Online monitoring method for voltage transformer

Abstract

The invention discloses an online monitoring method for a voltage transformer, belongs to the technical field of power systems, and is used for solving the problems of low online monitoring efficiency, low data processing speed and high storage cost of the voltage transformer in the prior art. Comprising an acquisition module, a data processing module, a fault diagnosis module, a database and a personnel terminal module. The acquisition frequency is dynamically adjusted through the acquisition module, unnecessary data acquisition is reduced through intelligent data acquisition, the storage cost is reduced, the data processing module sends an operation parameter set which may diagnose a fault to the fault diagnosis module, and the fault type and grade are accurately predicted in combination with the fault diagnosis module. According to the method, the fault diagnosis module is helped to perform fault analysis efficiently and specifically, the storage duration of the data in the database is intelligently adjusted according to the analysis result of the data diagnosis change line, storage resources are helped to be optimized, unnecessary data storage is reduced, and therefore the storage cost is reduced.

Description

Technical Field

[0001] The invention belongs to the technical field of power systems, and in particular relates to an online monitoring method for a voltage transformer. Background Art

[0002] With the rapid development of power systems, voltage transformers, as key equipment in power systems, are crucial to ensuring the safe operation of power grids in terms of stability and reliability.

[0003] The existing online monitoring method of voltage transformer requires real-time collection, processing and storage of a large amount of data, which can easily lead to slower processing speed, affect the timeliness of monitoring and increase storage costs. For this reason, we propose an online monitoring method for voltage transformer. Summary of the invention

[0004] The purpose of the present invention is to provide a voltage transformer online monitoring method to solve the problems raised in the above background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions: a voltage transformer online monitoring method, comprising an acquisition module, a data processing module, a fault diagnosis module, a database, and a personnel terminal module; Data acquisition module: used to divide the working time of the voltage transformer into a low-frequency acquisition period, a medium-frequency acquisition period and a high-frequency acquisition period according to the voltage and current values ​​of the primary and secondary sides of the voltage transformer, and set corresponding acquisition intervals to collect the voltage transformer operation parameter set; Data processing module: receiving and analyzing the voltage transformer operating parameter set collected by the data acquisition module, obtaining the data processing result and sending it to the fault diagnosis module; wherein the data processing result includes the abnormal parameter set, the data diagnosis value, the data transmission value, the predicted severity coefficient of the operating parameter corresponding to the data transmission value, and the predicted severity coefficient of the abnormal parameter set; at the same time, the operating parameter set is sent to the storage library for data recording; Fault diagnosis module: used to analyze the data sent by the data processing module and predict the fault type and level; Database: used to store historical data and real-time data of voltage transformers; Personnel terminal module: used to receive the prediction results of the fault diagnosis module and notify the inspection personnel.

[0006] Preferably, the specific process of the acquisition module dividing the working time of the voltage transformer into a low-frequency acquisition period, a medium-frequency acquisition period and a high-frequency acquisition period according to the voltage values ​​and current values ​​of the primary side and the secondary side of the voltage transformer is as follows: Step 101: Divide the daily working time of the voltage transformer into a number of equal detection time lengths m, where m=1, ..., n; Step 102: for each detection duration, collect historical data of average voltage and average current values ​​of the primary and secondary sides of the voltage transformer within the detection duration in the last three working days, add the average voltage and average current values ​​of the primary and secondary sides within the same detection duration, and obtain their averages to obtain the primary side voltage average value YVi, current average value YIi, secondary side voltage average value RVi, current average value RIi within the detection period, i represents the number of the detection period, i=1, 2, ..., n; use the formula: VTi=YVi*a1+YIi*a2+RVi*a3+RIi*a4 to obtain the voltage transformer state evaluation value VTi within each detection duration, where a1, a2, a3, a4 are preset weight coefficients; Step 103: presetting three collection time periods, which are divided into a low-frequency collection time period, a medium-frequency collection time period, and a high-frequency collection time period, each collection time period corresponds to a state evaluation value interval, matching the state evaluation value of the voltage transformer in each detection time length with the state evaluation value interval corresponding to the three collection time periods, and obtaining the collection time period corresponding to each detection time length; Step 104: Each of the three acquisition periods has a preset update state evaluation threshold. When the state evaluation value of the voltage transformer within the detection time exceeds the preset update state evaluation threshold of the corresponding acquisition period, the detection time is marked as an update period, and the update matching process is triggered, specifically: Step 1041: collecting the state evaluation value of the voltage transformer in the update period, and obtaining the matching evaluation value of the voltage transformer in the update period by multiplying the state evaluation value of the voltage transformer in the update period by the environmental impact coefficient in the update period; Step 1042: rematching the matching evaluation value of the voltage transformer in the update period with the state evaluation value intervals corresponding to the three collection periods to obtain the collection period rematched with the update period; Step 1043: Divide the update period into the re-matching collection period.

[0007] Preferably, the process of setting a collection interval for a collection period and collecting an operation parameter set is as follows: Step 105: obtaining the average state evaluation value of the voltage transformer in each collection period in the last three working days, presetting the collection interval coefficient, and obtaining the collection frequency of each collection period by multiplying the average state evaluation value of the voltage transformer in each collection period by the preset collection interval coefficient; Step 106: According to the collection frequency of each collection period, the voltage transformer operating parameter set is collected and sent to the data processing module. The voltage transformer operating parameter set includes primary side voltage value, primary side current value, secondary side voltage value, secondary side current value, insulation resistance value, winding temperature value, core temperature value, and ambient temperature value.

[0008] Preferably, the process of the data processing module analyzing the abnormal parameter set is: Step 201: the collection module matches each operating parameter in the operating parameter set with its corresponding preset normal value interval according to the collection interval for each operating parameter value in the operating parameter set and each operating parameter in the preset operating parameter set. If an operating parameter set exists and a certain operating parameter is not within its corresponding preset normal value range, the operating parameter is marked as a fault parameter, and the operating parameter is marked as an abnormal parameter set, and the abnormal parameter constant set is sent to the fault diagnosis module.

[0009] Preferably, the process of the data processing module analyzing the data diagnosis value, the data transmission value and the predicted severity coefficient of the operating parameter corresponding to the data transmission value is: If all the operating parameters in the operating parameter set are within the corresponding preset normal value range, and each preset operating parameter is set with a corresponding standard value, the deviation value Xij between all the operating parameters in the operating parameter set and their corresponding standard values ​​is calculated, where j represents the number of the operating parameter, j=1, 2, ..., n; i represents the number of the collection time, i=1, 2, ..., n; According to the importance of the operating parameters to fault diagnosis, a weight coefficient Wij is assigned to each operating parameter. The data diagnosis value YCi corresponding to the acquisition time i is obtained using the formula: YCi=∑j=1_n(Xij*Wij); where Xij and Wij represent the deviation value of the operating parameter j at the acquisition time i and the corresponding weight coefficient respectively; Step 202: Plot the relationship between the data diagnostic value YCi and the change between the two into a curve graph, such as Figure 2 , connect the data diagnosis values ​​YCi at each acquisition moment to form a data diagnosis change line; draw a preset data diagnosis change threshold line in the curve chart, and mark the data diagnosis value YCi at any acquisition moment that is greater than the preset data diagnosis change threshold at the corresponding moment as the data transmission value SFi; calculate the difference between the data transmission value SFi and the preset data diagnosis change threshold at the corresponding moment, and mark it as the alarm shutdown value BTi; preset the alarm shutdown threshold, when the alarm shutdown value BTi is greater than the preset alarm shutdown threshold, generate an alarm shutdown command and send it to the personnel terminal module; after receiving the alarm shutdown command, the personnel terminal module will notify the relevant monitoring personnel to immediately cut off the power line operation of the voltage transformer to stop it from working; Step 203: Send the operating parameter set corresponding to the data transmission value SFi to the fault diagnosis module, and at the same time obtain the slope value Ki of the data diagnosis change line between the acquisition time corresponding to the data transmission value SFi and the previous acquisition time, and use the formula: βi=SFi*b1+Ki*b2 to obtain the predicted severity coefficient βi of the operating parameter corresponding to the data transmission value; send the predicted severity coefficient βi to the fault diagnosis module; wherein b1 and b2 are preset weight coefficients, and b1 is greater than b2, so that β is always greater than zero. It can be seen from the formula that when Ki is greater than zero, the larger Ki is, the larger β is, and when Ki is less than zero, the larger the absolute value of Ki is, the smaller β is; For the abnormal parameter set, set the preset normal value interval of the fault parameters in the abnormal parameter set, obtain the upper limit and lower limit of the preset normal value interval, calculate the difference between the numerical value of the fault parameter in the abnormal parameter set and the upper limit and lower limit of the preset normal value interval, and obtain the upper limit deviation difference and the lower limit deviation difference; compare the upper limit deviation difference with the lower limit deviation difference, select the smallest value between the two as the fault deviation value Gij, and use the formula: Gβi=∑j=1_m(Gij*d1+m*d2) to obtain the predicted severity coefficient Gβi of the abnormal parameter set; where d1 and d2 are preset weight coefficients, m represents the total number of fault parameters in the abnormal parameter set, and the predicted severity coefficient Gβi of the abnormal parameter set is sent to the fault diagnosis module.

[0010] Preferably, the process in which the data processing module sends the operating parameter set to the storage library for data recording is: Step 204: obtain the number of times the data sending value appears within the detection time FTm, and at the same time obtain the sum of the areas where the data diagnosis change line is above the preset data diagnosis change threshold line and the area enclosed by it within the detection time, and mark it as the threshold exceeding area FSm; use the formula: CTm=U1+(FTm*c1+FSm*c2)*U2 to obtain the storage time CTm of the operating data set collected within the detection time in the repository; wherein U1 is the basic data storage time, U2 is the preset storage time coefficient, and c1 and c2 are preset weight coefficients; the storage time of the abnormal parameter set in the repository can be directly set by yourself.

[0011] Preferably, the process of the fault diagnosis module predicting the fault type according to the operating parameter set corresponding to the data transmission value of the data processing module is as follows: Step 301: for each fault type, extract the operating parameter set when the fault occurs from the historical data, calculate the correlation between each operating parameter in the operating parameter set and this type of fault using the Pearson correlation coefficient, select the top three operating parameters with the highest correlation, and combine them into a fault detection parameter set, so as to obtain the fault detection parameter set corresponding to each fault type, wherein the historical data is the data extracted from the database, and the fault detection parameter set data corresponding to the new fault type can be manually input into the database; Step 302: receiving the operating parameter set and the abnormal parameter set corresponding to the data transmission value SFi from the data processing module, selecting three key operating parameters corresponding to each fault type from the operating parameter set and the abnormal parameter set corresponding to the data transmission value SFi, and forming multiple diagnostic parameter sets; for each diagnostic parameter set, calculating the correlation between each operating parameter therein and the corresponding operating parameter in the fault detection parameter set of the corresponding fault type, and adding the correlation values ​​of the three operating parameters in each diagnostic parameter set to obtain the total correlation value between the diagnostic parameter set and the corresponding fault type; Step 303: Compare the total correlation values ​​of all diagnostic parameter sets formed based on the operating parameter set corresponding to the data sending value SFi and the abnormal parameter set, and select the diagnostic parameter set with the highest total correlation value, mark it as the matching diagnostic parameter set, and update the corresponding fault detection parameter set correlation threshold according to the preset fault type. Compare the total correlation value corresponding to the matching diagnostic parameter set with the preset fault type corresponding fault detection parameter set correlation threshold. If the total correlation value corresponding to the matching diagnostic parameter set is larger, the fault type corresponding to the matching diagnostic parameter set is used as the predicted fault type corresponding to the data sending value SFi. If the total correlation value corresponding to the matching diagnostic parameter set is smaller, update step 301 and re-execute steps 301-303.

[0012] Preferably, the fault level prediction process is: Step 304: Obtain the diagnostic parameter set corresponding to the predicted fault type, calculate the difference between each operating parameter in the diagnostic parameter set and the corresponding standard parameter and sum them up to obtain the predicted fault type deviation value PCi, substitute the predicted fault type deviation value PCi and the data transmission value predicted severity coefficient βi into the preset formula: LVi=PCi*d1+βi*d2, and obtain the predicted fault type level evaluation value LVi, where d1 and d2 are preset weight coefficients; When there is an abnormal parameter set, the formula: LVi=PCi*d1+Gβi*d2 is used to obtain the predicted fault type level evaluation value LVi; Step 305: Set all fault types to correspond to three preset fault levels, namely, level one fault level, level two fault level, and level three fault level, and each fault level corresponds to a level evaluation value interval. The higher the level, the larger the minimum and maximum values ​​in the corresponding level evaluation value interval. Match the predicted fault type level evaluation value LVi with the corresponding preset fault type fault level evaluation value interval to obtain the predicted fault type fault level, and send the predicted fault type and the predicted fault type fault level to the personnel terminal module.

[0013] Preferably, the working process of the personnel terminal module is: After the personnel terminal module receives the predicted fault type and the predicted fault type fault level, it extracts the location information of the voltage transformer that is predicted to fail, and integrates the predicted fault type, predicted fault type fault level and location into a notification and sends it to the mobile phone of the inspection and maintenance personnel, notifying the inspection and maintenance personnel to perform inspection and maintenance on the voltage transformer that is predicted to fail.

[0014] Compared with the prior art, the present invention has the following beneficial effects: (1) A voltage transformer online monitoring method divides the detection time into different collection time periods according to the voltage transformer status evaluation value within the detection time period, and obtains the collection frequency of each collection time period by multiplying the average status evaluation value of the voltage transformer in each collection time period with a preset collection interval coefficient. In this way, the collection interval is set for different detection time periods, which ensures the adaptability and flexibility of the collection of the operating parameter set, reduces the frequent collection in the low power period, and thus improves the efficiency of data processing.

[0015] (2) A method for online monitoring of a voltage transformer, wherein a data processing module receives a voltage transformer operating parameter set from an acquisition module and calculates a data diagnostic value for evaluating the current state of the voltage transformer, and compares the calculated data diagnostic value with a preset threshold value. If the data diagnostic value exceeds the threshold value, the data at that moment will be marked as a data transmission value, indicating that there may be a risk of failure. The operating parameter set corresponding to the marked data transmission value is sent to a fault diagnosis module. In this way, the operating parameter sets at different moments can be screened first, and the operating parameter sets that may diagnose a fault are sent to the fault diagnosis module for analysis, which helps the fault diagnosis module to perform fault analysis efficiently and specifically. By analyzing the data transmission value and the slope value of the data diagnosis change line, the predicted severity coefficient is calculated, which helps the fault diagnosis module to evaluate the potential severity of the fault. According to the analysis results of the data diagnosis change line, the storage time of the data in the database is intelligently adjusted, which helps to optimize storage resources and reduce unnecessary data storage, thereby reducing storage costs.

[0016] (3) A voltage transformer online monitoring method analyzes historical fault data to identify the operating parameters most relevant to a specific fault type, providing a basis for subsequent real-time data analysis. The real-time collected operating parameters are matched with historical fault characteristics to determine which parameters are most likely to indicate the current fault state. By comparing the total correlation values ​​of different diagnostic parameter sets, the most likely fault type is predicted, which helps to quickly locate the problem. The fault level evaluation value is matched with a preset fault level evaluation value interval to determine the specific level of the fault, which helps the maintenance team decide what level of response measures to take. The prediction results and fault level information are sent to the personnel terminal module to ensure that maintenance personnel can receive fault notifications in a timely manner and take corresponding maintenance measures. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a flowchart of the present invention; Figure 2 A curve diagram showing the changes of the data diagnostic value with time in the present invention; DETAILED DESCRIPTION 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.

[0018] Embodiment 1 See also Figure 1-Figure 2 , the present invention provides a voltage transformer online monitoring method, including an acquisition module, a data processing module, a fault diagnosis module, a database, and a personnel terminal module; The data acquisition module divides the working time of the voltage transformer into low-frequency acquisition period, medium-frequency acquisition period, and high-frequency acquisition period according to the voltage and current values ​​of the primary and secondary sides of the voltage transformer, and sets the acquisition intervals respectively. The specific steps are as follows: Step 101: Divide the daily working time of the voltage transformer into a number of equal detection time lengths m, where m=1, ..., n; Step 102: for each detection duration, collect historical data of average voltage and average current values ​​of the primary and secondary sides of the voltage transformer within the detection duration in the last three working days, add the average voltage and average current values ​​of the primary and secondary sides within the same detection duration, and obtain their averages to obtain the primary side voltage average value YVi, current average value YIi, secondary side voltage average value RVi, current average value RIi within the detection period, i represents the number of the detection period, i=1, 2, ..., n; use the formula: VTi=YVi*a1+YIi*a2+RVi*a3+RIi*a4 to obtain the voltage transformer state evaluation value VTi within each detection duration, where a1, a2, a3, a4 are preset weight coefficients; Step 103: presetting three collection time periods, which are divided into a low-frequency collection time period, a medium-frequency collection time period, and a high-frequency collection time period, each collection time period corresponds to a state evaluation value interval, matching the state evaluation value of the voltage transformer in each detection time length with the state evaluation value interval corresponding to the three collection time periods, and obtaining the collection time period corresponding to each detection time length; Step 104: Each of the three acquisition periods has a preset update state evaluation threshold. When the state evaluation value of the voltage transformer within the detection time exceeds the preset update state evaluation threshold of the corresponding acquisition period, the detection time is marked as an update period, and the update matching process is triggered, specifically: Step 1041: collecting the state evaluation value of the voltage transformer in the update period, and obtaining the matching evaluation value of the voltage transformer in the update period by multiplying the state evaluation value of the voltage transformer in the update period by the environmental impact coefficient in the update period; Step 1042: rematching the matching evaluation value of the voltage transformer in the update period with the state evaluation value intervals corresponding to the three collection periods to obtain the collection period rematched with the update period; Step 1043: Divide the update period into the rematched collection period; Step 105: obtaining the average state evaluation value of the voltage transformer in each collection period in the last three working days, presetting the collection interval coefficient, and obtaining the collection frequency of each collection period by multiplying the average state evaluation value of the voltage transformer in each collection period by the preset collection interval coefficient; Step 106: According to the collection frequency of each collection period, the voltage transformer operating parameter set is collected and sent to the data processing module, and the voltage transformer operating parameter set includes the primary side voltage value, the primary side current value, the secondary side voltage value, the secondary side current value, the insulation resistance value, the winding temperature value, the core temperature value, and the ambient temperature value; It should be noted that the voltage transformer status evaluation value within the detection time is obtained by analyzing the average voltage values ​​of the primary side and the secondary side within the same detection time; Primary voltage: refers to the voltage connected to the main line of the power system. It is the actual operating voltage of the power grid and is usually a higher voltage level. Secondary voltage: It is the voltage output by the voltage transformer after voltage transformation. It is a lower voltage used by equipment for measurement, protection and control. According to the voltage transformer status evaluation value within the detection time, the detection time is divided into different collection time periods. The collection frequency of each collection time period is obtained by multiplying the average status evaluation value of the voltage transformer in each collection time period with the preset collection interval coefficient. In this way, the collection interval is set for different detection times, which ensures the adaptability and flexibility of the operation parameter set collection, reduces the frequent collection in low power periods, and thus improves the efficiency of data processing.

[0019] The data processing module is used to receive the voltage transformer operating parameter set collected by the collection module within the collection frequency and process it. The specific process is as follows: Step 201: the collection module matches each operating parameter in the operating parameter set with its corresponding preset normal value interval according to the collection interval for each operating parameter value in the operating parameter set and each operating parameter in the preset operating parameter set. If the operating parameter set exists and a certain operating parameter is not within its corresponding preset normal value range, the operating parameter is marked as a fault parameter, and the operating parameter is marked as an abnormal parameter set, and the abnormal parameter constant set is sent to the fault diagnosis module; If all the operating parameters in the operating parameter set are within the corresponding preset normal value range, and each preset operating parameter is set with a corresponding standard value, the deviation value Xij between all the operating parameters in the operating parameter set and their corresponding standard values ​​is calculated, where j represents the number of the operating parameter, j=1, 2, ..., n; i represents the number of the collection time, i=1, 2, ..., n; According to the importance of the operating parameters to fault diagnosis, a weight coefficient Wij is assigned to each operating parameter. The data diagnosis value YCi corresponding to the acquisition time i is obtained using the formula: YCi=∑j=1_n(Xij*Wij); Xij and Wij represent the deviation value of the operating parameter j at the acquisition time i and the corresponding weight coefficient respectively; YCi=∑j=1_n(Xij*Wij), which means: sum the product of the deviation value of all operating parameters and the corresponding weight coefficient at the i-th acquisition time; Step 202: Plot the relationship between the data diagnostic value YCi and the change between the two into a curve graph, such as Figure 2, connect the data diagnosis values ​​YCi at each acquisition moment to form a data diagnosis change line; draw a preset data diagnosis change threshold line in the curve chart, and mark the data diagnosis value YCi at any acquisition moment that is greater than the preset data diagnosis change threshold at the corresponding moment as the data transmission value SFi; calculate the difference between the data transmission value SFi and the preset data diagnosis change threshold at the corresponding moment, and mark it as the alarm shutdown value BTi; preset the alarm shutdown threshold, when the alarm shutdown value BTi is greater than the preset alarm shutdown threshold, generate an alarm shutdown command and send it to the personnel terminal module; after receiving the alarm shutdown command, the personnel terminal module will notify the relevant monitoring personnel to immediately cut off the power line operation of the voltage transformer to stop it from working; Step 203: Send the operating parameter set corresponding to the data transmission value SFi to the fault diagnosis module, and at the same time obtain the slope value Ki of the data diagnosis change line between the acquisition time corresponding to the data transmission value SFi and the previous acquisition time, and use the formula: βi=SFi*b1+Ki*b2 to obtain the predicted severity coefficient βi of the operating parameter corresponding to the data transmission value; send the predicted severity coefficient βi to the fault diagnosis module; wherein b1 and b2 are preset weight coefficients, and b1 is greater than b2, so that β is always greater than zero. It can be seen from the formula that when Ki is greater than zero, the larger Ki is, the larger β is, and when Ki is less than zero, the larger the absolute value of Ki is, the smaller β is; For the abnormal parameter set, set the preset normal value interval of the fault parameter in the abnormal parameter set, obtain the upper limit and lower limit of the preset normal value interval, calculate the difference between the numerical value of the fault parameter in the abnormal parameter set and the upper limit and lower limit of the preset normal value interval, and obtain the upper limit deviation difference and the lower limit deviation difference; compare the upper limit deviation difference with the lower limit deviation difference, select the smallest value between the two as the fault deviation value Gij, and use the formula: Gβi=∑j=1_m(Gij*d1+m*d2)to obtain the abnormal The predicted severity coefficient Gβi of the constant parameter set; wherein d1 and d2 are preset weight coefficients, m represents the total number of fault parameters in the abnormal parameter set, and the predicted severity coefficient Gβi of the abnormal parameter set is sent to the fault diagnosis module; wherein Gβi=∑j=1_m(Gij*d1+m*d2) means: at the i-th acquisition time, the deviation value Gij of all fault parameters multiplied by the assigned preset weight coefficient d1 plus the total number of fault parameters in the abnormal parameter set m multiplied by the assigned preset weight coefficient d2 are summed; Step 204: Send the operating parameter set collected during the detection time to the storage library for data recording, so that the staff can retrieve the work data; obtain the number of data transmission value occurrences FTm during the detection time, and at the same time obtain the sum of the areas above the preset data diagnostic change threshold line and the area enclosed by the data diagnosis change line during the detection time, and mark it as the threshold exceeding area FSm; use the formula: CTm=U1+(FTm*c1+FSm*c2)*U2 to obtain the storage time CTm of the operating data set collected during the detection time in the storage library; wherein U1 is the basic data storage time, U2 is the preset storage time coefficient, c1 and c2 are the preset weight coefficients; for the storage time of the abnormal parameter set in the storage library, the storage time can be directly set by yourself.

[0020] It should be noted that the data processing module receives the voltage transformer operating parameter set from the acquisition module, analyzes the operating parameter value at each moment, calculates the data diagnosis value, which is used to evaluate the current state of the voltage transformer, and compares the calculated data diagnosis value with the preset threshold value. If the data diagnosis value exceeds the threshold value, the data at this moment will be marked as a data transmission value, indicating that there may be a risk of failure. The operating parameter set corresponding to the marked data transmission value is sent to the fault diagnosis module. In this way, the operating parameter sets at different moments can be screened first, and the operating parameter sets that may diagnose faults are sent to the fault diagnosis module for analysis, which helps the fault diagnosis module to perform fault analysis efficiently and specifically. If the difference between the data transmission value and the preset threshold exceeds the alarm shutdown threshold, the system will immediately trigger an alarm and notify the monitoring personnel to take emergency measures, such as cutting off the power supply, to prevent the fault from worsening. By analyzing the slope value of the data transmission value and the data diagnosis change line, the predicted severity coefficient is calculated, which helps the fault diagnosis module to evaluate the potential severity of the fault. According to the analysis results of the data diagnosis change line, the storage time of the data in the database is intelligently adjusted, which helps to optimize storage resources and reduce unnecessary data storage, thereby reducing storage costs.

[0021] The fault diagnosis module predicts the fault type and fault level according to the operating parameter set and abnormal parameter set corresponding to the data transmission value of the data processing module. The specific process is as follows: Step 301: for each fault type, extract the operating parameter set when the fault occurs from the historical data, calculate the correlation between each operating parameter in the operating parameter set and this type of fault using the Pearson correlation coefficient, select the top three operating parameters with the highest correlation, and combine them into a fault detection parameter set, so as to obtain the fault detection parameter set corresponding to each fault type, wherein the historical data is the data extracted from the database, and the fault detection parameter set data corresponding to the new fault type can be manually input into the database; Step 302: receiving the operating parameter set and the abnormal parameter set corresponding to the data transmission value SFi from the data processing module, selecting three key operating parameters corresponding to each fault type from the operating parameter set and the abnormal parameter set corresponding to the data transmission value SFi, and forming multiple diagnostic parameter sets; for each diagnostic parameter set, calculating the correlation between each operating parameter therein and the corresponding operating parameter in the fault detection parameter set of the corresponding fault type, and adding the correlation values ​​of the three operating parameters in each diagnostic parameter set to obtain the total correlation value between the diagnostic parameter set and the corresponding fault type; Step 303: compare the total correlation values ​​of all diagnostic parameter sets formed based on the operating parameter set corresponding to the data transmission value SFi and the abnormal parameter set, select the diagnostic parameter set with the highest total correlation value, mark it as the matching diagnostic parameter set, preset the fault type corresponding to the fault detection parameter set correlation threshold, compare the total correlation value corresponding to the matching diagnostic parameter set with the preset fault type corresponding to the fault detection parameter set correlation threshold, if the total correlation value corresponding to the matching diagnostic parameter set is larger, then use the fault type corresponding to the matching diagnostic parameter set as the predicted fault type corresponding to the data transmission value SFi, if the total correlation value corresponding to the matching diagnostic parameter set is smaller, update step 301, and re-execute steps 301-303; Step 304: Obtain the diagnostic parameter set corresponding to the predicted fault type, calculate the difference between each operating parameter in the diagnostic parameter set and the corresponding standard parameter and sum them up to obtain the predicted fault type deviation value PCi, substitute the predicted fault type deviation value PCi and the data transmission value predicted severity coefficient βi into the preset formula: LVi=PCi*d1+βi*d2, and obtain the predicted fault type level evaluation value LVi, where d1 and d2 are preset weight coefficients; When there is an abnormal parameter set, the formula: LVi=PCi*d1+Gβi*d2 is used to obtain the predicted fault type level evaluation value LVi; Step 305: Set all fault types to correspond to three preset fault levels, namely, level one fault level, level two fault level, and level three fault level, and each fault level corresponds to a level evaluation value interval. The higher the level, the larger the minimum and maximum values ​​in the corresponding level evaluation value interval. Match the predicted fault type level evaluation value LVi with the corresponding preset fault type fault level evaluation value interval to obtain the predicted fault type fault level, and send the predicted fault type and the predicted fault type fault level to the personnel terminal module.

[0022] It should be noted that by analyzing historical fault data, the operating parameters most relevant to specific fault types are identified, providing a basis for subsequent real-time data analysis. The real-time collected operating parameters are matched with historical fault characteristics to determine which parameters are most likely to indicate the current fault state. By comparing the total correlation values ​​of different diagnostic parameter sets, the most likely fault type is predicted, which helps to quickly locate the problem. The fault level assessment value is matched with the preset fault level assessment value range to determine the specific level of the fault, which helps the maintenance team decide what level of response measures to take. The prediction results and fault level information are sent to the personnel terminal module to ensure that maintenance personnel can receive fault notifications in a timely manner and take corresponding maintenance measures.

[0023] After the personnel terminal module receives the predicted fault type and the predicted fault type fault level, it extracts the location information of the voltage transformer that is predicted to fail, and integrates the predicted fault type, predicted fault type fault level and location into a notification and sends it to the mobile phone of the inspection and maintenance personnel, notifying the inspection and maintenance personnel to perform inspection and maintenance on the voltage transformer that is predicted to fail.

[0024] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A voltage transformer online monitoring method, comprising an acquisition module, a data processing module, a fault diagnosis module, a database, and a personnel terminal module, characterized in that: Data acquisition module: used to divide the working time of the voltage transformer into a low-frequency acquisition period, a medium-frequency acquisition period and a high-frequency acquisition period according to the voltage and current values ​​of the primary and secondary sides of the voltage transformer, and set corresponding acquisition intervals to collect the voltage transformer operation parameter set; Data processing module: receiving and analyzing the voltage transformer operating parameter set collected by the data acquisition module, obtaining the data processing result and sending it to the fault diagnosis module; wherein the data processing result includes the abnormal parameter set, the data diagnosis value, the data transmission value, the predicted severity coefficient of the operating parameter corresponding to the data transmission value, and the predicted severity coefficient of the abnormal parameter set; at the same time, the operating parameter set is sent to the storage library for data recording; Fault diagnosis module: used to analyze the data sent by the data processing module and predict the fault type and level; Database: used to store historical data and real-time data of voltage transformers; Personnel terminal module: used to receive the prediction results of the fault diagnosis module and notify the inspection personnel.

2. A voltage transformer online monitoring method according to claim 1, characterized in that: The specific process of the acquisition module dividing the working time of the voltage transformer into a low-frequency acquisition period, a medium-frequency acquisition period and a high-frequency acquisition period according to the voltage and current values ​​of the primary and secondary sides of the voltage transformer is as follows: Step 101: Divide the daily working time of the voltage transformer into a number of equal detection time lengths m, where m=1, ..., n; Step 102: for each detection duration, collect historical data of average voltage and average current values ​​of the primary and secondary sides of the voltage transformer within the detection duration in the last three working days, add the average voltage and average current values ​​of the primary and secondary sides within the same detection duration, and obtain their averages to obtain the primary side voltage average value YVi, current average value YIi, secondary side voltage average value RVi, current average value RIi within the detection period, i represents the number of the detection period, i=1, 2, ..., n; use the formula: VTi=YVi*a1+YIi*a2+RVi*a3+RIi*a4 to obtain the voltage transformer state evaluation value VTi within each detection duration, where a1, a2, a3, a4 are preset weight coefficients; Step 103: presetting three collection time periods, which are divided into a low-frequency collection time period, a medium-frequency collection time period, and a high-frequency collection time period, each collection time period corresponds to a state evaluation value interval, matching the state evaluation value of the voltage transformer in each detection time length with the state evaluation value interval corresponding to the three collection time periods, and obtaining the collection time period corresponding to each detection time length; Step 104: Each of the three acquisition periods has a preset update state evaluation threshold. When the state evaluation value of the voltage transformer within the detection time exceeds the preset update state evaluation threshold of the corresponding acquisition period, the detection time is marked as an update period, and the update matching process is triggered, specifically: Step 1041: collecting the state evaluation value of the voltage transformer in the update period, and obtaining the matching evaluation value of the voltage transformer in the update period by multiplying the state evaluation value of the voltage transformer in the update period by the environmental impact coefficient in the update period; Step 1042: rematching the matching evaluation value of the voltage transformer in the update period with the state evaluation value intervals corresponding to the three collection periods to obtain the collection period rematched with the update period; Step 1043: Divide the update period into the re-matching collection period.

3. A voltage transformer online monitoring method according to claim 2, characterized in that: The process of setting the collection interval for the collection period and collecting the operating parameter set is as follows: Step 105: obtaining the average state evaluation value of the voltage transformer in each collection period in the last three working days, presetting the collection interval coefficient, and obtaining the collection frequency of each collection period by multiplying the average state evaluation value of the voltage transformer in each collection period by the preset collection interval coefficient; Step 106: According to the collection frequency of each collection period, the voltage transformer operating parameter set is collected and sent to the data processing module. The voltage transformer operating parameter set includes primary side voltage value, primary side current value, secondary side voltage value, secondary side current value, insulation resistance value, winding temperature value, core temperature value, and ambient temperature value.

4. A voltage transformer online monitoring method according to claim 3, characterized in that: The process of the data processing module analyzing the abnormal parameter set is as follows: Step 201: the collection module matches each operating parameter in the operating parameter set with its corresponding preset normal value interval according to the collection interval for each operating parameter value in the operating parameter set and each operating parameter in the preset operating parameter set. If an operating parameter set exists and a certain operating parameter is not within its corresponding preset normal value range, the operating parameter is marked as a fault parameter, and the operating parameter is marked as an abnormal parameter set, and the abnormal parameter constant set is sent to the fault diagnosis module.

5. A voltage transformer online monitoring method according to claim 4, characterized in that: The process of the data processing module analyzing the data diagnosis value, the data transmission value and the predicted severity coefficient of the operating parameter corresponding to the data transmission value is as follows: If all the operating parameters in the operating parameter set are within the corresponding preset normal value range, and each preset operating parameter is set with a corresponding standard value, the deviation value Xij between all the operating parameters in the operating parameter set and their corresponding standard values ​​is calculated, where j represents the number of the operating parameter, j=1, 2, ..., n; i represents the number of the collection time, i=1, 2, ..., n; According to the importance of the operating parameters to fault diagnosis, a weight coefficient Wij is assigned to each operating parameter. The data diagnosis value YCi corresponding to the acquisition time i is obtained using the formula: YCi=∑j=1_n(Xij*Wij); Xij and Wij represent the deviation value of the operating parameter j at the acquisition time i and the corresponding weight coefficient respectively; Step 202: Draw a curve graph of the relationship between the data diagnostic value YCi and the preset data diagnostic change threshold value, connect the data diagnostic value YCi at each acquisition time, and form a data diagnostic change line; draw a preset data diagnostic change threshold line in the curve graph, and mark the data diagnostic value whose data diagnostic value YCi at any acquisition time is greater than the preset data diagnostic change threshold value at the corresponding time as the data transmission value SFi; calculate the difference between the data transmission value SFi and the preset data diagnostic change threshold value at the corresponding time, and mark it as the alarm stop value BTi; preset the alarm stop threshold, when the alarm stop value BTi is greater than the preset alarm stop threshold, generate an alarm stop instruction and send it to the personnel terminal module; After receiving the alarm shutdown command, the personnel terminal module will notify the relevant monitoring personnel to immediately cut off the power line of the voltage transformer to stop it from working; Step 203: Send the operating parameter set corresponding to the data transmission value SFi to the fault diagnosis module, and at the same time obtain the slope value Ki of the data diagnosis change line between the acquisition time corresponding to the data transmission value SFi and the previous acquisition time, and use the formula: βi=SFi*b1+Ki*b2 to obtain the predicted severity coefficient βi of the operating parameter corresponding to the data transmission value; send the predicted severity coefficient βi to the fault diagnosis module; wherein b1 and b2 are preset weight coefficients, and b1 is greater than b2, so that β is always greater than zero. It can be seen from the formula that when Ki is greater than zero, the larger Ki is, the larger β is, and when Ki is less than zero, the larger the absolute value of Ki is, the smaller β is; For the abnormal parameter set, set a preset normal value interval of the fault parameter in the abnormal parameter set, obtain the upper limit value and the lower limit value of the preset normal value interval, and perform difference calculations on the numerical value of the fault parameter in the abnormal parameter set and the upper limit value and the lower limit value of the preset normal value interval, respectively, to obtain the upper limit deviation difference value and the lower limit deviation difference value; The upper limit deviation difference is compared with the lower limit deviation difference, and the smallest value between the two is selected as the fault deviation value Gij. The predicted severity coefficient Gβi of the abnormal parameter set is obtained using the formula: Gβi=∑j=1_m(Gij*d1+m*d2); where d1 and d2 are preset weight coefficients, and m represents the total number of fault parameters in the abnormal parameter set. The predicted severity coefficient Gβi of the abnormal parameter set is sent to the fault diagnosis module.

6. A voltage transformer online monitoring method according to claim 5, characterized in that: The process of the data processing module sending the operating parameter set to the storage library for data recording is as follows: Step 204: obtain the number of times the data sending value appears within the detection time FTm, and at the same time obtain the sum of the areas where the data diagnosis change line is above the preset data diagnosis change threshold line and the area enclosed by it within the detection time, and mark it as the threshold exceeding area FSm; use the formula: CTm=U1+(FTm*c1+FSm*c2)*U2 to obtain the storage time CTm of the operating data set collected within the detection time in the repository; wherein U1 is the basic data storage time, U2 is the preset storage time coefficient, and c1 and c2 are preset weight coefficients; the storage time of the abnormal parameter set in the repository can be directly set by yourself.

7. A voltage transformer online monitoring method according to claim 6, characterized in that: The fault diagnosis module predicts the fault type according to the operating parameter set corresponding to the data transmission value of the data processing module: Step 301: for each fault type, extract the operating parameter set when the fault occurs from the historical data, calculate the correlation between each operating parameter in the operating parameter set and this type of fault using the Pearson correlation coefficient, select the top three operating parameters with the highest correlation, and combine them into a fault detection parameter set, so as to obtain the fault detection parameter set corresponding to each fault type, wherein the historical data is the data extracted from the database, and the fault detection parameter set data corresponding to the new fault type can be manually input into the database; Step 302: receiving the operating parameter set and the abnormal parameter set corresponding to the data transmission value SFi from the data processing module, selecting three key operating parameters corresponding to each fault type from the operating parameter set and the abnormal parameter set corresponding to the data transmission value SFi, and forming multiple diagnostic parameter sets; for each diagnostic parameter set, calculating the correlation between each operating parameter therein and the corresponding operating parameter in the fault detection parameter set of the corresponding fault type, and adding the correlation values ​​of the three operating parameters in each diagnostic parameter set to obtain the total correlation value between the diagnostic parameter set and the corresponding fault type; Step 303: Compare the total correlation values ​​of all diagnostic parameter sets formed based on the operating parameter set corresponding to the data sending value SFi and the abnormal parameter set, and select the diagnostic parameter set with the highest total correlation value, mark it as the matching diagnostic parameter set, and update the corresponding fault detection parameter set correlation threshold according to the preset fault type. Compare the total correlation value corresponding to the matching diagnostic parameter set with the preset fault type corresponding fault detection parameter set correlation threshold. If the total correlation value corresponding to the matching diagnostic parameter set is larger, the fault type corresponding to the matching diagnostic parameter set is used as the predicted fault type corresponding to the data sending value SFi. If the total correlation value corresponding to the matching diagnostic parameter set is smaller, update step 301 and re-execute steps 301-303.

8. A voltage transformer online monitoring method according to claim 7, characterized in that: The fault level prediction process is: Step 304: Obtain the diagnostic parameter set corresponding to the predicted fault type, calculate the difference between each operating parameter in the diagnostic parameter set and the corresponding standard parameter and sum them up to obtain the predicted fault type deviation value PCi, substitute the predicted fault type deviation value PCi and the data transmission value predicted severity coefficient βi into the preset formula: LVi=PCi*d1+βi*d2, and obtain the predicted fault type level evaluation value LVi, where d1 and d2 are preset weight coefficients; When there is an abnormal parameter set, the formula: LVi=PCi*d1+Gβi*d2 is used to obtain the predicted fault type level evaluation value LVi; Step 305: Set all fault types to correspond to three preset fault levels, namely, level one fault level, level two fault level, and level three fault level, and each fault level corresponds to a level evaluation value interval. The higher the level, the larger the minimum and maximum values ​​in the corresponding level evaluation value interval. Match the predicted fault type level evaluation value LVi with the corresponding preset fault type fault level evaluation value interval to obtain the predicted fault type fault level, and send the predicted fault type and the predicted fault type fault level to the personnel terminal module.

9. A voltage transformer online monitoring method according to claim 8, characterized in that: The working process of the personnel terminal module is: After the personnel terminal module receives the predicted fault type and the predicted fault type fault level, it extracts the location information of the voltage transformer that is predicted to fail, and integrates the predicted fault type, predicted fault type fault level and location into a notification and sends it to the mobile phone of the inspection and maintenance personnel, notifying the inspection and maintenance personnel to perform inspection and maintenance on the voltage transformer that is predicted to fail.

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