CN115542064B - Real-time monitoring method and device for error state of mutual inductor - Google Patents

Real-time monitoring method and device for error state of mutual inductor Download PDF

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CN115542064B
CN115542064B CN202211524416.4A CN202211524416A CN115542064B CN 115542064 B CN115542064 B CN 115542064B CN 202211524416 A CN202211524416 A CN 202211524416A CN 115542064 B CN115542064 B CN 115542064B
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汤博
赵言涛
刘宇轩
徐虎
王建忠
刘名成
汪龙峰
汪攀
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Wasion Group Co Ltd
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Abstract

The invention discloses a real-time monitoring method for the error state of a mutual inductor, wherein the method comprises the following steps: collecting related data sets of the secondary side of the mutual inductor under different error states; establishing a prediction model between the related data set of the secondary side of the transformer and a transformer state matrix of the transformer in different error states based on the related data set; and carrying out real-time monitoring on the error state of the mutual inductor on the online sampling data through the prediction model. The invention also discloses a real-time monitoring device for the error state of the mutual inductor. The invention solves the problem of accurately and quickly monitoring the error state of the mutual inductor in real time and outputting the result under the condition of no power failure.

Description

Real-time monitoring method and device for error state of mutual inductor
Technical Field
The invention relates to the technical field of electric power, in particular to a real-time monitoring method and device for the error state of a mutual inductor.
Background
At present, a Capacitor Voltage Transformer (CVT) has been widely used in an electric power system, not only at a line outlet of a substation, but also in a bus to replace an electromagnetic voltage transformer. In the aspect of electric energy metering, a CVT is a main component device of gateway metering, the error of the CVT determines the metering accuracy, a power plant and a power grid are separately checked for benefits, and the accuracy of the electric energy metering is related to the economic benefits among the power grid, an electric field and users. In actual operation, the system is restricted by multiple factors of design level and manufacturing process, especially the influence of system operation processes such as line switching, input or exit of certain equipment and the like, and the CVT operation fault rate is high, which seriously influences the safe and stable operation of a power grid. How to effectively monitor the error state of the CVT and timely find and eliminate faults has important practical significance and is one of the bases of digital intelligent upgrading and modifying development of a power grid.
Patent document with application number CN202021594243.X discloses a measurement mutual-inductor measurement error on-line measuring system, including first measurement mutual-inductor on-line measuring instrument and second measurement mutual-inductor on-line measuring instrument, first measurement mutual-inductor on-line measuring instrument is connected with the first voltage transformer and the first current transformer of power transmission line first end respectively, second measurement mutual-inductor on-line measuring instrument respectively with the second voltage transformer and the second current transformer of power transmission line second end are connected, first measurement mutual-inductor on-line measuring instrument with the online measuring instrument clock synchronization of second measurement mutual-inductor. The system cannot solve the problem of how to accurately and quickly monitor the error state of the mutual inductor in real time and output a result under the condition of no power failure. Therefore, a method and a device for real-time monitoring of an error state of a transformer are urgently needed to be provided, so as to solve the problem of how to accurately and quickly perform real-time monitoring of the error state of the transformer and output a result under the condition of no power outage.
Disclosure of Invention
The invention mainly aims to provide a method and a device for monitoring the error state of a mutual inductor in real time, which solve the problem of accurately and quickly monitoring the error state of the mutual inductor in real time and outputting a result under the condition of no power failure.
In order to achieve the above object, in a first aspect, the present invention provides a method for monitoring an error state of a transformer in real time, wherein the method includes the following steps:
s1, collecting related data sets of a secondary side of a mutual inductor in different error states; the related data set comprises a secondary side amplitude data matrix and a phase angle data matrix of the mutual inductor;
s2, establishing a prediction model between a related data set of the secondary side of the mutual inductor and a mutual inductor state matrix of the mutual inductor in different error states based on the related data set; the establishing of the prediction model between the mutual inductor secondary side related data set and the mutual inductor state matrix under different error states of the mutual inductor comprises the following steps: establishing a prediction model between an amplitude data matrix and a transformer state matrix and a prediction model between a phase angle data matrix and the transformer state matrix;
and S3, carrying out real-time monitoring on the error state of the mutual inductor by the online sampling data through the prediction model.
In one preferable scheme, the error state of the transformer comprises: normal state, warning state and abnormal state.
In a preferred embodiment, the related data set of the secondary side of the transformer in step S1 is:
Figure 659291DEST_PATH_IMAGE001
and k is an integral multiple of the number of the mutual inductors, and n is the number of sampling data points.
In one preferred embodiment, the establishing a prediction model between the mutual inductor state matrix in different error states and the mutual inductor secondary side related data set in step S2 specifically includes:
s21, carrying out standardization processing on the related data set and a mutual inductor state matrix;
s22, respectively extracting principal components of the relevant data sets and the mutual inductor state matrix;
s23, calculating a regression coefficient, performing regression modeling on the normalized related data set and a transformer state matrix through the principal components of the related data set, and recording the regression coefficient and a residual error matrix;
and S24, completing construction of a prediction model based on the regression coefficient and the principal component of the related data set.
In one preferable embodiment, after the step S23 of calculating the regression coefficient, the method further includes:
performing iteration judgment, calculating a matrix norm of the residual matrix, comparing the matrix norm with a preset residual threshold, and ending iteration when the matrix norm is smaller than the residual threshold; otherwise, the above steps S21 to S23 are repeated.
In one preferred embodiment, in step S3, the online sampling data is subjected to real-time monitoring of the error state of the transformer through the prediction model, specifically:
s31, preprocessing the online sampling data;
s32, calculating the principal component of the online sampling data;
and S33, calculating a predicted value of the error state of the mutual inductor based on the principal components and the regression coefficient of the online sampling data, and judging the error state of the mutual inductor.
In one preferable embodiment, after the step S32 calculates the principal component of the online sampling data, the method further includes:
and calculating a residual error matrix of the online sampling data according to the principal component of the online sampling data and the regression coefficient, and performing iterative judgment.
In one preferred embodiment, the step S33 determines that the error state of the transformer is specifically:
if the predicted value of the error state of the mutual inductor is within a first threshold interval, the mutual inductor is in a normal state; if the predicted value of the error state of the mutual inductor is within a second threshold interval, the mutual inductor is in a warning state; and if the predicted value of the error state of the mutual inductor is within a third threshold interval, the mutual inductor is in an abnormal state.
In a second aspect, the present invention further provides a device for monitoring the error status of a transformer in real time, wherein the device includes a storage unit and a processing unit, and a computer program operable on the processing unit is stored in the storage unit; when the processing unit executes the computer program, the method for monitoring the error state of the transformer in real time is realized.
In the technical scheme of the invention, the real-time monitoring method for the error state of the mutual inductor comprises the following steps: collecting related data sets of the secondary side of the mutual inductor under different error states; the related data set comprises a secondary side amplitude data matrix and a phase angle data matrix of the mutual inductor; establishing a prediction model between the related data set of the secondary side of the transformer and a transformer state matrix of the transformer in different error states based on the related data set; the establishing of the prediction model between the mutual inductor secondary side related data set and the mutual inductor state matrix under different error states of the mutual inductor comprises the following steps: establishing a prediction model between an amplitude data matrix and a transformer state matrix and a prediction model between a phase angle data matrix and the transformer state matrix; and carrying out real-time monitoring on the error state of the mutual inductor on the online sampling data through the prediction model. The invention solves the problem of accurately and quickly monitoring the error state of the mutual inductor in real time and outputting the result under the condition of no power failure.
According to the invention, the online sampling data is subjected to real-time monitoring of the error state of the transformer through the prediction model, the predicted value of the error state of the transformer is calculated, and the qualitative monitoring of the error state of the transformer is converted into the quantitative monitoring of the predicted value of the error state of the transformer, so that the requirements of different applications are met.
According to the invention, the prediction model has low requirement on the quantity of sample data, and has stronger practicability on scenes and environments with few data samples but many detection variables.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the structures shown in the drawings without creative efforts.
Fig. 1 is a schematic flow chart of a real-time monitoring method for an error state of a transformer according to an embodiment of the present invention;
FIG. 2 is a flowchart illustrating step S2 according to an embodiment of the present invention;
fig. 3 is a flowchart illustrating step S3 according to an embodiment of the present invention.
The implementation, functional features and advantages of the present invention will be further described with reference to the accompanying drawings.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, are within the scope of the present invention.
In addition, the descriptions related to "first", "second", etc. in the present invention are only for descriptive purposes and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature.
Moreover, the technical solutions in the embodiments of the present invention may be combined with each other, but it is necessary to be based on the realization of the technical solutions by those skilled in the art, and when the technical solutions are contradictory to each other or cannot be realized, such a combination of the technical solutions should not be considered to exist, and is not within the protection scope claimed by the present invention.
Example 1:
referring to fig. 1-3, according to an aspect of the present invention, the present invention provides a real-time monitoring method for an error state of a transformer, wherein the method includes the following steps:
s1, collecting related data sets of a secondary side of a mutual inductor in different error states; the related data set comprises a secondary side amplitude data matrix and a phase angle data matrix of the mutual inductor;
s2, establishing a prediction model between a related data set of a secondary side of the mutual inductor and a mutual inductor state matrix under different error states of the mutual inductor based on the related data set; the establishing of the prediction model between the mutual inductor state matrix under different error states and the mutual inductor secondary side related data set comprises the following steps: establishing a prediction model between an amplitude data matrix and a transformer state matrix and a prediction model between a phase angle data matrix and the transformer state matrix;
and S3, carrying out real-time monitoring on the error state of the mutual inductor by the online sampling data through the prediction model.
Specifically, in this embodiment, the transformer error state includes: normal state, warning state, and abnormal state.
Specifically, in this embodiment, the relevant data set of the secondary side of the transformer in step S1 is:
Figure 731153DEST_PATH_IMAGE002
wherein k is an integral multiple of the number of the mutual inductors, and n is the number of the sampling data points;
the related data set comprises a secondary side amplitude data matrix and a phase angle data matrix of the mutual inductor; and respectively establishing a secondary side amplitude data matrix of the transformer and a prediction model between the phase angle data matrix and a transformer state matrix based on the amplitude data matrix and the phase angle data matrix, wherein the step of establishing the prediction model between the amplitude data matrix and the transformer state matrix is the same as the step of establishing the prediction model between the phase angle data matrix and the transformer state matrix.
Specifically, in this embodiment, a voltage transformer is taken as an example, and a prediction model between the amplitude data matrix and a transformer state matrix is established as an example for explanation; the secondary side amplitude data matrix of the voltage transformer is as follows:
Figure 155050DEST_PATH_IMAGE003
wherein the content of the first and second substances,
Figure 21374DEST_PATH_IMAGE004
is an amplitude data matrix;
Figure 484717DEST_PATH_IMAGE005
wherein the content of the first and second substances,
Figure 94690DEST_PATH_IMAGE006
is a phase angle data matrix;
the mutual inductor state matrix of the voltage mutual inductor under different error states is as follows:
Figure 123825DEST_PATH_IMAGE007
wherein the content of the first and second substances,
Figure 911784DEST_PATH_IMAGE008
is a mutual inductor state matrix.
Specifically, in this embodiment, the establishing a prediction model between the transformer secondary side related data set and the transformer state matrix in step S2 includes: establishing a prediction model between an amplitude data matrix and a transformer state matrix and a prediction model between a phase angle data matrix and the transformer state matrix; the establishment of the prediction model between the amplitude data matrix and the transformer state matrix is the same as the establishment steps of the phase angle data matrix and the transformer state matrix, and the establishment of the model between the amplitude data matrix and the transformer state matrix is taken as an example for explanation.
Specifically, in the present embodiment, the correlated data set and the transformer state matrix are subjected to normalization processing in step S21, and sampling data are subjected to normalization processing, so that the data are comparable; specifically, the amplitude data matrix and the mutual inductor state matrix are subjected to standardization processing; amplitude data matrix
Figure 924739DEST_PATH_IMAGE009
Consisting of k row vectors, a single row vector corresponding to the n-times sampled data of a single voltage transformer, matrix ^ ing the amplitude data>
Figure 338403DEST_PATH_IMAGE010
Each row vector is processed as follows:
Figure 425308DEST_PATH_IMAGE011
calculating the said
Figure 899015DEST_PATH_IMAGE012
Average value of (d):
Figure 117375DEST_PATH_IMAGE013
according to the above
Figure 865888DEST_PATH_IMAGE014
The variance is calculated as follows:
Figure 869616DEST_PATH_IMAGE015
finally, the average value is obtained according to calculation
Figure 264957DEST_PATH_IMAGE016
And variance->
Figure 721346DEST_PATH_IMAGE017
And carrying out standardization processing on the amplitude data matrix to obtain:
Figure 742392DEST_PATH_IMAGE018
Figure 849894DEST_PATH_IMAGE019
similarly, the mutual inductor state matrix is subjected to standardization processing to obtain:
Figure 868665DEST_PATH_IMAGE020
specifically, in this embodiment, in step S22, the principal components of the relevant data set and the transformer state matrix are respectively extracted; specifically, the principal components of the amplitude data matrix and the mutual inductor state matrix are respectively extracted, and the principal components are obtained according to the amplitude data matrix
Figure 812351DEST_PATH_IMAGE021
And the transformer status matrix>
Figure 902666DEST_PATH_IMAGE022
Obtaining an autocovariance matrix thereof:
Figure 366140DEST_PATH_IMAGE023
decomposing the eigenvalue of the autocovariance matrix, and respectively removing the eigenvector corresponding to the maximum eigenvalue
Figure 555813DEST_PATH_IMAGE024
And &>
Figure 986794DEST_PATH_IMAGE025
And calculating to obtain main component->
Figure 84063DEST_PATH_IMAGE026
And/or>
Figure 651311DEST_PATH_IMAGE027
：
Figure 916945DEST_PATH_IMAGE028
Specifically, in this embodiment, in step S23, regression coefficients are calculated, the normalized correlation data set and the transformer state matrix are regression-modeled by the principal components of the correlation data set,recording a regression coefficient and a residual error matrix; the method specifically comprises the following steps: principal component calculated by the amplitude data matrix
Figure 38484DEST_PATH_IMAGE029
Performing regression modeling on the amplitude data matrix and the mutual inductor state matrix respectively to obtain a coefficient ^ greater than or equal to>
Figure 424598DEST_PATH_IMAGE030
，/>
Figure 111931DEST_PATH_IMAGE031
And residual matrix->
Figure 440144DEST_PATH_IMAGE032
，/>
Figure 580138DEST_PATH_IMAGE033
：
Figure 534057DEST_PATH_IMAGE034
/>
Figure 75896DEST_PATH_IMAGE035
Specifically, in the present embodiment, after the regression coefficient is calculated in step S23, the method further includes:
performing iteration judgment, calculating a matrix norm of the residual matrix, comparing the matrix norm with a preset residual threshold, and ending iteration when the matrix norm is smaller than the residual threshold; otherwise, repeating the steps S21 to S23; specifically, a residual matrix is calculated
Figure 371749DEST_PATH_IMAGE036
，/>
Figure 202301DEST_PATH_IMAGE037
Is greater than or equal to>
Figure 930217DEST_PATH_IMAGE038
，/>
Figure 592143DEST_PATH_IMAGE039
And the matrix norm is ≥ r>
Figure 262158DEST_PATH_IMAGE040
，/>
Figure 376745DEST_PATH_IMAGE041
Respectively associated with a predetermined residual threshold value->
Figure 430324DEST_PATH_IMAGE042
，/>
Figure 212335DEST_PATH_IMAGE043
Comparing, if the matrix norm
Figure 787673DEST_PATH_IMAGE044
，/>
Figure 389556DEST_PATH_IMAGE045
Are both smaller than the preset residual threshold value->
Figure 990433DEST_PATH_IMAGE046
，/>
Figure 361371DEST_PATH_IMAGE047
If not, the iteration is ended, otherwise, the residual error matrix is processed
Figure 373189DEST_PATH_IMAGE048
，/>
Figure 196789DEST_PATH_IMAGE049
Steps S21 to S23 are repeatedly performed.
Specifically, in this embodiment, in the step S24, the construction of the prediction model is completed based on the regression coefficient and the principal component of the relevant data set; specifically, based on the regression coefficient
Figure 834312DEST_PATH_IMAGE050
，/>
Figure 794178DEST_PATH_IMAGE051
And a principal component of the relevant data set>
Figure 976898DEST_PATH_IMAGE052
Completing the construction of a prediction model, and setting the iteration number as->
Figure 818952DEST_PATH_IMAGE053
Then the principal component mapping vector of each iteration can be obtained
Figure 230473DEST_PATH_IMAGE054
And the regression coefficient->
Figure 576003DEST_PATH_IMAGE055
，/>
Figure 195204DEST_PATH_IMAGE056
I.e. the model parameters:
Figure 993395DEST_PATH_IMAGE057
specifically, in this embodiment, the real-time monitoring of the error state of the transformer is performed on the online sampled data through the prediction model in step S3, which includes that the amplitude data matrix and the phase angle data matrix of the online sampling are respectively subjected to the prediction model constructed in step S2 to obtain the prediction values of the error state of the transformer with respect to the amplitude data and the phase angle data, the error state of the transformer is judged according to the prediction values, and the real-time monitoring of the error state of the transformer is performed through the prediction model according to the amplitude data matrix and the phase angle data matrix of the online sampling.
Specifically, in this embodiment, in step S31, the preprocessing is performed on the online sampled data, specifically, the normalizing is performed on the amplitude data matrix of the online sampling by:
Figure 661137DEST_PATH_IMAGE058
amplitude data matrix of on-line sampling after standardization processing
Figure 110442DEST_PATH_IMAGE059
Stacking the matrix in columns:
Figure 900543DEST_PATH_IMAGE060
wherein p is the number of the voltage transformers, k is the integral multiple of p, and the stacking times are the integer of k/p.
Specifically, in this embodiment, the principal component of the online sampling data is calculated in step S32; in particular, vectors are mapped through principal components of each iteration
Figure 920452DEST_PATH_IMAGE061
And a matrix of amplitude data sampled on-line>
Figure 719781DEST_PATH_IMAGE062
Calculating a principal component ^ on the ith iteration>
Figure 525057DEST_PATH_IMAGE063
：
Figure 220480DEST_PATH_IMAGE064
Specifically, in this embodiment, after the step S32 calculates the principal component of the online sampling data, the method further includes: calculating a residual error matrix of the online sampling data through the principal component of the online sampling data and the regression coefficient, and performing iterative judgment; specifically, the principal component of the iteration through the ith time
Figure 993264DEST_PATH_IMAGE065
Amplitude data matrix for online sampling
Figure 65125DEST_PATH_IMAGE066
And the regression coefficient->
Figure 177438DEST_PATH_IMAGE067
Calculating a residual ≥ for the ith iteration>
Figure 558610DEST_PATH_IMAGE068
：
Figure 553110DEST_PATH_IMAGE069
If the iteration number i is less than the iteration number
Figure 694242DEST_PATH_IMAGE070
Then to->
Figure 723378DEST_PATH_IMAGE071
Re-executing step S32 for input and re-calculating its residual error if the number of iterations i is less than the number of iterations >>
Figure 963866DEST_PATH_IMAGE072
Then the following steps are performed. />
Specifically, in this embodiment, in step S33, a predicted value of the error state of the transformer is calculated based on the principal component and the regression coefficient of the online sampled data, and the error state of the transformer is determined, specifically, the predicted value is determined according to the principal component of the online sampled amplitude data matrix
Figure 461975DEST_PATH_IMAGE073
And the regression coefficient->
Figure 875638DEST_PATH_IMAGE074
Calculating a first prediction of an amplitude-based error state of a voltage transformerThe value:
Figure 24860DEST_PATH_IMAGE075
and similarly, according to principal components and regression coefficients of the phase angle data matrix of the online sampling
Figure 232988DEST_PATH_IMAGE076
Calculating to obtain a second predicted value of the error state of the voltage transformer based on the phase angle;
specifically, in this embodiment, the step S33 of judging the error state of the transformer specifically includes: if the predicted value of the error state of the mutual inductor is within a first threshold interval, the mutual inductor is in a normal state; if the predicted value of the error state of the mutual inductor is within a second threshold interval, the mutual inductor is in a warning state; and if the predicted value of the error state of the mutual inductor is in a third threshold interval, the mutual inductor is in an abnormal state. In the invention, if the voltage transformer is based on a first predicted value of an error state of an amplitude value, and a second predicted value of the error state of the voltage transformer based on a phase angle is positioned in a first threshold interval or a second threshold interval or a third threshold interval, the voltage transformer is respectively in a normal state or a warning state or an abnormal state; if the first predicted value of the error state of the voltage transformer based on the amplitude value is located in a second threshold interval, and the second predicted value of the error state of the voltage transformer based on the phase angle is located in a first threshold interval or a second threshold interval or a third threshold interval, the voltage transformer is in a warning state or an abnormal state respectively; and if the first predicted value of the error state of the voltage transformer based on the amplitude value is located in a third threshold interval and the second predicted value of the error state of the voltage transformer based on the phase angle is located in the first threshold interval or the second threshold interval or the third threshold interval, the voltage transformer is in an abnormal state.
Specifically, in the embodiment, the on-line monitoring and fault diagnosis work of the CVT on the operation under the condition of no power outage is carried out, so that the CVT measurement error state can be mastered in real time, the operation and maintenance work of the CVT can be known in a targeted manner, and the method has important significance for ensuring the safe, stable and economic operation of a power system. Meanwhile, aiming at the related technical route of online monitoring and fault diagnosis of metering errors of the CVT, the research method can be popularized to online monitoring schemes of other types of power transformers, and planning and design of subsequent series products and schemes can be greatly promoted. On the other hand, the method has very important practical significance for power grid company customers: the equipment condition can be known without equipment power failure, and the operation efficiency of the power system is improved; the voltage during monitoring is the equipment operating voltage, and defects and abnormity can be found more sensitively than the voltage during preventive test; a large amount of data obtained by online monitoring and judgment and analysis of the data can provide a basis for state maintenance, and the defects of traditional preventive maintenance are overcome.
Example 2:
according to another aspect of the invention, the invention provides a mutual inductor error state real-time monitoring device, which comprises a storage unit and a processing unit, wherein a computer program which can run on the processing unit is stored in the storage unit; when the processing unit executes the computer program, the method for monitoring the error state of the transformer in real time is realized.
The above description is only a preferred embodiment of the present invention, and is not intended to limit the scope of the present invention, and all modifications and equivalents made by the contents of the present specification and the accompanying drawings, or directly/indirectly applied to other related technical fields, which are within the spirit of the present invention, are included in the scope of the present invention.

Claims (8)

1. A real-time monitoring method for the error state of a mutual inductor is characterized by comprising the following steps:
s1, collecting related data sets of a secondary side of a mutual inductor in different error states; the related data set comprises a secondary side amplitude data matrix and a phase angle data matrix of the mutual inductor;
s2, establishing a prediction model between a related data set of the secondary side of the mutual inductor and a mutual inductor state matrix of the mutual inductor in different error states based on the related data set; the establishing of the prediction model between the mutual inductor secondary side related data set and the mutual inductor state matrix under different error states of the mutual inductor comprises the following steps: establishing a prediction model between an amplitude data matrix and a transformer state matrix and a prediction model between a phase angle data matrix and the transformer state matrix;
in the step S2, a prediction model between a relevant data set of a secondary side of the transformer and a transformer state matrix of the transformer in different error states is established, and the specific steps are as follows:
s21, carrying out standardization processing on the related data set and a mutual inductor state matrix;
s22, respectively extracting principal components of the relevant data sets and the mutual inductor state matrix;
s23, calculating regression coefficients, performing regression modeling on the standardized relevant data set and a transformer state matrix through the principal components of the relevant data set, and recording the regression coefficients and a residual error matrix;
s24, building a prediction model based on the regression coefficient and the principal component of the related data set;
the state matrix of the mutual inductor under different error states of the mutual inductor is as follows:
Figure QLYQS_1
，
Figure QLYQS_2
the transformer state matrix ^ for the state of the kth transformer at the nth sampled data point>
Figure QLYQS_3
The states of the sampling data points of the transformers comprise a normal state, a warning state and an abnormal state;
and S3, carrying out real-time monitoring on the error state of the mutual inductor by the online sampling data through the prediction model.
2. The real-time mutual inductor error state monitoring method according to claim 1, wherein the mutual inductor error state comprises: normal state, warning state, and abnormal state.
3. The method for monitoring the error state of the mutual inductor in real time according to claim 1, wherein the related data sets of the secondary side of the mutual inductor in the step S1 are as follows:
Figure QLYQS_4
，
wherein x is sampling data, k is integral multiple of the number of the mutual inductors, and n is the number of the sampling data points.
4. The method for monitoring the error state of the mutual inductor according to claim 1, wherein after the step S23 of calculating the regression coefficient, the method further comprises:
performing iteration judgment, calculating a matrix norm of the residual error matrix, comparing the matrix norm with a preset residual error threshold value, and ending iteration when the matrix norm is smaller than the residual error threshold value; otherwise, the above steps S21 to S23 are repeated.
5. The method for monitoring the error state of the transformer in real time according to claim 4, wherein in the step S3, online sampling data is subjected to real-time monitoring of the error state of the transformer through the prediction model, and the method comprises the following specific steps:
s31, preprocessing the online sampling data;
s32, calculating the principal component of the online sampling data;
and S33, calculating a predicted value of the error state of the mutual inductor based on the principal components and the regression coefficient of the online sampling data, and judging the error state of the mutual inductor.
6. The method for real-time monitoring of the error state of the mutual inductor according to claim 5, wherein after the step S32 of calculating the principal component of the online sampling data, the method further comprises:
and calculating a residual error matrix of the online sampling data according to the principal component of the online sampling data and the regression coefficient, and performing iterative judgment.
7. The method for monitoring the error state of the mutual inductor in real time according to claim 5, wherein the step S33 is to judge that the error state of the mutual inductor is specifically:
if the predicted value of the error state of the mutual inductor is within a first threshold interval, the mutual inductor is in a normal state; if the predicted value of the error state of the mutual inductor is within a second threshold interval, the mutual inductor is in a warning state; and if the predicted value of the error state of the mutual inductor is in a third threshold interval, the mutual inductor is in an abnormal state.
8. The real-time monitoring device for the error state of the mutual inductor is characterized by comprising a storage unit and a processing unit, wherein a computer program which can run on the processing unit is stored in the storage unit; the processing unit, when executing the computer program, implements a method for real-time monitoring of the error state of a transformer according to any one of claims 1-7.
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