CN114065114A - Method and system for predicting metering error of capacitive voltage transformer - Google Patents

Method and system for predicting metering error of capacitive voltage transformer Download PDF

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CN114065114A
CN114065114A CN202210050187.0A CN202210050187A CN114065114A CN 114065114 A CN114065114 A CN 114065114A CN 202210050187 A CN202210050187 A CN 202210050187A CN 114065114 A CN114065114 A CN 114065114A
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刘思成
刘发志
何为
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Wuhan Gelanruo Intelligent Technology Co ltd
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Abstract

The invention provides a method and a system for predicting metering error of a capacitor voltage transformer, wherein the method comprises the following steps: respectively calculating the day additional error and the day additional error to be predicted of the capacitor voltage transformer CVT, and calculating the transformer error prediction theoretical value f of the day to be predictedL(ii) a Analysis mutual inductor historical error prediction estimation value sequence F0Determining an ARIMA model according to the autocorrelation coefficient graph and the partial autocorrelation coefficient graph; will sequence F0Inputting the error prediction observation value f of the mutual inductor to be predicted into an ARIMA modelG(ii) a Predicting theoretical value f according to mutual inductor error of day to be predictedLAnd the error prediction observed value f of the mutual inductor to be predictedGCalculating daily mutual inductance to be predictedError prediction estimation f1. The invention respectively calculates the error prediction theoretical values f of the mutual inductor of the days to be predictedLAnd mutual inductor error prediction observed value fGAnd combining the two predicted values to obtain a final predicted estimated value so as to improve the accuracy of CVT metering error state prediction.

Description

Method and system for predicting metering error of capacitive voltage transformer
Technical Field
The invention relates to the field of electric power measurement online monitoring, in particular to a method and a system for predicting a measurement error of a capacitor voltage transformer.
Background
As an important component of the electric energy metering device, the accuracy and the reliability of the metering performance of the mutual inductor directly relate to the fairness and the justice of electric energy trade settlement. The Capacitor Voltage Transformer (CVT) is used as a voltage transformation instrument by voltage division of a series capacitor and voltage reduction and isolation of an electromagnetic transformer, and can couple carrier frequency to a power transmission line for long-distance communication, selective high-frequency line protection, remote control and other functions. Compared with the conventional electromagnetic voltage transformer, the capacitor voltage transformer has the advantages of high impact insulation strength, simplicity in manufacturing, small size, light weight and the like, and has many advantages in economy and safety.
In the actual operation process of CVT, mutual-inductor error receives influences such as collection principle and adverse circumstances can appear measuring deviation in its working life and transfinites, consequently not only need carry out accurate quick diagnosis when its metering error is out of tolerance, and is further, need make timely prediction to CVT metering error's degradation trend to relevant operation maintainer arranges the work of overhauing and maintaining, if can not discover in time that mutual-inductor state degrades, will influence the electric wire netting and move.
In order to avoid inaccuracy of a secondary information system information source, reduce loss of electric energy metering and ensure normal operation of a measurement and control protection device, statistical methods, theoretical calculation and other methods are used for predicting a transformer error value at present, how to integrate existing data and predict a CVT error value and a change trend thereof more accurately so as to early warn risks appearing in the CVT is a technical problem.
Disclosure of Invention
The invention provides a method and a system for predicting metering error of a capacitor voltage transformer, aiming at the technical problems in the prior art.
According to a first aspect of the present invention, there is provided a method for predicting a metering error of a capacitor voltage transformer, comprising:
respectively calculating the current day additional error and the to-be-predicted day additional error of the capacitor voltage transformer CVT according to the current day external environment factor data and the to-be-predicted day external environment factor data predicted values;
calculating a transformer error prediction theoretical value f of the day to be predicted according to the day additional error of the CVT and the day additional error to be predictedL；
Obtaining a mutual inductor historical error prediction estimation value sequence F of a preset historical time period before the current day0Drawing a mutual inductor error prediction estimation value sequence F0The autocorrelation coefficient map and the partial autocorrelation coefficient map;
determining a historical error prediction estimation value sequence F of the mutual inductor according to the characteristics of the autocorrelation coefficient graph and the partial autocorrelation coefficient graph0A conforming ARIMA model;
predicting the historical error of the mutual inductor to estimate a sequence F0Inputting the obtained data into the ARIMA model to obtain an error prediction observation value f of the mutual inductor to be predictedG；
Predicting theoretical value f according to mutual inductor error of day to be predictedLAnd the error prediction observed value f of the mutual inductor to be predictedGCalculating the error prediction estimated value f of the mutual inductor to be predicted1。
On the basis of the technical scheme, the invention can be improved as follows.
Optionally, the external environment factor data at least includes temperature, power supply frequency of the CVT and secondary load of the CVT; the method for calculating the current day additional error and the to-be-predicted day additional error of the capacitor voltage transformer CVT respectively according to the current day external environment factor data and the to-be-predicted day external environment factor data prediction value comprises the following steps:
calculating the temperature additional error of the CVT on the current day according to the external environment factor data on the current day
Figure 350426DEST_PATH_IMAGE001
Power supply frequency added error
Figure 714542DEST_PATH_IMAGE002
And secondary load added error
Figure 82246DEST_PATH_IMAGE003
；
Calculating the temperature additional error of the CVT day to be predicted according to the external environment factor data prediction value of the day to be predicted
Figure 58161DEST_PATH_IMAGE004
Power supply frequency added error
Figure 550322DEST_PATH_IMAGE005
And secondary load added error
Figure 604997DEST_PATH_IMAGE006
；
Correspondingly, calculating the transformer error prediction theoretical value f of the day to be predicted according to the day additional error of the CVT and the day additional error to be predictedLThe method comprises the following steps:
according to the temperature additional error, the power frequency additional error and the secondary load additional error of the CVT on the same day, the temperature additional error, the power frequency additional error and the secondary load additional error of the CVT on the day to be predicted and the current transformer error prediction theoretical value f0Calculating the error prediction theoretical value f of the daily mutual inductor to be predictedL:
Figure 942437DEST_PATH_IMAGE007
。
Optionally, the sequence F of the historical error prediction estimation values of the mutual inductor in the preset historical time period before the current day is obtained0Drawing a mutual inductor error prediction estimation value sequence F0The autocorrelation coefficient map and the partial autocorrelation coefficient map of (1) include:
predicting estimated value sequence F of historical error of the mutual inductor by using unit root test method0Performing stationarity test to test the historical error prediction estimation value sequence F of the mutual inductor0If there is a unit root, recording the difference by increasing the difference order until the unit root test is passedA processing order;
predicting estimated value sequence F of historical error of the mutual inductor based on differential processing order0Carrying out differential processing to obtain a mutual inductor historical error prediction estimation value sequence F 'after differential processing'0;
Drawing a differential processed mutual inductor historical error prediction estimation value sequence F'0The autocorrelation coefficient map and the partial autocorrelation coefficient map.
Optionally, the sequence F of the historical error prediction estimation values of the transformer is determined according to the characteristics of the autocorrelation coefficient map and the partial autocorrelation coefficient map0A conforming ARIMA model comprising:
analyzing the tailing of the autocorrelation coefficient graph and the truncation of the partial autocorrelation coefficient graph, and determining the historical error prediction estimation value sequence F of the mutual inductor based on the tailing characteristic of the autocorrelation coefficient graph and the truncation characteristic of the partial autocorrelation coefficient graph0A conforming ARIMA model and model parameters.
Optionally, the theoretical value f is predicted according to the error of the transformer on the day to be predictedLAnd the error prediction observed value f of the mutual inductor to be predictedGCalculating the error prediction estimated value f of the mutual inductor to be predicted1The method comprises the following steps:
calculating an optimization coefficient based on historical observed value errors and historical theoretical value errors of the mutual inductor, wherein the historical observed value errors of the mutual inductor are the mean values of differences between a daily mutual inductor error prediction observed value and a corresponding prediction error estimated value in a preset historical time period before the current day, and the historical theoretical value errors of the mutual inductor are the mean values of differences between a daily mutual inductor error prediction theoretical value and a corresponding prediction error estimated value in a preset historical time period before the current day;
predicting theoretical value f according to mutual inductor error of day to be predictedLAnd the error prediction observation value f of the mutual inductor to be predictedGAnd calculating the error prediction estimation value of the mutual inductor to be predicted according to the optimization coefficient.
Optionally, the calculating an optimization coefficient based on the historical observed value error and the historical theoretical value error of the transformer includes:
Figure 166001DEST_PATH_IMAGE008
；
wherein e isGIs the historical observation error of the transformer, eLAnd K is an optimization coefficient.
Optionally, the theoretical value f is predicted according to the error of the transformer on the day to be predictedLAnd the error prediction observation value f of the mutual inductor to be predictedGAnd the optimization coefficient is used for calculating the error prediction estimated value f of the mutual inductor to be predicted1The method comprises the following steps:
Figure 845376DEST_PATH_IMAGE009
；
wherein k is an optimization coefficient.
According to a second aspect of the present invention, there is provided a capacitance voltage transformer metering error prediction system, comprising:
the first calculation module is used for respectively calculating the current day additional error and the to-be-predicted day additional error of the capacitor voltage transformer CVT according to the current day external environment factor data and the to-be-predicted day external environment factor data predicted value; calculating a transformer error prediction theoretical value f of the day to be predicted according to the day additional error of the CVT and the day additional error to be predictedL；
A drawing module for obtaining a mutual inductor historical error prediction estimation value sequence F of a preset historical time period before the current day0Drawing a mutual inductor error prediction estimation value sequence F0The autocorrelation coefficient map and the partial autocorrelation coefficient map;
a determining module, configured to determine a historical error prediction estimation value sequence F of the transformer according to the characteristics of the autocorrelation coefficient map and the partial autocorrelation coefficient map0A conforming ARIMA model;
an obtaining module, configured to predict the historical error estimation value sequence F of the transformer0Inputting the obtained data into the ARIMA model to obtain an error prediction observation value f of the mutual inductor to be predictedG；
Second calculation modelA block for predicting the theoretical value f according to the mutual inductor error of the day to be predictedLAnd the error prediction observed value f of the mutual inductor to be predictedGCalculating the error prediction estimated value f of the mutual inductor to be predicted1。
According to a third aspect of the present invention, there is provided an electronic device comprising a memory, a processor for implementing the steps of the capacitance voltage transformer metering error prediction method when executing a computer management class program stored in the memory.
According to a fourth aspect of the present invention, there is provided a computer readable storage medium having stored thereon a computer management like program, which when executed by a processor, implements the steps of the capacitive voltage transformer metering error prediction method.
The invention provides a method and a system for predicting metering error of a capacitor voltage transformer, which are used for respectively calculating a transformer error prediction theoretical value f of a day to be predictedLAnd mutual inductor error prediction observed value fGAnd combining the two predicted values to obtain a final predicted estimated value so as to improve the accuracy of CVT metering error state prediction.
Drawings
Fig. 1 is a flowchart of a method for predicting a metering error of a capacitive voltage transformer according to an embodiment of the present invention;
FIG. 2 is a schematic circuit diagram of a capacitive voltage transformer;
FIG. 3 is a schematic diagram showing comparison of observed values and differences between optimized and error reference values;
fig. 4 is a schematic structural diagram of a system for predicting a metering error of a capacitor voltage transformer according to an embodiment of the present invention;
FIG. 5 is a schematic diagram of a hardware structure of a possible electronic device provided in the present invention;
fig. 6 is a schematic diagram of a hardware structure of a possible computer-readable storage medium according to the present invention.
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.
Example one
A method for predicting a metering error of a capacitor voltage transformer, referring to fig. 1, the method for predicting the metering error comprises:
s1, respectively calculating the current day additional error and the to-be-predicted day additional error of the capacitor voltage transformer CVT according to the current day external environment factor data and the to-be-predicted day external environment factor data prediction value; calculating a transformer error prediction theoretical value f of the day to be predicted according to the day additional error of the CVT and the day additional error to be predictedL。
As an embodiment, the external environmental factor data includes at least a temperature, a power supply frequency of the CVT, and a secondary load of the CVT; the method for calculating the current day additional error and the to-be-predicted day additional error of the capacitor voltage transformer CVT respectively according to the current day external environment factor data and the to-be-predicted day external environment factor data prediction value comprises the following steps:
calculating the temperature additional error of the CVT on the current day according to the external environment factor data on the current day
Figure 167773DEST_PATH_IMAGE010
Power supply frequency added error
Figure 902379DEST_PATH_IMAGE011
And secondary load added error
Figure 462674DEST_PATH_IMAGE012
；
Calculating the temperature additional error of the CVT day to be predicted according to the external environment factor data prediction value of the day to be predicted
Figure 79993DEST_PATH_IMAGE013
Power supply frequency added error
Figure 827369DEST_PATH_IMAGE014
And secondary load added error
Figure 522924DEST_PATH_IMAGE015
。
Correspondingly, calculating the transformer error prediction theoretical value f of the day to be predicted according to the day additional error of the CVT and the day additional error to be predictedLThe method comprises the following steps: according to the temperature additional error, the power frequency additional error and the secondary load additional error of the CVT on the same day, the temperature additional error, the power frequency additional error and the secondary load additional error of the CVT on the day to be predicted and the current transformer error prediction theoretical value f0Calculating the error prediction theoretical value f of the daily mutual inductor to be predictedL:
Figure 937725DEST_PATH_IMAGE016
。
Optionally, in the embodiment of the invention, the theoretical value of the error prediction of the daily transformer to be predicted of the capacitor voltage transformer CVT is obtained through physical model calculation. The basic working principle of the capacitor voltage transformer is shown in fig. 2, wherein C1 and C2 are respectively a high-voltage capacitor and a medium-voltage capacitor of a capacitive voltage divider, and an intermediate transformer T1, a compensation reactor L, a damping device D and an overvoltage protection device G jointly form an electromagnetic unit part. After the CVT is connected into a high-voltage system, a primary high-voltage signal is converted into a lower intermediate-voltage signal through the capacitive voltage divider, the insulation requirement of an electromagnetic unit is lowered, and the secondary high-voltage signal is converted into a required secondary small signal through the intermediate transformer and is used for metering, measurement and control, protection, communication and the like. The secondary output of the CVT has multiple windings depending on the requirements, where 1a1n (2 a2n, 3a3 n) is the main secondary winding terminal and dadn is the residual voltage winding terminal.
Specifically, calculating the error prediction theoretical value f of the mutual inductor to be predictedLThe method mainly comprises the following steps:
acquiring the time sequence data of the predicted capacitor voltage transformer, using the existing error estimation algorithm for the electrical parameter to obtain the CVT ratio error prediction estimation value through a station error estimation system, using the error estimation algorithm as the error historical reference value of the CVT of the capacitor voltage transformer, wherein the error historical reference value belongs to the daily average of the comprehensive ratio error, and is used for correcting the future ratio error of the CVTAnd (4) poor prediction. CVT error historical reference value sequence F for collecting historical one quarter0I.e. the daily prediction estimation value of the mutual inductor error in a certain time period, also called the series F of the historical prediction estimation values of the mutual inductor error0And the current day (day before the day to be predicted) transformer error prediction estimated value f0。
Recording external environment data factors of the day, such as temperature, power supply frequency, secondary load, and external environment factor data forecast values of the day to be forecasted (next day), and calculating corresponding additional errors of the day, wherein the calculation formula of the temperature additional errors comprises the following steps:
Figure 191857DEST_PATH_IMAGE017
wherein S is the rated load,
Figure 426530DEST_PATH_IMAGE018
is a constant; a iscThe temperature coefficient is a constant, delta t is the difference between the temperature of the measuring point and 20 ℃, omeganIs the rated angular frequency; c1Is a high-voltage capacitor; c2Is a low-voltage capacitor; u shape1For the primary voltage of the intermediate transformer, the temperature of the measuring point can be simplified into the average temperature in the day, and the additional temperature error delta f is obtained(T)。
The calculation formula of the power supply frequency additional error is as follows:
Figure 191355DEST_PATH_IMAGE019
wherein S is the rated load,
Figure 460662DEST_PATH_IMAGE020
is a constant, ω is the mean angular frequency of the day, ωnFor rated angular frequency, C1 is a high voltage capacitor, C2 is a low voltage capacitor, and U1 is the primary voltage of the intermediate transformer.
The calculation formula of the secondary load additional error is as follows:
Figure 888626DEST_PATH_IMAGE021
wherein, U2Is a secondary voltage, I2Is the current of the secondary load, and is,
Figure 610594DEST_PATH_IMAGE022
is the secondary load power factor angle, R1And X1For primary winding and leakage reactance, R2' and X2Is a secondary winding resistance and leakage reactance converted to the primary side, fL0Is the ratio difference when the CVT is unloaded.
Calculating the temperature additional error, the power frequency additional error and the secondary load additional error of the current day through formulas (1) to (3) according to the acquired external environment factor data of the current day, and respectively recording the temperature additional error, the power frequency additional error and the secondary load additional error as the temperature additional error, the power frequency additional error and the secondary load additional error of the current day
Figure 647951DEST_PATH_IMAGE023
。
Similarly, according to the acquired external environment factor data forecast value of the day to be predicted, the temperature additional error, the frequency additional error and the secondary load additional error of the day to be predicted are respectively calculated according to the formulas (1) to (3) and are respectively recorded as
Figure 771765DEST_PATH_IMAGE024
。
Respectively calculating the temperature additional error, the frequency additional error, the secondary load additional error of the day and the temperature additional error, the frequency additional error and the secondary load additional error of the day to be predicted, and calculating the theoretical value f of the error of the mutual inductor of the day to be predictedL：
Figure 367700DEST_PATH_IMAGE025
(4)。
S2, obtaining a mutual inductor historical error prediction estimation value sequence F of a preset historical time period before the current day0Drawing a mutual inductor error prediction estimation value sequence F0The autocorrelation coefficient map and the partial autocorrelation coefficient map; according to the characteristics of the autocorrelation coefficient graph and the partial autocorrelation coefficient graph, determining the historical error of the mutual inductorSequence of difference prediction estimates F0A conforming ARIMA (differential Integrated Moving Average Autoregressive model) model; predicting the historical error of the mutual inductor to estimate a sequence F0Inputting the obtained data into the ARIMA model to obtain an error prediction observation value f of the mutual inductor to be predictedG。
As an embodiment, the obtaining of the mutual inductor historical error prediction estimation value sequence F of the preset historical time period before the current day0Drawing a mutual inductor error prediction estimation value sequence F0The autocorrelation coefficient map and the partial autocorrelation coefficient map of (1) include: predicting estimated value sequence F of historical error of the mutual inductor by using unit root test method0Performing stationarity test to test the historical error prediction estimation value sequence F of the mutual inductor0If the unit root exists, recording the differential processing order by increasing the differential order until the unit root is checked; predicting estimated value sequence F of historical error of the mutual inductor based on differential processing order0Carrying out differential processing to obtain a mutual inductor historical error prediction estimation value sequence F 'after differential processing'0Drawing a sequence F 'of historical error prediction estimated values of the mutual inductor after differential processing'0The autocorrelation coefficient map and the partial autocorrelation coefficient map.
Determining a historical error prediction estimation value sequence F of the mutual inductor according to the characteristics of the autocorrelation coefficient graph and the partial autocorrelation coefficient graph0A conforming ARIMA model comprising: analyzing the tailing of the autocorrelation coefficient graph and the truncation of the partial autocorrelation coefficient graph, and determining the historical error prediction estimation value sequence F of the mutual inductor based on the tailing characteristic of the autocorrelation coefficient graph and the truncation characteristic of the partial autocorrelation coefficient graph0A conforming ARIMA model and model parameters.
Specifically, the obtained historical error prediction estimation value sequence F of the mutual inductor0Drawing a time sequence curve as a data set, performing stationarity test on the time sequence curve by using a unit root test method, wherein the unit root test is to test whether a unit root exists in a sequence, increasing a differential order until the unit root test is passed, and recording differential processingThe order d is processed based on the obtained difference, and the estimated value sequence F is predicted for the historical error of the mutual inductor0Differential processing is carried out to obtain a sequence F 'after differential processing'0。
Drawing sequence F'0The autocorrelation coefficient map and the partial autocorrelation coefficient map of (a):
wherein the autocorrelation coefficients are expressed as:
Figure 576965DEST_PATH_IMAGE026
wherein the content of the first and second substances,
Figure 667280DEST_PATH_IMAGE027
is a selected time period error sequence F 'of the mutual inductor'0H is a hysteresis order, n is a transformer error sequence F'0The self-correlation coefficient expresses the self-correlation before and after the mutual inductor error data, and h is used as an abscissa and the self-correlation coefficient is used as an ordinate for drawing.
Provided with a mutual inductor error sequence X = (X)a，xb，xc) The partial autocorrelation coefficient is an error segment x in the middle of the cullingcThe correlation between the front and rear of the error data itself after the interference is expressed as:
Figure 661912DEST_PATH_IMAGE028
(6)；
rab（c）indicating x in the reject error sequencecAfter interference of a segment, xa，xbThe autocorrelation coefficient of (a).
For differentially processed sequence F'0Calculating the autocorrelation coefficient and the partial autocorrelation coefficient of each data respectively, and drawing the sequence F'0The autocorrelation coefficient map and the partial autocorrelation coefficient map. By plotting the two graphs, the partial autocorrelation coefficients should be zero after p-th order, which is said to have truncation, the autocorrelation coefficients cannot be zero after a certain step (truncation) but decay exponentially (or in the form of a sine wave), which is said to have smearing,according to the method and the device, the sequence F of the historical error prediction estimation value of the mutual inductor is determined by analyzing the trailing characteristic of the autocorrelation coefficient graph and the truncation characteristic of the partial autocorrelation coefficient graph0A conforming ARIMA model and model parameters.
Prepared from sequence F'0Leading the data into an ARIMA model to obtain a prediction model M and obtain an error prediction observation value f of the mutual inductor to be predictedG。
S3, predicting the theoretical value f according to the transformer error of the day to be predictedLAnd the error prediction observed value f of the mutual inductor to be predictedGCalculating the error prediction estimated value f of the mutual inductor to be predicted1。
As an example, the theoretical value f is predicted according to the mutual inductor error of the day to be predictedLAnd the error prediction observed value f of the mutual inductor to be predictedGCalculating the error prediction estimated value f of the mutual inductor to be predicted1The method comprises the following steps: calculating an optimization coefficient based on historical observed value errors and historical theoretical value errors of the mutual inductor, wherein the historical observed value errors of the mutual inductor are the mean values of differences between a daily mutual inductor error prediction observed value and a corresponding prediction error estimated value in a preset historical time period before the current day, and the historical theoretical value errors of the mutual inductor are the mean values of differences between a daily mutual inductor error prediction theoretical value and a corresponding prediction error estimated value in a preset historical time period before the current day; predicting theoretical value f according to mutual inductor error of day to be predictedLAnd the error prediction observation value f of the mutual inductor to be predictedGAnd calculating the error prediction estimation value of the mutual inductor to be predicted according to the optimization coefficient.
It can be understood that the error prediction theoretical value f of the daily mutual inductor to be predicted is obtained through the stepsLAnd the error prediction observed value fGAnd the optimal estimation idea is introduced into the CVT metering error prediction method so as to achieve the aim of improving the prediction accuracy.
f0For the known error reference value of the current day transformer (namely the error prediction estimation value of the current day transformer), fLFor the calculated error prediction theoretical value of the daily mutual inductor to be predicted, fGPredicting observed values, namely ARIMA models, for the errors of the mutual inductors to be predictedValue of (a), (b), f)1And predicting an estimated value for the finally obtained error of the daily mutual inductor to be predicted.
Firstly, updating an equation Kalman gain calculation formula by referring to a Kalman filtering algorithm, and designing an optimization coefficient, wherein an optimization coefficient calculation formula K is as follows:
Figure 648323DEST_PATH_IMAGE029
(7)；
wherein e isGObtaining the average value of the difference between an error prediction observation value and an error reference value of the current day by using an ARIMA algorithm for the historical observation value error of the mutual inductor, namely the historical measurement error of the mutual inductor in one quarter; e.g. of the typeLAnd obtaining the average value of the difference between the error prediction theoretical value and the error reference value of the current day by using an external environment additional error calculation method for the historical theoretical value error of the transformer, namely the historical quarter transformer metering error. Historical observed value error e of mutual inductorGAnd the historical theoretical value error e of the mutual inductorLThe calculation formula of (2) is as follows:
Figure 874379DEST_PATH_IMAGE030
(8);
wherein i refers to the ith day of a certain period of history.
Finally obtained daily mutual inductor error prediction estimation value f to be predicted1The calculation formula of (2) is as follows:
Figure 237227DEST_PATH_IMAGE031
(9);
wherein f isGAnd fLRespectively predicting an observed value and a theoretical value of the error for the daily mutual inductor to be predicted, f1And obtaining an error prediction estimated value of the mutual inductor to be predicted on a day, and using the error prediction estimated value as an optimized error prediction value result of the mutual inductor for reference of workers. Referring to fig. 3, a graph is presented comparing the effect of the estimated transformer error prediction (optimized) values with the observed transformer error prediction values. Wherein, 5 months of data are applied to an optimization algorithm, and the error prediction estimation values of the mutual inductor are respectively calculatedAnd the difference value between the error prediction observation value and the error reference value of the mutual inductor is drawn in the figure 3, and the result shows that the difference value between the optimal value and the error reference value is generally smaller, which indicates that the prediction accuracy of the optimal algorithm is higher.
Example two
A capacitive voltage transformer metering error prediction system, see fig. 4, includes a first calculation module 401, a drawing module 402, a determination module 403, an acquisition module 404, and a second calculation module 405.
The first calculating module 401 is configured to calculate an additional error of the capacitor voltage transformer CVT on the same day and an additional error of the capacitor voltage transformer CVT on the day according to the external factor environmental data on the same day and the predicted value of the external factor environmental data on the day to be predicted; calculating a transformer error prediction theoretical value f of the day to be predicted according to the day additional error of the CVT and the day additional error to be predictedL(ii) a A drawing module 402, configured to obtain a transformer historical error prediction estimation value sequence F of a preset historical time period before the current day0Drawing a mutual inductor error prediction estimation value sequence F0The autocorrelation coefficient map and the partial autocorrelation coefficient map; a determining module 403, configured to determine a historical error prediction estimation value sequence F of the transformer according to the characteristics of the autocorrelation coefficient map and the partial autocorrelation coefficient map0A conforming ARIMA model; an obtaining module 404, configured to obtain the historical error prediction estimation value sequence F of the transformer0Inputting the obtained data into the ARIMA model to obtain an error prediction observation value f of the mutual inductor to be predictedG(ii) a A second calculation module 405, configured to predict the theoretical value f according to the transformer error on the day to be predictedLAnd the error prediction observed value f of the mutual inductor to be predictedGCalculating the error prediction estimated value f of the mutual inductor to be predicted1。
It can be understood that the capacitive voltage transformer metering error prediction system provided by the present invention corresponds to the capacitive voltage transformer metering error prediction methods provided by the foregoing embodiments, and the related technical features of the capacitive voltage transformer metering error prediction system may refer to the related technical features of the capacitive voltage transformer metering error prediction method, and are not described herein again.
EXAMPLE III
Referring to fig. 5, fig. 5 is a schematic view of an embodiment of an electronic device according to an embodiment of the invention. As shown in fig. 5, an electronic device 500 according to an embodiment of the present invention includes a memory 510, a processor 520, and a computer program 511 stored in the memory 510 and executable on the processor 520, where the processor 520 implements the capacitance voltage transformer metering error prediction method according to the first embodiment when the computer program 511 is executed by the processor 520.
Example four
Referring to fig. 6, fig. 6 is a schematic diagram of an embodiment of a computer-readable storage medium according to the present invention. As shown in fig. 6, the present embodiment provides a computer-readable storage medium 600, on which a computer program 611 is stored, and when executed by a processor, the computer program 611 implements the capacitance voltage transformer metering error prediction method according to the first embodiment.
The method and the system for predicting the metering error of the capacitive voltage transformer respectively calculate the theoretical value f of the error prediction of the transformer on the day to be predictedLAnd mutual inductor error prediction observed value fGAnd combining the two predicted values, and adding new algorithm comprehensive prediction data to obtain a final prediction estimated value so as to improve the accuracy of CVT metering error state prediction.
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 (10)

1. A metering error prediction method for a capacitor voltage transformer is characterized by comprising the following steps:
respectively calculating the current day additional error and the to-be-predicted day additional error of the capacitor voltage transformer CVT according to the current day external environment factor data and the to-be-predicted day external environment factor data predicted values;
calculating a transformer error prediction theoretical value f of the day to be predicted according to the day additional error of the CVT and the day additional error to be predictedL；
Obtaining a mutual inductor historical error prediction estimation value sequence F of a preset historical time period before the current day0Drawing a mutual inductor error prediction estimation value sequence F0The autocorrelation coefficient map and the partial autocorrelation coefficient map;
determining a historical error prediction estimation value sequence F of the mutual inductor according to the characteristics of the autocorrelation coefficient graph and the partial autocorrelation coefficient graph0A conforming ARIMA model;
predicting the historical error of the mutual inductor to estimate a sequence F0Inputting the obtained data into the ARIMA model to obtain an error prediction observation value f of the mutual inductor to be predictedG；
Predicting theoretical value f according to mutual inductor error of day to be predictedLAnd the error prediction observed value f of the mutual inductor to be predictedGCalculating the error prediction estimated value f of the mutual inductor to be predicted1。
2. The metering error prediction method of claim 1, wherein the external environmental factor data includes at least a temperature, a power supply frequency of the CVT, and a secondary load of the CVT; the method for calculating the current day additional error and the to-be-predicted day additional error of the capacitor voltage transformer CVT respectively according to the current day external environment factor data and the to-be-predicted day external environment factor data prediction value comprises the following steps:
calculating the temperature additional error of the CVT on the current day according to the external environment factor data on the current day
Figure 201321DEST_PATH_IMAGE001
Power supply frequency added error
Figure 430046DEST_PATH_IMAGE002
And secondary load added error
Figure 6521DEST_PATH_IMAGE003
；
Calculating the temperature additional error of the CVT day to be predicted according to the external environment factor data prediction value of the day to be predicted
Figure 214779DEST_PATH_IMAGE004
Power supply frequency added error
Figure 357047DEST_PATH_IMAGE005
And secondary load added error
Figure 216726DEST_PATH_IMAGE006
；
Correspondingly, calculating the transformer error prediction theoretical value f of the day to be predicted according to the day additional error of the CVT and the day additional error to be predictedLThe method comprises the following steps:
according to the temperature additional error, the power frequency additional error and the secondary load additional error of the CVT on the same day, the temperature additional error, the power frequency additional error and the secondary load additional error of the CVT on the day to be predicted and the current transformer error prediction theoretical value f0Calculating the error prediction theoretical value f of the daily mutual inductor to be predictedL:
Figure 546076DEST_PATH_IMAGE007
。
3. The metering error prediction method of claim 1, wherein the obtaining of the historical error prediction estimation value of the transformer at a preset historical time period before the current daySequence F0Drawing a mutual inductor error prediction estimation value sequence F0The autocorrelation coefficient map and the partial autocorrelation coefficient map of (1) include:
predicting estimated value sequence F of historical error of the mutual inductor by using unit root test method0Performing stationarity test to test the historical error prediction estimation value sequence F of the mutual inductor0If the unit root exists, recording the differential processing order by increasing the differential order until the unit root is checked;
predicting estimated value sequence F of historical error of the mutual inductor based on differential processing order0Carrying out differential processing to obtain a mutual inductor historical error prediction estimation value sequence F 'after differential processing'0;
Drawing a differential processed mutual inductor historical error prediction estimation value sequence F'0The autocorrelation coefficient map and the partial autocorrelation coefficient map.
4. The metering error prediction method according to claim 1 or 3, wherein the sequence F of the historical error prediction estimation values of the mutual inductor is determined according to the characteristics of the autocorrelation coefficient map and the partial autocorrelation coefficient map0A conforming ARIMA model comprising:
analyzing the tailing of the autocorrelation coefficient graph and the truncation of the partial autocorrelation coefficient graph, and determining the historical error prediction estimation value sequence F of the mutual inductor based on the tailing characteristic of the autocorrelation coefficient graph and the truncation characteristic of the partial autocorrelation coefficient graph0A conforming ARIMA model and model parameters.
5. The metering error prediction method according to claim 1, wherein the theoretical value f is predicted according to the transformer error of the day to be predictedLAnd the error prediction observed value f of the mutual inductor to be predictedGCalculating the error prediction estimated value f of the mutual inductor to be predicted1The method comprises the following steps:
calculating an optimization coefficient based on historical observed value errors and historical theoretical value errors of the mutual inductor, wherein the historical observed value errors of the mutual inductor are the mean values of differences between a daily mutual inductor error prediction observed value and a corresponding prediction error estimated value in a preset historical time period before the current day, and the historical theoretical value errors of the mutual inductor are the mean values of differences between a daily mutual inductor error prediction theoretical value and a corresponding prediction error estimated value in a preset historical time period before the current day;
predicting theoretical value f according to mutual inductor error of day to be predictedLAnd the error prediction observation value f of the mutual inductor to be predictedGAnd calculating the error prediction estimation value of the mutual inductor to be predicted according to the optimization coefficient.
6. The metering error prediction method of claim 5, wherein the calculating optimization coefficients based on historical observed value errors and historical theoretical value errors of the transformer comprises:
Figure 558026DEST_PATH_IMAGE008
；
wherein e isGIs the historical observation error of the transformer, eLAnd K is an optimization coefficient.
7. The metering error prediction method according to claim 5 or 6, characterized in that the theoretical value f is predicted according to the transformer error on the day to be predictedLAnd the error prediction observation value f of the mutual inductor to be predictedGAnd the optimization coefficient is used for calculating the error prediction estimated value f of the mutual inductor to be predicted1The method comprises the following steps:
Figure 289221DEST_PATH_IMAGE009
；
wherein k is an optimization coefficient.
8. A capacitive voltage transformer metering error prediction system, comprising:
a first computing module for calculating the environmental factor data and the waiting time of the current dayPredicting a daily external environment factor data prediction value, and respectively calculating a daily additional error of the capacitor voltage transformer CVT and a daily additional error to be predicted; calculating a transformer error prediction theoretical value f of the day to be predicted according to the day additional error of the CVT and the day additional error to be predictedL；
A drawing module for obtaining a mutual inductor historical error prediction estimation value sequence F of a preset historical time period before the current day0Drawing a mutual inductor error prediction estimation value sequence F0The autocorrelation coefficient map and the partial autocorrelation coefficient map;
a determining module, configured to determine a historical error prediction estimation value sequence F of the transformer according to the characteristics of the autocorrelation coefficient map and the partial autocorrelation coefficient map0A conforming ARIMA model;
an obtaining module, configured to predict the historical error estimation value sequence F of the transformer0Inputting the obtained data into the ARIMA model to obtain an error prediction observation value f of the mutual inductor to be predictedG；
A second calculation module for predicting the theoretical value f according to the mutual inductor error of the day to be predictedLAnd the error prediction observed value f of the mutual inductor to be predictedGCalculating the error prediction estimated value f of the mutual inductor to be predicted1。
9. An electronic device, comprising a memory, a processor for implementing the steps of the capacitive voltage transformer metering error prediction method according to any one of claims 1 to 7 when executing a computer management class program stored in the memory.
10. A computer-readable storage medium, having stored thereon a computer management-like program which, when executed by a processor, carries out the steps of the method for predicting a metering error of a capacitor voltage transformer according to any one of claims 1 to 7.
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