CN114781165A - Multi-feature information fusion power transformer winding health diagnosis method - Google Patents
Multi-feature information fusion power transformer winding health diagnosis method Download PDFInfo
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Abstract
The invention provides a multi-feature information fused power transformer winding health diagnosis method, which comprises the following steps: s1, collecting data of current, voltage and output power of the transformer, and carrying out signal offset calculation on the collected data to obtain a linear deviation degree; s2, according to the linear deviation degree in the acquisition period, carrying out error calculation of the data, and calculating the fault probability according to the error function of the data; and S3, calculating the accuracy and the recall rate of the transformer winding data according to the fault probability, and evaluating the health state of the transformer through a clustering algorithm.
Description
Technical Field
The invention relates to the field of data analysis, in particular to a multi-feature information fused power transformer winding health diagnosis method.
Background
The transformer is one of the most important and critical electrical devices in the power system, and the safety and reliability of the operation of the transformer are directly related to the safety and stability of the power system. In the long-term operation process of the power transformer, various short-circuit current surges often occur, and particularly when short-circuit faults occur at the outlet and near area of the transformer, the transformer winding is heated rapidly by tens of times or even hundreds of times of electrodynamic force generated in normal operation due to the huge short-circuit surge current, and the transformer winding is easy to damage or deform under the condition. During transportation, installation or cover hanging, power transformers are often subjected to accidental jostling and vibration, which also tend to cause the windings to deform. Proved by a large amount of experimental research and practice, transformer winding deformation has obvious accumulative effect, after transformer winding deformation accumulates to a certain degree, transformer winding's dynamic stability will receive destruction, anti short circuit ability descends by a wide margin, will lose dynamic balance when transformer winding suffers the short circuit impact once more, thereby lead to the emergence of major accident, current monitoring method has the short circuit impedance method, the voiceprint monitoring method, the pulse injection method, these methods have the fault discovery untimely, the monitoring is sensitive inadequately, the injected energy produces the influence to the winding, need the monitoring of having a power failure, therefore, but a real-time high accuracy on-line monitoring non-invasive monitoring method is needed urgently.
Disclosure of Invention
The invention aims to at least solve the technical problems in the prior art, and particularly creatively provides a multi-characteristic information fused power transformer winding health diagnosis method.
In order to achieve the above object, the present invention provides a method for diagnosing health of a power transformer winding with multi-feature information fusion, comprising the following steps:
s1, collecting data of current, voltage and output power of the transformer, and carrying out signal offset calculation on the collected data to obtain a linear deviation degree;
s2, according to the linear deviation degree in the acquisition period, carrying out error calculation of the data, and calculating the fault probability according to the error function of the data;
and S3, calculating the accuracy and the recall rate of the transformer winding data according to the fault probability, and evaluating the health state of the transformer through a clustering algorithm.
Preferably, the S1 includes:
s1-1, collecting the current value of the transformer winding, and in one collection period, obtaining a current data set of { I }iWhere 1,2,3,4,5, …, n, the acquired voltage dataset is { V }jWhere j is 1,2,3,4,5, …, n, the acquired power dataset is { P }k}(k＝1,2,3,4,5,…,n)，
State vector, T, in the course of the acquired current data setICurrent overload record, current transmission temperature record, transformer temperature record; state vector, T, in the course of the acquired voltage data setV(voltage load, voltage fluctuation record); state vector, T, in the course of the acquired power data setPPower distortion recording, power regulation recording, power output recording.
Preferably, the S1 further includes:
s1-2, according to the linear trend of the collected data of the state vector, the data deviation degree is calculated,
when the influence of the interference threshold μ increases, the linear deviation thereof increases in order to equalize the equations; the interference threshold value has a small proportion for evaluating the deviation of current, voltage and power in the transformer, so that the health condition result of the transformer is subjected to preliminary deviation judgment, the calculation describes the actual running state of the transformer, the preliminary state evaluation of the transformer is reflected by the threshold value calculation,
wherein,
is the amplitude of the signal for the change in current,
the characteristic value is reconstructed for the current signal,
is the amplitude of the signal for the voltage change,
the characteristic value is reconstructed for the voltage signal,
is the amplitude of the signal for the power change,
the eigenvalues are reconstructed for the power signal.
Preferably, the S1 further includes:
s1-3, traversing the amplitudes of the current, voltage and power data in the sampling period, and distinguishing and identifying the current, voltage and power data through a linear deviation degree calculation formula according to the amplitudes as reference values of transformer health detection;
obtaining the linear deviation degree A according to the ratio of the peak value of the corresponding data to the average value of the amplitude valuesI，AV，AP；
Preferably, the S2 includes:
s2-1, carrying out error operation on the linear deviation value to obtain reconstructed characteristic value average values AVEI, AVEV and AVEP of the current, voltage and power data; obtaining deviation values of the current, voltage and power data according to the difference of the reconstructed characteristic values of the current, voltage and power data, obtaining a standardized characteristic value according to the ratio of the deviation values to the absolute deviation values,
preferably, the S2 further includes:
s2-2, according to the normalized eigenvalue and the sum result after the absolute value of the reconstructed eigenvalue of current, voltage and power is squared,
performing normalization calculation
According to the result of the normalized calculation, the current, voltage and power values of the transformer are judged to be within
0＜UI,UV,UPWhen the value is less than or equal to 0.4, the health condition of the transformer winding is good,
if 0.4 < UI,UV,UPWhen the fault risk is less than or equal to 0.8, the fault risk exists;
if 0.8 < UI,UV,UPIf < 1, there is a significant risk.
Preferably, the S2 further includes:
s2-3, carrying out average fault probability analysis on current, voltage and power in the existing fault risk interval to ensure the accuracy of data;
lambda is an empirical judgment threshold, characteristic sorting is carried out according to data of the characteristic vector, the average fault probability of a fault risk period existing in the acquisition cycle is determined and solved for current, voltage and power data,
the mean failure probability is calculated as
Preferably, the S3 includes:
s3-1, after obtaining the average fault probability, calculating the accuracy,
s3-2, calculating the recall rate,
preferably, S3-3, the calculated score is
In summary, due to the adoption of the technical scheme, the invention has the beneficial effects that:
the method can judge the current, voltage or power deviation of the transformer winding in certain time periods, calculate and grade the extracted time periods, realize the health safety check of the transformer winding in the whole time period according to corresponding supervision calculation, and provide data reference for users.
Additional aspects and advantages of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.
Drawings
The above and/or additional aspects and advantages of the present invention will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
FIG. 1 is a general schematic of the present invention;
FIG. 2 is a schematic diagram of the working method of the present invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like reference numerals refer to the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are illustrative only for the purpose of explaining the present invention, and are not to be construed as limiting the present invention.
As shown in fig. 1 and 2, the invention discloses a multi-feature information fused power transformer winding health diagnosis method, which comprises the following steps:
s1, collecting data of current, voltage and output power of the transformer, and carrying out signal offset calculation on the collected data to obtain a linear deviation degree;
s2, calculating the error of the data according to the linear deviation degree in the acquisition period, and calculating the fault probability according to the error function of the data;
and S3, calculating the accuracy and the recall rate of the transformer winding data according to the fault probability, and evaluating the health state of the transformer through a clustering algorithm.
And data collection is carried out through the current sensor, the voltage sensor and the power sensor, and finally data analysis operation is carried out through the processor.
The S1 includes:
s1-1, collecting the current value of the transformer winding, and in a collecting period, obtaining a current data set as { I }iGet the number of voltages (i ═ 1,2,3,4,5, …, n), getThe data set is { VjJ ═ 1,2,3,4,5, …, n), the acquired power data set being { Pk}(k＝1,2,3,4,5,…,n)，
State vector, T, in the course of the acquired current data setICurrent overload record, current transmission temperature record, transformer temperature record; state vector, T, in the course of the acquired voltage data setV(voltage load, voltage fluctuation record); state vector, T, in the course of the acquired power data setPPower distortion recording, power regulation recording, power output recording;
s1-2, according to the linear trend of the collected data of the state vector, the data deviation degree is calculated,
when the influence of the interference threshold μ increases, the linear deviation thereof increases in order to equalize the equations; the interference threshold value has a small proportion for evaluating the deviation of current, voltage and power in the transformer, so that the health condition result of the transformer is subjected to preliminary deviation judgment, the calculation describes the actual running state of the transformer, the preliminary state evaluation of the transformer is reflected by the threshold value calculation,
wherein,
is the amplitude of the signal for the change in current,
the characteristic value is reconstructed for the current signal,
is the amplitude of the signal for the voltage change,
the characteristic value is reconstructed for the voltage signal,
is the amplitude of the signal for the power change,
reconstructing the characteristic value for the power signal, and adjusting the corresponding reconstructed characteristic value according to the signal amplitude changes of current, voltage and power so as to ensure equality of equations and extract the signal abnormality of the corresponding acquisition point;
s1-3, traversing the amplitude of the current, voltage and power data in the sampling period, and distinguishing and identifying the current, voltage and power data through a linear deviation degree calculation formula according to the amplitude as a reference value for the health detection of the transformer;
obtaining the linear deviation degree A according to the ratio of the peak value of the corresponding data to the average value of the amplitudeI，AV，AP；
If the deviation degree difference is small, the corresponding data stability is good, otherwise, the stability is poor; and carrying out fault probability analysis on the data with good stability.
The S2 includes:
s2-1, carrying out error operation on the linear deviation value to obtain the reconstructed eigenvalue average value AVE of the current, voltage and power dataI，AVEV，AVEP(ii) a And the difference of the reconstructed characteristic values of the current, voltage and power data, obtaining deviation values of the current, voltage and power data, obtaining a standardized characteristic value according to the ratio of the deviation values to the absolute deviation values,
s2-2, according to the normalized eigenvalue and the sum result after the absolute value of the reconstructed eigenvalue of current, voltage and power is squared,
performing normalization calculation
According to the result of the normalized calculation, the current, voltage and power values of the transformer are judged to be within
0＜UI,UV,UPWhen the value is less than or equal to 0.4, the health condition of the transformer winding is good,
if 0.4 < UI,UV,UPWhen the fault risk is less than or equal to 0.8, the fault risk exists;
if 0.8 < UI,UV,UPIf the number is less than 1, serious risks exist;
s2-3, for the transformer winding with good state and great risk, corresponding data can be directly screened out, but U is more than 0.4I,UV,UPWhen the current, the voltage and the power in the existing fault risk interval are less than or equal to 0.8, the average fault probability analysis is required to be carried out, and the accuracy of data is ensured;
lambda is an empirical judgment threshold, characteristic sorting is carried out according to data of the characteristic vectors, the average fault probability of fault risk periods in the acquisition cycle is determined and solved for current, voltage and power data,
the mean failure probability is calculated as
the S3 includes:
s3-1, after obtaining the average fault probability, calculating the accuracy,
s3-2, calculating the recall rate,
s3-3, calculating the score
According to the calculation scores, the deviation of the current, the voltage or the power of the transformer winding in certain time periods is judged, the calculation scores are carried out on the extracted time periods, the health safety check of the transformer winding in all time periods can be realized according to corresponding supervision calculation, data reference is provided for users, abnormal time period characteristics are calibrated, and the abnormal time period characteristics are pushed to a cloud platform.
While embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that: various changes, modifications, substitutions and alterations can be made to the embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.
Claims (9)
1. A multi-feature information fused power transformer winding health diagnosis method is characterized by comprising the following steps:
s1, collecting data of current, voltage and output power of the transformer, and carrying out signal offset calculation on the collected data to obtain a linear deviation degree;
s2, calculating the error of the data according to the linear deviation degree in the acquisition period, and calculating the fault probability according to the error function of the data;
and S3, calculating the accuracy and the recall rate of the transformer winding data according to the fault probability, and evaluating the health state of the transformer through a clustering algorithm.
2. The multi-signature information fusion power transformer winding health diagnosis method according to claim 1, wherein the S1 includes:
s1-1, collecting the current value of the transformer winding, and in a collecting period, obtaining a current data set as { I }iWhere 1,2,3,4,5, …, n, the acquired voltage dataset is { V }jJ ═ 1,2,3,4,5, …, n), the acquired power data set being { Pk}(k＝1,2,3,4,5,…,n)，
State vector, T, in the course of the acquired current data setICurrent overload record, current transmission temperature record, transformer temperature record; state vector, T, in the course of the acquired voltage data setV(voltage load, voltage fluctuation record); state vector, T, in the course of the acquired power data setPPower distortion recording, power regulation recording, power output recording).
3. The multi-signature information fusion power transformer winding health diagnosis method of claim 2, wherein the S1 further comprises:
s1-2, according to the linear trend of the collected data of the state vector, the data deviation degree is calculated,
when the influence of the interference threshold μ increases, the linear deviation thereof increases in order to equalize the equations; the interference threshold value has a small proportion for evaluating the deviation of current, voltage and power in the transformer, so that the health condition result of the transformer is subjected to preliminary deviation judgment, the calculation describes the actual running state of the transformer, the preliminary state evaluation of the transformer is reflected by the threshold value calculation,
wherein,
is the amplitude of the signal for the change in current,
the characteristic value is reconstructed for the current signal,
is the amplitude of the signal for the voltage change,
the characteristic value is reconstructed for the voltage signal,
is the amplitude of the signal for the power change,
the eigenvalues are reconstructed for the power signal.
4. The multi-signature information fusion power transformer winding health diagnosis method of claim 2, wherein the S1 further comprises:
s1-3, traversing the amplitude of the current, voltage and power data in the sampling period, and distinguishing and identifying the current, voltage and power data through a linear deviation degree calculation formula according to the amplitude as a reference value for the health detection of the transformer;
obtaining the linear deviation degree A according to the ratio of the peak value of the corresponding data to the average value of the amplitude valuesI，AV，AP；
5. The multi-signature information fusion power transformer winding health diagnosis method according to claim 1, wherein the S2 comprises:
s2-1, carrying out error operation on the linear deviation value to obtain the reconstructed eigenvalue average value AVE of the current, voltage and power dataI，AVEV，AVEP(ii) a And the difference of the reconstructed characteristic values of the current, voltage and power data, obtaining deviation values of the current, voltage and power data, obtaining a standardized characteristic value according to the ratio of the deviation values to the absolute deviation values,
6. the multi-signature information fusion power transformer winding health diagnosis method according to claim 1, wherein the S2 further comprises:
s2-2, according to the normalized eigenvalue and the sum result after the absolute value of the reconstructed eigenvalue of current, voltage and power is squared,
performing normalization calculation
According to the result of the normalization calculation, the current, voltage and power values of the transformer are judged to be in
0＜UI,UV,UPWhen the value is less than or equal to 0.4, the health condition of the transformer winding is good,
if 0.4 < UI,UV,UPWhen the fault risk is less than or equal to 0.8, the fault risk exists;
if 0.8 < UI,UV,UPIf < 1, there is a significant risk.
7. The multi-signature information fusion power transformer winding health diagnosis method of claim 1, wherein the S2 further comprises:
s2-3, carrying out average fault probability analysis on current, voltage and power in the existing fault risk interval to ensure the accuracy of data;
lambda is an empirical judgment threshold, characteristic sorting is carried out according to data of the characteristic vector, the average fault probability of a fault risk period existing in the acquisition cycle is determined and solved for current, voltage and power data,
the mean failure probability is calculated as
8. The multi-signature information fusion power transformer winding health diagnosis method according to claim 1, wherein the S3 comprises:
s3-1, after obtaining the average fault probability, calculating the accuracy,
s3-2, calculating the recall rate,
9. the multi-signature information fusion power transformer winding health diagnosis method according to claim 8, wherein the S3 further comprises:
s3-3, calculating the score
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117214592A (en) * | 2023-11-09 | 2023-12-12 | 国网甘肃省电力公司白银供电公司 | Fault monitoring management system and method for power transformer |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117214592A (en) * | 2023-11-09 | 2023-12-12 | 国网甘肃省电力公司白银供电公司 | Fault monitoring management system and method for power transformer |
| CN117214592B (en) * | 2023-11-09 | 2024-03-15 | 国网甘肃省电力公司白银供电公司 | Fault monitoring management system and method for power transformer |
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Application publication date: 20220722 |