Power transformer loss calculation method and system

Abstract

The invention relates to the technical field of transformer loss detection, in particular to a power transformer loss calculation method and system, and the method comprises the steps: selecting a voltage data window for voltage data at each moment through a voltage fitting curve in the operation process of a power transformer within a preset time period; determining the potential loss anomaly degree of the voltage data window at each moment; correcting the initial similar capacitance limit of the sub-window in the voltage data window according to the potential loss anomaly degree to obtain the corrected similar capacitance limit of the sub-window in the voltage data window; calculating a fuzzy entropy value of a voltage data window of the voltage data at each moment according to the corrected similar tolerance limit, and determining that the voltage data in the voltage data window is abnormal and the abnormality is caused by the loss of the power transformer under the condition that the fuzzy entropy value is greater than a threshold value; and calculating the loss of the power transformer. Therefore, the accuracy of the calculated loss calculation result of the power transformer is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of transformer loss detection, and in particular to a method and system for calculating power transformer loss. Background Art

[0002] A power transformer is an electrical device used to exchange AC voltage and current and transmit and distribute electrical energy in a power system. As a core device in a power system, it is widely used in the transmission and distribution of electricity. With the increasing complexity of power systems, the safe and stable operation of power transformers is crucial to the stability of power systems.

[0003] In some scenarios, it is necessary to calculate the loss of the power transformer in order to understand the state of the power transformer. At present, the fuzzy entropy method is often used to detect abnormalities in the voltage data of the power transformer during operation, identify abnormal voltage data in the voltage data of the power transformer, and then use the abnormal voltage data to calculate the loss of the power transformer. However, when the fuzzy entropy method calculates the fuzzy entropy values ​​in the corresponding windows of all voltage data points, it provides the same similarity tolerance for the calculation of the fuzzy membership between each sub-window in the corresponding window of each voltage data point, so as to calculate the loss of the power transformer. In actual scenarios, the changes in voltage data caused by the failure of the power transformer usually have similar characteristics to the changes in voltage data caused by the loss of the power transformer itself, that is, both will cause changes in voltage data. Therefore, the above method leads to a low accuracy rate in detecting abnormalities in voltage data, which in turn leads to inaccurate calculation results of the loss of the power transformer. Summary of the invention

[0004] In order to solve the technical problem that the accuracy of abnormal detection of voltage data is low, which leads to inaccurate loss calculation results of power transformers, the purpose of the present invention is to provide a method and system for calculating power transformer losses. The technical solutions adopted are as follows: In a first aspect, an embodiment of the present invention provides a method for calculating the loss of a power transformer, comprising: selecting a voltage data window for voltage data at each moment through a voltage fitting curve during the operation of the power transformer within a predetermined time period; determining the potential loss abnormality degree of the voltage data window at each moment according to the voltage data window at each moment and the adjacent data segments of the voltage data window at each moment, wherein the adjacent data segments are multiple data segments continuously adjacent to each other before the first voltage data in the voltage data window of the voltage data at each moment; correcting the initial similarity tolerance of the subwindow in the voltage data window according to the potential loss abnormality degree to obtain the corrected similarity tolerance of the subwindow in the voltage data window; calculating the fuzzy entropy value of the voltage data window of the voltage data at each moment according to the corrected similarity tolerance, and when the fuzzy entropy value is greater than a threshold value, determining that the voltage data in the voltage data window is abnormal and the abnormality is caused by the loss of the power transformer; and calculating the loss of the power transformer using the time period corresponding to the voltage data window where the abnormal voltage data is located and the rated power of the power transformer.

[0005] Optionally, determining the potential loss abnormality of the voltage data window at each moment based on the voltage data window at each moment and the adjacent data segments of the voltage data window at each moment includes: determining the right-skewed distribution factor of the voltage data window based on the voltage data in the voltage data window at each moment; determining the right-skewed distribution reliability of the voltage data window at each moment based on the first minimum voltage value of the voltage data in the voltage data window at each moment and the second minimum voltage value of the voltage data in the adjacent data segments adjacent to the voltage data window at each moment; determining the downward fluctuation degree of the voltage data in the voltage data window at each moment based on the right-skewed distribution factor and the right-skewed distribution reliability; determining the smooth characteristic factor of the voltage data window at each moment based on the time interval between the maximum voltage value and the first minimum voltage value in the voltage data window at each moment and the variance of the change in the voltage data at each moment in the voltage data window; determining the potential loss abnormality of the voltage data window at each moment using the downward fluctuation degree, the smooth characteristic factor, the first minimum voltage value in the voltage data window at each moment and the voltage data at the final neighborhood comparison moment at each moment.

[0006] Optionally, determining the right-skewed distribution factor of the voltage data window based on the voltage data in the voltage data window at each moment includes: calculating a first difference between the median of the voltage data in the voltage data window and an average voltage data value of the voltage data in the voltage data window; and normalizing the first difference to obtain the right-skewed distribution factor.

[0007] Optionally, determining the right-biased arrangement reliability of the voltage data window at each moment based on the first minimum voltage value of the voltage data in the voltage data window at each moment and the second minimum voltage value of the voltage data in the adjacent data segments adjacent to the voltage data window at each moment includes: calculating the second difference between each second minimum voltage value and the first minimum voltage value, and superimposing each second difference to obtain a first superimposed value; and normalizing the first superimposed value to obtain the right-biased arrangement reliability.

[0008] Optionally, determining the downward fluctuation degree of the voltage data in the voltage data window at each moment according to the right-skewed distribution factor and the right-skewed distribution reliability includes: determining the sum of the right-skewed distribution factor and the right-skewed distribution reliability as the downward fluctuation degree.

[0009] Optionally, determining the smooth characteristic factor of the voltage data window at each moment based on the time interval between the maximum voltage value and the first minimum voltage value in the voltage data window at each moment and the variance of the change in voltage data at each moment in the voltage data window includes: determining the ratio between the time interval and the variance as the smooth characteristic factor.

[0010] Optionally, the potential loss abnormality degree of the voltage data window at each moment is determined by using the downward fluctuation degree, the smooth characteristic factor, the first minimum voltage value in the voltage data window at each moment and the voltage data at the final neighborhood comparison moment at each moment, including: calculating the third difference between the voltage data at each final neighborhood comparison moment and the first minimum voltage value, and superimposing each third difference to obtain a second superimposed value; performing inverse proportional normalization processing on the second superimposed value to obtain a first normalized value; and determining the first product of the first normalized value, the downward fluctuation degree and the smooth characteristic factor as the potential loss abnormality degree.

[0011] Optionally, the initial similarity tolerance of the subwindow in the voltage data window is corrected according to the degree of potential loss abnormality, and the corrected similarity tolerance of the subwindow in the voltage data window includes: the initial similarity tolerance of the subwindow in the voltage data window in a preset fuzzy entropy algorithm and the length and number of the subwindows; the potential loss abnormality degree is inversely normalized to obtain a second normalized value; and the second product between the second normalized value and the initial similarity tolerance is determined as the corrected similarity tolerance of the subwindow in the voltage data window.

[0012] Optionally, calculating the loss of the power transformer using the time period corresponding to the voltage data window where the abnormal voltage data occurs and the rated power of the power transformer includes: superimposing the time periods corresponding to all the voltage data windows where the abnormal voltage data occurs to obtain the superposition time; and determining the third product between the superposition time and the rated power as the loss of the power transformer.

[0013] In a second aspect, an embodiment of the present invention provides a power transformer loss calculation system, comprising: a processor and a memory; wherein the memory is used to store a computer program that can be run on the processor; and the processor is used to execute the program stored in the memory to implement the steps of the power transformer loss calculation method mentioned in the first aspect.

[0014] The present invention has the following beneficial effects: firstly, a voltage data window is selected for voltage data at each moment through a voltage fitting curve during operation of a power transformer within a predetermined time period; then, the potential loss abnormality degree of the voltage data window at each moment is determined according to the voltage data window at each moment and the adjacent data segments of the voltage data window at each moment, wherein the adjacent data segments are a plurality of data segments continuously adjacent to the first voltage data in the voltage data window of the voltage data at each moment; and the initial similarity tolerance of the subwindow in the voltage data window is corrected according to the potential loss abnormality degree to obtain the corrected similarity tolerance of the subwindow in the voltage data window; secondly, the fuzzy entropy value of the voltage data window of the voltage data at each moment is calculated according to the corrected similarity tolerance, and when the fuzzy entropy value is greater than a threshold value, it is determined that the voltage data in the voltage data window is abnormal and the abnormality is caused by the loss of the power transformer; finally, the loss of the power transformer is calculated using the time period corresponding to the voltage data window where the abnormal voltage data is located and the rated power of the power transformer.

[0015] In this way, the embodiment of the present invention analyzes the change characteristics of the voltage data of the power transformer and introduces the potential loss abnormality degree to specifically adjust the similarity tolerance of the sub-window in the voltage data window of each voltage data in the fuzzy entropy algorithm. This method can more accurately reflect the true characteristics of the voltage change of the power transformer, effectively distinguish the abnormal voltage change caused by the fault and the abnormal voltage change caused by the power transformer's own loss, reduce the occurrence of false detection, improve the accuracy of the abnormal detection of the voltage data in the power transformer, reduce the false detection rate of the abnormal voltage data, and thus improve the accuracy of the calculated loss calculation result of the power transformer. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art descriptions are 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 these drawings without paying creative work.

[0017] Figure 1 A flow chart of a method for calculating power transformer loss provided by one embodiment of the present invention; Figure 2A schematic diagram of the structure of a power transformer loss calculation system provided by one embodiment of the present invention; Figure 3 A schematic structural diagram of a power transformer loss calculation system provided in another embodiment of the present invention. DETAILED DESCRIPTION

[0018] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following is a detailed description of a method and system for calculating power transformer losses proposed by the present invention, its specific implementation, structure, features and effects, in conjunction with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0019] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0020] A specific scheme of a method for calculating power transformer losses provided by the present invention is described in detail below in conjunction with the accompanying drawings.

[0021] Embodiment 1: See also Figure 1 , which shows a flow chart of a method for calculating power transformer losses provided by an embodiment of the present invention, including: S101, selecting a voltage data window for voltage data at each moment through a voltage fitting curve during operation of a power transformer within a predetermined time period.

[0022] Specifically, the embodiment of the present invention installs a voltage sensor at the input end of the power transformer to record the voltage data during the operation of the power transformer. The predetermined time period can be determined according to actual conditions. In the embodiment of the present invention, the value is 2 hours, and the acquisition frequency is five times per second.

[0023] Furthermore, the embodiment of the present invention uses an analog-to-digital conversion device to digitally convert voltage data to obtain a digital representation of voltage data, and then uses the least squares curve fitting technology to fit the digital voltage data at each moment during the operation of the power transformer within a predetermined time period to obtain a voltage fitting curve. The voltage fitting curve includes a plurality of voltage data points, and then a voltage data window is selected from the voltage fitting curve for the voltage data point at each moment.

[0024] Furthermore, the size of the voltage data window is preset to be , the voltage data corresponding to the sampling time before The voltage data at each moment is used as a window of each voltage data. It is worth noting that the size of the voltage data window can also be determined according to the actual scenario, and the embodiment of the present invention does not limit this.

[0025] S102, determining the potential loss abnormality degree of the voltage data window at each moment according to the voltage data window at each moment and the adjacent data segments of the voltage data window at each moment.

[0026] The adjacent data segments are a plurality of data segments that are continuous and adjacent to the first voltage data in the voltage data window of the voltage data at each moment.

[0027] Specifically, the embodiment of the present invention can select multiple continuous adjacent data segments for the voltage data window of the voltage data at each moment. The number of adjacent data segments can be determined according to actual conditions, and the value in the embodiment of the present invention is 6. For example: for any voltage data window, the 300 voltage data before the sampling moment of the first voltage data in the voltage data window are used as the first adjacent data segment of the voltage data window. The voltage data at the 300 sampling moments before the first voltage data in the first adjacent data segment are used as the second adjacent data segment of the voltage data window, and so on, until the 6th adjacent data segment of the voltage data window is obtained. Among them, the voltage data in each adjacent data segment is different, but the number is the same. In addition, if the amount of data before the sampling moment of the voltage data window is insufficient to construct its adjacent data segments, six minutes of voltage data can be collected in advance to ensure that the adjacent data segments of the voltage data window can be constructed.

[0028] Furthermore, when the power transformer itself has losses, the corresponding change in the voltage passing through the power transformer is a decrease. Therefore, when analyzing the change characteristics of the voltage data during the operation of the power transformer, if the numerical distribution characteristics of the voltage data in the voltage data window of each voltage data are more in line with the right-skewed distribution, and the minimum value of the voltage data in each voltage data window is smaller than the minimum value of its adjacent data segment, it means that the voltage data value in the voltage data window is lower, and the power transformer is more likely to have potential abnormal losses in the time period corresponding to the voltage data window, and the corresponding voltage data will have a greater degree of downward fluctuation. Therefore, the embodiment of the present invention determines the potential loss abnormality of the voltage data window based on the downward fluctuation degree of the voltage data window.

[0029] Furthermore, when determining the degree of downward fluctuation, the embodiment of the present invention determines the degree of downward fluctuation of the voltage data by analyzing the numerical distribution of the voltage data in the voltage data window. When the voltage data in the voltage data window is closer to the right-skewed distribution, the numerical performance of the voltage data in the voltage data window is lower, indicating that the corresponding change of the voltage passing through the power transformer is reduced, and the power transformer is more likely to have potential abnormal losses in the time period corresponding to the voltage data window, and the corresponding downward fluctuation degree will also be greater.

[0030] Further, as an optional embodiment of the present invention, determining the potential loss abnormality of the voltage data window at each moment according to the voltage data window at each moment and the adjacent data segments of the voltage data window at each moment includes: determining the right-skewed distribution factor of the voltage data window according to the voltage data in the voltage data window at each moment; determining the right-skewed distribution reliability of the voltage data window at each moment according to the first minimum voltage value of the voltage data in the voltage data window at each moment and the second minimum voltage value of the voltage data in the adjacent data segments adjacent to the voltage data window at each moment; determining the downward fluctuation degree of the voltage data in the voltage data window at each moment according to the right-skewed distribution factor and the right-skewed distribution reliability; determining the smooth characteristic factor of the voltage data window at each moment according to the time interval between the maximum voltage value and the first minimum voltage value in the voltage data window at each moment and the variance of the change in the voltage data at each moment in the voltage data window; determining the potential loss abnormality of the voltage data window at each moment by using the downward fluctuation degree, the smooth characteristic factor, the first minimum voltage value in the voltage data window at each moment and the voltage data at the final neighborhood comparison moment at each moment.

[0031] Specifically, as an optional embodiment of the present invention, determining the right-skewed distribution factor of the voltage data window based on the voltage data in the voltage data window at each moment includes: calculating a first difference between the median of the voltage data in the voltage data window and the average voltage data value of the voltage data in the voltage data window; normalizing the first difference to obtain the right-skewed distribution factor.

[0032] The embodiment of the present invention uses the following formula to calculate the right-skewed distribution factor: In the above formula, Indicates The right-skewed distribution factor of a voltage data window. Indicates The median of the voltage data in a voltage data window. Indicates The average voltage data value of the voltage data in a voltage data window. Represents the normalization function, which is used to Perform normalization.

[0033] Furthermore, when there is loss in the power transformer itself, the corresponding change in the voltage passing through the power transformer is a decrease. Therefore, the smaller the minimum value of the voltage data in the voltage data window is compared to the minimum value of the voltage data in its adjacent data segment, the greater the confidence of the right-biased distribution of the voltage data in the voltage data window, and the more likely the power transformer is to have potential abnormal losses in the time period corresponding to the voltage data window, and the greater the corresponding degree of downward fluctuation will be.

[0034] Further, as an optional embodiment of the present invention, according to the first minimum voltage value of the voltage data in the voltage data window at each moment and the second minimum voltage value of the voltage data in the adjacent data segments adjacent to the voltage data window at each moment, determining the right-biased arrangement reliability of the voltage data window at each moment includes: calculating the second difference between each second minimum voltage value and the first minimum voltage value, and superimposing each second difference to obtain a first superimposed value; normalizing the first superimposed value to obtain the right-biased arrangement reliability.

[0035] Specifically, the embodiment of the present invention specifically uses the following formula to calculate the right-biased distribution reliability: In the above formula, Indicates The right-biased distribution of voltage data within a voltage data window is used to arrange the confidence. Indicates The number of adjacent data segments in a voltage data window. Indicates The first voltage data window The minimum value of the voltage data in two adjacent data segments is the second minimum voltage value. Indicates The minimum value of the voltage data in a voltage data window is the first minimum voltage value. Represents the normalization function, which is used to Perform normalization.

[0036] Furthermore, when the numerical distribution of the voltage data in the voltage data window is closer to the right-skewed distribution, it means that the power transformer is more likely to have potential abnormal losses in the time period corresponding to the voltage data window, and the corresponding downward fluctuation degree will be greater. As an optional embodiment of the present invention, determining the downward fluctuation degree of the voltage data in the voltage data window at each moment according to the right-skewed distribution factor and the right-skewed distribution reliability includes: determining the sum of the right-skewed distribution factor and the right-skewed distribution reliability as the downward fluctuation degree.

[0037] Specifically, the embodiment of the present invention specifically uses the following formula to calculate the degree of decline fluctuation: In the above formula, Indicates The degree of voltage fluctuation within a voltage data window. Indicates The right-biased distribution of voltage data within a voltage data window is used to arrange the confidence. Indicates The right-skewed distribution factor of a voltage data window.

[0038] Furthermore, the degree of downward fluctuation of the voltage data in each voltage data window is determined by analyzing whether there is a fluctuation of voltage value reduction in the time period corresponding to a voltage data window. However, the voltage change caused by the aging failure of the load equipment usually has similar characteristics to the change of the voltage data caused by the loss of the power transformer itself, that is, both will cause the reduction of the voltage data in the pipeline. Therefore, only by analyzing the numerical performance of the voltage data in the voltage data window, it is not possible to distinguish the reduction of the voltage value caused by the above two situations, which will eventually lead to errors in the abnormal detection results of the voltage data. The embodiment of the present invention analyzes that there is a difference between the change trend characteristics of the voltage data caused by the aging failure of the load equipment and the change trend of the voltage data caused by the loss of the power transformer itself. The change of the voltage data caused by the aging failure of the load equipment is more drastic, but it will return to stability in a short time, while the change of the voltage data caused by the loss of the power transformer itself is relatively gentle and continuous.

[0039] Therefore, the embodiment of the present invention analyzes the numerical variation characteristics of the voltage data in each voltage data window, and optimizes the degree of downward fluctuation of the voltage data in each voltage data window to obtain the potential loss abnormality degree of each voltage data window. Among them, the lower the relative value of the voltage data at the last sampling moment in each voltage data window in the time period corresponding to the voltage data window, and the smoother the numerical variation between the maximum voltage value and the minimum voltage value in each voltage data window, it can be explained that the value of the voltage data in the voltage data window is continuous and the possibility of a gradual decrease is greater, so it can be explained that the more likely the power transformer itself is to have losses in the time period corresponding to this voltage data window, the greater the potential loss abnormality degree in the voltage data window will be.

[0040] Further, as an optional embodiment of the present invention, determining the smooth characteristic factor of the voltage data window at each moment based on the time interval between the maximum voltage value and the first minimum voltage value in the voltage data window at each moment and the variance of the change in voltage data at each moment in the voltage data window includes: determining the ratio between the time interval and the variance as the smooth characteristic factor.

[0041] Specifically, the embodiment of the present invention is In the voltage data window The voltage data at the first sampling moment is The absolute value of the difference between the voltage data at the sampling time is recorded as In the voltage data window The change amount of the voltage data at each sampling moment. In the embodiment of the present invention, the flat characteristic factor of the voltage data window at each moment is calculated by the following formula: In the above formula, Indicates The flat characteristic factor within a voltage data window. Indicates The time interval between the maximum voltage value and the minimum value (the first minimum voltage value) in a voltage data window. Indicates The variance of the voltage data changes at all sampling moments within a voltage data window.

[0042] In the above formula, The larger the The longer it takes for the maximum voltage value in a voltage data window to change to the minimum voltage value, the The smoother the change in value between the maximum voltage value and the minimum voltage value in a voltage data window, the greater the smooth characteristic factor. The smaller the The more consistent the voltage reduction between each group of adjacent sampling moments in the interval from the maximum voltage value to the minimum voltage value in the voltage data window, the more consistent the voltage reduction between each group of adjacent sampling moments in the interval from the maximum voltage value to the minimum voltage value in the voltage data window. The smoother the change in value between the maximum voltage and the minimum voltage in a voltage data window, the greater the credibility, and the corresponding smooth characteristic factor will also be greater.

[0043] Furthermore, the greater the degree of fluctuation of the voltage data in the voltage data window, the more likely it is that the power transformer will have an abnormal risk in the time period corresponding to the voltage data window, and the corresponding potential loss abnormality should also be greater. The smaller the value of the voltage data at the last sampling time in the voltage data window, the more likely it is that the minimum voltage in the voltage data window will appear at the last sampling time. Then the voltage change characteristics in the voltage data window are more consistent with the characteristics of the continuous decrease in voltage value caused by the loss of the power transformer itself, which can be explained that the power transformer itself is more likely to have a loss risk in the time period corresponding to the voltage data window, and the corresponding potential loss abnormality should also be greater. In addition, the larger the smooth characteristic factor in the voltage data window, the greater the possibility that the voltage value in the voltage data window is continuous and gradually reduced. Then the voltage change characteristics in the voltage data window are more consistent with the characteristics of the continuous decrease in voltage value caused by the loss of the power transformer itself, which can be explained that the power transformer itself is more likely to have a loss risk in the time period corresponding to the voltage data window, and the corresponding potential loss abnormality should also be greater.

[0044] Further, as an optional embodiment of the present invention, the potential loss abnormality degree of the voltage data window at each moment is determined by utilizing the downward fluctuation degree, the smooth characteristic factor, the first minimum voltage value in the voltage data window at each moment, and the voltage data at the final neighborhood comparison moment at each moment, including: calculating the third difference between the voltage data at each final neighborhood comparison moment and the first minimum voltage value, and superimposing each third difference to obtain a second superimposed value; performing inverse proportional normalization processing on the second superimposed value to obtain a first normalized value; determining the first product of the first normalized value, the downward fluctuation degree, and the smooth characteristic factor as the potential loss abnormality degree.

[0045] Specifically, the embodiment of the present invention is The five sampling moments before the last sampling moment in the voltage data window are used as the final neighborhood comparison moment, wherein the final neighborhood comparison moment includes the last sampling moment. Further, the embodiment of the present invention uses the following formula to calculate the potential loss abnormality degree: In the formula, Indicates The potential loss anomaly level for each voltage data window. Indicates The minimum value of the voltage data in a voltage data window is the first minimum voltage value. Indicates The number of all final neighborhood comparison moments within a voltage data window. Indicates In the voltage data window The voltage data at the final neighborhood comparison moment. Indicates The flat characteristic factor within a voltage data window. Indicates The degree of voltage fluctuation within a voltage data window. (-) indicates the inverse normalization function, which is used to Perform inverse proportional normalization.

[0046] So far, the embodiment of the present invention has acquired the potential loss abnormality degree of the voltage data window of each voltage data.

[0047] S103, correcting the initial similarity tolerance of the sub-window in the voltage data window according to the potential loss abnormality degree, to obtain a corrected similarity tolerance of the sub-window in the voltage data window.

[0048] Specifically, the greater the potential loss anomaly in each voltage data window, the greater the possibility that the voltage data in the voltage data window is caused by the loss of the power transformer itself. Therefore, in order to more accurately and conveniently identify the loss risk of the voltage data itself, the similarity tolerance between the sub-windows should be smaller when calculating the fuzzy entropy value in the voltage data window to ensure that the final calculated fuzzy entropy value is larger.

[0049] Furthermore, as an optional embodiment of the present invention, the initial similarity tolerance of the sub-window in the voltage data window is corrected according to the potential loss abnormality degree, and the corrected similarity tolerance of the sub-window in the voltage data window includes: the initial similarity tolerance of the sub-window in the voltage data window in the preset fuzzy entropy algorithm and the length and number of the sub-windows; the potential loss abnormality degree is inversely normalized to obtain a second normalized value; and the second product between the second normalized value and the initial similarity tolerance is determined as the corrected similarity tolerance of the sub-window in the voltage data window.

[0050] Specifically, in the embodiment of the present invention, the initial similarity tolerance, the length and number of the sub-windows can be determined according to actual conditions. In the embodiment of the present invention, the initial similarity tolerance of the sub-windows in the preset voltage data window is The length of the sub-window is set to 10, and the number is set to the length of the voltage data window - the length of each sub-window + 1.

[0051] Furthermore, the embodiment of the present invention uses the following formula to calculate the modified similarity tolerance: In the above formula, Indicates the calculation The fuzzy entropy value within a voltage data window is the modified similarity tolerance between sub-windows. Indicates The potential loss anomaly level for each voltage data window. Represents the initial similarity tolerance. (-) indicates the inverse normalization function, which is used to Perform inverse proportional normalization.

[0052] At this point, the modified similarity tolerance between sub-windows when calculating the fuzzy entropy value in each voltage data window is obtained. The modified similarity tolerance is a parameter set for calculating the fuzzy entropy value in the voltage data window using the fuzzy entropy algorithm.

[0053] S104, calculating the fuzzy entropy value of the voltage data window of the voltage data at each moment according to the modified similarity tolerance, and when the fuzzy entropy value is greater than a threshold, determining that the voltage data in the voltage data window is abnormal and the abnormality is caused by the loss of the power transformer.

[0054] Specifically, in the embodiment of the present invention, the known technology is used to calculate the initial fuzzy entropy value in the voltage data window of each voltage data by using the modified similarity tolerance, and then the initial fuzzy entropy value is normalized to obtain the fuzzy entropy value of each voltage data window. Among them, the use of the modified similarity tolerance to calculate the initial fuzzy entropy value in the voltage data window of each voltage data can refer to the known technology, and it can be understood that the modified similarity tolerance is used as the original similarity tolerance in the fuzzy entropy calculation process to achieve the acquisition operation of the initial fuzzy entropy value, and the embodiment of the present invention will not be repeated here.

[0055] Furthermore, the threshold can be set according to the actual situation. If the fuzzy entropy value is greater than 0.9, it can be said that the voltage data in this voltage data window is abnormal. The abnormal voltage data in the time period corresponding to the voltage data window is caused by the loss of the power transformer itself. When the embodiment of the present invention determines that the power transformer itself has loss, it issues an early warning and starts the automatic alarm program to notify the operator to take timely measures. The threshold value can be determined according to the actual situation, and the embodiment of the present invention is not limited here.

[0056] S105, calculating the loss of the power transformer by using the time period corresponding to the voltage data window where the abnormal voltage data is located and the rated power of the power transformer.

[0057] Specifically, after determining that the abnormal voltage data is caused by the loss of the power transformer itself, as an optional embodiment of the present invention, the loss of the power transformer is calculated using the time period corresponding to the voltage data window where the abnormal voltage data is located and the rated power of the power transformer, including: superimposing the time periods corresponding to the voltage data windows where all the abnormal voltage data are located to obtain the superposition time; determining the third product between the superposition time and the rated power as the loss of the power transformer.

[0058] More specifically, the embodiment of the present invention superimposes the time periods corresponding to the voltage data windows where all abnormal voltage data are located to obtain the superimposed time, and then obtains the rated power of the power transformer from the manual of the power transformer, and multiplies the superimposed time and the rated power to obtain the loss of the power transformer.

[0059] The embodiment of the present invention analyzes the change characteristics of the voltage data of the power transformer and introduces the potential loss abnormality degree to specifically adjust the similarity tolerance of the sub-window in the voltage data window of each voltage data in the fuzzy entropy algorithm. This method can more accurately reflect the true characteristics of the voltage change of the power transformer, effectively distinguish the abnormal voltage change caused by the fault and the abnormal voltage change caused by the power transformer's own loss, improve the accuracy of the abnormal detection of the voltage data in the power transformer, reduce the abnormal false detection rate of the voltage data, and thus improve the accuracy of the calculated loss calculation result of the power transformer.

[0060] Embodiment 2: Corresponding to the power transformer loss calculation method provided in the above embodiment, based on the same technical concept, an embodiment of the present invention further provides a power transformer loss calculation system, which is used to execute the above power transformer loss calculation method. Figure 2 A schematic diagram of a power transformer loss calculation system provided by an embodiment of the present invention is shown in FIG. Figure 2As shown. The power transformer loss calculation system 200 includes: a selection module 201, which is used to select a voltage data window for voltage data at each moment through a voltage fitting curve during the operation of the power transformer in a predetermined time period; a determination module 202, which is used to determine the potential loss abnormality of the voltage data window at each moment according to the voltage data window at each moment and the adjacent data segments of the voltage data window at each moment, wherein the adjacent data segments are a plurality of data segments that are continuously adjacent to the first voltage data in the voltage data window of the voltage data at each moment; a correction module 203, which is used to correct the initial similarity tolerance of the subwindow in the voltage data window according to the potential loss abnormality, and obtain the corrected similarity tolerance of the subwindow in the voltage data window; the determination module 202, which is also used to calculate the fuzzy entropy value of the voltage data window of the voltage data at each moment according to the corrected similarity tolerance, and when the fuzzy entropy value is greater than a threshold value, it is determined that the voltage data in the voltage data window is abnormal and the abnormality is caused by the loss of the power transformer; a calculation module 204, which is used to calculate the loss of the power transformer using the time period corresponding to the voltage data window where the abnormal voltage data is located and the rated power of the power transformer.

[0061] The embodiment of the present invention analyzes the change characteristics of the voltage data of the power transformer and introduces the potential loss abnormality degree to specifically adjust the similarity tolerance of the sub-window in the voltage data window of each voltage data in the fuzzy entropy algorithm. This method can more accurately reflect the true characteristics of the voltage change of the power transformer, effectively distinguish the abnormal voltage change caused by the fault and the abnormal voltage change caused by the power transformer's own loss, improve the accuracy of the abnormal detection of the voltage data in the power transformer, reduce the abnormal false detection rate of the voltage data, and thus improve the accuracy of the calculated loss calculation result of the power transformer.

[0062] Embodiment three: Corresponding to the power transformer loss calculation method provided in the above embodiment, based on the same technical concept, an embodiment of the present invention further provides a power transformer loss calculation system, which is used to execute the above power transformer loss calculation method. Figure 3 A schematic diagram of a power transformer loss calculation system provided by another embodiment of the present invention is shown in FIG. Figure 3 The power transformer loss calculation system may have relatively large differences due to different configurations or performances, and may include one or more processors 301 and memory 302, the memory 302 is used to store computer programs that can be run on the processor 301, and the processor 301 is used to execute the program stored in the memory 302 to achieve the above Figure 1The memory 302 may be a temporary storage or a permanent storage. The application stored in the memory 302 may include one or more modules (not shown in the figure), each of which may include a series of computer executable instructions in the power transformer loss calculation system.

[0063] Furthermore, the processor 301 can be configured to communicate with the memory 302, and execute a series of computer executable instructions in the memory 302 on the power transformer loss calculation system. The power transformer loss calculation system can also include one or more power supplies 303, one or more wired or wireless network interfaces 304, one or more input and output interfaces 305, and one or more keyboards 306.

[0064] Specifically in this embodiment, the power transformer loss calculation system includes a processor, a communication interface, a memory and a communication bus; wherein the processor, the communication interface and the memory communicate with each other through the bus; the memory is used to store computer programs; the processor is used to execute the programs stored in the memory to achieve the above Figure 1 The various steps in the method embodiment are similar to those in the method embodiment, and have the beneficial effects of the above method embodiments. To avoid repetition, the embodiments of the present invention will not be described in detail here.

[0065] It should be noted that the power transformer loss calculation system provided in the embodiment of the present invention and the power transformer loss calculation method provided in the embodiment of the present invention are based on the same application concept. Therefore, the specific implementation of this embodiment can refer to the implementation of the aforementioned power transformer loss calculation method, and has the same or similar beneficial effects, and the repeated parts will not be repeated.

[0066] It should be noted that the sequence of the above embodiments of the present invention is only for description and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0067] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

Claims

1. A method for calculating power transformer losses, characterized in that: The power transformer loss calculation method comprises: Selecting a voltage data window for voltage data at each moment by using a voltage fitting curve during operation of the power transformer within a predetermined time period; Determine the potential loss abnormality degree of the voltage data window at each moment according to the voltage data window at each moment and the adjacent data segments of the voltage data window at each moment, wherein the adjacent data segments are a plurality of data segments continuously adjacent to the first voltage data in the voltage data window of the voltage data at each moment; Correcting the initial similarity tolerance of the sub-window in the voltage data window according to the potential loss abnormality degree to obtain a corrected similarity tolerance of the sub-window in the voltage data window; Calculating a fuzzy entropy value of a voltage data window of voltage data at each moment according to the modified similarity tolerance, and determining that the voltage data in the voltage data window is abnormal and the abnormality is caused by the loss of the power transformer when the fuzzy entropy value is greater than a threshold value; The loss of the power transformer is calculated using a time period corresponding to a voltage data window in which the abnormal voltage data is located and the rated power of the power transformer.

2. The method for calculating power transformer loss according to claim 1, characterized in that: The step of determining the potential loss abnormality degree of the voltage data window at each moment according to the voltage data window at each moment and the adjacent data segments of the voltage data window at each moment comprises: Determining a right-skewed distribution factor of the voltage data window according to voltage data in the voltage data window at each moment; Determine the right-biased distribution reliability of the voltage data window at each moment according to the first minimum voltage value of the voltage data in the voltage data window at each moment and the second minimum voltage value of the voltage data in the adjacent data segment adjacent to the voltage data window at each moment; Determine the degree of downward fluctuation of the voltage data in the voltage data window at each moment according to the right-skewed distribution factor and the right-skewed distribution confidence; Determine a smooth characteristic factor of the voltage data window at each moment according to the time interval between the maximum voltage value and the first minimum voltage value in the voltage data window at each moment and the variance of the change amount of the voltage data at each moment in the voltage data window; The potential loss abnormality degree of the voltage data window at each moment is determined by using the decline fluctuation degree, the smooth characteristic factor, the first minimum voltage value in the voltage data window at each moment and the voltage data at the final neighborhood comparison moment at each moment.

3. The method for calculating power transformer loss according to claim 2, characterized in that: The step of determining the right-skew distribution factor of the voltage data window according to the voltage data in the voltage data window at each moment comprises: Calculate a first difference between a median of the voltage data in the voltage data window and an average voltage data value of the voltage data in the voltage data window; The first difference is normalized to obtain the right-skewed distribution factor.

4. The method for calculating power transformer loss according to claim 2, characterized in that: Determining the right-biased arrangement reliability of the voltage data window at each moment according to the first minimum voltage value of the voltage data in the voltage data window at each moment and the second minimum voltage value of the voltage data in the adjacent data segment adjacent to the voltage data window at each moment comprises: Calculating a second difference between each of the second minimum voltage values ​​and the first minimum voltage value, and superimposing each of the second difference values ​​to obtain a first superimposed value; The first superposition value is normalized to obtain the right-biased distribution reliability.

5. The method for calculating power transformer loss according to claim 2, characterized in that: The step of determining the degree of downward fluctuation of the voltage data in the voltage data window at each moment according to the right-skewed distribution factor and the right-skewed distribution confidence comprises: The sum of the right-skewed distribution factor and the right-skewed distribution confidence is determined as the downward fluctuation degree.

6. The method for calculating power transformer loss according to claim 2, characterized in that: Determining the smooth characteristic factor of the voltage data window at each moment according to the time interval between the maximum voltage value and the first minimum voltage value in the voltage data window at each moment and the variance of the change amount of the voltage data at each moment in the voltage data window includes: The ratio between the time interval and the variance is determined as the flat characteristic factor.

7. The method for calculating power transformer loss according to claim 2, characterized in that: Determining the potential loss abnormality degree of the voltage data window at each moment by using the decline fluctuation degree, the smooth characteristic factor, the first minimum voltage value in the voltage data window at each moment, and the voltage data at the final neighborhood comparison moment at each moment includes: Calculating a third difference between the voltage data at each of the final neighborhood comparison moments and the first minimum voltage value, and superimposing each of the third differences to obtain a second superimposed value; Performing inverse proportional normalization processing on the second superposition value to obtain a first normalized value; A first product of the first normalized value, the downward fluctuation degree, and the flat characteristic factor is determined as the potential loss abnormality degree.

8. The method for calculating power transformer loss according to claim 1, characterized in that: The step of correcting the initial similarity tolerance of the sub-window in the voltage data window according to the potential loss abnormality to obtain the corrected similarity tolerance of the sub-window in the voltage data window includes: Presetting the initial similarity tolerance of the sub-windows in the voltage data window in the fuzzy entropy algorithm and the length and number of the sub-windows; Performing inverse proportional normalization processing on the potential loss abnormality degree to obtain a second normalized value; A second product of the second normalized value and the initial similarity margin is determined as a modified similarity margin of the sub-window in the voltage data window.

9. The method for calculating power transformer loss according to claim 1, characterized in that: The calculating the loss of the power transformer by using the time period corresponding to the voltage data window where the abnormal voltage data is located and the rated power of the power transformer comprises: Superimposing the time periods corresponding to the voltage data windows where all abnormal voltage data are located to obtain superimposed time; A third product between the superposition time and the rated power is determined as the loss of the power transformer.

10. A power transformer loss calculation system, characterized in that: include: A processor and a memory; wherein the memory is used to store a computer program that can be run on the processor; A processor is used to execute the program stored in the memory to implement the steps of the power transformer loss calculation method as described in any one of claims 1 to 9.

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