CN113821938B - 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
- Publication number
- CN113821938B CN113821938B CN202111365391.3A CN202111365391A CN113821938B CN 113821938 B CN113821938 B CN 113821938B CN 202111365391 A CN202111365391 A CN 202111365391A CN 113821938 B CN113821938 B CN 113821938B
- Authority
- CN
- China
- Prior art keywords
- error
- mutual inductor
- value
- self
- sequence
- 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
Links
- 238000000034 method Methods 0.000 title claims abstract description 41
- 238000012360 testing method Methods 0.000 claims description 12
- 239000000654 additive Substances 0.000 claims description 10
- 230000000996 additive effect Effects 0.000 claims description 10
- 238000012545 processing Methods 0.000 claims description 7
- 230000002159 abnormal effect Effects 0.000 claims description 6
- 238000011156 evaluation Methods 0.000 claims description 6
- 238000001744 unit root test Methods 0.000 claims description 6
- 230000008569 process Effects 0.000 claims description 5
- 230000008030 elimination Effects 0.000 claims description 3
- 238000003379 elimination reaction Methods 0.000 claims description 3
- 239000013589 supplement Substances 0.000 claims description 3
- 238000010998 test method Methods 0.000 claims description 3
- 238000013112 stability test Methods 0.000 claims description 2
- YHXISWVBGDMDLQ-UHFFFAOYSA-N moclobemide Chemical compound C1=CC(Cl)=CC=C1C(=O)NCCN1CCOCC1 YHXISWVBGDMDLQ-UHFFFAOYSA-N 0.000 claims 4
- 238000010586 diagram Methods 0.000 description 14
- 238000004590 computer program Methods 0.000 description 11
- 239000003990 capacitor Substances 0.000 description 8
- 230000006870 function Effects 0.000 description 5
- 238000012986 modification Methods 0.000 description 5
- 230000004048 modification Effects 0.000 description 5
- 238000004364 calculation method Methods 0.000 description 2
- 230000008859 change Effects 0.000 description 2
- 238000004519 manufacturing process Methods 0.000 description 2
- 230000002411 adverse Effects 0.000 description 1
- 230000004931 aggregating effect Effects 0.000 description 1
- 230000032683 aging Effects 0.000 description 1
- 230000004075 alteration Effects 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 230000015556 catabolic process Effects 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 238000006731 degradation reaction Methods 0.000 description 1
- 238000003745 diagnosis Methods 0.000 description 1
- 238000009499 grossing Methods 0.000 description 1
- 238000009413 insulation Methods 0.000 description 1
- 238000002955 isolation Methods 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 238000005070 sampling Methods 0.000 description 1
- 230000009466 transformation Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
- G06F2119/08—Thermal analysis or thermal optimisation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
- G06F2119/12—Timing analysis or timing optimisation
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Computer Hardware Design (AREA)
- Evolutionary Computation (AREA)
- Geometry (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
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 CVT metering error state prediction is mapped to be a power grid information physical correlation state prediction, so that the influence of power grid fluctuation on an evaluation result is eliminated. 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 box line graph, and Q1 is the box line graphThe lower quartile of (A) is (B), and the 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,
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:
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.
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. Rejecting outliers can reduce the impact of outliers 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)。
Will missDifference estimate 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,
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,
is a time period selected error sequence F of the mutual inductor(y)H is the hysteresis order,n is the mutual inductor 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 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.
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)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)And temperature additive error predictionThe value and the frequency additional error predicted value are combined to obtain a target time point t mutual inductor error predicted value f(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 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.
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 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.
2. According to claimThe method of 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 obtaining the self error time sequence F of the mutual inductor corresponding to the short-term time range T(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,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.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111365391.3A CN113821938B (en) | 2021-11-18 | 2021-11-18 | Short-term prediction method and device for metering error state of mutual inductor |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111365391.3A CN113821938B (en) | 2021-11-18 | 2021-11-18 | Short-term prediction method and device for metering error state of mutual inductor |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113821938A CN113821938A (en) | 2021-12-21 |
| CN113821938B true CN113821938B (en) | 2022-02-18 |
Family
ID=78919313
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111365391.3A Active CN113821938B (en) | 2021-11-18 | 2021-11-18 | Short-term prediction method and device for metering error state of mutual inductor |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113821938B (en) |
Families Citing this family (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114065114B (en) * | 2022-01-17 | 2022-04-15 | 武汉格蓝若智能技术有限公司 | Method and system for predicting metering error of capacitive voltage transformer |
| 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 |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105184674A (en) * | 2015-09-07 | 2015-12-23 | 杭州凯达电力建设有限公司 | Electric energy metering error prediction method and device |
| CN106203732A (en) * | 2016-07-26 | 2016-12-07 | 国网重庆市电力公司 | Error in dipping computational methods based on ITD and time series analysis |
| CN110095744A (en) * | 2019-04-04 | 2019-08-06 | 国网江苏省电力有限公司电力科学研究院 | A kind of electronic mutual inductor error prediction method |
| CN110261809A (en) * | 2019-06-27 | 2019-09-20 | 中国电力科学研究院有限公司 | It is a kind of for determining the system and method for the operating status of capacitance type potential transformer |
| 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 |
| CN111814390A (en) * | 2020-06-18 | 2020-10-23 | 三峡大学 | Voltage transformer error prediction method based on transfer entropy and wavelet neural network |
-
2021
- 2021-11-18 CN CN202111365391.3A patent/CN113821938B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105184674A (en) * | 2015-09-07 | 2015-12-23 | 杭州凯达电力建设有限公司 | Electric energy metering error prediction method and device |
| CN106203732A (en) * | 2016-07-26 | 2016-12-07 | 国网重庆市电力公司 | Error in dipping computational methods based on ITD and time series analysis |
| CN110095744A (en) * | 2019-04-04 | 2019-08-06 | 国网江苏省电力有限公司电力科学研究院 | A kind of electronic mutual inductor error prediction method |
| CN110261809A (en) * | 2019-06-27 | 2019-09-20 | 中国电力科学研究院有限公司 | It is a kind of for determining the system and method for the operating status of capacitance type potential transformer |
| 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 |
| CN111814390A (en) * | 2020-06-18 | 2020-10-23 | 三峡大学 | Voltage transformer error prediction method based on transfer entropy and wavelet neural network |
Non-Patent Citations (2)
| Title |
|---|
| 基于本征时间尺度分解和时间序列分析的电能计量误差预测方法;万忠兵 等;《电气应用》;20170131;第77-84页 * |
| 电容式电压互感器计量误差状态评和预测方法研究;张竹;《中国博士学位论文全文数据库》;20190515;C042-40 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113821938A (en) | 2021-12-21 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN113821938B (en) | Short-term prediction method and device for metering error state of mutual inductor | |
| CN114065114B (en) | Method and system for predicting metering error of capacitive voltage transformer | |
| EP2870450B1 (en) | Method for determining modal parameters | |
| CN102062832B (en) | Power system online prewarning method based on microdisturbance signal low-frequency oscillation mode identification | |
| CN113887846B (en) | Out-of-tolerance risk early warning method for capacitive voltage transformer | |
| CN105699843A (en) | Electric cable running state diagnosis method and system | |
| Bhui et al. | Application of recurrence quantification analysis to power system dynamic studies | |
| CN105929340B (en) | A method of battery SOC is estimated based on ARIMA | |
| CN113777552B (en) | Method and system for predicting metering error of capacitive voltage transformer | |
| CN106933618B (en) | System upgrade evaluation method based on system parameter correlation coefficient | |
| CN111448467A (en) | Method and system for modeling and estimating battery capacity | |
| CN108304685A (en) | A kind of non-linear degradation equipment method for predicting residual useful life and system | |
| CN115616470B (en) | Method, system, equipment and medium for predicting metering error state of current transformer | |
| CN114169631B (en) | Oil field power load management and control system based on data analysis | |
| CN114295983B (en) | Battery thermal runaway early warning method, device, vehicle, equipment and storage medium | |
| US20150168465A1 (en) | Method and apparatus for electric power system distribution state estimations | |
| CN113325357A (en) | Voltage transformer error evaluation method and system based on output time series difference | |
| CN118091526B (en) | On-line monitoring method and device for measuring error of current transformer | |
| CN115587673A (en) | Voltage transformer error state prediction method and system | |
| CN114046869A (en) | Broadband oscillation information online monitoring method and system based on daily disturbance response of power system | |
| CN105426656B (en) | A kind of error calculation method and system | |
| CN112929214A (en) | Model construction method, device, equipment and storage medium | |
| CN112396535A (en) | Management method, device, equipment and storage medium of smart power grid | |
| CN108038279B (en) | Method and device for evaluating electromechanical transient recovery process of fan under asymmetric power grid fault | |
| CN114511088A (en) | Bayesian model updating method and system for structure damage recognition |
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 |
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. |
|
| CP01 | Change in the name or title of a patent holder |