CN118836932B

CN118836932B - A transformer vacuum oil filtration operation monitoring method and system

Info

Publication number: CN118836932B
Application number: CN202411320079.6A
Authority: CN
Inventors: 赵磊; 庞子皓; 屠月海; 王亦昌; 吴熙; 李知远; 钱佳琪; 许成程; 胡群丰; 张小帆; 王度
Original assignee: Construction Branch of State Grid Zhejiang Electric Power Co Ltd; Double Innovation Center of State Grid Zhejiang Electric Power Co Ltd
Current assignee: Construction Branch of State Grid Zhejiang Electric Power Co Ltd; Technology Innovation Center of State Grid Zhejiang Electric Power Co Ltd
Priority date: 2024-09-23
Filing date: 2024-09-23
Publication date: 2025-01-10
Anticipated expiration: 2044-09-23
Also published as: CN118836932A

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

Transformer vacuum oil filtering operation monitoring method and system

Technical Field

The application relates to the technical field of information monitoring, in particular to a method and a system for monitoring vacuum oil filtering operation of a transformer.

Background

With the continuous development of smart grid technology, requirements on the running stability and safety of a power system are higher and higher. Transformers are used as important equipment for changing voltage, and are widely used in places such as power substations, power plants, operation lines and the like, and the covered areas are continuously enlarged. The transformer oil can play roles of insulation, heat dissipation, arc extinction and the like, and the existing various transformers are improved by using the transformer oil, so that the efficient utilization of equipment is realized. Because the temperature of the equipment is increased during operation, transformer oil is contacted with oxygen under the action of high temperature and an electric field to generate insoluble colloid, so that the insulating property of the transformer is reduced, and therefore, the transformer is required to be filtered regularly. The related art only measures partial parameters of the vacuum oil filtering operation, and cannot comprehensively monitor the vacuum oil filtering operation.

Disclosure of Invention

The application provides a method and a system for monitoring vacuum oil filtering operation of a transformer, which solve the technical problem that the monitoring of the transformer is not complete enough during the vacuum oil filtering operation, achieve the technical effect of realizing the complete monitoring of the vacuum oil filtering operation and ensuring the normal operation of the transformer during the vacuum oil filtering operation.

In order to achieve the above purpose, the main technical scheme adopted by the application comprises the following steps:

In a first aspect, an embodiment of the present application provides a method for monitoring a vacuum oil filtering operation of a transformer, where the method includes:

Acquiring working data of the current period transformer in vacuum oil filtering operation, wherein the working data comprise a target current load curve, a target voltage load curve, a power load curve, a vacuum degree range and temperature data;

Obtaining the exchange power of the transformer according to the actual peak value of the current load of the transformer in the current period and the target critical voltage, wherein a power control error exists 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;

Performing operation state evaluation on 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;

and determining the working state score of the transformer in the current period when the vacuum oil filtering operation is performed according to the running load score, the vacuum degree score corresponding to the vacuum degree range and the temperature score corresponding to the temperature data so as to monitor the vacuum oil filtering operation of the transformer.

According to the transformer vacuum oil filtering operation monitoring method, on the basis of monitoring the vacuum degree and the temperature during the transformer vacuum oil filtering operation, the current load, the voltage load and the power load of the transformer are monitored, so that the operating load of the transformer is accurately mastered, meanwhile, the operating load of the transformer is scored, whether the transformer works normally is judged according to the operating load scoring, the operating load scoring is used as a basis for adjusting the working state of the transformer, and the stability and the safety of the transformer during the vacuum oil filtering operation are improved.

Optionally, the target current load curve and the target voltage load curve are obtained by:

according to the difference value 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 period, an 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 a time period corresponding to the predicted peak value of the target current load;

And adjusting an initial voltage load curve according to the corresponding relation between the voltage load weak node of the transformer and the maximum value of the target current load prediction peak value to obtain a target voltage load curve, and obtaining the target critical voltage according to the target voltage load curve.

According to the transformer vacuum oil filtering operation monitoring method, the transformer is adjusted according to the initial current load curve and the initial voltage load curve, so that the target current load curve and the target voltage load curve are obtained respectively, the transformer can work according to the target current load curve and the target voltage load curve, the fault rate of the transformer is reduced, and the stability of the transformer is improved.

Optionally, before the predicting peak value of the target current load of the transformer according to the current period, adjusting an initial current load curve to obtain the target current load curve, the method further includes:

predicting according to historical current load data of the transformer to obtain an initial current load prediction peak value of the transformer in the current period;

comparing the actual peak value of the current load of the transformer with the predicted peak value of the initial current load in the previous period in the same time period to obtain a predicted peak value error;

And adjusting the initial current load predicted peak value according to the predicted peak value error to obtain the target current load predicted peak value.

Optionally, the adjusting the initial current load predicted peak according to the predicted peak error to obtain the target current load predicted peak includes:

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

and if the predicted peak value error does not exceed the preset range, adjusting the initial current load predicted peak value based on a second current adjustment coefficient to obtain the target current load predicted peak value, wherein the second current adjustment coefficient is obtained according to the average value of the current peak value errors in different time periods, and the current peak value error is obtained according to the difference value between the predicted peak value error and the maximum value of the current load actual peak value in the previous period.

Optionally, before 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 predicted peak value, the method further includes:

determining voltage load weak nodes of the transformer based on continuous power flow calculation according to working parameters of all nodes in the transformer and a network admittance matrix among different nodes, wherein the working parameters comprise voltage and complex power;

And obtaining the initial critical voltage of the transformer according to the working parameters of each voltage load weak node.

Optionally, 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 predicted 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 maximum value of the target current load prediction peak value exists in the target current load curve corresponding to any voltage load weak node, the initial voltage load curve is adjusted based on a first voltage adjustment coefficient to obtain the target voltage load curve, wherein the first voltage adjustment coefficient is obtained according to the voltage of any voltage load weak node;

If the maximum value of the target current load prediction peak value does not exist in all target current load curves corresponding to the voltage load weak nodes, 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, the initial critical voltage and the rated voltage of the transformer;

and calculating the critical voltage according to the target voltage load curve to obtain the target critical voltage.

Optionally, the vacuum score is determined according to the following:

And determining the vacuum degree score according to the symmetry 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 magnitude of the symmetry difference.

Optionally, the temperature score is determined according to the following:

Weighting calculation is carried out according to the heating power, the heat preservation rate and the dielectric loss of the transformer, so as to obtain a temperature correlation coefficient;

And determining the temperature score according to the temperature correlation coefficient, wherein the temperature score is inversely proportional to the temperature correlation coefficient.

Optionally, the method further comprises:

determining the working state of the transformer according to the working state score of the transformer in the vacuum oil filtering operation in the current period;

if the working state of the transformer has abnormal conditions, corresponding measures are taken according to the abnormal conditions.

In a second aspect, an embodiment of the present application provides a transformer vacuum oil filtering operation monitoring system, the system including:

The working data acquisition module is used for acquiring working data of the current period transformer in vacuum oil filtering operation, wherein the working data comprise a target current load curve, a target voltage load curve, a power load curve, a vacuum degree range and temperature data;

The switching power calculation module is used for obtaining switching power of the transformer according to an actual peak value of the current load of the transformer in the current period and a target critical voltage, wherein a power control error exists between the switching power and an 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 running load scoring module is used for evaluating the running 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 a running load score;

And the working state scoring module is used for determining the working state score of the transformer in the vacuum oil filtering operation in the current period according to the running load score, the vacuum degree score corresponding to the vacuum degree range and the temperature score corresponding to the temperature data so as to monitor the vacuum oil filtering operation of the transformer.

In a third aspect, an embodiment of the present application provides a computer device, including a memory and a processor, where the memory and the processor are communicatively connected to each other, and the memory stores computer instructions, and the processor executes the computer instructions, thereby executing the transformer vacuum oil filtering operation monitoring method according to any one of the foregoing embodiments.

In a fourth aspect, an embodiment of the present application provides a computer readable storage medium having stored thereon computer instructions for causing a computer to perform the transformer oil vacuum operation monitoring method according to any one of the above embodiments.

In a fifth aspect, an embodiment of the present application provides a computer program product comprising computer instructions for causing a computer to perform the transformer vacuum oil filtration operation monitoring method according to any one of the above embodiments.

Drawings

In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings that are needed in the description of the embodiments or the prior art will be briefly described, and it is obvious that the drawings in the description below are some embodiments of the present application, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.

Fig. 1 is a step diagram of a method for monitoring a transformer vacuum oil filtering operation according to an embodiment of the present application;

FIG. 2 is a step diagram of a method for obtaining a target current load curve and a target voltage load curve according to an embodiment of the present application;

FIG. 3 is a flowchart of a method for monitoring a transformer vacuum oil filtering operation according to an embodiment of the present application;

Fig. 4 is a block diagram of a transformer vacuum oil filtering operation monitoring system according to an embodiment of the present application;

Fig. 5 is a schematic structural diagram of a computer device according to an embodiment of the present application.

The reference numerals of the drawings in the specification are as follows, namely, a working data acquisition module, a switching power calculation module, a running load scoring module and a working state scoring module.

Detailed Description

For the purpose of making the objects, technical solutions and advantages of the embodiments of the present application more apparent, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application, and it is apparent that the described embodiments are some embodiments of the present application, but not all embodiments of the present application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to fall within the scope of the application.

With the continuous development of smart grid technology, requirements on the running stability and safety of a power system are higher and higher. Transformers are used as important equipment for changing voltage, and are widely used in places such as power substations, power plants, operation lines and the like, and the covered areas are continuously enlarged. Transformer oil is usually added in the transformer, and the transformer oil can play roles in insulation, heat dissipation, arc extinction and the like, and the existing various transformers are improved by using the transformer oil, so that the high-efficiency utilization of equipment is realized. As the temperature of the equipment is increased during operation, the transformer oil is contacted with oxygen to generate insoluble colloid under the action of high temperature and an electric field, so that the insulating property of the transformer is reduced. Therefore, the transformer needs to be subjected to vacuum oil filtering operation regularly, insoluble colloid, gas and the like in the transformer oil are removed, and the insulating property of the transformer oil is recovered, so that the transformer can work normally, and the safety and stability of the transformer are improved.

In the related art, the data monitored by the transformer during the vacuum oil filtering operation comprise vacuum degree and temperature, wherein the proper vacuum degree can prevent oxygen or moisture from being carried out in the transformer, and reduce the influence on the transformer, so that the efficiency of the vacuum oil filtering operation is improved, and the proper temperature can reduce the solidification risk of the transformer oil, and is beneficial to accelerating the volatilization of moisture and gas in the transformer oil, so that the efficiency of the vacuum oil filtering operation is improved.

In a practical scenario, the oil filter and the transformer are usually integrated, and the transformer integrated machine integrates the oil filtering function into the transformer body, and usually comprises an automatic oil treatment, filtering and heating system. The integrated design reduces maintenance work, improves the overall reliability of the system, can treat oil quality in real time during operation, and keeps the optimal performance of the transformer. The transformer still works when the integrated machine is in vacuum oil filtering operation, so that whether the transformer can normally work and the working stability of the transformer have great influence on the vacuum oil filtering operation efficiency of the integrated machine of the transformer at the moment, and the working condition of the integrated machine of the transformer during the vacuum oil filtering operation can not be completely mastered only through monitoring the vacuum degree and the temperature in the related technology.

Based on the problems, the application provides a method and a system for monitoring vacuum oil filtering operation of a transformer, which can be used for integrating an oil filter and the transformer into an integrated machine and monitoring the working state of the transformer in the integrated machine during the vacuum oil filtering operation. The method comprises the steps of obtaining working data of a transformer in a current period during vacuum oil filtering operation, obtaining exchange power of the transformer according to an actual peak value and a target critical voltage of a current load of the transformer in the current period, evaluating the running 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 running load scores, and determining the working state scores of the transformer in the current period during vacuum oil filtering operation according to the running load scores, the vacuum degree scores corresponding to the vacuum degree range and the temperature scores corresponding to the temperature data so as to monitor the vacuum oil filtering operation of the transformer. The application solves the technical problem that the monitoring of the transformer is not complete enough during the vacuum oil filtering operation, realizes the complete monitoring of the vacuum oil filtering operation, and improves the stability and safety of the transformer during the vacuum oil filtering operation, thereby improving the efficiency of the vacuum oil filtering operation.

According to an embodiment of the present application, there is provided an embodiment of a transformer vacuum oil filtering operation monitoring method, it should be noted that the steps illustrated in the flowchart of the drawings may be performed in a computer system such as a set of computer executable instructions, and that although a logical order is illustrated in the flowchart, in some cases, the steps illustrated or described may be performed in an order different from that herein.

In this embodiment, a method for monitoring a vacuum oil filtering operation of a transformer is provided, which may be used for the transformer integrated with an oil filter, and fig. 1 is a step diagram of the method for monitoring a vacuum oil filtering operation of a transformer according to an embodiment of the present application, where as shown in the drawing, the method includes:

S100, working data of the current period transformer in vacuum oil filtering operation are obtained, wherein the working data comprise a target current load curve, a target voltage load curve, a power load curve, a vacuum degree range and temperature data.

S200, obtaining 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 period, wherein a power control error exists 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.

S300, performing operation state evaluation on 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.

S400, determining the working state score of the transformer in the vacuum oil filtering operation in the current period according to the operation load score, the vacuum degree score corresponding to the vacuum degree range and the temperature score corresponding to the temperature data so as to monitor the vacuum oil filtering operation of the transformer.

Specifically, the time length of the current period can be selected according to the actual scene, and any period can comprise the following time dimensions of day, week, month, year and the like. When the week time dimension is selected, a Zhou Fuhe curve can be obtained for the current load, the voltage load and the power load, the Zhou Fuhe curve reflects the change rule of the operation load in a week through serially connecting the daily load curves in the day, the power demand difference between the working day and the weekend is facilitated to be analyzed, and when the month or the year time dimension is selected, a month load curve or a year load curve can be obtained for the current load, the voltage load and the power load, and the month load curve or the year load curve can be formed through serially connecting the daily load curve or the week load curve, so that the change condition of each month load in a month or in a year is respectively shown, and the method has important significance for analyzing seasonal power demand change. It should be noted that, when data acquired in different periods are analyzed by comparison, the two periods for comparison should use the same time dimension.

Accordingly, the variation curves in different time dimensions can also be acquired for the vacuum range and temperature data. The change curve of the temperature data can reflect the temperature change of the transformer in the vacuum oil filtering operation, the change curve of the vacuum degree range can reflect the change range of the vacuum degree in the vacuum oil filtering operation, and whether the vacuum oil filtering operation is normally performed can be determined according to the change curve of the temperature data and the change curve of the vacuum degree range.

Further, a plurality of time periods may be included in any one cycle, and by setting a plurality of time periods to subdivide the cycle, the change in the operating load in any one cycle can be accurately grasped. For example, for a daily load curve, a time period with a time length of 6 hours can be set, so that a period corresponding to the daily load curve is divided into 4 time periods, and at this time, the running load variation trend of the transformer in different time periods in one day can be obtained through the load curves in different time periods. For example, for a transformer located in a commercial area, the power consumption in the commercial area is small in two time periods including 0 to 6 and 18 to 24 points, so the operating load of the transformer is small, and the power consumption in the commercial area is large in two time periods including 6 to 12 and 12 to 18 points, so the operating load of the transformer is large, and at this time, the two time periods including 6 to 12 and 12 to 18 points can be focused, so the transformer can be ensured to safely and stably operate, and the load requirement is met. It is understood that the length of the time period may be determined according to an actual scene.

Specifically, when a target current load curve, a target voltage load curve and a power load curve are obtained, working parameters of all nodes in the transformer are collected, and the collected working parameters comprise current and voltage. For the collected working parameters, the data which are greatly influenced by the environment are removed through screening, so that the influence of abnormal data on subsequent calculation is avoided.

In some embodiments, the data may be screened by introducing a Lagrange multiplier and a penalty factor for the current data, converting the current data related to the transformer nodes into an unconstrained problem, and obtaining a feature vector of the current data by solving the unconstrained problem, wherein the Lagrange multiplier integrates constraints of the current data, and the penalty factor is used for controlling the current data according to the constraints, thereby improving stability and accuracy of the feature vector. The feature vector is subjected to multi-modal transformation, where the multi-modal transformation includes fourier transformation or the like, to thereby obtain a frequency domain feature of the current data as a load feature of the current data. And clustering the current data according to the load characteristics, and obtaining an initial current load curve according to the cluster with the largest data quantity. Similarly, the voltage data may also be processed in the manner described above to obtain an initial voltage load curve.

Specifically, the power control error is the difference between the exchange power of the transformer and the expected power value, wherein the exchange power is the maximum power of the transformer in the current period. In this embodiment, the calculation mode of the switching power is the product between the actual peak value of the current load of the transformer and the target critical voltage, and according to the difference between the obtained switching power and the expected power value, it is determined whether the running load of the transformer meets the requirement or has a safety problem, when the power control error is positive, the switching power is lower than the expected power value, and when the power control error is negative, the switching power is higher than the expected power value. The method comprises the steps of monitoring the running load of a transformer according to a power control error, wherein the running load of the transformer is monitored according to the power control error, if the power control error is close to zero, the running load of the transformer meets the expectations and normally works in the current period, if the power control error is positive and is not close to zero, the running load of the transformer is too low and runs in the current period in a light load mode, if the power control error is negative and is not close to zero, the running load of the transformer is too high and runs in an overload mode in the current period, and the running load score of the transformer can be obtained according to the results.

Further, a point corresponding to the actual peak value of the current load in the target current load curve is referred to as a peak point, and a point corresponding to the target critical voltage in the target voltage load curve is referred to as a critical point. Before calculating the switching 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, and the calculation can be performed. In some embodiments, the matching may be performed by moving the time point corresponding to the peak point such that the time point corresponding to the peak point is the same as the time point corresponding to the critical point, and correspondingly, moving the time point corresponding to the critical point such that the time point corresponding to the critical point is the same as the time point corresponding to the peak point.

Further, since the time points at which the operation data are collected are discrete, there may be no point in the target current load curve or the target critical voltage in the target voltage load curve that can be corresponded, resulting in failure to match the peak point with the time point corresponding to the critical point. In some embodiments, the point with the highest similarity with the peak point can be found on the timestamp of the target current load curve according to the actual peak value of the current load, the interpolation point is obtained through interpolation calculation, the interpolation point is taken as the peak point, and accordingly, the point with the highest similarity with the critical point can be found on the timestamp of the target voltage load curve according to the target critical voltage, the interpolation point is obtained through interpolation calculation, and the interpolation point is taken as the critical point, so that the matching is performed.

Further, if the time difference between the peak point and the time point corresponding to the critical point exceeds the preset allowable time difference, it means that the peak point and the critical point do not occur in the same time period, and the exchange power of the transformer cannot be calculated by multiplying the peak point and the critical point. At this time, the average value of a plurality of current peaks in the current period can be obtained and used as the actual peak value of the current load corresponding to the peak point after matching, so as to calculate the exchange power, and correspondingly, 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.

In some embodiments, the operating load scoring of the transformer further comprises scoring according to the stability of the target current load curve, the target voltage load curve, and the power load curve, it being 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 scoring of the transformer, and 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 scoring of the transformer.

Further, the working state scoring form of the current period transformer in the vacuum oil filtering operation is as follows:

Wherein, Representing a working state score; scoring the vacuum degree; Scoring the operational load; Scoring the temperature; 、 And The weight coefficient can be adjusted according to the actual scene requirement to reflect the importance of each score in the working state score.

Referring to fig. 2, fig. 2 is a step diagram of a method for obtaining a target current load curve and a target voltage load curve according to an embodiment of the present application, as shown in the drawing, the target current load curve and the target voltage load curve are obtained by:

S120, adjusting an initial current load curve according to the difference value between the actual current load peak value and the target current load predicted peak value of the transformer in the current period to obtain the target current load curve, wherein the actual current load peak value is the peak value of the initial current load curve in a time period corresponding to the target current load predicted peak value.

S140, according to the corresponding relation between the voltage load weak node of the transformer and the maximum value of the target current load prediction peak value, an 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.

Specifically, the target current load curve is obtained through automatic adjustment according to the initial current load curve, and in the next period of the current period, the transformer works according to data in the target current load curve, so that the automatic adjustment of the working state of the transformer is realized. Correspondingly, the target voltage load curve is obtained by automatic adjustment according to the initial voltage load curve, and in the next period of the current period, the transformer works according to the data in the target voltage load curve, so that the automatic adjustment of the working state of the transformer is realized.

Through automatic adjustment, the transformer has a certain degree of self-adjusting capacity, so that the transformer can adapt to the environment in time and keep stable working when the external conditions change. It can be understood that if the external condition exceeds the self-adjusting capability of the transformer, the transformer cannot be automatically adjusted, and the running load score of the transformer is not satisfactory at this time, and a technician can judge whether the transformer needs to be manually adjusted according to the running load score.

As an embodiment of the present application, before the predicting peak value of the target current load of the transformer according to the current period, the adjusting the initial current load curve to obtain the target current load curve further includes:

s102, predicting according to historical current load data of the transformer to obtain an initial current load prediction peak value of the transformer in the current period.

S104, comparing the actual peak value of the current load of the transformer with the predicted peak value of the initial current load in the previous period in the same time period to obtain a predicted peak value error.

S106, adjusting the initial current load predicted peak value according to the predicted peak value error to obtain the target current load predicted peak value.

Specifically, when the initial current load curve is adjusted, the target current load predicted peak value of the transformer is taken as an adjustment basis, and the target current load predicted peak value is obtained after the initial current load predicted peak value is adjusted. The initial current load prediction peak value is obtained by prediction according to historical current load data of the transformer, and can be predicted by means of current average values and current change rates of the transformer in different periods in the historical current data, and a method for obtaining the initial current load prediction peak value is not particularly limited. It will be appreciated that the initial current load predicted peak represents preliminary predicted data on the magnitude and trend of the current during normal operation of the transformer during the current period.

Further, the initial current load predicted peak is adjusted by the predicted peak error. The predicted peak error is the difference between the actual peak value of the current load of the transformer in the previous period and the predicted peak value of the initial current load in the current period in the same time period, and the form is as follows:

Wherein, Representing a predicted peak error; Predicting a peak value for an initial current load of a present period; Is the actual peak of the current load for the previous cycle. If the prediction peak value error exceeds the preset range, the prediction accuracy is lower, the initial current load prediction peak value is not in the acceptable range, and the transformer is in an abnormal working state when working with the current size shown by the initial current load prediction peak value.

It can be understood that if the predicted peak value error does not exceed the preset range, only the initial current load predicted peak value needs to be finely adjusted so as to further improve the accuracy of prediction, and if the predicted peak value error exceeds the preset range, the initial current load predicted peak value needs to be adjusted by a set adjustment coefficient so as to correct the prediction result.

As an embodiment of the present application, the adjusting the initial current load predicted peak according to the predicted peak error to obtain the target current load predicted peak includes:

s108, if the predicted peak error exceeds a preset range, adjusting the initial current load predicted peak value based on a first current adjustment coefficient to obtain the target current load predicted peak value, wherein the first current adjustment coefficient is obtained according to the variance of the actual current load peak value of the transformer in the previous period.

S110, if the predicted peak value error does not exceed a preset range, the initial current load predicted peak value is adjusted based on a second current adjustment coefficient to obtain the target current load predicted peak value, wherein the second current adjustment coefficient is obtained according to the average value of the current peak value errors in different time periods, and the current peak value error is obtained according to the difference value between the predicted peak value error and the maximum value of the current load actual peak value of the previous period.

Specifically, for convenience of explanation, the period corresponding to the predicted peak error is set as the preset period. The first current adjustment factor is of the form:

Wherein, Representing a first current adjustment factor; the first period is the preset time period of the last period Actual peak values of the individual current loads; the average value of the actual peak values of all current loads in the previous period; Is the number of actual peaks 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, reflects the fluctuation condition of the actual peak value of the current load in the previous cycle, and is larger if the actual peak value distribution of the current load in the previous cycle is more dispersed and has larger change, and is smaller if the actual peak value distribution of the current load in the previous cycle is more concentrated and has smaller change.

Further, the second current adjustment coefficient is in the form of:

Wherein, Representing a second current adjustment factor; the actual peak value of the maximum current load in the preset time period of the previous period is set; Is the current period of Predicted peak error over a period of time; Is the number of time periods in the current cycle. It will be appreciated that the second current adjustment coefficient may fine tune the prediction when it is relatively accurate to further improve the accuracy of the prediction.

Further, the initial current load predicted peak value is adjusted according to the first current adjustment coefficient or the second current adjustment coefficient, and the adjustment form is as follows:

Wherein, Predicting a peak value for a target current load; Representing current adjustment coefficients, including a first current adjustment coefficient and a second current adjustment coefficient; Is the average of the predicted peak errors; The specific size of the constant term can be analyzed according to historical current load data of the transformer, and the optimal constant term is obtained through a method comprising an optimizing algorithm. The target current load predicted peak value and the initial current load predicted peak value in the above expression each correspond to the same time period, and the current adjustment coefficient used in the calculation corresponds to the time period. It will be appreciated that the predicted peak value of the target current load, derived from the above equation, represents the trend of the actual peak value of the current load under steady operation conditions. In other embodiments, the initial current load predicted peak may be adjusted in other ways, depending on actual scenario requirements.

As an embodiment of the present application, before 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 predicted peak value, the method further includes:

s132, determining voltage load weak nodes of the transformer based on continuous power flow calculation according to working parameters of all nodes in the transformer and network admittance matrixes among different nodes, wherein the working parameters comprise voltage and complex power.

S134, obtaining the initial critical voltage of the transformer according to the working parameters of the voltage load weak nodes.

Specifically, according to the current and the voltage of each node in the transformer, an equation of the current relation between each node is obtained based on kirchhoff current law, and an equation of the voltage relation between each node is obtained based on kirchhoff voltage law. It will be appreciated that the above equation is generally expressed based on the injection power and branch admittance of each node, illustratively for a single node havingSystems of individual nodes, availableA nonlinear equation of the form:

Wherein, Is the firstComplex power of individual nodes; Is the first Active power of individual nodes; Is the first Reactive power of individual nodes; Is the first The voltage of the individual nodes; Is the first Personal node and the firstAdmittance between the individual nodes for describing the relationship between current and voltage in the circuit; Is the first The conjugate complex number of the voltage of each node is used for calculating the real part and the imaginary part of complex power, namely active power and reactive power; Is the first The imaginary part of the reactive power of the individual nodes. According to the topological structure among the nodes in the transformer and the electrical parameters of the branches among the nodes, including resistance, reactance and the like, solving the equation to obtain admittance of each branch, filling the obtained result into a symmetric matrix to obtain a network admittance matrix, wherein elements in the network admittance matrix represent the electrical connection relations among different nodes, and the network topological structure corresponding to the corresponding node and the complex power corresponding to the electrical parameters can be determined according to the network admittance matrix.

Further, in the continuous power flow calculation process, working parameters of each node in the current period transformer are used as initial conditions of continuous power flow calculation, and parameters are used as parametersRepresenting the load level of the transformer, the parameters being increased stepwise in each iterationTo simulate an increase in load. And secondly, based on the working parameters of all the nodes in the current period transformer and the increasing direction of the load, the working parameters of all the nodes in the next period transformer are predicted, and the prediction method comprises a tangent method, a line cutting method and the like by way of example, so that the working parameters of all the nodes in the current period transformer are utilized to estimate the working parameters of all the nodes in the next period transformer.

Further, after the prediction results of the working parameters of all the nodes in the next period transformer are obtained, the prediction results are calibrated by using a Newton-Raphson method or other nonlinear solvers, and the precision of the prediction results is improved through multiple iterations, so that the calibrated prediction results meet the original tide equation. The Newton-Lapherson method is a numerical solution for solving a nonlinear equation, and is particularly suitable for finding the root of the equation. The Newton-Laporthson method has the advantages of rapid convergence and high calculation efficiency.

Further, as the load increases, the above steps of predicting and calibrating are repeated continuously, when the voltage breakdown point of any node is reached, the voltage of the node drops sharply as the load increases, which means that the node approaches a load critical state, and the working parameters capable of stable operation cannot be obtained by continuing iteration, and the iteration is ended, and the voltage value obtained at this time is used as the critical voltage of the node. Recording all results in continuous power flow calculation, obtaining a PV curve of each node in the transformer according to the recorded results, and representing the relation between the voltage and the transmission power in each node, wherein the information of the stability margin of the transformer system can be obtained according to the PV curve. And taking the node with the voltage suddenly reduced as a voltage load weak node, and obtaining the critical voltage of each voltage load weak node as an initial critical voltage.

As an embodiment of the present application, 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 predicted peak value to obtain the target voltage load curve, and obtaining the target critical voltage according to the target voltage load curve includes:

S136, if a maximum value of the target current load prediction peak value exists in a target current load curve corresponding to any voltage load weak node, adjusting the initial voltage load curve based on a first voltage adjustment coefficient to obtain the target voltage load curve, wherein the first voltage adjustment coefficient is obtained according to the voltage of any voltage load weak node.

S138, if the maximum value of the target current load prediction peak value does not exist in all target current load curves corresponding to the voltage load weak nodes, 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, the initial critical voltage and the rated voltage of the transformer.

S139, calculating the critical voltage according to the target voltage load curve to obtain the target critical voltage.

Specifically, the first voltage adjustment coefficient is related to the voltage of the voltage load weak node, and in a target current load curve corresponding to any voltage load weak node, if the maximum value of the target current load prediction peak exists, the critical voltage of the voltage load weak node is taken as the first voltage adjustment coefficient, and the first voltage adjustment coefficient is taken as an adjustment value to adjust the initial voltage load curve. Illustratively, the target voltage load curve obtained from the first voltage adjustment factor is in the form of:

Wherein, Representing a first voltage adjustment factor; an initial voltage load curve corresponding to the voltage load weak node; and a target voltage load curve corresponding to the voltage load weak node. In other embodiments, the initial voltage load curve may be adjusted in other ways, as desired in the actual scenario.

Further, the second voltage adjustment coefficient is in the form of:

Wherein, Representing the second voltage adjustment coefficient in percent form; Working parameters corresponding to voltage load weak nodes in the current period transformer; A threshold voltage of a weak node for the voltage load; Is the rated voltage of the transformer. And if the second voltage adjustment coefficient is negative, the voltage load weak node exceeds the critical point and enters an unstable area.

In some embodiments, the target voltage load curve derived from the second voltage adjustment factor is in the form of:

Wherein, Representing a second voltage adjustment factor. In other embodiments, the initial voltage load curve may be adjusted in other ways, as desired in the actual scenario.

Further, after the target voltage load curve is obtained, the critical voltage of each voltage load weak node in the transformer also changes, and needs to be recalculated. The calculation step of the target threshold voltage is the same as the step of obtaining the initial threshold voltage.

As one embodiment of the present application, the vacuum score is determined according to the following:

S410, determining the vacuum degree score according to the symmetry 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 magnitude of the symmetry difference.

Specifically, the optimal vacuum degree range is related to the designed vacuum degree range of the transformer, and the optimal vacuum degree range in the actual operation process is changed in real time with the change of external conditions. Illustratively, when the ambient temperature increases, the minimum value in the optimal vacuum degree range increases while the maximum value in the optimal vacuum degree range is hardly changed, and when the ambient temperature decreases, the minimum value in the optimal vacuum degree range decreases while the maximum value in the optimal vacuum degree range is hardly changed.

Further, the vacuum degree range obtained by measurement is set asThe optimal vacuum degree range of the transformer isThe symmetry difference between the two is. It will be appreciated that the vacuum degree score is inversely proportional to the magnitude of the symmetry difference, the larger the interval range encompassed by the symmetry difference, the lower the vacuum degree score, and the smaller the interval range encompassed by the symmetry difference, the higher the vacuum degree score.

As an embodiment of the application, the temperature score is determined according to the following manner:

s420, carrying out weighted calculation according to the heating power, the heat preservation rate and the dielectric loss of the transformer to obtain a temperature correlation coefficient.

S422, determining the temperature score according to the temperature correlation coefficient, wherein the temperature score is inversely proportional to the temperature correlation coefficient.

Specifically, the heating power of the transformer is related to the temperature difference between the inside and outside of the transformer, and when the inside temperature of the transformer is higher than the ambient temperature, the heating power increases. The form of the heating power of the transformer is as follows:

Wherein, Representing the heating power of the transformer; The heat conduction coefficient of the transformer is related to the material, structure and insulating medium of the transformer and is constant; Is the internal temperature of the transformer; Is the ambient temperature at which the transformer is located.

Further, the heat preservation rate of the transformer refers to the ratio of heat dissipated by the transformer through its housing to the total heating power in unit time, and is as follows:

Wherein, The heat preservation rate of the transformer is represented; the heat dissipated by the transformer through the shell can be calculated by measuring the shell temperature and the environment temperature of the transformer and by a heat conduction formula.

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

Wherein, Representing 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 Can be obtained by a dielectric constant measuring instrument.

Further, the form of the temperature correlation coefficient is as follows:

Wherein, Representing a temperature correlation coefficient; the normalized heating power; the heat preservation rate after normalization; is the normalized dielectric loss; 、 And Respectively weight coefficients, satisfy。

Illustratively, a transformerThe internal temperature of (2) was 50 ℃, the ambient temperature was 20 ℃, and the heating power was 2000W. At this time, the transformer can be obtained by calculationThe heat dissipated by the shell is 500W, the heat preservation rate is 0.25, and the transformer can be obtained by measurementThe dielectric loss of (2) was 0.01. Order theThe total number of the components is 0.4,The total number of the components is 0.3,0.4, The transformer is obtained by calculationThe temperature dependence coefficient of (2) is 0.301. Similarly, another transformerThe internal temperature of (2) is 60 ℃, the ambient temperature is 20 ℃, and the heating power is 2400W. At this time, the transformer can be obtained by calculationThe heat dissipated by the shell is 600W, the heat preservation rate is 0.25, and the transformer can be obtained by measurementIs 0.015. Order theThe total number of the components is 0.4,The total number of the components is 0.3,0.4, The transformer is obtained by calculationThe temperature dependence coefficient of (2) is 0.85.

The temperature-dependent coefficient reflects the overall thermal performance and insulation conditions of the transformer as a function of the internal temperature of the transformer, ranging fromThe larger the temperature correlation coefficient is, the positive correlation is formed between the thermal performance of the whole transformer and the internal temperature of the whole transformer, and the smaller the temperature correlation coefficient is, the no relation is formed between the thermal performance of the transformer and the internal temperature of the transformer. According to transformersAnd a transformerIt can be seen that as the temperature increases, the temperature dependence coefficient also increases, which indicates that the overall thermal performance and insulation condition of the transformer decreases with increasing temperature, and therefore the temperature score is inversely proportional to the temperature dependence coefficient, the higher the temperature dependence coefficient, the lower the temperature score, and the lower the temperature dependence coefficient, the higher the temperature score.

In some embodiments, the internal temperature of the transformer is also determined by monitoring the temperature, humidity, and flow of transformer oil during oil filtration operations. The temperature is collected by using high-precision and quick-response temperature sensors, including thermocouples, thermal resistors (such as PT100 and the like) or digital temperature sensors (such as DS18B20 and the like) and the like, and the sensors can accurately measure the temperature of the inside of the transformer and the oil filtering working area. The above-described sensor converts the temperature signal into an electrical signal (e.g., 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. Meanwhile, the data CAN also be transmitted to an upper computer or a cloud platform in a wired (such as RS485 or CAN bus and the like) or wireless (such as Wi-Fi or Bluetooth and the like) mode, and when the temperature exceeds a preset safety range, the microprocessor CAN trigger an alarm mechanism, such as audible and visual alarm or send alarm information through a communication interface. In addition, the microprocessor can also control the switch of the heating or cooling equipment according to the temperature change so as to maintain the temperature stability inside the transformer.

As an embodiment of the present application, the method further comprises:

s510, determining the working state of the transformer according to the working state score of the transformer in the vacuum oil filtering operation in the current period.

S520, if the working state of the transformer has abnormal conditions, corresponding measures are taken according to the abnormal conditions.

Specifically, the working state of the transformer is determined according to the working state scores and the comparison results of the individual scores and corresponding preset score thresholds. The method comprises the steps of setting a first work grading threshold value, a second work grading threshold value and a third work grading threshold value for the work state grading of the transformer, wherein the first work grading threshold value is higher than the second work grading threshold value, the second work grading threshold value is higher than the third work grading threshold value, judging that the transformer works normally if the work state grading is higher than the first work grading threshold value, judging that the work state of the transformer has abnormal conditions if the work state grading is lower than the first work grading threshold value and higher than the second work grading threshold value, and needing to perform slight early warning, judging that the work state of the transformer has serious abnormal conditions if the work state grading is lower than the second work grading threshold value and higher than the third work grading threshold value, and judging that the transformer has faults and needs to stop working if the work state grading is lower than the third work grading threshold value.

The vacuum degree score of the transformer is higher than the vacuum degree score threshold value, which indicates that the transformer works normally, and the vacuum degree score of the transformer is lower than the vacuum degree score threshold value, which indicates that the transformer has a problem of too low vacuum degree, and the vacuum degree score of the transformer needs to be correspondingly adjusted, including checking and replacing the vacuum pump. If the operating load score of the transformer is lower than the operating load score threshold, the problem that the operating load of the transformer is too high is solved, and corresponding adjustment is needed, including adjustment of the operating load distribution and the like. If the temperature score of the transformer is lower than the temperature score threshold, the transformer has an overhigh temperature, and corresponding adjustment is needed, including adding heat dissipation measures or optimizing fan layout and the like. The working stability of the transformer under the vacuum oil filtering operation is improved by adjusting the working parameters through the measures.

Referring to fig. 3, fig. 3 is a flowchart of a method for monitoring a transformer vacuum oil filtering operation according to an embodiment of the present application, as shown in the fig. 3, as an embodiment of the present application, a method for obtaining an exchange power of the transformer according to the present application includes the following steps:

S602, predicting according to historical current load data of the transformer to obtain an initial current load prediction peak value of the transformer in the current period.

S604, comparing the actual peak value of the current load of the transformer with the predicted peak value of the initial current load in the previous period in the same time period to obtain a predicted peak value error.

S606, if the predicted peak error exceeds a preset range, adjusting the initial current load predicted peak value based on a first current adjustment coefficient to obtain the target current load predicted peak value, wherein the first current adjustment coefficient is obtained according to the variance of the actual current load peak value of the transformer in the previous period.

S608, if the predicted peak value error does not exceed a preset range, the initial current load predicted peak value is adjusted based on a second current adjustment coefficient to obtain the target current load predicted peak value, wherein the second current adjustment coefficient is obtained according to the average value of the current peak value errors in different time periods, and the current peak value error is obtained according to the difference value between the predicted peak value error and the maximum value of the actual peak value of the current load in the previous period.

S610, adjusting the initial current load curve according to the difference value between the actual current load peak value and the target current load predicted peak value of the transformer in the current period to obtain the target current load curve, wherein the actual current load peak value is the peak value of the initial current load curve in a time period corresponding to the target current load predicted peak value.

S612, determining voltage load weak nodes of the transformer based on continuous power flow calculation according to working parameters of all nodes in the transformer and network admittance matrixes among different nodes, wherein the working parameters comprise voltage and complex power.

S614, obtaining the initial critical voltage of the transformer according to the working parameters of the voltage load weak nodes.

S616, if a maximum value of the target current load prediction peak value exists in a target current load curve corresponding to any voltage load weak node, adjusting the initial voltage load curve based on a first voltage adjustment coefficient to obtain the target voltage load curve, wherein the first voltage adjustment coefficient is obtained according to the voltage of any voltage load weak node.

S618, if the maximum value of the target current load prediction peak value does not exist in all target current load curves corresponding to the voltage load weak nodes, 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, the initial critical voltage and the rated voltage of the transformer.

S620, calculating the critical voltage according to the target voltage load curve to obtain the target critical voltage.

S622, obtaining 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 period, wherein a power control error exists between the exchange power and the expected power value of the transformer, and the actual peak value of the current load is obtained according to the target current load curve.

Specifically, through the steps, a target current load curve, a target voltage load curve, a power load curve and a power control error are obtained, so that the operation load of the transformer can be scored when the vacuum oil filtering operation is performed, whether the transformer normally operates or not is monitored, the vacuum oil filtering operation can be normally performed, the efficiency of the vacuum oil filtering operation is improved, and the working efficiency of the transformer is further improved.

Accordingly, referring to fig. 4, an embodiment of the present application provides a transformer vacuum oil filtering operation monitoring system, which includes:

The working data acquisition module 100 is used for acquiring working data of the current period transformer in vacuum oil filtering operation, wherein the working data comprise a target current load curve, a target voltage load curve, a power load curve, a vacuum degree range and temperature data.

And the switching power calculation module 200 is configured to obtain switching power of the transformer according to an actual peak value of a current load of the transformer in a current period and a target critical voltage, where a power control error exists between the switching power and an 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.

And the operation load scoring module 300 is configured to evaluate an 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, so as to obtain an operation load score.

The working state scoring module 400 is configured to determine a working state score of the transformer in the vacuum oil filtering operation in the current period according to the running load score, the vacuum degree score corresponding to the vacuum degree range, and the temperature score corresponding to the temperature data, so as to monitor the vacuum oil filtering operation of the transformer.

In some alternative embodiments, the working data acquisition module 100 includes:

the current load adjusting unit is used for adjusting an initial current load curve according to the difference value 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 period 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 a time period corresponding to the predicted peak value of the target current load.

And the voltage load adjusting unit is used for adjusting an initial voltage load curve according to the corresponding relation 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.

In some alternative embodiments, the operational data acquisition module 100 further comprises:

And the current peak value prediction unit is used for predicting according to the historical current load data of the transformer to obtain an initial current load prediction peak value of the transformer in the current period.

And the prediction error calculation unit is used for comparing the actual peak value of the current load of the transformer with the initial current load prediction peak value in the previous period in the same time period to obtain a prediction peak value error.

And the predicted peak value adjusting unit is used for adjusting the initial current load predicted peak value according to the predicted peak value error to obtain the target current load predicted peak value.

In some alternative embodiments, the predicted peak adjusting unit includes:

And the first prediction adjustment subunit is used for adjusting the initial current load prediction peak value, and 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 according to the variance of the actual current load peak value of the transformer in the previous period.

And the second prediction adjustment subunit is used for adjusting the initial current load prediction peak value, and adjusting the initial current load prediction peak value based on a second current adjustment coefficient to obtain the target current load prediction peak value if the prediction peak value error does not exceed a preset range, wherein the second current adjustment coefficient is obtained according to the average value of the current peak value errors in different time periods, and the current peak value error is obtained according to the difference value between the prediction peak value error and the maximum value of the current load actual peak value of the last period.

And the weak node determining unit is used for determining the voltage load weak node of the transformer based on continuous power flow calculation according to the working parameters of all the nodes in the transformer and the network admittance matrix among different nodes, wherein the working parameters comprise voltage and complex power.

And the initial critical voltage calculation unit is used for obtaining the initial critical voltage of the transformer according to the working parameters of each voltage load weak node.

In some alternative embodiments, the voltage load adjustment unit includes:

The first voltage adjustment subunit is used for adjusting an initial voltage load curve, and if the maximum value of the target current load prediction peak value exists in the target current load curve corresponding to any voltage load weak node, the initial voltage load curve is adjusted based on a first voltage adjustment coefficient to obtain the target voltage load curve, wherein the first voltage adjustment coefficient is obtained according to the voltage of any voltage load weak node.

And the second voltage adjustment subunit is used for adjusting an initial voltage load curve, and if the maximum value of the target current load prediction peak value 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 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, the initial critical voltage and the rated voltage of the transformer.

And the target critical voltage calculating subunit is used for calculating the critical voltage according to the target voltage load curve to obtain the target critical voltage.

In some alternative embodiments, the operational status scoring module 400 includes:

and the vacuum degree scoring unit is used for determining the vacuum degree score according to the symmetry 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 magnitude of the symmetry difference.

In some alternative embodiments, the operational status scoring module 400 further includes:

The temperature correlation coefficient calculation unit is used for carrying out weighted calculation according to the heating power, the heat preservation rate and the dielectric loss of the transformer to obtain a temperature correlation coefficient;

and the temperature scoring unit is used for determining the temperature score according to the temperature correlation coefficient, wherein the temperature score is inversely proportional to the temperature correlation coefficient.

In some alternative embodiments, the system further comprises:

the working state determining module is used for determining the working state of the transformer according to the working state score of the transformer in the vacuum oil filtering operation in the current period;

The abnormal condition processing module is used for processing abnormal conditions, and if the working state of the transformer has abnormal conditions, corresponding measures are taken according to the abnormal conditions.

Further functional descriptions of the above respective modules and units are the same as those of the above corresponding embodiments, and are not repeated here.

The transformer vacuum oil filtering 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 a memory that execute one or more software or firmware programs, and/or other devices that can provide the above functions.

Referring to fig. 5, fig. 5 is a schematic structural diagram of a computer device according to an embodiment of the present application, and the computer device includes one or more processors 10, a memory 20, and interfaces for connecting the components, including a high-speed interface and a low-speed interface. The various components are communicatively coupled to each other using different buses and may be mounted on a common motherboard or in other manners as desired. The processor may process instructions executing within the computer device, including instructions stored in or on memory to display graphical information of the GUI on an external input/output device, such as a display device coupled to the interface. In some alternative embodiments, multiple processors and/or multiple buses may be used, if desired, along with multiple memories and multiple memories. Also, multiple computer devices may be connected, each providing a portion of the necessary operations (e.g., as a server array, a set of blade servers, or a multiprocessor system). One processor 10 is illustrated in fig. 5.

The processor 10 may be a central processor, a network processor, or a combination thereof. The processor 10 may further include a hardware chip, among others. The hardware chip may be an application specific 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.

Wherein the memory 20 stores instructions executable by the at least one processor 10 to cause the at least one processor 10 to perform the methods shown in implementing the above embodiments.

The memory 20 may include a storage program area that may store an operating system, application programs required for at least one function, and a storage data area that may store data created according to the use of the computer device, etc. In addition, the memory 20 may include high-speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid-state storage device. In some alternative embodiments, memory 20 may optionally include memory located remotely from processor 10, which may be connected to the computer device via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.

The memory 20 may comprise volatile memory, such as random access memory, or nonvolatile memory, such as flash memory, hard disk or solid state disk, or the memory 20 may comprise a combination of the above types of memory.

The computer device also includes a communication interface 30 for the computer device to communicate with other devices or communication networks.

The embodiments of the present application also provide a computer readable storage medium, and the method according to the embodiments of the present application described above may be implemented in hardware, firmware, or as a computer code which may be recorded on a storage medium, or as original stored in a remote storage medium or a non-transitory machine readable storage medium downloaded through a network and to be stored in a local storage medium, so that the method described herein may be stored on such software process on a storage medium using a general purpose computer, a special purpose processor, or programmable or special purpose hardware. The storage medium may be a magnetic disk, an optical disk, a read-only memory, a random-access memory, a flash memory, a hard disk, a solid state disk, or the like, and further, the storage medium may further include a combination of the above types of memories. It will be appreciated that a computer, processor, microprocessor controller or programmable hardware includes a storage element that can store or receive software or computer code that, when accessed and executed by the computer, processor or hardware, implements the methods illustrated by the above embodiments.

Embodiments of the present application provide a computer program product comprising computer instructions stored in a computer-readable storage medium. The processor of the computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions to cause the computer device to perform the method of any of the embodiments of the application.

Although embodiments of the present application have been described in connection with the accompanying drawings, various modifications and variations may be made by those skilled in the art without departing from the spirit and scope of the application, and such modifications and variations fall within the scope of the application as defined by the appended claims.

The system, apparatus, module or unit set forth in the above embodiments may be implemented in particular by a computer chip or entity, or by a product having a certain function. One typical implementation is a computer. In particular, the computer may be, for example, a personal computer, a laptop computer, a cellular telephone, 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.

For convenience of description, the above devices are described as being functionally divided into various units, respectively. Of course, the functions of each element may be implemented in the same piece or pieces of software and/or hardware when implementing the present application.

It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.

The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises an element.

In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments. In particular, for system embodiments, since they are substantially similar to method embodiments, the description is relatively simple, as relevant to see a section of the description of method embodiments.

The foregoing is merely exemplary of the present application and is not intended to limit the present application. Various modifications and variations of the present application will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. which come within the spirit and principles of the application are to be included in the scope of the claims of the present application.

Although embodiments of the present application have been described in connection with the accompanying drawings, various modifications and variations may be made by those skilled in the art without departing from the spirit and scope of the application, and such modifications and variations are within the scope of the application as defined by the appended claims.

Claims

1. A method for monitoring vacuum oil filtering operation of a transformer, the method comprising:

Obtaining the exchange power of the transformer according to the actual peak value of the current load of the transformer in the current period and the target critical voltage, wherein a power control error exists 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, 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 period, and the calculation mode of the exchange power is the product between the actual peak value of the current load and the target critical voltage;

The target voltage load curve is obtained by adjusting an initial voltage load curve according to the corresponding relation between a voltage load weak node of the transformer and the maximum value of the target current load prediction peak value, so as to obtain the target voltage load curve, and the target critical voltage is obtained according to the target voltage load curve;

Before 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 predicted peak value to obtain the target voltage load curve, the method further comprises:

obtaining the initial critical voltage of the transformer according to the working parameters of each voltage load weak node;

The step of adjusting an 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:

calculating a critical voltage according to the target voltage load curve to obtain the target critical voltage;

2. The method according to claim 1, characterized in that the target current load curve is obtained by:

And adjusting an initial current load curve according to the difference value between the actual current load peak value and the target current load predicted peak value of the transformer in the current period to obtain the target current load curve, wherein the actual current load peak value is the peak value of the initial current load curve in a time period corresponding to the target current load predicted peak value.

3. The method of claim 2, wherein prior to said adjusting an initial current load curve to obtain said target current load curve based on said target current load predicted peak value of said transformer for a present period, further comprising:

4. A method according to claim 3, wherein said adjusting said initial current load predicted peak value based on said predicted peak error to obtain said target current load predicted peak value comprises:

5. The method of claim 1, wherein the vacuum score is determined according to the following:

6. The method of claim 1, wherein the temperature score is determined according to the following:

7. The method according to any one of claims 1 to 6, further comprising:

8. A transformer vacuum oil filtration operation monitoring system, the system comprising:

The switching power calculation module is used for obtaining switching power of the transformer according to an actual peak value of a current load of the transformer and a target critical voltage in a current period, wherein a power control error exists between the switching power and an expected power value of the transformer, the actual peak value of the current load is obtained according to the target current load curve, the target critical voltage is obtained by calculating the critical voltage according to the target voltage load curve, the switching power is the maximum power of the transformer in the current period, and the calculating mode of the switching power is the product between the actual peak value of the current load and the target critical voltage;

The working data acquisition module comprises a voltage load adjustment unit, a working data acquisition module and a working data acquisition module, wherein the voltage load adjustment unit is used for adjusting an initial voltage load curve according to the corresponding relation between a 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, and the working data acquisition module further comprises:

the weak node determining unit is used for determining voltage load weak nodes of the transformer based on continuous power flow calculation according to working parameters of all nodes in the transformer and a network admittance matrix among different nodes, wherein the working parameters comprise voltage and complex power;

an initial critical voltage calculation unit, configured to obtain an initial critical voltage of the transformer according to the working parameters of each voltage load weak node;

the voltage load adjustment unit includes:

The first voltage adjustment subunit is used for adjusting an initial voltage load curve, and if the maximum value of the target current load prediction peak value exists in a target current load curve corresponding to any voltage load weak node, the initial voltage load curve is adjusted based on a first voltage adjustment coefficient to obtain the target voltage load curve, wherein the first voltage adjustment coefficient is obtained according to the voltage of any voltage load weak node;

the second voltage adjustment subunit is used for adjusting an initial voltage load curve, and if the maximum value of the target current load prediction peak value 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 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, the initial critical voltage and the rated voltage of the transformer;

A target critical voltage calculating subunit, configured to perform critical voltage calculation according to the target voltage load curve, so as to obtain the target critical voltage;

9. A computer device, comprising:

A memory and a processor in communication with each other, the memory having stored therein computer instructions which, upon execution, cause the processor to perform the method of any of claims 1 to 7.

10. A computer readable storage medium having stored thereon computer instructions for causing a computer to perform the method of any one of claims 1 to 7.