Error change and state evaluation method for voltage transformers of same bus

Abstract

The invention discloses a method for evaluating error change and state of voltage transformers of the same bus, which comprises the following steps of: establishing a regional synchronous acquisition system, acquiring secondary side data of each transformer of the same bus in a region, and dividing the secondary side data of each transformer into a single-group transformer data set and a multi-group transformer data set; performing classification matching on the single-group class mutual inductor data set and the multi-group class mutual inductor data set to obtain a data set in a homologous time period; and based on the off-line verification error value of the mutual inductor and the error distribution state of each mutual inductor in the multiple groups of class mutual inductor data sets, evaluating the error change condition of each mutual inductor in the single group of class mutual inductor data sets. According to the invention, the technical problem of how to accurately and quickly realize the online detection of the metering error of the voltage transformer and output the result is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of transformers, and in particular to an error change and state evaluation method of a voltage transformer with a same busbar. Background Art

[0002] At present, voltage transformers, including capacitor voltage transformers, electronic voltage transformers and electromagnetic voltage transformers, have been widely used in power systems. Among them, capacitor voltage transformers (hereinafter referred to as CVT) have the advantages of high impulse insulation strength, simple manufacturing, small size, light weight and significant economy. They are not only used at the outlet of substation lines, but also widely used on busbars to replace electromagnetic voltage transformers. In actual operation, they are restricted by various factors such as design level and manufacturing process, especially the influence of system operation processes such as line switching, input or exit of certain equipment, and the high failure rate of CVT operation has a serious impact on the safe and stable operation of the power grid. How to effectively realize the online detection of CVT metering errors and timely discover and eliminate faults is of great practical significance and is also one of the foundations for the development of digital and intelligent upgrading and transformation of power grids. Carrying out online detection of CVT metering errors in operation without power outages, so as to grasp the state of CVT metering errors in real time and guide CVT operation and maintenance work in a more targeted manner, is of great significance for ensuring the safe, stable and economic operation of the power system. At the same time, the relevant technical routes and research methods for online detection of CVT metering errors can be extended to other types of online monitoring solutions for power transformers, which can greatly promote the planning and design of subsequent series products and solutions. Since there are two main factors affecting the error of the voltage transformer itself, one is external factors, namely secondary load, primary voltage, temperature and humidity, etc., and the other is the internal factors of the transformer itself, namely excitation current and winding impedance. The internal factors are determined by the design and manufacturing process of the transformer, and the external factors change in real time during the actual operation of the CVT, which will lead to inconsistent CVT error results when the online detection of CVT metering errors is carried out under non-stop power conditions. In a way, the interference caused by external factors on the error results is reduced when the online detection of CVT metering errors is carried out under non-stop power conditions, and the CVT error state and CVT inherent error changes can be more accurately evaluated. Therefore, it is urgent to propose a method for evaluating the error change and state of the voltage transformer on the same busbar to solve the technical problem of how to accurately and quickly realize the online detection of voltage transformer metering errors and output the results. Summary of the invention

[0003] The main purpose of the present invention is to propose a method for evaluating the error change and state of a voltage transformer on a same busbar, aiming to solve the technical problem of how to accurately and quickly realize online detection of voltage transformer metering errors and output the results.

[0004] To achieve the above object, the present invention provides a method for evaluating the error change and state of a voltage transformer on a same bus, wherein the method for evaluating the error change and state of a voltage transformer on a same bus comprises the following steps:

[0005] S1. Establish a regional synchronous acquisition system to collect secondary side data of each transformer on the same bus in the region, and divide the secondary side data of each transformer into single-group and multi-group transformer data sets;

[0006] S2, classifying and matching a single group of transformer-like data sets with multiple groups of transformer-like data sets to obtain data sets in the same source time period;

[0007] S3. Based on the offline verification error value of the transformer and the error distribution state of each transformer in multiple groups of transformer-like data sets, the error change of each transformer in a single group of transformer-like data sets is evaluated.

[0008] In one of the preferred solutions, the secondary side data of the transformer includes voltage amplitude, phase angle, and temperature and humidity.

[0009] One of the preferred solutions, step S1 divides the secondary side data of each transformer into single-group and multi-group transformer data sets, specifically:

[0010] According to the distribution of each transformer in different substations, the secondary side data of the transformers distributed in the same substation are divided into independent units, and all independent units are divided into single-group transformer-like data sets and multiple-group transformer-like data sets according to the number of transformers in the bus.

[0011] In one of the preferred solutions, step S2 is specifically:

[0012] S21, extracting an independent unit whose relative distance from a substation is less than a set threshold from each of the single-group transformer-like data set and the multiple-group transformer-like data set, and establishing a first multidimensional matrix and a second multidimensional matrix of the single-group transformer-like data set and the multiple-group transformer-like data set, respectively;

[0013] S22, dividing the first multidimensional matrix and the second multidimensional matrix according to the time window to obtain m first multidimensional matrix subsets and m second multidimensional matrix subsets respectively;

[0014] S23, randomly extracting a group of mutual inductor data from the second multidimensional matrix subset, forming a data matrix M with the first multidimensional matrix subset, and normalizing the data matrix M;

[0015] S24, extracting the same-phase data in the normalized data matrix M, and performing similarity calculation;

[0016] S25. If the similarity reaches a specified threshold, it can be determined that the data of the single-group mutual inductor-like data set and the multiple-group mutual inductor-like data sets selected by the data matrix M in the current time interval meet the homology requirement.

[0017] One of the preferred solutions, after step S22, further includes:

[0018] Outlier screening is performed on the first multidimensional matrix subset and the second multidimensional matrix subset for each time period.

[0019] One of the preferred solutions is that the normalization process of the data matrix M in step S23 is:

[0020]

[0021] Among them, X norm is the data in the normalized data matrix M, X is the data in the unnormalized data matrix M, X max , X min are the maximum and minimum values ​​in the data matrix M that has not been normalized.

[0022] One of the preferred solutions is that in step S24, similarity calculation is performed on the in-phase data in the normalized data matrix M based on Pearson correlation.

[0023] In one of the preferred solutions, the similarity is:

[0024]

[0025] Among them, r p1,p2 is the similarity between vector Ep1 and vector Ep2 with phase p in the data matrix M, cov() is the covariance, and σ is the standard deviation.

[0026] In one of the preferred solutions, step S3 is specifically:

[0027] S31, according to the real-time sampling data, calculate the error distribution of each channel of each mutual inductor in the same phase and position in the multiple sets of mutual inductor data sets, and compare it with the error offline verification data distribution, and evaluate the error distribution and error state of each channel of the multiple sets of mutual inductor;

[0028] S32, based on the data matrix M, filter out the secondary side data set of the mutual inductor and the corresponding data matrix M in the time period, calculate the average value of the sampling value ratio between the same phases, and obtain the first matrix D;

[0029] S33, evaluating the primary side voltage transformation ratio between the single-group type mutual inductor and the multiple-group type mutual inductor according to the offline verification data of each mutual inductor error and the average value of the sampling value ratio relationship between the single-group type mutual inductor and the multiple-group type mutual inductor in the same phase in the first matrix D;

[0030] S44. Evaluate the error state of a single group of mutual inductors at different times through the primary side voltage transformation ratio, the first matrix D, and the error distribution and error state of multiple groups of mutual inductors.

[0031] In one of the preferred solutions, the average value of the ratio of the sampling values ​​of the single-group mutual inductor and the multiple-group mutual inductor in the same phase in the first matrix D is:

[0032]

[0033] Wherein, D' is the average value of the proportional relationship between the A phase / B phase / C phase secondary side sampling values ​​of the single-group mutual inductor and the multi-group mutual inductor in the first matrix D, V 1 is the average value of the primary voltage sampling of a single group of transformers, V 2 is the average value of primary voltage sampling of multiple groups of transformers, e 1 is the offline verification error of a single group of transformers, e 2 Offline calibration error of multiple groups of transformers.

[0034] In the above technical scheme of the present invention, the error change and state evaluation method of the voltage transformer on the same busbar includes the following steps: establishing a regional synchronous acquisition system, collecting the secondary side data of each transformer on the same busbar in the region, and dividing the secondary side data of each transformer into a single-group and multi-group transformer data set; classifying and matching the single-group transformer data set with the multi-group transformer data set to obtain the data set under the same source time period; based on the transformer offline verification error value and the error distribution state of each transformer in the multi-group transformer data set, evaluating the error change of each transformer in the single-group transformer data set. The present invention solves the technical problem of how to accurately and quickly realize the online detection of voltage transformer metering errors and output the results.

[0035] In the present invention, when environmental factors, such as temperature and humidity, are close, data cleaning methods such as matching the secondary side amplitude of the same bus CVT with the same source data can be used to improve data quality and reduce the interference of external factors such as secondary load, power balance of three-phase lines, temperature and humidity and other environmental factors on the inherent error judgment of the transformer. Substituting the primary side voltage ratio information into the error calculation model can allow two transformers in different substations to serve as control groups for error state evaluation, thereby improving the accuracy of CVT error state evaluation under the same bus in different substations. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the structures shown in these drawings without paying any creative work.

[0037] Figure 1 The present invention is a schematic diagram of a method for evaluating the error change and state of a voltage transformer on a same busbar according to an embodiment of the present invention.

[0038] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with the implementation methods and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0039] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0040] In addition, in the present invention, the descriptions such as "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or implying their relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" or "second" may explicitly or implicitly include at least one of the features.

[0041] Furthermore, the technical solutions between the various embodiments of the present invention may be combined with each other, but this must be based on the fact that they can be implemented by ordinary technicians in the field. When the combination of technical solutions is mutually contradictory or cannot be implemented, it should be deemed that such combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0042] See also Figure 1 According to one aspect of the present invention, the present invention provides a method for evaluating the error change and state of a voltage transformer on a same bus, wherein the method for evaluating the error change and state of a voltage transformer on a same bus comprises the following steps:

[0043] S1. Establish a regional synchronous acquisition system to collect secondary side data of each transformer on the same bus in the region, and divide the secondary side data of each transformer into single-group and multi-group transformer data sets;

[0044] S2, classifying and matching a single group of transformer-like data sets with multiple groups of transformer-like data sets to obtain data sets in the same source time period;

[0045] S3. Based on the offline verification error value of the transformer and the error distribution state of each transformer in multiple groups of transformer-like data sets, the error change of each transformer in a single group of transformer-like data sets is evaluated.

[0046] Specifically, in this embodiment, according to the voltage signal change phenomenon and the related factors of the change, it can be known that on the same busbar in an independent substation, or on multiple buses of the same voltage level when the bus tie switch is connected, the change trend and amplitude of the voltage signal are approximately the same; it can be considered that when the primary side signals are of the same source and the environmental conditions are the same, the deviation between the secondary side voltage signals of the connected transformers is only reflected in their own inherent errors. In actual scenarios, when the substation generally uses a double busbar or multi-busbar wiring method, when the bus tie switch is connected, the primary side signals are of the same source and the environmental conditions are similar; for this type of transformer, in the process of error analysis and calculation, the secondary side information of each transformer can be used as a control group, and the error of each transformer can be obtained through the deviation change between the secondary side voltage signals of different transformers. For the scenario where only one group of transformers is connected on a single busbar in the substation, it is difficult to judge the error change through the secondary side voltage data of a single group of transformers due to the lack of a control group.

[0047] Specifically, in this embodiment, the secondary side data of the transformer include voltage amplitude, phase angle, temperature and humidity; the voltage amplitude, phase angle, temperature and humidity of two or more groups of transformer secondary sides on the same voltage level bus in the same area are collected in real time. The present invention does not make specific limitations and can be set according to needs.

[0048] Specifically, in this embodiment, the step S1 divides the secondary side data of each transformer into single-group and multi-group transformer data sets, specifically:

[0049] According to the distribution of each transformer in different substations, the secondary side data of the transformers distributed in the same substation are divided into independent units, and all independent units are divided into a single-group transformer data set and a multi-group transformer data set according to the number of transformers in the bus; if the number of transformers in the bus is 1, the transformers in the bus are divided into a single-group transformer data set, and the data is a single-group transformer data set; if the number of transformers in the bus is greater than 1, the transformers in the bus are divided into multiple groups of transformers, and the data is a multiple-group transformer data set.

[0050] Specifically, in this embodiment, the step S2 is specifically:

[0051] S21. According to the distance distribution of the power grid lines, an independent unit with a relative distance of a substation less than a set threshold is extracted from each of the single-group transformer data set and the multi-group transformer data set, and a first multidimensional matrix and a second multidimensional matrix of the single-group transformer data set and the multi-group transformer data set are respectively established; that is, multidimensional matrices are respectively established for the fundamental wave amplitude data, phase angle, temperature and humidity and other characteristic information of the connected transformer; EIG = [Amp, Ang, Temp, RH, ...], wherein EIG is the transformer data set, Amp is the fundamental wave amplitude data of the transformer, Ang is the phase angle of the transformer, Temp is the temperature, and RH is the humidity; the first multidimensional matrix is:

[0052]

[0053] The second multidimensional matrix is:

[0054]

[0055] Among them, S 1 is the first multidimensional matrix of a single set of transformer-like data sets, S 2 is the second multidimensional matrix of a single group of transformer-like data sets, E is the data characteristic information of a single transformer at each time coordinate, n is the number of transformer channels, and L is the data length;

[0056] S22, dividing the first multidimensional matrix and the second multidimensional matrix according to the time window, and obtaining m first multidimensional matrix subsets and second multidimensional matrix subsets respectively; according to the power distribution rules of the power grid, there is a bus with the same voltage level between two adjacent substations, and its power supply is different at different times, so it is necessary to further divide the established data set according to a certain time window, for example, according to the hour, and divide the first multidimensional matrix and the second multidimensional matrix into m first multidimensional matrix subsets and second multidimensional matrix subsets;

[0057] S23, randomly extracting a group of mutual inductor data from the second multidimensional matrix subset, and forming a data matrix M with the first multidimensional matrix subset, and normalizing the data matrix M; the data length of the data matrix M is L / m, and the number of mutual inductor groups is 2;

[0058] S24, extracting the same-phase data in the normalized data matrix M, and performing similarity calculation;

[0059] S25. If the similarity reaches a specified threshold, it can be determined that the data of the single-group mutual inductor-like data set and the multiple-group mutual inductor-like data sets selected by the data matrix M in the current time interval meet the homology requirement.

[0060] Specifically, in this embodiment, after step S22, the following steps are further included:

[0061] Outlier screening is performed on the first multidimensional matrix subset and the second multidimensional matrix subset of each simultaneous period. Based on problems such as abnormal data fluctuations, the data with large differences in environmental factors are eliminated from the first multidimensional matrix subset and the second multidimensional matrix subset of each simultaneous period. Then, the data samples are screened based on the fundamental wave amplitude validity detection, the change difference detection between channels in the same phase, etc., and the samples with large differences in fundamental wave amplitude and long-term running status in the same phase are eliminated.

[0062] Specifically, in this embodiment, the step S23 performs normalization processing on the data matrix M as follows:

[0063]

[0064] Among them, X norm is the data in the normalized data matrix M, X is the data in the unnormalized data matrix M, X max , X min are the maximum and minimum values ​​in the data matrix M that has not been normalized.

[0065] Specifically, in this embodiment, in step S24, similarity calculation is performed on the in-phase data in the normalized data matrix M based on the Pearson correlation; the similarity is:

[0066]

[0067] Among them, r p1,p2 is the similarity between vector Ep1 and vector Ep2 with phase p in the data matrix M, cov() is the covariance, and σ is the standard deviation; is the mean of the vector Ep1 with phase p in the data matrix M, is the mean of the vector Ep2 with phase p in the data matrix M; L / m is the data length of the vector Ep1 and the vector Ep2.

[0068] Specifically, in this embodiment, step S3 is as follows:

[0069] S31, according to the real-time sampling data, calculate the error distribution of each channel of each mutual inductor in the same phase and position in the multiple sets of mutual inductor data sets, and compare it with the error offline verification data distribution, and evaluate the error distribution and error state of each channel of the multiple sets of mutual inductor;

[0070] S32, based on the data matrix M, filter out the secondary side data set of the mutual inductor and the corresponding data matrix M in the time period, calculate the average value of the sampling value ratio between the same phases, and obtain the first matrix D; the first matrix is:

[0071]

[0072] Where D is the first matrix, is the average value of the secondary side sampling ratio of single-group mutual inductor and multiple-group mutual inductor in phase A during period t, is the average value of the secondary side sampling ratio of a single group of mutual inductors and multiple groups of mutual inductors in phase B during period t, is the average value of the secondary side sampling ratio of a single group of mutual inductors and multiple groups of mutual inductors in phase C during period t; t is E 1 With E 2 The number of data matrices M that satisfy the data homology, E 1 is the specified transformer selected from the single group of transformers, E 2 A specified mutual inductor selected from multiple groups of similar mutual inductors;

[0073] S33, evaluating the primary side voltage transformation ratio between the single-group type transformer and the multiple-group type transformer according to the offline verification data of each transformer error and the average value of the sampling value ratio relationship between the single-group type transformer and the multiple-group type transformer closest to the offline verification data date in the first matrix D between the same phases;

[0074] S44. Evaluate the error state of a single group of mutual inductors at different times through the primary side voltage transformation ratio, the first matrix D, and the error distribution and error state of multiple groups of mutual inductors.

[0075] Specifically, in this embodiment, the average value of the ratio of the sampling values ​​of the single-group mutual inductor and the multiple-group mutual inductor in the same phase in the first matrix D is:

[0076]

[0077] Wherein, D' is the average value of the proportional relationship between the A phase / B phase / C phase secondary side sampling values ​​of the single-group mutual inductor and the multi-group mutual inductor in the first matrix D, V 1 is the average value of the primary voltage sampling of a single group of transformers, V 2 is the average value of primary voltage sampling of multiple groups of transformers, e 1 is the offline verification error of a single group of transformers, e 2 is the offline verification error of multiple groups of similar transformers; ρ is the primary side voltage ratio.

[0078] Specifically, in this embodiment, the error states of the single group of mutual inductors at different times are:

[0079]

[0080] Among them, e is the error state of a single group of mutual inductors at different times, D is the data in the first matrix, e' is the real-time error of multiple groups of mutual inductors, and ρ is the primary side voltage ratio.

[0081] The above are only preferred embodiments of the present invention, and are not intended to limit the patent scope of the present invention. All equivalent structural changes made using the contents of the present invention's specification and drawings, or directly / indirectly applied in other related technical fields, are included in the patent protection scope of the present invention.

Claims

1. A method for evaluating the error change and state of a voltage transformer with a busbar, characterized in that: The following steps are involved: S1. Establish a regional synchronous acquisition system to collect secondary side data of each transformer on the same bus in the region, and divide the secondary side data of each transformer into single-group and multi-group transformer data sets; S2, classifying and matching a single group of transformer-like data sets with multiple groups of transformer-like data sets to obtain data sets in the same source time period; S3. Based on the offline verification error value of the transformer and the error distribution state of each transformer in multiple groups of transformer-like data sets, the error change of each transformer in a single group of transformer-like data sets is evaluated.

2. The method for error variation and state evaluation of a voltage transformer with a same bus according to claim 1, characterized in that: The secondary side data of the transformer include voltage amplitude, phase angle, temperature and humidity.

3. A method for evaluating the error change and state of a voltage transformer with a same busbar according to any one of claims 1 to 2, characterized in that: The step S1 divides the secondary side data of each transformer into single-group and multi-group transformer data sets, specifically: According to the distribution of each transformer in different substations, the secondary side data of the transformers distributed in the same substation are divided into independent units, and all independent units are divided into single-group transformer-like data sets and multiple-group transformer-like data sets according to the number of transformers in the bus.

4. The method for error variation and state evaluation of a voltage transformer with a same busbar according to claim 3, characterized in that: The step S2 is specifically: S21, extracting an independent unit whose relative distance from a substation is less than a set threshold from each of the single-group transformer-like data set and the multiple-group transformer-like data set, and establishing a first multidimensional matrix and a second multidimensional matrix of the single-group transformer-like data set and the multiple-group transformer-like data set, respectively; S22, dividing the first multidimensional matrix and the second multidimensional matrix according to the time window to obtain m first multidimensional matrix subsets and m second multidimensional matrix subsets respectively; S23, randomly extracting a group of mutual inductor data from the second multidimensional matrix subset, forming a data matrix M with the first multidimensional matrix subset, and normalizing the data matrix M; S24, extracting the same-phase data in the normalized data matrix M, and performing similarity calculation; S25. If the similarity reaches a specified threshold, it can be determined that the data of the single-group mutual inductor-like data set and the multiple-group mutual inductor-like data sets selected by the data matrix M in the current time interval meet the homology requirement.

5. The method for error variation and state evaluation of a voltage transformer with a same busbar according to claim 4, characterized in that: After step S22, the method further includes: Outlier screening is performed on the first multidimensional matrix subset and the second multidimensional matrix subset for each time period.

6. The method for error variation and state evaluation of a voltage transformer with a same busbar according to claim 4, characterized in that: In step S23, the data matrix M is normalized as follows: Among them, X norm is the data in the normalized data matrix M, X is the data in the unnormalized data matrix M, X max , X min are the maximum and minimum values ​​in the data matrix M that has not been normalized.

7. The method for error variation and state evaluation of a voltage transformer with a same busbar according to claim 4, characterized in that: In step S24, similarity calculation is performed on the in-phase data in the normalized data matrix M based on the Pearson correlation.

8. The method for error variation and state evaluation of voltage transformers with the same busbar according to claim 4, characterized in that: The similarity is: Among them, r p1,p2 is the similarity between vector Ep1 and vector Ep2 with phase p in the data matrix M, cov() is the covariance, and σ is the standard deviation.

9. The method for error variation and state evaluation of voltage transformers with the same busbar according to claim 4, characterized in that: The step S3 is specifically: S31, according to the real-time sampling data, calculate the error distribution of each channel of each mutual inductor in the same phase and position in the multiple sets of mutual inductor data sets, and compare it with the error offline verification data distribution, and evaluate the error distribution and error state of each channel of the multiple sets of mutual inductor; S32, based on the data matrix M, filter out the secondary side data set of the mutual inductor and the corresponding data matrix M in the time period, calculate the average value of the sampling value ratio between the same phases, and obtain the first matrix D; S33, evaluating the primary side voltage transformation ratio between the single-group type mutual inductor and the multiple-group type mutual inductor according to the offline verification data of each mutual inductor error and the average value of the sampling value ratio relationship between the single-group type mutual inductor and the multiple-group type mutual inductor in the same phase in the first matrix D; S44. Evaluate the error state of a single group of mutual inductors at different times through the primary side voltage transformation ratio, the first matrix D, and the error distribution and error state of multiple groups of mutual inductors.

10. A method for error variation and state evaluation of voltage transformers with the same busbar as claimed in claim 9, characterized in that: The average value of the sampling value ratio relationship between the single-group mutual inductor and the multiple-group mutual inductor in the same phase in the first matrix D is: Wherein, D' is the average value of the proportional relationship between the A phase / B phase / C phase secondary side sampling values ​​of the single-group mutual inductor and the multi-group mutual inductor in the first matrix D, V 1 is the average value of the primary voltage sampling of a single group of transformers, V 2 is the sampling mean of the primary side voltage of multiple groups of transformers, e1 is the offline verification error of a single group of transformers, and e2 is the offline verification error of multiple groups of transformers.

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