CN113821938A - Short-term prediction method and device for metering error state of mutual inductor - Google Patents
Short-term prediction method and device for metering error state of mutual inductor Download PDFInfo
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Abstract
The invention relates to a short-term prediction method and a short-term prediction device for a metering error state of a mutual inductor. And the error state of the mutual inductor is predicted in a short term, data support is provided for risk early warning of the mutual inductor, and the running stability of a power grid is further ensured.
Description
Technical Field
The invention relates to the technical field of on-line monitoring of electric power metering, in particular to a short-term prediction method and a short-term prediction device for a metering error state of a capacitor voltage transformer based on ARIMA.
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 capacitance voltage transformer has the advantages of high impact insulation strength, simple manufacture, small volume, light weight and the like, and has a plurality of 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, how to predict CVT error change trend so as to early warn risks appearing in the CVT is a technical problem.
In the prior art, on the basis of CVT metering error state evaluation, Q statistic representing CVT metering error state and a statistical control threshold value thereof are selected as prediction objects, and the state prediction of the CVT metering error is mapped to the state prediction of the physical correlation of power grid information, so as to eliminate the influence of power grid fluctuation on an evaluation result. The result shows that the CVT metering error state prediction model based on the Q-ARMA can predict the change trend of the metering error state of the CVT at the next sampling moment or a certain time period in the future, but the accuracy is not high.
Disclosure of Invention
The invention provides a short-term prediction method and a short-term prediction device for a metering error state of a capacitor voltage transformer based on ARIMA (auto-regressive integrated moving average) aiming at the technical problems in the prior art.
The technical scheme for solving the technical problems is as follows:
in a first aspect, the invention provides a short-term prediction method for a metering error state of a mutual inductor, which comprises the following steps:
s1, acquiring time series data of the observed system, and obtaining an error estimation value f through a station-level error estimation system(g)And estimate time series F(g)；
S2, selecting two CVT error influence factors of temperature and frequency, and respectively calculating temperature additional error delta f(Tem)And frequency additive error Δ f(w)Stripping CVT error data to obtain the self error f of the mutual inductor on the same day(y)：f(y)=f(g)-Δf(Tem)-Δf(w)And self-error time series F(y)；
S3, using self-error time sequence F(y)An ARIMA model M is constructed, trend prediction is carried out on the error state of the mutual inductor, and the self error prediction value f of the mutual inductor at the target time point t is obtained(p)And self error prediction value time series F in target time range T(p)；
S4, respectively calculating the temperature additional error predicted value and the frequency additional error predicted value of the mutual inductor at the target time point t, and calculating the self error predicted value f of the mutual inductor at the target time point t(p)Combining the predicted value with the predicted value of the temperature additional error and the predicted value of the frequency additional error to obtain a predicted value f of the error of the mutual inductor at the target time point t(future)And predicting a value time series F within the target time range T(future)And outputting a transformer error state prediction curve.
Further, step S1 includes: time series F of estimated values by using box plot method(g)Error estimate f in (1)(g)And (3) abnormal value processing:
time series F for estimated values(g)Each error estimate f in(g)If Q1-1.5IQR is less than or equal to f(g)Keeping the Q3+1.5IQR or less, otherwise, removing; wherein Q3 is the upper quartile point in the boxplot, Q1 is the lower quartile point in the boxplot, and IQR is Q3-Q1;
time series of estimated values F(g)In the method, error estimation values f of a certain time point due to abnormal elimination or missing collection(g)And a front-back mean value method is adopted for supplement.
Further, step S3 includes:
s301, carrying out self-error time sequence F on the mutual inductor(y)As a data set, a time series curve is drawn, a unit root test is used for stability test, if F(y)Is a non-stationary sequence, and is subjected to stationary treatment by a difference method to obtain a sequence F'(y)；
S302, drawing a sequence F'(y)The sequence F 'is determined through characteristic judgment of the autocorrelation coefficient graph and the partial autocorrelation coefficient graph'(y)A conforming ARIMA model and model parameters;
s303, sequence F'(y)Importing the ARIMA model into a prediction model M;
s304, selecting a short-term time range T, wherein T is less than or equal to 7, and predicting the self error prediction value f of the mutual inductor at each time point T in the short-term time range T by using a prediction model M(p)Further obtaining the self error time sequence F of the mutual inductor corresponding to the short-term time range T(p)。
Further, in step S301, the unit root test is performed by using a PP test method, which includes:
calculating PP test statistics
：
In the formula (I), the compound is shown in the specification,
as an estimator of Newey-West,
the variance obtained when the DF test was performed beforehand,γ 0is 0 order of autocovariance, T is the time sequence length, MSE is the mean square error;
given a significance level of 5%, the statistics will be tested
Compare with the threshold corresponding to 5% significance level in the table of threshold values: PP test statistic less than critical value indicates F(y)There is no unit root, is a stationary sequence, otherwise is a non-stationary sequence.
Further, if F(y)Is a non-stationary sequence, and is subjected to stationary treatment by a difference method to obtain a sequence F'(y)The method comprises the following steps:
when the differential order is d'(y)=F(y)(i)-F(y)(i-d)Obtaining each differential value f'(y)And sequence F'(y)，F(y)(i)Represents sequence F(y)The ith value of;
the difference order is incremented until the order d of the difference process is recorded by the unit root check.
Further, in step S4, adding an error prediction value to the temperature of the transformer at the target time point t, and calculating according to temperature information tem (t) at the target time point t; and default the frequency additional error predicted value of the mutual inductor at the target time point t to be 0.
In a second aspect, the present invention provides a short-term prediction apparatus for a mutual inductor metering error state, including:
error estimation value acquisition module for acquiringTime series data of the observed system are obtained through a station-level error evaluation system to obtain an error estimation value f(g)And estimate time series F(g)；
The self error value obtaining module selects two CVT error influence factors of temperature and frequency and respectively calculates the temperature additional error delta f(Tem)And frequency additive error Δ f(w)Stripping CVT error data to obtain the self error f of the mutual inductor on the same day(y)：f(y)=f(g)-Δf(Tem)-Δf(w)And self-error time series F(y)；
A self error prediction value acquisition module which utilizes a self error time sequence F(y)An ARIMA model M is constructed, trend prediction is carried out on the error state of the mutual inductor, and the self error prediction value f of the mutual inductor at the target time point t is obtained(p)And self error prediction value time series F in the target time range(p)；
An error predicted value obtaining module which respectively calculates the temperature additional error predicted value and the frequency additional error predicted value of the mutual inductor at the target time point t and enables the self error predicted value f of the mutual inductor at the target time point t to be predicted(p)Combining the predicted value with the predicted value of the temperature additional error and the predicted value of the frequency additional error to obtain a predicted value f of the error of the mutual inductor at the target time point t(future)And predicting a value time series F within the target time range T(future)And outputting a transformer error state prediction curve.
In a third aspect, the present invention provides an electronic device comprising:
a memory for storing a computer software program;
and the processor is used for reading and executing the computer software program so as to realize the short-term prediction method of the metering error state of the mutual inductor in the first aspect of the invention.
In a fourth aspect, the present invention provides a non-transitory computer readable storage medium having stored therein a computer software program for implementing a method for short-term prediction of a transformer metering error state according to the first aspect of the present invention.
The invention has the beneficial effects that: and the error state of the mutual inductor is predicted in a short term, data support is provided for risk early warning of the mutual inductor, and the running stability of a power grid is further ensured.
Drawings
Fig. 1 is a flowchart of a short-term prediction method for a metering error state of a transformer according to an embodiment of the present invention;
fig. 2 is a structural diagram of a short-term prediction apparatus for a metering error state of a transformer according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of an electronic device according to the present invention;
fig. 4 is a schematic structural diagram of a computer-readable storage medium according to the present invention.
Detailed Description
The principles and features of this invention are described below in conjunction with the following drawings, which are set forth by way of illustration only and are not intended to limit the scope of the invention.
Fig. 1 is a flowchart of a short-term prediction method for a metering error state of a transformer according to an embodiment of the present invention, and as shown in fig. 1, the method includes the following steps:
s1, acquiring time series data of the observed system, and obtaining an error estimation value f through a station-level error estimation system(g)And estimate time series F(g)。
And acquiring time series data of the observed system, acquiring an error estimation value through a station-level error evaluation system, and acquiring relevant data in the station of the day. In actual operation, a coarse error caused by human factors and the like may occur, and abnormal value processing needs to be performed on the acquired error data.
Time series F for estimated values(g)Each error estimate f in(g)If Q1-1.5IQR is less than or equal to f(g)Keeping the Q3+1.5IQR, otherwise, rejecting. Wherein Q3 is the upper quartile point in the boxplot, Q1 is the lower quartile point in the boxplot, and IQR is Q3-Q1. Time series of estimated values F(g)In the method, error estimation values f of a certain time point due to abnormal elimination or missing collection(g)And a front-back mean value method is adopted for supplement. Eliminating outliers can reduce outliersInfluence of constant values in prediction.
S2, selecting two CVT error influence factors of temperature and frequency, and respectively calculating temperature additional error delta f(Tem)And frequency additive error Δ f(w)Stripping CVT error data to obtain the self error f of the mutual inductor on the same day(y)：f(y)=f(g)-Δf(Tem)-Δf(w)And self-error time series F(y)。
Error estimation value f(g)Subtracting the additional error to obtain the error generated by the aging of the mutual inductor and other factors, and selecting two factors of temperature and frequency, wherein the temperature additional error delta f(Tem)The calculation method of (a) is as follows:
wherein S is a rated load, sin phi is a constant, acAs temperature coefficient,. DELTA.tem is the difference between the average temperature of the day and 20 ℃ and ωnAt a nominal angular frequency, C1Is a high-voltage capacitor, C2Is a low-voltage capacitor, U1Is the primary voltage of the intermediate transformer.
The frequency additive error Δ f(w)The calculation method of (a) is as follows:
wherein S is the rated load, sin phi is a constant, omega is the average angular frequency of the day, omeganAt a nominal angular frequency, C1Is a high-voltage capacitor, C2Is a low-voltage capacitor, U1Is the primary voltage of the intermediate transformer.
S3, using self-error time sequence F(y)An ARIMA model M is constructed, trend prediction is carried out on the error state of the mutual inductor, and the self error prediction value f of the mutual inductor at the target time point t is obtained(p)And self error prediction value time series F in target time range T(p)。
S301, forming a time sequence F for the error data of the mutual inductor(y)Drawing a time sequence curve as a data set, performing stationarity test by using a unit root test, namely, testing whether a unit root exists in a sequence, and calculating PP test statistic by using a PP test method in the unit root test
：
In the formula (I), the compound is shown in the specification,
as an estimator of Newey-West,
the variance obtained when the DF test was performed beforehand,γ 0is 0 order of autocovariance, T is the time sequence length, MSE is the mean square error;
given a significance level of 5%, the statistics will be tested
Compare with the threshold corresponding to 5% significance level in the table of threshold values: PP test statistic less than critical value indicates F(y)There is no unit root, is a stationary sequence, otherwise is a non-stationary sequence. According to the test result, if F(y)In the non-stationary sequence, smoothing is carried out by using a difference method, and f 'is carried out when the difference order is d'(y)=F(y)(i)-F(y)(i-d)Obtaining each differential value f'(y)And sequence F'(y)The difference order is incremented until the order d of the difference process is recorded by the unit root check.
S302, drawing a sequence F'(y)The autocorrelation coefficient map and the partial autocorrelation coefficient map.
Wherein the autocorrelation coefficients are expressed as:
wherein the content of the first and second substances,
is a time period selected error sequence F of the mutual inductor(y)H is a hysteresis order, n is a transformer error sequence F(y)The self-correlation coefficient expresses the self-correlation before and after the mutual inductor error data.
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:
rab（c）indicating x in the reject error sequencecAfter a period of time, xa,xbThe correlation coefficient of (2). When the two graphs are drawn, the partial autocorrelation coefficient should be zero after p-th order, which is said to have truncation, and the autocorrelation coefficient cannot be zero after a certain step (truncation) but decays exponentially (or in the form of a sine wave), which is said to have tailing. Determining sequence F 'through feature judgment of two graphs'(y)A conforming ARIMA model and model parameters.
S303, sequence F'(y)Importing the ARIMA model into a prediction model M;
s304, selecting a short-term time range T, wherein T is less than or equal to 7, and predicting the self error prediction value f of the mutual inductor at each time point T in the short-term time range T by using a prediction model M(p)Further obtaining the self error time sequence F of the mutual inductor corresponding to the short-term time range T(p)。
It should be noted here that the time point T is in units of days, T ∈ T, T = {1, 2.
S4, respectively calculating the target time pointst the temperature additional error predicted value and the frequency additional error predicted value of the mutual inductor are used for predicting the self error predicted value f of the mutual inductor at the target time point t(p)Combining the predicted value with the predicted value of the temperature additional error and the predicted value of the frequency additional error to obtain a predicted value f of the error of the mutual inductor at the target time point t(future)And predicting a value time series F within the target time range T(future)And outputting a transformer error state prediction curve.
Obtaining temperature information Tem (t) predicted corresponding to time according to external data such as weather forecast, and then calculating a temperature additional error predicted value delta f 'of a mutual inductor at a target time point t according to the temperature information Tem (t) at the target time point t'(Tem)(ii) a Frequency additional error predicted value delta f 'of target time point t mutual inductor'(w)Default is 0, and the target time point t is the transformer error prediction value f(future)=Δf’(Tem)+f(p)Aggregating error prediction values f of target time points within the target time range T(future)To obtain F(future)According to F(future)And drawing a short-term error state prediction curve for analysis and judgment of related operation maintenance personnel.
Fig. 2 is a structural diagram of a short-term prediction apparatus for a metering error state of a transformer according to an embodiment of the present invention. As shown in fig. 2, the apparatus includes:
an error estimation value acquisition module for acquiring time series data of the observed system and obtaining an error estimation value f through a station-level error evaluation system(g)And estimate time series F(g)；
The self error value obtaining module selects two CVT error influence factors of temperature and frequency and respectively calculates the temperature additional error delta f(Tem)And frequency additive error Δ f(w)Stripping CVT error data to obtain the self error f of the mutual inductor on the same day(y)：f(y)=f(g)-Δf(Tem)-Δf(w)And self-error time series F(y)；
A self error prediction value acquisition module which utilizes a self error time sequence F(y)Constructing an ARIMA model M, and performing trend prediction on the error state of the transformer to obtainSelf error prediction value f of mutual inductor at target time point t(p)And self error prediction value time series F in the target time range(p)；
An error predicted value obtaining module which respectively calculates the temperature additional error predicted value and the frequency additional error predicted value of the mutual inductor at the target time point t and enables the self error predicted value f of the mutual inductor at the target time point t to be predicted(p)Combining the predicted value with the predicted value of the temperature additional error and the predicted value of the frequency additional error to obtain a predicted value f of the error of the mutual inductor at the target time point t(future)And predicting a value time series F within the target time range T(future)And outputting a transformer error state prediction curve.
Referring to fig. 3, fig. 3 is a schematic diagram of an embodiment of an electronic device according to an embodiment of the invention. As shown in fig. 3, an embodiment of the present invention provides an electronic device 500, which includes a memory 510, a processor 520, and a computer program 511 stored in the memory 520 and executable on the processor 520, wherein the processor 520 executes the computer program 511 to implement the following steps:
s1, acquiring time series data of the observed system, and obtaining an error estimation value f through a station-level error estimation system(g)And estimate time series F(g)；
S2, selecting two CVT error influence factors of temperature and frequency, and respectively calculating temperature additional error delta f(Tem)And frequency additive error Δ f(w)Stripping CVT error data to obtain the self error f of the mutual inductor on the same day(y)：f(y)=f(g)-Δf(Tem)-Δf(w)And self-error time series F(y)；
S3, using self-error time sequence F(y)An ARIMA model M is constructed, trend prediction is carried out on the error state of the mutual inductor, and the self error prediction value f of the mutual inductor at the target time point t is obtained(p)And self error prediction value time series F in target time range T(p)；
S4, respectively calculating the temperature additional error predicted value and the frequency additional error predicted value of the mutual inductor at the target time point t, and calculating the self error of the mutual inductor at the target time point tPredicted value f(p)Combining the predicted value with the predicted value of the temperature additional error and the predicted value of the frequency additional error to obtain a predicted value f of the error of the mutual inductor at the target time point t(future)And predicting a value time series F within the target time range T(future)And outputting a transformer error state prediction curve.
Referring to fig. 4, fig. 4 is a schematic diagram illustrating an embodiment of a computer-readable storage medium according to the present invention. As shown in fig. 4, the present embodiment provides a computer-readable storage medium 600 having a computer program 611 stored thereon, the computer program 611, when executed by a processor, implementing the steps of:
s1, acquiring time series data of the observed system, and obtaining an error estimation value f through a station-level error estimation system(g)And estimate time series F(g)；
S2, selecting two CVT error influence factors of temperature and frequency, and respectively calculating temperature additional error delta f(Tem)And frequency additive error Δ f(w)Stripping CVT error data to obtain the self error f of the mutual inductor on the same day(y)：f(y)=f(g)-Δf(Tem)-Δf(w)And self-error time series F(y)；
S3, using self-error time sequence F(y)An ARIMA model M is constructed, trend prediction is carried out on the error state of the mutual inductor, and the self error prediction value f of the mutual inductor at the target time point t is obtained(p)And self error prediction value time series F in target time range T(p)；
S4, respectively calculating the temperature additional error predicted value and the frequency additional error predicted value of the mutual inductor at the target time point t, and calculating the self error predicted value f of the mutual inductor at the target time point t(p)Combining the predicted value with the predicted value of the temperature additional error and the predicted value of the frequency additional error to obtain a predicted value f of the error of the mutual inductor at the target time point t(future)And predicting a value time series F within the target time range T(future)And outputting a transformer error state prediction curve.
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 (9)
1. A short-term prediction method for a metering error state of a mutual inductor is characterized by comprising the following steps:
s1, acquiring time series data of the observed system, and obtaining an error estimation value f through a station-level error estimation system(g)And estimate time series F(g)；
S2, selecting two CVT error influence factors of temperature and frequency, and respectively calculating temperature additional error delta f(Tem)And frequency additive error Δ f(w)Stripping CVT error data to obtain the self error f of the mutual inductor on the same day(y)：f(y)=f(g)-Δf(Tem)-Δf(w)And self-error time series F(y)；
S3, using self-error time sequence F(y)An ARIMA model M is constructed, trend prediction is carried out on the error state of the mutual inductor, and the self error prediction value f of the mutual inductor at the target time point t is obtained(p)And self error prediction value time series F in target time range T(p)；t∈T；
S4, respectively calculating the temperature additional error predicted value and the frequency additional error predicted value of the mutual inductor at the target time point t, and calculating the self error predicted value of the mutual inductor at the target time point tf(p)Combining the predicted value with the predicted value of the temperature additional error and the predicted value of the frequency additional error to obtain a predicted value f of the error of the mutual inductor at the target time point t(future)And predicting a value time series F within the target time range T(future)And outputting a transformer error state prediction curve.
2. The method according to claim 1, wherein step S1 includes: time series F of estimated values by using box plot method(g)Error estimate f in (1)(g)And (3) abnormal value processing:
time series F for estimated values(g)Each error estimate f in(g)If Q1-1.5IQR is less than or equal to f(g)Keeping the Q3+1.5IQR or less, otherwise, removing; wherein Q3 is the upper quartile point in the boxplot, Q1 is the lower quartile point in the boxplot, and IQR is Q3-Q1;
time series of estimated values F(g)In the method, error estimation values f of a certain time point due to abnormal elimination or missing collection(g)And a front-back mean value method is adopted for supplement.
3. The method according to claim 1, wherein step S3 includes:
s301, carrying out self-error time sequence F on the mutual inductor(y)As a data set, a time series curve is drawn, a unit root test is used for stability test, if F(y)Is a non-stationary sequence, and is subjected to stationary treatment by a difference method to obtain a sequence F'(y)；
S302, drawing a sequence F'(y)The sequence F 'is determined through characteristic judgment of the autocorrelation coefficient graph and the partial autocorrelation coefficient graph'(y)A conforming ARIMA model and model parameters;
s303, sequence F'(y)Importing the ARIMA model into a prediction model M;
s304, selecting a short-term time range T, wherein T is less than or equal to 7, and predicting the self error prediction value f of the mutual inductor at each time point T in the short-term time range T by using a prediction model M(p)Further obtain short-term time range T pairsSelf error time sequence F of corresponding mutual inductor(p)。
4. The method of claim 3, wherein in step S301, the unit root test is performed by using a PP test method, which comprises:
calculating PP test statistics
：
In the formula (I), the compound is shown in the specification,λ 2(q) is an estimate of Newey-West,
the variance obtained when the DF test was performed beforehand,γ 0is 0 order of autocovariance, T is the time sequence length, MSE is the mean square error;
given a significance level of 5%, the statistics will be tested
Compare with the threshold corresponding to 5% significance level in the table of threshold values: PP test statistic less than critical value indicates F(y)There is no unit root, is a stationary sequence, otherwise is a non-stationary sequence.
5. The method of claim 4, wherein F is(y)Is a non-stationary sequence, and is subjected to stationary treatment by a difference method to obtain a sequence F'(y)The method comprises the following steps:
when the differential order is d'(y)=F(y)(i)-F(y)(i-d)Obtaining each differential value f'(y)And sequence F'(y)，F(y)(i)Represents sequence F(y)The ith value of;
the difference order is incremented until the order d of the difference process is recorded by the unit root check.
6. The method according to claim 1, characterized in that in step S4, the temperature of the mutual inductor at the target time point t is calculated according to temperature information tem (t) at the target time point t, with an error predicted value; and default the frequency additional error predicted value of the mutual inductor at the target time point t to be 0.
7. A short-term prediction device for a metering error state of a mutual inductor is characterized by comprising:
an error estimation value acquisition module for acquiring time series data of the observed system and obtaining an error estimation value f through a station-level error evaluation system(g)And estimate time series F(g)；
The self error value obtaining module selects two CVT error influence factors of temperature and frequency and respectively calculates the temperature additional error delta f(Tem)And frequency additive error Δ f(w)Stripping CVT error data to obtain the self error f of the mutual inductor on the same day(y)：f(y)=f(g)-Δf(Tem)-Δf(w)And self-error time series F(y)；
A self error prediction value acquisition module which utilizes a self error time sequence F(y)An ARIMA model M is constructed, trend prediction is carried out on the error state of the mutual inductor, and the self error prediction value f of the mutual inductor at the target time point t is obtained(p)And self error prediction value time series F in the target time range(p)；
An error predicted value obtaining module which respectively calculates the temperature additional error predicted value and the frequency additional error predicted value of the mutual inductor at the target time point t and enables the self error predicted value f of the mutual inductor at the target time point t to be predicted(p)Combining the predicted value with the predicted value of the temperature additional error and the predicted value of the frequency additional error to obtain a predicted value f of the error of the mutual inductor at the target time point t(future)And predicting a value time series F within the target time range T(future)And outputting a transformer error state prediction curve.
8. An electronic device, comprising:
a memory for storing a computer software program;
a processor for reading and executing the computer software program to implement a short-term prediction method of the mutual inductor metering error state as claimed in any one of claims 1 to 6.
9. A non-transitory computer readable storage medium having stored therein a computer software program for implementing a transformer metering error state short-term prediction method according to any one of claims 1-6.
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