CN115542064A - 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 PDFInfo
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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
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 accounted for benefit, and the accuracy of the electric energy metering is concerned with the economic benefit 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 transformer in real time, which solve the problem of accurately and quickly monitoring the error state of the transformer 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 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 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 preferred embodiment, the error state of the transformer includes: normal state, warning state, and abnormal state.
In one preferable embodiment, the related data set of the secondary side of the transformer in the step S1 is:
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 a 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 preferred 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 in 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 state of a transformer in real time, where the device includes a storage unit and a processing unit, and the storage unit stores therein a computer program that is executable on the processing 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 above technical solution of the present invention, the method for monitoring the error state of the mutual inductor in real time 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 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 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 able to be realized by a person skilled in the art, and when the technical solutions are contradictory or cannot be realized, the combination of the technical solutions should be considered to be absent, and is not within the protection scope of 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 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 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 on 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:
wherein k is an integral multiple of the number of the mutual inductors, and n is the number of 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:
wherein the content of the first and second substances,
is an amplitude data matrix;
wherein the content of the first and second substances,
is a phase angle data matrix;
the mutual inductor state matrix of the voltage mutual inductor under different error states is as follows:
wherein the content of the first and second substances,
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 normalized in step S21, and sampling data is normalized, so that the data is comparable; specifically, the amplitude data matrix and the mutual inductor state matrix are subjected to standardization processing; amplitude data matrix
The amplitude data matrix consists of k row vectors, a single row vector corresponds to n times of sampling data of a single voltage transformer, and the amplitude data matrix
Each row vector is processed as follows:
calculating the said
Average value of (d):
according to the above
The variance is calculated as follows:
finally, the average value is obtained according to calculation
Sum variance
And carrying out standardization processing on the amplitude data matrix to obtain:
similarly, the mutual inductor state matrix is subjected to standardization processing to obtain:
specifically, in this embodiment, in step S22, 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
And mutual inductor state matrix
Obtaining an autocovariance matrix thereof:
decomposing the eigenvalue of the autocovariance matrix, and respectively removing the eigenvector corresponding to the maximum eigenvalue
And
and calculating to obtain principal component
And
：
specifically, in this embodiment, in step S23, a regression coefficient is calculated, regression modeling is performed on the normalized correlated data set and the transformer state matrix through the principal component of the correlated data set, and the regression coefficient and the residual error matrix are recorded; the method specifically comprises the following steps: principal component calculated by the amplitude data matrix
Performing regression modeling on the amplitude data matrix and the mutual inductor state matrix respectively to obtain coefficients
，
And residual matrix
，
：
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 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, repeating the steps S21 to S23; specifically, a residual matrix is calculated
，
Of matrix norm
，
And applying the matrix norm
，
Respectively with preset residual threshold
，
Comparing, if the matrix norm
，
Are all less than the preset residual threshold
，
If so, the iteration is ended, otherwise, the residual error matrix is processed
，
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
，
And principal components of related data sets
Completing the construction of a prediction model, and setting the iteration number as
Then principal component mapping vectors for each iteration can be obtained
And the regression coefficient
，
I.e. the model parameters:
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 online sampling, and includes:
amplitude data matrix of on-line sampling after standardization processing
Performing matrix stacking by columns:
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
And an online sampled amplitude data matrix
Calculating to obtain the principal component of the ith iteration
：
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 according to 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
Amplitude data matrix for online sampling
And regression coefficients
Computing the residual of the ith iteration
：
If the iteration number i is less than the iteration number
Then, in turn
Re-executing step S32 for input and re-calculating its residual error if the iteration number i is less than the iteration number
Then the following steps are performed.
In particular toIn this embodiment, in step S33, a transformer error state prediction value 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 error state is determined according to the principal component of the online sampled amplitude data matrix
And regression coefficients
Calculating a first predicted value of the error state of the voltage transformer based on the amplitude value:
similarly, according to the principal component and regression coefficient of the phase angle data matrix of the online sampling
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 determining 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 first predicted value of the error state of the voltage transformer based on the amplitude value and the second predicted value of the error state of the voltage transformer based on the phase angle are positioned in a first threshold interval or a second threshold interval or a third threshold interval, the 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 online monitoring and fault diagnosis work of the metering error of the CVT under the condition of no power outage is carried out, so that the metering error state of the CVT can be mastered in real time, the operation and maintenance work of the CVT can be known in a targeted manner, and the online monitoring and fault diagnosis 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 as well: 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 not intended to limit the scope of the present invention, and all modifications and equivalents of the present invention, which are made by the contents of the present specification and the accompanying drawings, or directly/indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (9)
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 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.
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:
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 in real time according to claim 1, wherein a prediction model between a mutual inductor secondary side related data set and a mutual inductor state matrix under different error states of the mutual inductor is established in the step S2, and the method comprises the following specific steps:
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.
5. The method for real-time monitoring of the error state of the mutual inductor according to claim 4, wherein after the step S23 of calculating the regression coefficient, the method further comprises:
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.
6. The method for monitoring the error state of the mutual inductor according to claim 5, wherein the online sampling data is used for real-time monitoring of the error state of the mutual inductor through the prediction model in the step S3, and the method comprises the following specific steps:
s31, preprocessing the online sampling data;
s32, calculating a 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.
7. The method for real-time monitoring of the error state of the mutual inductor according to claim 6, 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.
8. The method for monitoring the error state of the mutual inductor in real time according to claim 6, 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.
9. 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 1-8.
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