error variation limit comprises:
collecting error value e of first inspection report of each VTi norPositive error regulation limit + e of voltage transformer file informationlimAnd negative error specification limit-elim;
Reporting the first inspection error value e of each VTi norDetermining a positive error variation limit Δ e by the difference from the error specification limit of the file informationpi=elim-ei norAnd negative error variation limit
△eni=-elim-ei nor。
3. The method of claim 2, wherein the error variation limit and the mapping relation are used to calculate a corresponding statistic, and the control limit t of each VT is defined based on the contribution rate of the statisticiThe method comprises the following steps:
according to the positive error variation limit value delta epiAnd negative error regulation limit value delta eniAnd the mapping relation is calculated
Figure 382598DEST_PATH_IMAGE016
And
Figure 230468DEST_PATH_IMAGE017
；
based on
Figure 14753DEST_PATH_IMAGE018
、
Figure 973482DEST_PATH_IMAGE017
And expected value size E (Q) of the modeling phasexi nor) Determining a forward control limit t for each VTpiAnd a negative control limit tniThe calculation formula is as follows:
Figure 769400DEST_PATH_IMAGE019
；
Figure 788171DEST_PATH_IMAGE020
。
4. the method for online identification of voltage transformer according to claim 3, wherein said monitoring data set
Figure 810485DEST_PATH_IMAGE021
The real-time contribution rate index Q of the single point data is calculatedxiWhen Q isxiGreater than the control limit tiAnd then, judging the suspected over-tolerance of VT, comprising the following steps:
based on QxiJudging the error drift direction of VT, and according to the error drift direction of VT, making QxiControl limits t associated with respective directionsiMake a comparison if QxiIf the corresponding control limit is exceeded, the VT is suspected to be out of tolerance.
5. The online identification method for the out-of-tolerance of the voltage transformer according to claim 4, wherein the step of calculating an evaluation error mean value of all monitoring data of the out-of-limit time segment where the suspected out-of-tolerance VT is located, and if the evaluation error mean value exceeds an error regulation limit value in the archive information, determining that the out-of-limit time segment is out-of-tolerance comprises the steps of:
calculating the mean value E (Q) of the Q statistic of the monitoring data of the time slice based on the monitoring data of the suspected out-of-tolerance VT in the time slice, and calculating the mean value E (Q) and the mean value E (Q) of the Q statistic of the monitoring data of the time slicexi nor) Size of the variation ofDetermining a corresponding error variation Δ ei；
Error variation quantity delta eiError value e from the first inspection reporti norThe mean value e of the evaluation errors is obtained by superpositioni est；
To estimate the mean error ei estWhether the error of the file information exceeds the specified limit
Figure 572905DEST_PATH_IMAGE022
And judging whether the VT suspected to be out of tolerance is out of tolerance in the time slice.
CN202110785972.6A 2021-07-12 2021-07-12 Online out-of-tolerance identification method for voltage transformer Active CN113239132B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202110785972.6A CN113239132B (en) 2021-07-12 2021-07-12 Online out-of-tolerance identification method for voltage transformer

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202110785972.6A CN113239132B (en) 2021-07-12 2021-07-12 Online out-of-tolerance identification method for voltage transformer

Publications (2)

Publication Number Publication Date
CN113239132A CN113239132A (en) 2021-08-10
CN113239132B true CN113239132B (en) 2021-09-24

Family

ID=77135438

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202110785972.6A Active CN113239132B (en) 2021-07-12 2021-07-12 Online out-of-tolerance identification method for voltage transformer

Country Status (1)

Country Link
CN (1) CN113239132B (en)

Families Citing this family (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114579933B (en) * 2022-05-05 2022-08-12 威胜集团有限公司 Error measurement optimization method and device, electronic equipment and readable storage medium
CN115980647B (en) * 2022-11-02 2023-08-11 国网安徽省电力有限公司营销服务中心 CVT abnormal state identification method and device based on group information
CN115587673B (en) * 2022-11-10 2023-04-07 武汉格蓝若智能技术股份有限公司 Voltage transformer error state prediction method and system
CN115685045B (en) * 2022-12-08 2023-04-07 华中科技大学 Online evaluation method for voltage transformer
CN115932702B (en) * 2023-03-14 2023-05-26 武汉格蓝若智能技术股份有限公司 Virtual standard based voltage transformer online operation calibration method and device

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103278791A (en) * 2013-05-10 2013-09-04 国家电网公司 Electronic transformer amplitude and phase error checking system with networked detection function
CN111474510A (en) * 2020-04-25 2020-07-31 华中科技大学 Error evaluation method and system for voltage transformer with non-stable output
CN111537938A (en) * 2020-03-31 2020-08-14 国网江西省电力有限公司电力科学研究院 Error short-time prediction method for electronic transformer based on intelligent algorithm
CN112485747A (en) * 2020-11-28 2021-03-12 国网江西省电力有限公司供电服务管理中心 Error state evaluation method for capacitor voltage transformer
CN113032728A (en) * 2021-03-04 2021-06-25 国网湖南省电力有限公司 Voltage transformer state evaluation method and system based on data driving error evaluation result

Family Cites Families (18)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104933012B (en) * 2015-05-08 2018-04-10 西南交通大学 A kind of measured deviation On-line Fault recognition methods of traction substation transformer
CN105184674B (en) * 2015-09-07 2018-12-18 杭州凯达电力建设有限公司 A kind of electric energy metering error prediction technique and equipment
CN105354663B (en) * 2015-10-20 2020-01-31 杭州电力设备制造有限公司 Power transformer error analysis method and system based on time series similarity
CN106772200B (en) * 2017-01-25 2023-07-21 云南电网有限责任公司电力科学研究院 CVT metering error abnormity evaluation method and system based on capacitance-to-ground current
CN107255792A (en) * 2017-06-20 2017-10-17 华中科技大学 A kind of electronic type voltage transformer error on-line monitoring method and system
CN109359822B (en) * 2018-09-21 2022-02-15 国网江苏省电力有限公司电力科学研究院 Electronic voltage transformer measurement state evaluation method and system
CN109444791B (en) * 2018-11-09 2020-09-18 华中科技大学 Error state evaluation method and system for capacitor voltage transformer
EP3660523B1 (en) * 2018-11-27 2023-04-19 Siemens Aktiengesellschaft Method, device and system for determining the location of a fault on a line of an electrical energy supply network
CN109902735B (en) * 2019-02-22 2022-06-07 武汉格蓝若智能技术有限公司 Method for realizing running error assessment of three-phase voltage transformer in transformer substation
CN110333474A (en) * 2019-06-28 2019-10-15 中国电力科学研究院有限公司 A kind of method and system of on-line checking mutual inductor metering abnormality
CN111025222A (en) * 2019-12-31 2020-04-17 三峡大学 Electronic voltage transformer state monitoring method based on recursion principal component analysis
CN111796233B (en) * 2020-09-04 2020-11-27 武汉格蓝若智能技术有限公司 Method for evaluating secondary errors of multiple voltage transformers in double-bus connection mode
CN112068064B (en) * 2020-09-18 2023-08-25 国网重庆市电力公司营销服务中心 Method for calculating errors of operation three-phase voltage transformer with background influence subtracted
CN112068065B (en) * 2020-10-12 2022-06-21 武汉格蓝若智能技术有限公司 Voltage transformer state early warning method and device and storage medium
CN112098915B (en) * 2020-11-05 2021-02-05 武汉格蓝若智能技术有限公司 Method for evaluating secondary errors of multiple voltage transformers under double-bus segmented wiring
CN112285634B (en) * 2020-12-18 2021-03-23 华中科技大学 Method for identifying abnormal measurement error of high-voltage transformer in double-bus structure
CN113050017A (en) * 2021-03-02 2021-06-29 合肥工业大学 Intelligent error state monitoring and fault diagnosis system for electronic transformer
CN112731260B (en) * 2021-03-30 2021-07-09 华中科技大学 Online evaluation method for error state of voltage transformer based on concept drift recognition

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103278791A (en) * 2013-05-10 2013-09-04 国家电网公司 Electronic transformer amplitude and phase error checking system with networked detection function
CN111537938A (en) * 2020-03-31 2020-08-14 国网江西省电力有限公司电力科学研究院 Error short-time prediction method for electronic transformer based on intelligent algorithm
CN111474510A (en) * 2020-04-25 2020-07-31 华中科技大学 Error evaluation method and system for voltage transformer with non-stable output
CN112485747A (en) * 2020-11-28 2021-03-12 国网江西省电力有限公司供电服务管理中心 Error state evaluation method for capacitor voltage transformer
CN113032728A (en) * 2021-03-04 2021-06-25 国网湖南省电力有限公司 Voltage transformer state evaluation method and system based on data driving error evaluation result

Also Published As

Publication number Publication date
CN113239132A (en) 2021-08-10

Similar Documents

Publication Publication Date Title
CN113239132B (en) Online out-of-tolerance identification method for voltage transformer
CN110095744B (en) Error prediction method for electronic transformer
CN106505557B (en) Remote measurement error identification method and device
CN112098915B (en) Method for evaluating secondary errors of multiple voltage transformers under double-bus segmented wiring
WO2020185990A1 (en) Method and apparatus for composite load calibration for a power system
CN109818349B (en) Power grid robust state prediction method based on multidimensional state matrix sliding matching
CN109583520B (en) State evaluation method of cloud model and genetic algorithm optimization support vector machine
CN109034400B (en) Transformer substation abnormal measurement data prediction platform system
CN116125361B (en) Voltage transformer error evaluation method, system, electronic equipment and storage medium
EP2978095A1 (en) Power system operation
CN109284933B (en) Electronic transformer state evaluation system and method based on mathematical statistics
CN108199891B (en) Cps network attack identification method based on artificial neural network multi-angle comprehensive decision
CN117060409B (en) Automatic detection and analysis method and system for power line running state
CN103413044A (en) Substation measurement information based method for estimating local topology of electric power system
CN112731260A (en) Online evaluation method for error state of voltage transformer based on concept drift recognition
EP3968479A1 (en) Systems and methods for automatic power topology discovery
CN115372816A (en) Power distribution switchgear operation fault prediction system and method based on data analysis
CN115169704A (en) CVT error state prediction method and device based on increment integrated learning model
CN114065875A (en) Power grid fault identification system based on big data
CN113985339A (en) Error diagnosis method, system, equipment and storage medium for intelligent electric meter
CN106874676B (en) State evaluation method for electric energy metering device
CN115480204A (en) Current transformer operation error online evaluation optimization method based on big data deduction
Papadopoulos et al. A three-level distributed architecture for the real-time monitoring of modern power systems
CN108845285B (en) Electric energy metering device detection method and system
CN115685045B (en) Online evaluation method for voltage transformer

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant
CP01 Change in the name or title of a patent holder
CP01 Change in the name or title of a patent holder

Address after: Room 1803-1805, building 2-07, guanggu.core center, 303 Guanggu Avenue, Donghu New Technology Development Zone, Wuhan City, Hubei Province, 430000

Patentee after: Wuhan Gelanruo Intelligent Technology Co.,Ltd.

Address before: Room 1803-1805, building 2-07, guanggu.core center, 303 Guanggu Avenue, Donghu New Technology Development Zone, Wuhan City, Hubei Province, 430000

Patentee before: WUHAN GLORY ROAD INTELLIGENT TECHNOLOGY Co.,Ltd.