A transformer vacuum oil filtration operation monitoring method and system

Abstract

The present application relates to the field of information monitoring technology, and discloses a transformer vacuum oil filtration operation monitoring method and system, wherein the method includes: obtaining the working data of the transformer in the current cycle during the vacuum oil filtration operation; obtaining the exchange power of the transformer according to the actual peak value of the current load of the transformer in the current cycle and the target critical voltage; evaluating the operating state of the transformer according to the power control error, the target current load curve, the target voltage load curve and the power load curve to obtain the operating load score; determining the working state score of the transformer in the current cycle during the vacuum oil filtration operation according to the operating load score, the vacuum score corresponding to the vacuum range and the temperature score corresponding to the temperature data, so as to monitor the vacuum oil filtration operation of the transformer. Its beneficial effect is that it realizes the comprehensive monitoring of the vacuum oil filtration operation and ensures that the transformer can work normally during the vacuum oil filtration operation.

Description

Technical Field

[0001] The present application relates to the field of information monitoring technology, and in particular to a transformer vacuum oil filtration operation monitoring method and system. Background Art

[0002] With the continuous development of smart grid technology, the requirements for the stability and safety of power system operation are getting higher and higher. As an important device for changing voltage, transformers are mostly used in substations, power plants, operating lines and other places, and the areas they cover are constantly expanding. Among them, transformer oil can play the role of insulation, heat dissipation, arc extinguishing, etc. The existing transformers are improved with transformer oil to achieve efficient use of equipment. As the temperature of the equipment rises during operation, the transformer oil comes into contact with oxygen under the action of high temperature and electric field to generate insoluble colloid, which reduces the insulation performance of the transformer. Therefore, it is necessary to filter the transformer oil regularly. The relevant technology only measures some parameters of the vacuum oil filtration operation and cannot comprehensively monitor the vacuum oil filtration operation. Summary of the invention

[0003] The present application provides a transformer vacuum oil filtration operation monitoring method and system, which solves the technical problem of insufficient comprehensive monitoring of the transformer during vacuum oil filtration operation, achieves the technical effect of realizing comprehensive monitoring of the vacuum oil filtration operation and ensuring that the transformer can work normally during vacuum oil filtration operation.

[0004] In order to achieve the above objectives, the main technical solutions adopted in this application include:

[0005] In a first aspect, an embodiment of the present application provides a transformer vacuum oil filtration operation monitoring method, the method comprising:

[0006] Obtaining the working data of the transformer in the vacuum oil filtering operation in the current cycle; wherein the working data includes a target current load curve, a target voltage load curve, a power load curve, a vacuum range and temperature data;

[0007] The exchange power of the transformer is obtained according to the actual peak value of the current load of the transformer and the target critical voltage in the current cycle; wherein there is a power control error between the exchange power and the expected power value of the transformer, the actual peak value of the current load is obtained according to the target current load curve, and the target critical voltage is obtained by calculating the critical voltage according to the target voltage load curve;

[0008] Evaluate the operating state of the transformer according to the power control error, the target current load curve, the target voltage load curve and the power load curve to obtain an operating load score;

[0009] According to the operating load score, the vacuum score corresponding to the vacuum range and the temperature score corresponding to the temperature data, the working state score of the transformer during the vacuum oil filtration operation in the current cycle is determined to monitor the vacuum oil filtration operation of the transformer.

[0010] The transformer vacuum oil filtration operation monitoring method proposed in the embodiment of the present application monitors the vacuum degree and temperature during the transformer vacuum oil filtration operation, and monitors the current load, voltage load and power load of the transformer to accurately grasp the operating load of the transformer; at the same time, the operating load of the transformer is scored, and whether the transformer is working normally is determined according to the operating load score, and the operating load score is used as the basis for adjusting the working state of the transformer, thereby improving the stability and safety of the transformer during the vacuum oil filtration operation.

[0011] Optionally, the target current load curve and the target voltage load curve are obtained in the following manner:

[0012] According to the difference between the actual peak value of the current load and the predicted peak value of the target current load of the transformer in the current cycle, the initial current load curve is adjusted to obtain the target current load curve; wherein the actual peak value of the current load is the peak value of the initial current load curve in the time period corresponding to the predicted peak value of the target current load;

[0013] According to the corresponding relationship between the voltage load weak node of the transformer and the maximum value of the target current load prediction peak value, the initial voltage load curve is adjusted to obtain the target voltage load curve, and the target critical voltage is obtained according to the target voltage load curve.

[0014] The transformer vacuum oil filtration operation monitoring method proposed in the embodiment of the present application adjusts the transformer according to the initial current load curve and the initial voltage load curve respectively, thereby obtaining the target current load curve and the target voltage load curve respectively, so that the transformer can work according to the target current load curve and the target voltage load curve, thereby reducing the failure rate of the transformer and improving the stability of the transformer.

[0015] Optionally, before adjusting the initial current load curve according to the target current load prediction peak value of the transformer in the current cycle to obtain the target current load curve, the method further includes:

[0016] Perform prediction based on the historical current load data of the transformer to obtain the predicted initial current load peak value of the transformer in the current cycle;

[0017] Comparing the actual peak value of the current load of the transformer in the previous cycle with the predicted peak value of the initial current load in the same time period to obtain a predicted peak error;

[0018] The initial current load prediction peak value is adjusted according to the prediction peak value error to obtain the target current load prediction peak value.

[0019] Optionally, the adjusting the initial current load prediction peak value according to the prediction peak value error to obtain the target current load prediction peak value includes:

[0020] If the predicted peak value error exceeds a preset range, the initial current load predicted peak value is adjusted based on a first current adjustment coefficient to obtain the target current load predicted peak value; wherein the first current adjustment coefficient is obtained based on the variance of the actual peak value of the current load of the transformer in the previous cycle;

[0021] If the predicted peak error does not exceed a preset range, the initial current load predicted peak is adjusted based on a second current adjustment coefficient to obtain the target current load predicted peak; wherein, the second current adjustment coefficient is obtained based on the average value of the current peak error in different time periods, and the current peak error is obtained based on the difference between the predicted peak error and the maximum value of the actual peak value of the current load in the previous cycle.

[0022] Optionally, before adjusting the initial voltage load curve according to the correspondence between the voltage load weak node of the transformer and the maximum value of the target current load prediction peak value to obtain the target voltage load curve, the method further includes:

[0023] According to the operating parameters of each node in the transformer and the network admittance matrix between different nodes, the voltage load weak node of the transformer is determined based on continuous power flow calculation; wherein the operating parameters include voltage and complex power;

[0024] The initial critical voltage of the transformer is obtained according to the operating parameters of each of the voltage load weak nodes.

[0025] Optionally, adjusting the initial voltage load curve according to the correspondence between the voltage load weak node of the transformer and the maximum value of the target current load prediction peak value to obtain the target voltage load curve, and obtaining the target critical voltage according to the target voltage load curve includes:

[0026] If the target current load curve corresponding to any voltage load weak node has the maximum value of the target current load prediction peak value, the initial voltage load curve is adjusted based on the first voltage adjustment coefficient to obtain the target voltage load curve; wherein the first voltage adjustment coefficient is obtained according to the voltage magnitude of any voltage load weak node;

[0027] If the maximum value of the target current load prediction peak does not exist in the target current load curves corresponding to all the voltage load weak nodes, the initial voltage load curve is adjusted based on the second voltage adjustment coefficient to obtain the target voltage load curve; wherein the second voltage adjustment coefficient is obtained according to the current working voltage of the transformer, the initial critical voltage and the rated voltage;

[0028] The critical voltage is calculated according to the target voltage load curve to obtain the target critical voltage.

[0029] Optionally, the vacuum score is determined according to the following method:

[0030] The vacuum degree score is determined according to the symmetric difference between the vacuum degree range and the optimal vacuum degree range of the transformer; wherein the vacuum degree score is inversely proportional to the size of the symmetric difference.

[0031] Optionally, the temperature score is determined according to the following method:

[0032] A temperature correlation coefficient is obtained by performing a weighted calculation according to the heating power, thermal insulation rate and dielectric loss of the transformer;

[0033] The temperature score is determined according to the temperature correlation coefficient; wherein the temperature score is inversely proportional to the temperature correlation coefficient.

[0034] Optionally, the method further comprises:

[0035] Determining the working state of the transformer according to the working state score of the transformer during vacuum oil filtering operation in the current cycle;

[0036] If there is an abnormality in the working state of the transformer, corresponding measures are taken according to the abnormality.

[0037] In a second aspect, an embodiment of the present application provides a transformer vacuum oil filtration operation monitoring system, the system comprising:

[0038] A working data acquisition module is used to obtain working data of the transformer in the vacuum oil filtration operation in the current cycle; wherein the working data includes a target current load curve, a target voltage load curve, a power load curve, a vacuum range and temperature data;

[0039] an exchange power calculation module, configured to obtain the exchange power of the transformer according to the actual peak value of the current load of the transformer and the target critical voltage in the current cycle; wherein there is a power control error between the exchange power and the expected power value of the transformer, the actual peak value of the current load is obtained according to the target current load curve, and the target critical voltage is obtained by calculating the critical voltage according to the target voltage load curve;

[0040] An operation load scoring module, used to evaluate the operation state of the transformer according to the power control error, the target current load curve, the target voltage load curve and the power load curve to obtain an operation load score;

[0041] The working status scoring module is used to determine the working status score of the transformer during the vacuum oil filtration operation in the current cycle according to the operating load score, the vacuum score corresponding to the vacuum range and the temperature score corresponding to the temperature data, so as to monitor the vacuum oil filtration operation of the transformer.

[0042] In a third aspect, an embodiment of the present application provides a computer device, comprising: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the transformer vacuum oil filtration operation monitoring method described in any one of the above embodiments by executing the computer instructions.

[0043] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, on which computer instructions are stored, and the computer instructions are used to enable a computer to execute the transformer vacuum oil filtration operation monitoring method described in any one of the above embodiments.

[0044] In a fifth aspect, an embodiment of the present application provides a computer program product, including computer instructions, wherein the computer instructions are used to enable a computer to execute the transformer vacuum oil filtration operation monitoring method described in any one of the above embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the specific implementation methods of the present application or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0046] Figure 1 A step diagram of a transformer vacuum oil filtration operation monitoring method provided in an embodiment of the present application;

[0047] Figure 2A step diagram of a method for obtaining a target current load curve and a target voltage load curve in an embodiment of the present application;

[0048] Figure 3 A flow chart of a transformer vacuum oil filtration operation monitoring method provided in an embodiment of the present application;

[0049] Figure 4 A module diagram of a transformer vacuum oil filtration operation monitoring system provided in an embodiment of the present application;

[0050] Figure 5 A schematic diagram of the structure of a computer device provided in an embodiment of the present application.

[0051] Among them, the figure numbers of the drawings in the specification are as follows: 100. working data acquisition module, 200. switching power calculation module, 300. operation load scoring module, 400. working status scoring module. DETAILED DESCRIPTION

[0052] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present application.

[0053] With the continuous development of smart grid technology, the requirements for the stability and safety of power system operation are getting higher and higher. As an important device for changing voltage, transformers are mostly used in substations, power plants, operating lines and other places, and the areas they cover are constantly expanding. Transformer oil is usually added to the transformer, which can play the role of insulation, heat dissipation, arc extinguishing, etc. The existing transformers are improved with transformer oil to achieve efficient use of equipment. As the temperature of the equipment rises during operation, the transformer oil comes into contact with oxygen under the action of high temperature and electric field to generate insoluble colloid, which will reduce the insulation performance of the transformer. Therefore, the transformer needs to perform vacuum oil filtration operations regularly to remove insoluble colloids and gases in the transformer oil, restore the insulation performance of the transformer oil, so that the transformer can work normally and improve the safety and stability of the transformer.

[0054] In the related art, the data monitored during the vacuum oil filtration operation of the transformer include vacuum degree and temperature. Among them, the appropriate vacuum degree can prevent oxygen or moisture from entering the transformer, reduce the impact on the transformer, and thus improve the efficiency of the vacuum oil filtration operation; the appropriate temperature can reduce the risk of solidification of the transformer oil, and at the same time help accelerate the volatilization of moisture and gas in the transformer oil, thereby improving the efficiency of the vacuum oil filtration operation.

[0055] In actual scenarios, oil filters and transformers are usually integrated into one, and the transformer integrated machine integrates the oil filtering function into the transformer body, usually including automatic oil treatment, filtration and heating systems. This integrated design reduces maintenance work, improves the overall reliability of the system, and can process oil quality in real time during operation to maintain the best performance of the transformer. When this integrated machine is performing vacuum oil filtering, the transformer is still working. Therefore, whether the transformer can work normally and the stability of the transformer's operation have a great impact on the vacuum oil filtering efficiency of the transformer integrated machine. Only through the monitoring of vacuum degree and temperature in related technologies, it is impossible to fully grasp the working conditions of the transformer integrated machine during vacuum oil filtering.

[0056] Based on the above problems, the present application provides a transformer vacuum oil filtering operation monitoring method and system, which can be used to integrate the oil filter and the transformer into an integrated machine, and monitor the working state of the transformer in the integrated machine during the vacuum oil filtering operation. Among them, the method includes: obtaining the working data of the transformer in the vacuum oil filtering operation in the current cycle; obtaining the exchange power of the transformer according to the actual peak value of the current load and the target critical voltage of the transformer in the current cycle; evaluating the operating state of the transformer according to the power control error, the target current load curve, the target voltage load curve and the power load curve to obtain the operating load score; determining the working state score of the transformer in the vacuum oil filtering operation in the current cycle according to the operating load score, the vacuum score corresponding to the vacuum range and the temperature score corresponding to the temperature data, so as to monitor the vacuum oil filtering operation of the transformer. The present application solves the technical problem of insufficient comprehensive monitoring of the transformer during the vacuum oil filtering operation, realizes comprehensive monitoring of the vacuum oil filtering operation, improves the stability and safety of the transformer during the vacuum oil filtering operation, and thus improves the efficiency of the vacuum oil filtering operation.

[0057] According to an embodiment of the present application, an embodiment of a transformer vacuum oil filtration operation monitoring method is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0058] In this embodiment, a transformer vacuum oil filtration operation monitoring method is provided, which can be used for the above-mentioned transformer integrated with the oil filter. Figure 1 The step diagram of the transformer vacuum oil filtration operation monitoring method provided in the embodiment of the present application is shown in the figure, and the method includes:

[0059] S100. Obtaining the working data of the transformer in the vacuum oil filtration operation in the current cycle; wherein the working data includes a target current load curve, a target voltage load curve, a power load curve, a vacuum range and temperature data.

[0060] S200. Obtain the exchange power of the transformer according to the actual peak value of the current load of the transformer in the current cycle and the target critical voltage; wherein there is a power control error between the exchange power and the expected power value of the transformer, the actual peak value of the current load is obtained according to the target current load curve, and the target critical voltage is obtained by calculating the critical voltage according to the target voltage load curve.

[0061] S300. Evaluate the operating state of the transformer according to the power control error, the target current load curve, the target voltage load curve and the power load curve to obtain an operating load score.

[0062] S400. Determine the working state score of the transformer during vacuum oil filtration operation in the current cycle according to the operating load score, the vacuum score corresponding to the vacuum range and the temperature score corresponding to the temperature data, so as to monitor the vacuum oil filtration operation of the transformer.

[0063] Specifically, the time length of the current cycle can be selected according to the actual scenario, and any cycle may include the following time dimensions: day, week, month and year. Among them, when the day time dimension is selected, a daily load curve can be obtained for the current load, voltage load and power load. The daily load curve shows the change of the transformer operating load within 24 hours a day, which helps to understand the fluctuation law of power demand within a day; when the week time dimension is selected, a weekly load curve can be obtained for the current load, voltage load and power load. The weekly load curve reflects the change law of the operating load within a week by connecting the daily load curves every day, which helps to analyze the difference in power demand between weekdays and weekends; when the month or year time dimension is selected, a monthly load curve or an annual load curve can be obtained for the current load, voltage load and power load. Both are formed by connecting the daily load curve or the weekly load curve, respectively showing the change of the load every day in a month or the load of each month in a year, which is of great significance for analyzing the change of seasonal power demand. It should be noted that when the data obtained in different cycles are analyzed by comparison, the two cycles used for comparison should use the same time dimension.

[0064] Correspondingly, the change curves of vacuum range and temperature data under different time dimensions can also be collected. Among them, the change curve of temperature data can reflect the temperature change of the transformer during the vacuum oil filtration operation, and the change curve of vacuum range can reflect the change range of vacuum degree during the vacuum oil filtration operation. According to the change curves of temperature data and vacuum range, it can be determined whether the vacuum oil filtration operation is carried out normally.

[0065] Furthermore, any cycle may include multiple time periods. By setting multiple time periods to subdivide the cycle, the operating load changes in any cycle can be accurately grasped. For example, for the daily load curve, a time period with a time length of 6 hours can be set, so that the cycle corresponding to the daily load curve is divided into 4 time periods. At this time, the load curves in different time periods can be used to obtain the operating load change trend of the transformer in different time periods of the day. Exemplarily, for a transformer located in a commercial area, in the two time periods including 0 o'clock to 6 o'clock and 18 o'clock to 24 o'clock, the power consumption in the commercial area is small, so the operating load of the transformer is small; in the two time periods including 6 o'clock to 12 o'clock and 12 o'clock to 18 o'clock, the power consumption in the commercial area is large, so the operating load of the transformer is large. At this time, the two time periods including 6 o'clock to 12 o'clock and 12 o'clock to 18 o'clock can be focused on to ensure that the transformer can work safely and stably and meet the load requirements. It can be understood that the length of the time period can be determined according to the actual scenario.

[0066] Specifically, when obtaining the target current load curve, target voltage load curve and power load curve, the working parameters of each node in the transformer are first collected, including current and voltage. For the collected working parameters, it is necessary to filter and remove data that is greatly affected by the environment, so as to avoid the influence of abnormal data on subsequent calculations.

[0067] In some embodiments, the data can be screened in the following manner: for current data, Lagrange multipliers and penalty factors are introduced to convert the current data related to the transformer node into an unconstrained problem, and the characteristic vector of the current data is obtained by solving the unconstrained problem; wherein, the Lagrange multipliers integrate the constraints of the current data, and the penalty factors are used to control the current data according to the constraints, thereby improving the stability and accuracy of the characteristic vector. The characteristic vector is subjected to multimodal conversion, and the multimodal conversion here includes Fourier transform, etc., so as to obtain the frequency domain characteristics of the current data as the load characteristics of the current data. The current data is clustered according to the load characteristics, and the initial current load curve is obtained according to the cluster with the largest amount of data. Similarly, the voltage data can also be processed in the above manner to obtain the initial voltage load curve.

[0068] Specifically, the power control error is the difference between the exchange power of the transformer and the expected power value, where the exchange power is the maximum power of the transformer in the current cycle. In this embodiment, the exchange power is calculated by multiplying the actual peak value of the transformer's current load and the target critical voltage. According to the difference between the obtained exchange power and the expected power value, it is judged whether the operating load of the transformer meets the requirements or there is a safety problem. When the power control error is a positive number, it means that the exchange power is lower than the expected power value, and when the power control error is a negative number, it means that the exchange power is higher than the expected power value. For the power load curve, the operating load of the transformer is also monitored according to the power control error. If the power control error is close to zero, it means that the operating load of the transformer meets expectations and works normally in the current cycle; if the power control error is positive and not close to zero, it means that the operating load of the transformer is too low and it is light-loaded in the current cycle; if the power control error is negative and not close to zero, it means that the operating load of the transformer is too high and it is overloaded in the current cycle; according to the above results, the operating load score of the transformer can be obtained.

[0069] Furthermore, the point corresponding to the actual peak value of the current load in the target current load curve is recorded as the peak point, and the point corresponding to the target critical voltage in the target voltage load curve is recorded as the critical point. Before calculating the exchange power, it is necessary to match the time points corresponding to the peak point and the critical point so that the peak point and the critical point are at the same time, so that the calculation can be performed. In some embodiments, the matching can be performed by the following method: the time point corresponding to the peak point is moved so that the time point corresponding to the peak point is the same as the time point corresponding to the critical point; accordingly, the time point corresponding to the critical point can also be moved so that the time point corresponding to the critical point is the same as the time point corresponding to the peak point.

[0070] Furthermore, since the time points for collecting working data are discrete, there may not be a corresponding point in the target current load curve for the actual peak value of the current load or in the target voltage load curve for the target critical voltage, resulting in the inability to match the time points corresponding to the peak point and the critical point. In some embodiments, at this time, based on the actual peak value of the current load, the point with the highest similarity to the peak point can be found on the timestamp of the target current load curve, and the interpolation point can be obtained by interpolation calculation, and the interpolation point can be used as the peak point for matching; accordingly, based on the target critical voltage, the point with the highest similarity to the critical point can be found on the timestamp of the target voltage load curve, and the interpolation point can be obtained by interpolation calculation, and the interpolation point can be used as the critical point for matching.

[0071] Furthermore, if the time difference between the time points corresponding to the peak point and the critical point exceeds the preset allowable time difference, it means that the two do not appear in the same time period, and the exchange power of the transformer cannot be calculated by multiplying the two. At this time, the exchange power can be calculated by obtaining the average value of several current peaks in the current cycle as the actual peak value of the current load corresponding to the peak point after matching; accordingly, the adjacent critical points adjacent to the critical point can also be taken, and the average value of the critical voltages of the adjacent critical points can be taken as the target critical voltage corresponding to the critical point after matching, so as to calculate the exchange power.

[0072] In some embodiments, the operating load score of the transformer also includes scoring based on the stability of the target current load curve, the target voltage load curve, and the power load curve. It can be understood that the higher the stability of the target current load curve, the target voltage load curve, and the power load curve, the higher the operating load score of the transformer; the lower the stability of the target current load curve, the target voltage load curve, and the power load curve, the lower the operating load score of the transformer.

[0073] Furthermore, the working status score of the current cycle transformer during vacuum oil filtration operation is as follows:

[0074]

[0075] in, Indicates the work status score; Score the vacuum level; Score the operational load; Score the temperature; , and is the weight coefficient, which can be adjusted according to the actual scenario needs to reflect the importance of each score in the work status score.

[0076] The transformer vacuum oil filtration operation monitoring method provided in the present embodiment monitors the vacuum degree and temperature during the transformer vacuum oil filtration operation, and monitors the current load, voltage load and power load of the transformer to accurately grasp the operating load of the transformer; at the same time, the operating load of the transformer is scored, and whether the transformer is working normally is determined according to the operating load score, and the operating load score is used as the basis for adjusting the working state of the transformer, thereby improving the stability and safety of the transformer during the vacuum oil filtration operation.

[0077] Reference Figure 2 As shown, Figure 2 This is a step diagram of a method for obtaining a target current load curve and a target voltage load curve in an embodiment of the present application. As shown in the figure, as an embodiment of the present application, the target current load curve and the target voltage load curve are obtained in the following manner:

[0078] S120. According to the difference between the actual current load peak and the target current load predicted peak of the transformer in the current cycle, the initial current load curve is adjusted to obtain the target current load curve; wherein the actual current load peak is the peak value of the initial current load curve in the time period corresponding to the target current load predicted peak value.

[0079] S140. According to the correspondence between the voltage load weak node of the transformer and the maximum value of the target current load prediction peak value, the initial voltage load curve is adjusted to obtain the target voltage load curve, and the target critical voltage is obtained according to the target voltage load curve.

[0080] Specifically, the target current load curve is obtained by automatically adjusting the initial current load curve. In the next cycle of the current cycle, the transformer works according to the data in the target current load curve, thereby realizing automatic adjustment of the transformer working state. Correspondingly, the target voltage load curve is obtained by automatically adjusting the initial voltage load curve. In the next cycle of the current cycle, the transformer works according to the data in the target voltage load curve, thereby realizing automatic adjustment of the transformer working state.

[0081] Through automatic adjustment, the transformer has a certain degree of self-regulation ability, so that when external conditions change, it can adapt to the environment in time and maintain stable operation. It is understandable that if the external conditions exceed the self-regulation ability of the transformer, the transformer cannot adjust automatically. At this time, it means that the operating load score of the transformer does not meet the requirements. The technicians can judge whether the transformer needs to be manually adjusted based on the operating load score.

[0082] The transformer vacuum oil filtration operation monitoring method proposed in the embodiment of the present application adjusts the transformer according to the initial current load curve and the initial voltage load curve respectively, thereby obtaining the target current load curve and the target voltage load curve respectively, so that the transformer can work according to the target current load curve and the target voltage load curve, thereby reducing the failure rate of the transformer and improving the stability of the transformer.

[0083] As an embodiment of the present application, before adjusting the initial current load curve according to the target current load prediction peak value of the transformer in the current cycle to obtain the target current load curve, the method further includes:

[0084] S102. Predicting based on the historical current load data of the transformer, to obtain the predicted initial current load peak value of the transformer in the current cycle.

[0085] S104. Compare the actual peak value of the current load of the transformer in the previous cycle with the predicted peak value of the initial current load in the same time period to obtain a predicted peak value error.

[0086] S106. Adjust the initial current load prediction peak value according to the prediction peak value error to obtain the target current load prediction peak value.

[0087] Specifically, when adjusting the initial current load curve, the target current load prediction peak value of the transformer is used as the basis for adjustment, and the target current load prediction peak value is obtained after adjusting the initial current load prediction peak value. Among them, the initial current load prediction peak value is obtained by prediction based on the historical current load data of the transformer. For example, it can be predicted by the mean current size and current change rate of the transformer in different cycles in the historical current data. The method for obtaining the initial current load prediction peak value is not specifically limited here. It can be understood that the initial current load prediction peak value represents the preliminary prediction data of the current size and change trend when the transformer is working normally in the current cycle.

[0088] Furthermore, the initial current load prediction peak is adjusted by the prediction peak error. The prediction peak error is the difference between the actual peak current load of the transformer in the previous cycle and the initial current load prediction peak in the current cycle in the same time period, and is in the following form:

[0089]

[0090] in, represents the prediction peak error; Predicting the peak value for the initial current load of the current cycle; It is the actual peak value of the current load in the previous cycle. If the predicted peak error does not exceed the preset range, it means that the prediction accuracy is high, the initial current load predicted peak is within the acceptable range, and the transformer is in normal working state when it works with the current size indicated by the initial current load predicted peak; if the predicted peak error exceeds the preset range, it means that the prediction accuracy is low, the initial current load predicted peak is not within the acceptable range, and the transformer is in abnormal working state when it works with the current size indicated by the initial current load predicted peak.

[0091] It can be understood that if the predicted peak error does not exceed the preset range, it is only necessary to fine-tune the initial current load predicted peak value to further improve the accuracy of the prediction; if the predicted peak error exceeds the preset range, it is necessary to adjust the initial current load predicted peak value with the set adjustment coefficient to correct the prediction result.

[0092] As an embodiment of the present application, the adjusting the initial current load prediction peak value according to the prediction peak value error to obtain the target current load prediction peak value includes:

[0093] S108. If the predicted peak error exceeds the preset range, the initial current load predicted peak is adjusted based on the first current adjustment coefficient to obtain the target current load predicted peak; wherein the first current adjustment coefficient is obtained based on the variance of the actual peak current load of the transformer in the previous cycle.

[0094] S110. If the predicted peak error does not exceed the preset range, the initial current load predicted peak is adjusted based on the second current adjustment coefficient to obtain the target current load predicted peak; wherein, the second current adjustment coefficient is obtained based on the average value of the current peak error in different time periods, and the current peak error is obtained based on the difference between the predicted peak error and the maximum value of the actual peak value of the current load in the previous cycle.

[0095] Specifically, for the convenience of explanation, the time period corresponding to the predicted peak error is set as a preset time period. The first current adjustment coefficient is in the form of:

[0096]

[0097] in, represents the first current adjustment coefficient; The first time period in the previous cycle The actual peak value of the current load; It is the average value of the actual peak values ​​of all current loads in the previous cycle; It is the number of actual peak values ​​of the current load in the previous cycle. It can be understood that the first current adjustment coefficient is the variance of the actual peak value of the current load in the previous cycle, reflecting the fluctuation of the actual peak value of the current load in the previous cycle. If the actual peak value of the current load in the previous cycle is more dispersed and varies greatly, the larger the first current adjustment coefficient is; if the actual peak value of the current load in the previous cycle is more concentrated and varies less, the smaller the first current adjustment coefficient is.

[0098] Furthermore, the second current adjustment coefficient is in the following form:

[0099]

[0100] in, represents the second current adjustment coefficient; It is the maximum actual peak value of the current load within the preset time period of the previous cycle; For the current cycle The peak prediction error in the time period; is the number of time periods in the current cycle. It is understandable that the second current adjustment coefficient can be used to fine-tune the prediction when the prediction is relatively accurate, so as to further improve the prediction accuracy.

[0101] Furthermore, the initial current load prediction peak value is adjusted according to the first current adjustment coefficient or the second current adjustment coefficient, and the adjustment form is as follows:

[0102]

[0103] in, predicting peak values ​​for target current loads; represents a current adjustment coefficient, including a first current adjustment coefficient and a second current adjustment coefficient; is the average value of the prediction peak error; is a constant term, and its specific size can be analyzed based on the historical current load data of the transformer, and the best constant term can be obtained by methods including optimization algorithms. It should be noted that the target current load prediction peak value and the initial current load prediction peak value in the above formula correspond to the same time period, and the current adjustment coefficient used in the calculation also corresponds to the time period. It can be understood that the target current load prediction peak value obtained by the above formula represents the trend of the actual peak value of the current load under stable operation. In other embodiments, the initial current load prediction peak value can also be adjusted in other ways according to the actual scene requirements.

[0104] As an embodiment of the present application, before adjusting the initial voltage load curve according to the correspondence between the voltage load weak node of the transformer and the maximum value of the target current load prediction peak value to obtain the target voltage load curve, the method further includes:

[0105] S132. According to the operating parameters of each node in the transformer and the network admittance matrix between different nodes, the voltage load weak node of the transformer is determined based on continuous power flow calculation; wherein the operating parameters include voltage and complex power.

[0106] S134. Obtaining the initial critical voltage of the transformer according to the operating parameters of each of the voltage load weak nodes.

[0107] Specifically, according to the current and voltage of each node in the transformer, the equation of the current relationship between the nodes is obtained based on Kirchhoff's current law, and the equation of the voltage relationship between the nodes is obtained based on Kirchhoff's voltage law. It can be understood that the above equations are usually expressed based on the injected power and branch admittance of each node. For example, for a transformer with For a system with 10 nodes, we can get A nonlinear equation of the following form:

[0108]

[0109] in, For the The complex power of each node; For the Active power of each node; For the Reactive power of each node; For the The voltage of each node; For the Nodes and The mutual admittance between nodes is used to describe the relationship between current and voltage in the circuit; For the The conjugate complex number of the voltage at each node is used to calculate the real and imaginary parts of the complex power, that is, the active power and reactive power; It is The imaginary part of the reactive power of each node. According to the topological structure between the nodes in the transformer and the electrical parameters of the branches between the nodes, including resistance and reactance, solving the above equation can obtain the admittance of each branch, and the results are filled into the symmetric matrix to obtain the network admittance matrix. The elements in the network admittance matrix represent the electrical connection relationship between different nodes. According to the network admittance matrix, the network topological structure corresponding to the corresponding node and the complex power corresponding to the electrical parameters can be determined.

[0110] Furthermore, in the continuous power flow calculation process, the operating parameters of each node in the current cycle transformer are first used as the initial conditions for the continuous power flow calculation, and the parameters Represents the load level of the transformer, and gradually increases the parameter in each iteration The value of is used to simulate the load increase. Secondly, based on the working parameters of each node in the transformer of the current cycle and the direction of load increase, the working parameters of each node in the transformer of the next cycle are predicted. For example, the prediction method includes the tangent method and the secant method, etc., and the working parameters of each node in the transformer of the current cycle are used to estimate the working parameters of each node in the transformer of the next cycle.

[0111] Furthermore, after obtaining the prediction results of the working parameters of each node in the transformer in the next cycle, the prediction results are calibrated using the Newton-Raphson method or other nonlinear solvers, and the accuracy of the prediction results is improved through multiple iterations so that the calibrated prediction results meet the original power flow equation. The Newton-Raphson method is a numerical solution method for solving nonlinear equations, especially for finding the roots of equations. This method is an iterative algorithm that gradually approaches the solution of the equation by linearization. The Newton-Raphson method has the advantages of fast convergence and high computational efficiency.

[0112] Furthermore, as the load continues to increase, and the above prediction and calibration steps are repeated, when the voltage collapse point of any node is reached, the voltage of the node drops sharply as the load increases, indicating that the node is close to the critical load state. Continuing the iteration cannot obtain the working parameters that can operate stably. At this time, the iteration is terminated, and the voltage value obtained at this time is used as the critical voltage of the node. All the results in the continuous power flow calculation are recorded, and the PV curve of each node in the transformer is obtained according to the recorded results, which is used to represent the relationship between the voltage and the transmission power in each node. According to the PV curve, the stability margin of the transformer system can be obtained. The nodes where the voltage drops sharply are regarded as voltage load weak nodes, and the critical voltage of each voltage load weak node is obtained as the initial critical voltage.

[0113] As an embodiment of the present application, the initial voltage load curve is adjusted according to the correspondence between the voltage load weak node of the transformer and the maximum value of the target current load prediction peak value to obtain the target voltage load curve, and the target critical voltage is obtained according to the target voltage load curve, including:

[0114] S136. If the target current load curve corresponding to any voltage load weak node contains the maximum value of the target current load prediction peak, the initial voltage load curve is adjusted based on the first voltage adjustment coefficient to obtain the target voltage load curve; wherein the first voltage adjustment coefficient is obtained based on the voltage size of any voltage load weak node.

[0115] S138. If the maximum value of the target current load predicted peak does not exist in the target current load curves corresponding to all the voltage load weak nodes, the initial voltage load curve is adjusted based on the second voltage adjustment coefficient to obtain the target voltage load curve; wherein the second voltage adjustment coefficient is obtained based on the current operating voltage of the transformer, the initial critical voltage and the rated voltage.

[0116] S139. Calculate the critical voltage according to the target voltage load curve to obtain the target critical voltage.

[0117] Specifically, the first voltage adjustment coefficient is related to the voltage magnitude of the voltage load weak node. In the target current load curve corresponding to any voltage load weak node, if there is a maximum value of the target current load prediction peak, the critical voltage of the voltage load weak node is used as the first voltage adjustment coefficient, and the initial voltage load curve is adjusted with the first voltage adjustment coefficient as the adjustment value. Exemplarily, the target voltage load curve obtained according to the first voltage adjustment coefficient is in the form of:

[0118]

[0119] in, represents the first voltage adjustment coefficient; is the initial voltage load curve corresponding to the voltage load weak node; is the target voltage load curve corresponding to the voltage load weak node. In other embodiments, the initial voltage load curve may also be adjusted in other forms according to actual scene requirements.

[0120] Furthermore, the second voltage adjustment coefficient is in the following form:

[0121]

[0122] in, Indicates the second voltage adjustment coefficient in percentage form; is the working parameter of the corresponding voltage load weak node in the transformer in the current cycle; is the critical voltage of the weak node of the voltage load; is the rated voltage of the transformer. If the second voltage adjustment coefficient is a positive number, it means that the voltage load weak node is working in the stable area; if the second voltage adjustment coefficient is a negative number, it means that the voltage load weak node exceeds the critical point and enters the unstable area.

[0123] In some embodiments, the target voltage load curve obtained according to the second voltage adjustment coefficient is in the form of:

[0124]

[0125] in, In other embodiments, the initial voltage load curve may be adjusted in other forms according to actual scene requirements.

[0126] Furthermore, after obtaining the target voltage load curve, the critical voltage of each voltage load weak node in the transformer also changes and needs to be recalculated. The calculation steps of the target critical voltage are the same as the steps of obtaining the initial critical voltage.

[0127] As an embodiment of the present application, the vacuum score is determined according to the following method:

[0128] S410. Determine the vacuum degree score according to the symmetric difference between the vacuum degree range and the optimal vacuum degree range of the transformer; wherein the vacuum degree score is inversely proportional to the size of the symmetric difference.

[0129] Specifically, the optimal vacuum range is related to the designed vacuum range of the transformer, and the optimal vacuum range in the actual operation process changes in real time with the change of external conditions. For example, when the ambient temperature increases, the minimum value in the optimal vacuum range increases, while the maximum value in the optimal vacuum range remains almost unchanged; when the ambient temperature decreases, the minimum value in the optimal vacuum range decreases, while the maximum value in the optimal vacuum range remains almost unchanged.

[0130] Furthermore, let the measured vacuum range be , at this time the optimal vacuum range of the transformer is , then the symmetric difference between the two is It can be understood that the vacuum score is inversely proportional to the size of the symmetry difference. The larger the range of the symmetry difference, the lower the vacuum score; the smaller the range of the symmetry difference, the higher the vacuum score.

[0131] As an embodiment of the present application, the temperature score is determined according to the following method:

[0132] S420. Perform weighted calculation based on the heating power, thermal insulation rate and dielectric loss of the transformer to obtain a temperature correlation coefficient.

[0133] S422. Determine the temperature score according to the temperature correlation coefficient; wherein the temperature score is inversely proportional to the temperature correlation coefficient.

[0134] Specifically, the heating power of the transformer is related to the temperature difference between the inside and outside of the transformer. When the internal temperature of the transformer is higher than the ambient temperature, the heating power will increase. The heating power of the transformer is in the following form:

[0135]

[0136] in, Indicates the heating power of the transformer; is the thermal conductivity of the transformer, which is related to the material, structure and insulation medium of the transformer and is a constant; is the internal temperature of the transformer; is the ambient temperature of the transformer.

[0137] Furthermore, the insulation rate of the transformer refers to the ratio of the heat lost by the transformer through its casing per unit time to the total heating power, and its form is as follows:

[0138]

[0139] in, Indicates the insulation rate of the transformer; It is the heat dissipated by the transformer through the casing, which can be calculated by measuring the transformer casing temperature and ambient temperature using the heat conduction formula.

[0140] Furthermore, the dielectric loss of the transformer is an important indicator of the degree of oil aging, which can be calculated by measuring the dielectric constant and loss tangent in the oil. The form of the dielectric loss of the transformer is as follows:

[0141]

[0142] in, Indicates the dielectric loss of the transformer; is the real part of the complex dielectric constant; is the imaginary part of the complex dielectric constant; and It can be obtained through dielectric constant measuring instruments.

[0143] Furthermore, the temperature dependence coefficient is in the following form:

[0144]

[0145] in, represents the temperature correlation coefficient; is the normalized heating power; is the normalized insulation rate; is the normalized dielectric loss; , and are weight coefficients respectively, satisfying .

[0146] For example, the transformer The internal temperature is 50℃, the ambient temperature is 20℃, and the heating power is 2000W. At this time, the transformer can be obtained by calculation. The heat dissipated through the shell is 500W, and the insulation rate is 0.25. By measuring, we can get the transformer The dielectric loss is 0.01. is 0.4, is 0.3, is 0.4, and the transformer can be obtained by calculation The temperature correlation coefficient of another transformer is 0.301. The internal temperature is 60℃, the ambient temperature is 20℃, and the heating power is 2400W. At this time, the transformer can be calculated The heat dissipated through the shell is 600W, and the insulation rate is 0.25. By measuring, we can get the transformer The dielectric loss is 0.015. is 0.4, is 0.3, is 0.4, and the transformer can be obtained by calculation The temperature correlation coefficient is 0.85.

[0147] The temperature correlation coefficient reflects the change of the overall thermal performance and insulation condition of the transformer with the internal temperature of the transformer, and its range is The larger the temperature correlation coefficient, the more positively correlated the overall thermal performance of the transformer is with its internal temperature; the smaller the temperature correlation coefficient, the less positively correlated the thermal performance of the transformer is with its internal temperature. and transformer It can be seen that as the temperature increases, the temperature correlation coefficient also increases, which indicates that the overall thermal performance and insulation condition of the transformer decrease with increasing temperature. Therefore, the temperature score is inversely proportional to the temperature correlation coefficient. The higher the temperature correlation coefficient, the lower the temperature score; the lower the temperature correlation coefficient, the higher the temperature score.

[0148] In some embodiments, the internal temperature of the transformer is also determined by monitoring the temperature, humidity, and flow rate of the transformer oil during the oil filter operation. The temperature is collected using a high-precision, fast-response temperature sensor, including a thermocouple, a thermal resistor (such as PT100, etc.) or a digital temperature sensor (such as DS18B20, etc.), which can accurately measure the temperature inside the transformer and the oil filter working area. The above-mentioned sensor converts the temperature signal into an electrical signal (such as a voltage or resistance change, etc.) for processing by the microprocessor. The microprocessor analyzes and processes the temperature data through a built-in algorithm and stores the processed temperature data in a local memory for subsequent analysis. At the same time, the data can also be transmitted to the host computer or cloud platform via wired (such as RS485 or CAN bus, etc.) or wireless (such as Wi-Fi or Bluetooth, etc.). When the temperature exceeds the preset safety range, the microprocessor will trigger an alarm mechanism, such as an audible and visual alarm or send an alarm message through a communication interface. In addition, the microprocessor can also control the switch of the heating or cooling device according to the temperature change to maintain the temperature stability inside the transformer.

[0149] As an embodiment of the present application, the method further includes:

[0150] S510. Determine the working state of the transformer according to the working state score of the transformer during the vacuum oil filtration operation in the current cycle.

[0151] S520. If there is an abnormality in the working state of the transformer, take corresponding measures according to the abnormality.

[0152] Specifically, the working state of the transformer is determined according to the working state score and the comparison results of each individual score with the corresponding preset score threshold. Exemplarily, the working state score of the transformer is set to a first working score threshold, a second working score threshold and a third working score threshold, wherein the first working score threshold is higher than the second working score threshold, and the second working score threshold is higher than the third working score threshold; if the working state score is higher than the first working score threshold, it is determined that the transformer is working normally; if the working state score is lower than the first working score threshold but higher than the second working score threshold, it is determined that the working state of the transformer is abnormal and a slight warning is required; if the working state score is lower than the second working score threshold but higher than the third working score threshold, it is determined that the working state of the transformer is seriously abnormal and a serious warning is required; if the working state score is lower than the third working score threshold, it is determined that the transformer is faulty and needs to stop working.

[0153] For example, if the vacuum score of the transformer is higher than the vacuum score threshold, it means that the transformer is working normally; if the vacuum score of the transformer is lower than the vacuum score threshold, it means that the transformer has a problem of low vacuum degree, and corresponding adjustments need to be made, including checking and replacing the vacuum pump. If the operating load score of the transformer is higher than the operating load score threshold, it means that the transformer is working normally; if the operating load score of the transformer is lower than the operating load score threshold, it means that the transformer has a problem of too high operating load, and corresponding adjustments need to be made, including adjusting the operating load distribution. If the temperature score of the transformer is higher than the temperature score threshold, it means that the transformer is working normally; if the temperature score of the transformer is lower than the temperature score threshold, it means that the transformer has a problem of too high temperature, and corresponding adjustments need to be made, including adding heat dissipation measures or optimizing the fan layout. The above measures are used to adjust the working parameters to improve the stability of the transformer under vacuum oil filtration.

[0154] Reference Figure 3 As shown, Figure 3 A flowchart of a transformer vacuum oil filtration operation monitoring method provided in an embodiment of the present application is shown in the figure. As an embodiment of the present application, the method for obtaining the exchange power of the transformer in the present application includes the following steps:

[0155] S602. Predicting based on the historical current load data of the transformer, to obtain the predicted initial current load peak value of the transformer in the current cycle.

[0156] S604. Compare the actual peak value of the current load of the transformer in the previous cycle with the predicted peak value of the initial current load in the same time period to obtain a predicted peak value error.

[0157] S606. If the predicted peak error exceeds the preset range, the initial current load predicted peak is adjusted based on the first current adjustment coefficient to obtain the target current load predicted peak; wherein the first current adjustment coefficient is obtained based on the variance of the actual peak current load of the transformer in the previous cycle.

[0158] S608. If the predicted peak error does not exceed the preset range, the initial current load predicted peak is adjusted based on the second current adjustment coefficient to obtain the target current load predicted peak; wherein, the second current adjustment coefficient is obtained based on the average value of the current peak error in different time periods, and the current peak error is obtained based on the difference between the predicted peak error and the maximum value of the actual peak value of the current load in the previous cycle.

[0159] S610. According to the difference between the actual current load peak and the target current load predicted peak of the transformer in the current cycle, the initial current load curve is adjusted to obtain the target current load curve; wherein the actual current load peak is the peak value of the initial current load curve in the time period corresponding to the target current load predicted peak value.

[0160] S612. Determine the voltage load weak node of the transformer based on continuous power flow calculation according to the operating parameters of each node in the transformer and the network admittance matrix between different nodes; wherein the operating parameters include voltage and complex power.

[0161] S614. Obtain the initial critical voltage of the transformer according to the operating parameters of each of the voltage load weak nodes.

[0162] S616. If the target current load curve corresponding to any voltage load weak node contains the maximum value of the target current load predicted peak value, the initial voltage load curve is adjusted based on the first voltage adjustment coefficient to obtain the target voltage load curve; wherein the first voltage adjustment coefficient is obtained based on the voltage size of any voltage load weak node.

[0163] S618. If the maximum value of the target current load predicted peak does not exist in the target current load curves corresponding to all the voltage load weak nodes, the initial voltage load curve is adjusted based on the second voltage adjustment coefficient to obtain the target voltage load curve; wherein the second voltage adjustment coefficient is obtained based on the current operating voltage of the transformer, the initial critical voltage and the rated voltage.

[0164] S620. Calculate the critical voltage according to the target voltage load curve to obtain a target critical voltage.

[0165] S622. Obtain the exchange power of the transformer based on the actual peak current load of the transformer in the current cycle and the target critical voltage; wherein there is a power control error between the exchange power and the expected power value of the transformer, and the actual peak current load is obtained based on the target current load curve.

[0166] Specifically, through the above steps, the target current load curve, the target voltage load curve, the power load curve and the power control error are obtained, so that the operating load of the transformer during the vacuum oil filtration operation can be scored, and whether the transformer is operating normally can be monitored, thereby ensuring that the vacuum oil filtration operation can proceed normally, improving the efficiency of the vacuum oil filtration operation, and then improving the working efficiency of the transformer.

[0167] Accordingly, please refer to Figure 4 The embodiment of the present application provides a transformer vacuum oil filtration operation monitoring system, the system comprising:

[0168] The working data acquisition module 100 is used to obtain working data of the transformer in the vacuum oil filtering operation in the current cycle; wherein the working data includes a target current load curve, a target voltage load curve, a power load curve, a vacuum range and temperature data.

[0169] The exchange power calculation module 200 is used to obtain the exchange power of the transformer according to the actual peak value of the current load of the transformer and the target critical voltage in the current cycle; wherein there is a power control error between the exchange power and the expected power value of the transformer, the actual peak value of the current load is obtained according to the target current load curve, and the target critical voltage is obtained by critical voltage calculation according to the target voltage load curve.

[0170] The operation load scoring module 300 is used to evaluate the operation state of the transformer according to the power control error, the target current load curve, the target voltage load curve and the power load curve to obtain an operation load score.

[0171] The working status scoring module 400 is used to determine the working status score of the transformer during the vacuum oil filtration operation in the current cycle according to the operating load score, the vacuum score corresponding to the vacuum range and the temperature score corresponding to the temperature data, so as to monitor the vacuum oil filtration operation of the transformer.

[0172] In some optional implementations, the working data acquisition module 100 includes:

[0173] A current load adjustment unit is used to adjust the initial current load curve according to the difference between the actual current load peak and the target current load predicted peak of the transformer in the current cycle to obtain the target current load curve; wherein the actual current load peak is the peak value of the initial current load curve in the time period corresponding to the target current load predicted peak value.

[0174] The voltage load adjustment unit is used to adjust the initial voltage load curve according to the corresponding relationship between the voltage load weak node of the transformer and the maximum value of the target current load prediction peak value to obtain the target voltage load curve, and obtain the target critical voltage according to the target voltage load curve.

[0175] In some optional implementations, the working data acquisition module 100 further includes:

[0176] The current peak prediction unit is used to make a prediction based on the historical current load data of the transformer to obtain the initial current load prediction peak value of the transformer in the current cycle.

[0177] The prediction error calculation unit is used to compare the actual peak value of the current load of the transformer in the previous cycle with the predicted peak value of the initial current load in the same time period to obtain the predicted peak value error.

[0178] The predicted peak value adjustment unit is used to adjust the initial current load predicted peak value according to the predicted peak value error to obtain the target current load predicted peak value.

[0179] In some optional implementations, the predicted peak value adjustment unit includes:

[0180] The first prediction adjustment subunit is used to adjust the initial current load prediction peak value. If the prediction peak value error exceeds a preset range, the initial current load prediction peak value is adjusted based on a first current adjustment coefficient to obtain the target current load prediction peak value; wherein the first current adjustment coefficient is obtained based on the variance of the actual peak value of the current load of the transformer in the previous cycle.

[0181] The second prediction adjustment subunit is used to adjust the initial current load prediction peak value. If the prediction peak value error does not exceed a preset range, the initial current load prediction peak value is adjusted based on a second current adjustment coefficient to obtain the target current load prediction peak value; wherein, the second current adjustment coefficient is obtained based on the average value of the current peak error in different time periods, and the current peak error is obtained based on the difference between the prediction peak error and the maximum value of the actual peak value of the current load in the previous cycle.

[0182] In some optional implementations, the working data acquisition module 100 further includes:

[0183] A weak node determination unit is used to determine the voltage load weak node of the transformer based on continuous power flow calculation according to the operating parameters of each node in the transformer and the network admittance matrix between different nodes; wherein the operating parameters include voltage and complex power.

[0184] The initial critical voltage calculation unit is used to obtain the initial critical voltage of the transformer according to the working parameters of each of the voltage load weak nodes.

[0185] In some optional implementations, the voltage load adjustment unit includes:

[0186] The first voltage adjustment subunit is used to adjust the initial voltage load curve. If the target current load curve corresponding to any voltage load weak node has a maximum value of the target current load prediction peak, the initial voltage load curve is adjusted based on the first voltage adjustment coefficient to obtain the target voltage load curve; wherein the first voltage adjustment coefficient is obtained according to the voltage size of any voltage load weak node.

[0187] The second voltage adjustment subunit is used to adjust the initial voltage load curve. If the maximum value of the target current load prediction peak does not exist in the target current load curves corresponding to all the voltage load weak nodes, the initial voltage load curve is adjusted based on the second voltage adjustment coefficient to obtain the target voltage load curve; wherein the second voltage adjustment coefficient is obtained based on the current operating voltage of the transformer, the initial critical voltage and the rated voltage.

[0188] The target critical voltage calculation subunit is used to perform critical voltage calculation according to the target voltage load curve to obtain the target critical voltage.

[0189] In some optional implementations, the work status scoring module 400 includes:

[0190] The vacuum degree scoring unit is used to determine the vacuum degree score according to the symmetric difference between the vacuum degree range and the optimal vacuum degree range of the transformer; wherein the vacuum degree score is inversely proportional to the size of the symmetric difference.

[0191] In some optional implementations, the working status scoring module 400 further includes:

[0192] A temperature correlation coefficient calculation unit, used for performing weighted calculation according to the heating power, thermal insulation rate and dielectric loss of the transformer to obtain the temperature correlation coefficient;

[0193] A temperature scoring unit is used to determine the temperature score according to the temperature correlation coefficient; wherein the temperature score is inversely proportional to the temperature correlation coefficient.

[0194] In some optional embodiments, the system further comprises:

[0195] A working state determination module, used to determine the working state of the transformer according to the working state score of the transformer during vacuum oil filtering operation in the current cycle;

[0196] The abnormal situation processing module is used to process abnormal situations. If there is an abnormal situation in the working state of the transformer, corresponding measures are taken according to the abnormal situation.

[0197] The further functional description of each of the above modules and units is the same as that of the above corresponding embodiments and will not be repeated here.

[0198] The transformer vacuum oil filtration operation monitoring system in this embodiment is presented in the form of a functional unit, where the unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that executes one or more software or fixed programs, and / or other devices that can provide the above functions.

[0199] See also Figure 5 , Figure 5 It is a structural diagram of a computer device provided by an embodiment of the present application. As shown in the figure, the computer device includes: one or more processors 10, a memory 20, and an interface for connecting various components, including a high-speed interface and a low-speed interface. Each component uses different buses to communicate with each other, and can be installed on a common motherboard or installed in other ways as needed. The processor can process instructions executed in the computer device, including instructions stored in or on the memory to display the graphical information of the GUI on an external input / output device (such as a display device coupled to the interface). In some optional embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Similarly, multiple computer devices can be connected, and each device provides some necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Figure 5 A processor 10 is taken as an example.

[0200] The processor 10 may be a central processing unit, a network processor or a combination thereof. The processor 10 may further include a hardware chip. The hardware chip may be a dedicated integrated circuit, a programmable logic device or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general purpose array logic or any combination thereof.

[0201] The memory 20 stores instructions executable by at least one processor 10, so that the at least one processor 10 executes the method shown in the above embodiment.

[0202] The memory 20 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store data created according to the use of the computer device, etc. In addition, the memory 20 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some optional embodiments, the memory 20 may optionally include a memory remotely arranged relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0203] The memory 20 may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a flash memory, a hard disk or a solid state drive; the memory 20 may also include a combination of the above types of memory.

[0204] The computer device further comprises a communication interface 30 for the computer device to communicate with other devices or a communication network.

[0205] The embodiment of the present application also provides a computer-readable storage medium. The above method according to the embodiment of the present application can be implemented in hardware, firmware, or can be implemented as a computer code that can be recorded in a storage medium, or can be implemented as a computer code that is originally stored in a remote storage medium or a non-temporary machine-readable storage medium and will be stored in a local storage medium through a network download, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only storage memory, a random access memory, a flash memory, a hard disk or a solid-state hard disk, etc.; further, the storage medium can also include a combination of the above types of memories. It can be understood that a computer, a processor, a microprocessor controller, or programmable hardware includes a storage component that can store or receive software or computer code. When the software or computer code is accessed and executed by a computer, a processor, or hardware, the method shown in the above embodiment is implemented.

[0206] The embodiment of the present application provides a computer program product, which includes computer instructions, which are stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the method of any embodiment of the present application.

[0207] Although the embodiments of the present application are described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present application, and such modifications and variations are all within the scope defined by the appended claims.

[0208] The systems, devices, modules or units described in the above embodiments may be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, the computer may be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

[0209] For the convenience of description, the above device is described in various units according to their functions. Of course, when implementing the present application, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0210] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0211] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1A device that provides the functions specified in a block or multiple blocks.

[0212] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0213] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0214] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0215] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0216] The above is only an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

[0217] Although the embodiments of the present application have been described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present application, and such modifications and variations are all within the scope defined by the appended claims.

Claims

1. A transformer vacuum oil filtration operation monitoring method, characterized in that: The method comprises: Obtaining the working data of the transformer in the vacuum oil filtering operation in the current cycle; wherein the working data includes a target current load curve, a target voltage load curve, a power load curve, a vacuum range and temperature data; The exchange power of the transformer is obtained according to the actual peak value of the current load of the transformer in the current cycle and the target critical voltage; wherein there is a power control error between the exchange power and the expected power value of the transformer, the actual peak value of the current load is obtained according to the target current load curve, and the target critical voltage is obtained by calculating the critical voltage according to the target voltage load curve; the exchange power is the maximum power of the transformer in the current cycle, and the exchange power is calculated by multiplying the actual peak value of the current load and the target critical voltage; The target voltage load curve is obtained by: adjusting the initial voltage load curve according to the corresponding relationship between the voltage load weak node of the transformer and the maximum value of the target current load prediction peak value to obtain the target voltage load curve, and obtaining the target critical voltage according to the target voltage load curve; Before adjusting the initial voltage load curve according to the correspondence between the voltage load weak node of the transformer and the maximum value of the target current load prediction peak value to obtain the target voltage load curve, the method further includes: According to the operating parameters of each node in the transformer and the network admittance matrix between different nodes, the voltage load weak node of the transformer is determined based on continuous power flow calculation; wherein the operating parameters include voltage and complex power; Obtaining the initial critical voltage of the transformer according to the working parameters of each of the voltage load weak nodes; The adjusting the initial voltage load curve according to the correspondence between the voltage load weak node of the transformer and the maximum value of the target current load prediction peak value to obtain the target voltage load curve, and obtaining the target critical voltage according to the target voltage load curve includes: If the target current load curve corresponding to any voltage load weak node has the maximum value of the target current load prediction peak value, the initial voltage load curve is adjusted based on the first voltage adjustment coefficient to obtain the target voltage load curve; wherein the first voltage adjustment coefficient is obtained according to the voltage magnitude of any voltage load weak node; If the maximum value of the target current load prediction peak does not exist in the target current load curves corresponding to all the voltage load weak nodes, the initial voltage load curve is adjusted based on the second voltage adjustment coefficient to obtain the target voltage load curve; wherein the second voltage adjustment coefficient is obtained according to the current working voltage of the transformer, the initial critical voltage and the rated voltage; Calculating the critical voltage according to the target voltage load curve to obtain the target critical voltage; Evaluate the operating state of the transformer according to the power control error, the target current load curve, the target voltage load curve and the power load curve to obtain an operating load score; According to the operating load score, the vacuum score corresponding to the vacuum range and the temperature score corresponding to the temperature data, the working state score of the transformer during the vacuum oil filtration operation in the current cycle is determined to monitor the vacuum oil filtration operation of the transformer.

2. The method according to claim 1, characterized in that The target current load curve is obtained by: According to the difference between the actual current load peak and the target current load predicted peak of the transformer in the current cycle, the initial current load curve is adjusted to obtain the target current load curve; wherein the actual current load peak is the peak value of the initial current load curve in the time period corresponding to the target current load predicted peak value.

3. The method according to claim 2, characterized in that Before adjusting the initial current load curve according to the target current load prediction peak value of the transformer in the current cycle to obtain the target current load curve, the method further includes: Perform prediction based on the historical current load data of the transformer to obtain the predicted initial current load peak value of the transformer in the current cycle; Comparing the actual peak value of the current load of the transformer in the previous cycle with the predicted peak value of the initial current load in the same time period to obtain a predicted peak error; The initial current load prediction peak value is adjusted according to the prediction peak value error to obtain the target current load prediction peak value.

4. The method according to claim 3, characterized in that: The adjusting the initial current load prediction peak value according to the prediction peak value error to obtain the target current load prediction peak value includes: If the predicted peak value error exceeds a preset range, the initial current load predicted peak value is adjusted based on a first current adjustment coefficient to obtain the target current load predicted peak value; wherein the first current adjustment coefficient is obtained based on the variance of the actual peak value of the current load of the transformer in the previous cycle; If the predicted peak error does not exceed a preset range, the initial current load predicted peak is adjusted based on a second current adjustment coefficient to obtain the target current load predicted peak; wherein, the second current adjustment coefficient is obtained based on the average value of the current peak error in different time periods, and the current peak error is obtained based on the difference between the predicted peak error and the maximum value of the actual peak value of the current load in the previous cycle.

5. The method according to claim 1, characterized in that The vacuum rating is determined as follows: The vacuum degree score is determined according to the symmetric difference between the vacuum degree range and the optimal vacuum degree range of the transformer; wherein the vacuum degree score is inversely proportional to the size of the symmetric difference.

6. The method according to claim 1, characterized in that The temperature score is determined as follows: A temperature correlation coefficient is obtained by performing a weighted calculation according to the heating power, thermal insulation rate and dielectric loss of the transformer; The temperature score is determined according to the temperature correlation coefficient; wherein the temperature score is inversely proportional to the temperature correlation coefficient.

7. The method according to any one of claims 1 to 6, characterized in that The method further comprises: Determine the working state of the transformer according to the working state score of the transformer during vacuum oil filtering operation in the current cycle; If there is an abnormality in the working state of the transformer, corresponding measures are taken according to the abnormality.

8. A transformer vacuum oil filtration operation monitoring system, characterized in that: The system comprises: A working data acquisition module is used to obtain working data of the transformer in the vacuum oil filtration operation in the current cycle; wherein the working data includes a target current load curve, a target voltage load curve, a power load curve, a vacuum range and temperature data; An exchange power calculation module, used to obtain the exchange power of the transformer according to the actual peak value of the current load of the transformer and the target critical voltage in the current cycle; wherein there is a power control error between the exchange power and the expected power value of the transformer, the actual peak value of the current load is obtained according to the target current load curve, and the target critical voltage is obtained by calculating the critical voltage according to the target voltage load curve; the exchange power is the maximum power of the transformer in the current cycle, and the exchange power is calculated by multiplying the actual peak value of the current load by the target critical voltage; The working data acquisition module includes: a voltage load adjustment unit, which is used to adjust the initial voltage load curve according to the corresponding relationship between the voltage load weak node of the transformer and the maximum value of the target current load prediction peak value to obtain the target voltage load curve, and obtain the target critical voltage according to the target voltage load curve; the working data acquisition module also includes: A weak node determination unit, configured to determine the voltage load weak node of the transformer based on continuous power flow calculation according to the operating parameters of each node in the transformer and the network admittance matrix between different nodes; wherein the operating parameters include voltage and complex power; An initial critical voltage calculation unit, used to obtain the initial critical voltage of the transformer according to the working parameters of each of the voltage load weak nodes; The voltage load adjustment unit comprises: A first voltage adjustment subunit is used to adjust the initial voltage load curve, and if the target current load curve corresponding to any voltage load weak node has the maximum value of the target current load prediction peak value, the initial voltage load curve is adjusted based on the first voltage adjustment coefficient to obtain the target voltage load curve; wherein the first voltage adjustment coefficient is obtained according to the voltage magnitude of any voltage load weak node; A second voltage adjustment subunit is used to adjust the initial voltage load curve, if the target current load curves corresponding to all the voltage load weak nodes do not have the maximum value of the target current load prediction peak value, then the initial voltage load curve is adjusted based on a second voltage adjustment coefficient to obtain the target voltage load curve; wherein the second voltage adjustment coefficient is obtained according to the current working voltage of the transformer, the initial critical voltage and the rated voltage; a target critical voltage calculation subunit, configured to calculate the critical voltage according to the target voltage load curve to obtain the target critical voltage; An operation load scoring module, used to evaluate the operation state of the transformer according to the power control error, the target current load curve, the target voltage load curve and the power load curve to obtain an operation load score; The working status scoring module is used to determine the working status score of the transformer during the vacuum oil filtration operation in the current cycle according to the operating load score, the vacuum score corresponding to the vacuum range and the temperature score corresponding to the temperature data, so as to monitor the vacuum oil filtration operation of the transformer.

9. A computer device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the method according to any one of claims 1 to 7 by executing the computer instructions.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the method according to any one of claims 1 to 7.

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