Abnormal data correction method and device, electronic equipment and readable storage medium

Abstract

The invention relates to the technical field of power equipment monitoring, and discloses an abnormal data correction method and device, electronic equipment and a readable storage medium. The method comprises the following steps: acquiring an actual oil domain temperature sequence with abnormal data; obtaining a pre-constructed transformer model; the pre-constructed transformer model is established by using simulation software according to the transformer operation parameters of the actual transformer corresponding to the actual oil domain temperature sequence; performing first temperature field simulation calculation on the pre-constructed transformer model to obtain a first simulation oil domain temperature sequence; and correcting abnormal elements in the actual oil domain temperature sequence according to the first simulated oil domain temperature sequence and the actual oil domain temperature sequence to obtain a correction result. The method does not depend on historical or actually measured data, the accurate simulated oil domain temperature sequence is obtained through simulation by building the transformer model with the operation parameters consistent with the actual transformer, and the actually detected abnormal oil temperature can be corrected with the simulated oil domain temperature sequence as the standard.

Description

Technical Field

[0001] The present disclosure relates to the technical field of power equipment monitoring, and in particular to an abnormal data correction method, device, electronic device and readable storage medium. Background Art

[0002] As the scale of modern power grids continues to expand, society's electricity demand is also growing, and higher requirements are placed on the performance and work efficiency of electrical equipment. As a key equipment in the power grid, the efficient and stable operation of power transformers determines the stability of the power grid and is also related to the transformation to a new type of power grid. The accurate correction of abnormal transformer oil temperature data is of great significance for the normal operation and maintenance of mechanical equipment and avoiding unnecessary maintenance and downtime caused by erroneous data analysis.

[0003] At present, the abnormal transformer oil temperature data can be corrected by using statistical methods by analyzing historical data or a large amount of measured data. Common statistical methods include regression analysis, time series analysis, cluster analysis, etc. However, the accuracy of such correction methods depends on the quality and completeness of the historical data or measured data used. If the data is insufficient, noisy, missing or outliers exist, the correction results may be affected; in addition, sufficient historical data or measured data are required to establish a statistical model. If the data sampling frequency is insufficient or uneven, the correction results may also be limited. Therefore, it is urgent to propose a method that can accurately correct abnormal transformer oil temperature data. Summary of the invention

[0004] In view of the above situation, the embodiments of the present disclosure provide an abnormal data correction method, device, electronic device and readable storage medium, aiming to solve the above problem or at least partially solve the above problem.

[0005] In a first aspect, an embodiment of the present disclosure provides a method for correcting abnormal data, the method comprising:

[0006] Obtain the actual oil field temperature series with abnormal data;

[0007] Obtaining a pre-built transformer model; the pre-built transformer model is established using simulation software according to transformer operating parameters of an actual transformer corresponding to the actual oil domain temperature sequence;

[0008] Performing a first temperature field simulation calculation on the pre-built transformer model to obtain a first simulated oil domain temperature sequence;

[0009] According to the first simulated oil domain temperature sequence and the actual oil domain temperature sequence, abnormal elements in the actual oil domain temperature sequence are corrected to obtain a correction result.

[0010] In a second aspect, the present disclosure also provides an abnormal data correction device, the device comprising:

[0011] An abnormal data acquisition module is used to obtain the actual oil field temperature sequence with abnormal data;

[0012] A model acquisition model is used to acquire a pre-built transformer model; the pre-built transformer model is established by using simulation software according to transformer operating parameters of an actual transformer corresponding to the actual oil domain temperature sequence;

[0013] A simulation module, used for performing a first temperature field simulation calculation on the pre-built transformer model to obtain a first simulation oil domain temperature sequence;

[0014] The correction module is used to correct the abnormal elements in the actual oil domain temperature sequence according to the first simulated oil domain temperature sequence and the actual oil domain temperature sequence to obtain a correction result.

[0015] In a third aspect, an embodiment of the present disclosure further provides an electronic device, comprising: a processor; and a memory arranged to store computer executable instructions, wherein when the executable instructions are executed, the processor executes the steps of the above-mentioned abnormal data correction method.

[0016] In a fourth aspect, an embodiment of the present disclosure further provides a computer-readable storage medium, which stores one or more programs. When the one or more programs are executed by an electronic device including multiple application programs, the electronic device executes the steps of the above-mentioned abnormal data correction method.

[0017] By means of the above technical scheme, the abnormal data correction method, device, electronic device and readable storage medium provided by the embodiment of the present disclosure, compared with the prior art, the correction scheme provided by the embodiment of the present disclosure does not rely on the quality, quantity and integrity of historical data or measured data, but by obtaining the actual oil domain temperature sequence with abnormal data; using simulation software, according to the transformer operating parameters of the actual transformer, a pre-constructed transformer model is established; the pre-constructed transformer model is simulated and calculated for the first temperature field to obtain the first simulated oil domain temperature sequence of the transformer; by comparing the first simulated oil domain temperature sequence with the actual oil domain temperature sequence, the abnormal elements in the actual oil domain temperature sequence are corrected to obtain the correction result. It can be seen that since the operating parameters of the pre-constructed transformer model are consistent with the actual transformer, the first simulated oil domain temperature sequence obtained by simulation can more accurately reflect the value and change of the insulating oil temperature in the actual transformer, so the first simulated oil domain temperature sequence is used as the standard to correct the abnormal oil temperature actually detected. By correcting the abnormal transformer oil temperature data, the operating status of the transformer can be better monitored, and the maintenance and maintenance strategies can be optimized, thereby improving the reliability and operating efficiency of the transformer, which has high reliability and engineering practice significance.

[0018] The above description is only an overview of the technical solution of the present disclosure. In order to more clearly understand the technical means of the present disclosure, it can be implemented according to the contents of the specification. In order to make the above and other purposes, features and advantages of the present disclosure more obvious and easy to understand, the specific implementation methods of the present disclosure are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The drawings described herein are used to provide a further understanding of the present disclosure and constitute a part of the present disclosure. The illustrative embodiments of the present disclosure and their descriptions are used to explain the present disclosure and do not constitute an improper limitation on the present disclosure. In the drawings:

[0020] Figure 1 A schematic diagram of a process of an abnormal data correction method provided by an embodiment of the present disclosure is shown;

[0021] Figure 2 A front view of an initial transformer model provided by an embodiment of the present disclosure is shown;

[0022] Figure 3 A schematic diagram of the structure of an abnormal data correction device provided by an embodiment of the present disclosure is shown;

[0023] Figure 4 A schematic structural diagram of an electronic device provided by an embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0024] In order to make the purpose, technical solutions and advantages of the present disclosure clearer, the technical solutions of the present disclosure will be clearly and completely described below in combination with the specific embodiments of the present disclosure and the corresponding drawings. Obviously, the described embodiments are only part of the embodiments of the present disclosure, not all of the embodiments. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present disclosure.

[0025] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, further definition and explanation thereof is not required in subsequent drawings.

[0026] It should be noted that the terms "first", "second", etc. in the specification and claims of the present disclosure and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that such usage is interchangeable where appropriate, so that the embodiments of the present disclosure described herein can be implemented in an order other than those illustrated or described herein. In addition, the term "including" and its variants are to be interpreted as open-ended terms meaning "including but not limited to".

[0027] As introduced above, in the prior art, abnormal transformer oil temperature data can be corrected by using statistical methods by analyzing historical data or a large amount of measured data. Common statistical methods include regression analysis, time series analysis, cluster analysis, etc. However, the accuracy of such correction methods depends on the quality and completeness of the historical data or measured data used. If the data is insufficient, noisy, missing or outliers are present, the correction results may be affected; in addition, sufficient historical data or measured data are required to establish a statistical model. If the data sampling frequency is insufficient or uneven, the correction results may also be limited. Based on this, the present invention proposes an abnormal data correction method, device, electronic device and readable storage medium, and the present disclosure is described in detail below through specific embodiments.

[0028] To facilitate understanding of the present embodiment, a method for correcting abnormal data disclosed in the embodiment of the present disclosure is first introduced in detail. The execution subject of the method for correcting abnormal data provided in the embodiment of the present disclosure is generally a computer device with certain computing capabilities, and the computer device includes, for example: a terminal device or a server or other processing device, and the terminal device may be a user equipment (UE), a mobile device, a user terminal, a terminal, etc. In some possible implementations, the method for correcting abnormal data may be implemented by a processor calling a computer-readable instruction stored in a memory.

[0029] Figure 1The flowchart of the abnormal data correction method provided by the embodiment of the present disclosure is shown. Figure 1 It can be seen that the embodiment of the present disclosure at least includes steps S101 to S104:

[0030] Step S101: Acquire the actual oil region temperature sequence with abnormal data.

[0031] During implementation, a temperature sensor can be used to detect the temperature of the insulating oil in the transformer in real time. Through analysis, abnormal data can be located in the detected temperature data, so as to obtain the actual oil domain temperature sequence including the abnormal data. Here, the temperature in the actual oil domain temperature sequence can be the top oil temperature of the insulating oil, or the middle oil temperature of the insulating oil, etc., which is not limited in the embodiments of the present disclosure. It should be noted that the temperature in the subsequent first simulation oil domain temperature sequence is consistent with the temperature in the actual oil domain temperature sequence. For example, if the temperature in the actual oil domain temperature sequence is the top oil temperature, the temperature in the subsequent first simulation oil domain temperature sequence is also the top oil temperature.

[0032] Step S102: obtaining a pre-built transformer model; the pre-built transformer model is established by using simulation software according to transformer operating parameters of an actual transformer corresponding to the actual oil domain temperature sequence.

[0033] Here, the simulation software is a software tool that can simulate the interaction and influence between multiple physical fields, such as COMSOL Multiphysics, ANSYS Multiphysics and other software, which is not limited in the embodiments of the present disclosure. During implementation, after obtaining the actual oil domain temperature sequence, the transformer operating parameters of the actual transformer that generates the sequence can be obtained from the transformer workstation, and the pre-constructed transformer model can be constructed in the simulation software according to the transformer operating parameters.

[0034] Step S103: performing a first temperature field simulation calculation on the pre-built transformer model to obtain a first simulated oil domain temperature sequence.

[0035] After simulating the actual running transformer to obtain the pre-constructed transformer model, the simulation software can be used to perform temperature field simulation calculations on the simulation model to obtain the temperature field distribution data of the pre-constructed transformer model, so that the first simulation oil domain temperature sequence can be obtained from the temperature field distribution data.

[0036] Step S104: According to the first simulated oil domain temperature sequence and the actual oil domain temperature sequence, the abnormal elements in the actual oil domain temperature sequence are corrected to obtain a correction result.

[0037] After obtaining the first simulated oil domain temperature sequence, the correct oil temperature data corresponding to the abnormal data in the first simulated oil domain temperature sequence can be found by comparing the first simulated oil domain temperature sequence with the actual oil domain temperature sequence, and the abnormal oil temperature data can be replaced with the correct oil temperature data, thereby correcting the abnormal oil temperature data of the transformer and obtaining a correction result.

[0038] from Figure 1 It can be seen from the method shown that, compared with the prior art, the correction scheme provided by the embodiment of the present disclosure does not rely on the quality, quantity and integrity of historical data or measured data, but obtains the actual oil domain temperature sequence with abnormal data; uses simulation software to establish a pre-built transformer model according to the transformer operating parameters of the actual transformer; performs a first temperature field simulation calculation on the pre-built transformer model to obtain the first simulated oil domain temperature sequence of the transformer; and corrects the abnormal elements in the actual oil domain temperature sequence by comparing the first simulated oil domain temperature sequence with the actual oil domain temperature sequence to obtain the correction result. It can be seen that since the operating parameters of the pre-built transformer model are consistent with the actual transformer, the first simulated oil domain temperature sequence obtained by simulation can more accurately reflect the value and change of the insulating oil temperature in the actual transformer. Therefore, the first simulated oil domain temperature sequence is used as the standard to correct the abnormal oil temperature actually detected. By correcting the abnormal transformer oil temperature data, the operating status of the transformer can be better monitored, and the maintenance and maintenance strategies can be optimized, thereby improving the reliability and operating efficiency of the transformer, which has high reliability and engineering practice significance.

[0039] Considering the key role of transformer model construction in realizing abnormal oil temperature data correction, the process of constructing the transformer model is described in detail below. In one possible implementation, the transformer operating parameters include: geometric parameter data, thermal property parameter data, electrical performance parameter data, thermal convection parameter data, and excitation source parameter data; the pre-constructed transformer model is generated according to the following method:

[0040] Step A1: construct an initial transformer model according to the geometric parameter data.

[0041] Step A2: According to the thermal property parameter data, a plurality of thermal property parameters of each component in the initial transformer model are set to obtain a first transformer model.

[0042] Step A3: Determine the load loss and no-load loss of at least one component in the first transformer model according to the electrical performance parameter data, so as to add a thermal load at a specified position of the first transformer model to obtain a second transformer model.

[0043] Step A4: setting the boundary conditions of the second transformer model according to the thermal convection parameter data to obtain a third transformer model.

[0044] Step A5: According to the excitation source parameter data, the excitation source is set and meshing is performed on the third transformer model to obtain the transformer model.

[0045] In this embodiment, the transformer operating parameters include: geometric parameter data, thermal attribute parameter data, electrical performance parameter data, thermal convection parameter data, and excitation source parameter data. Among them, the geometric parameter data is used to characterize the physical structure of the transformer, such as the length, width, height, winding wire diameter, number of turns, core size, etc. of the transformer. The thermal attribute parameter data is used to characterize the thermal properties of each material in the transformer, such as thermal conductivity, specific heat capacity, thermal expansion coefficient, etc. The electrical performance parameter data is used to characterize the electrical characteristics and performance of the transformer, such as rated voltage, current, active power, reactive power, resistance, capacitance, inductance, conductivity, etc. The thermal convection parameter data is used to characterize the efficiency and effect of heat transfer between a fluid (such as air or transformer oil) and a solid surface (such as a transformer component). The excitation source parameter data is used to characterize the characteristics of the external energy source that provides power to the transformer.

[0046] In order to obtain a pre-built transformer model, the initial transformer model can be constructed using simulation software according to the geometric parameter data. Specifically, for example, the tools in the COMSOL software drawing toolbar (such as rectangles, circles, line segments, etc.) can be used to set the transformer components such as the main winding, the secondary winding, and the core in accordance with the actual transformer. Then, the geometric parameter data of each component can be used to set the respective geometric parameters, such as the number of turns of the winding, the size of the core, and other parameters, so as to obtain the initial transformer model.

[0047] In a possible implementation manner, the initial transformer model includes: a winding, an iron core, and insulating oil; the primary winding of the winding is connected to a power supply, and the secondary winding is connected to a load, the winding is wound on the iron core, and the insulating oil is filled between the winding and the iron core; wherein the winding is a cylinder; and the iron core is a cylinder.

[0048] The initial transformer model in this embodiment ignores the pull plates, clamps, outer wall structures of the oil tank and other parts in the actual transformer, and only retains the windings, iron cores and insulating oil. Considering that the inter-turn insulation between the transformer windings has little effect on heat dissipation, the inter-turn insulation of the windings is ignored, and the transformer windings are equivalent to components of a cylindrical structure. Because the iron core of the transformer is composed of multiple layers of silicon steel sheets stacked together and the cross-section is approximately circular, the transformer core is simplified to a cylinder. Figure 2The front view of the initial transformer model provided by the embodiment of the present disclosure is shown. This embodiment can greatly reduce the simulation calculation time of the subsequent model by simplifying the transformer model and appropriately ignoring factors that have little impact on the calculation results, thereby improving the correction efficiency of abnormal data.

[0049] After the initial transformer model is obtained, multiple thermal property parameters of each component in the initial transformer model can be set according to the thermal property parameter data to obtain the first transformer model. In specific implementation, for example, simulation software can be used to select a certain component, and the corresponding thermal property parameters can be defined according to the thermal property parameter data by setting constants, setting interpolation functions, and defining user-defined functions.

[0050] Through research, it is found that the working state of the heat exchange system in the main transformer room has a significant impact on the heat transfer process between the outside of the oil domain and the outside air. For example, when the heat exchange system starts to operate, the temperature inside the transformer begins to drop, and the heat exchange between the insulating oil and the environment is accelerated, thereby improving the heat dissipation effect of the internal heat energy of the transformer. The difference in temperature will affect the thermal property parameters of the internal components of the transformer, thereby affecting the oil temperature value obtained by subsequent simulation calculations. Based on this, in a possible implementation of the present disclosure, the thermal property parameter data includes metal parameter data and insulating oil parameter data; according to the thermal property parameter data, multiple thermal property parameters of each component in the initial transformer model are set to obtain a first transformer model, including:

[0051] Step A21: setting a plurality of thermal property parameters of the metal material in the initial transformer model according to the metal parameter data.

[0052] Step A22: Obtain the working status of the heat exchange system corresponding to the actual transformer.

[0053] Step A23: Determine a target interpolation function group according to the working state; wherein the target interpolation function group includes multiple interpolation functions, and the interpolation function is determined according to the insulating oil parameter data within the target temperature range corresponding to the working state, and is used to characterize the functional relationship between thermal property parameters and temperature.

[0054] Step A24: using the objective function group, setting a plurality of thermal property parameters of the insulating oil in the initial transformer model to obtain the first transformer model.

[0055] It should be noted that this embodiment does not limit the order of setting the thermal property parameters of the metal material and setting the thermal property parameters of the insulating oil.

[0056] Considering that the thermal properties of metal materials in the transformer are less affected by temperature, the metal parameter data can be used to set multiple thermal property parameters of the metal materials in the initial transformer model. Here, the metal parameter data includes parameter values ​​of multiple thermal property parameters for various types of metal materials. Exemplarily, the following Table 1 is a table of thermal property parameters of metal materials. For example, the iron core of the initial transformer model can be selected in the simulation software, and the constant pressure heat capacity of the iron core is set to 460J / (kg·K), the thermal conductivity is 45W / (m·K), and the density is 7650kg / m3.

[0057] Table 1 Thermal property parameters of metal materials

[0058]

[0059]

[0060] Considering that the thermal properties of the insulating oil in the transformer are greatly affected by temperature, the working state (on / off) of the heat exchange system corresponding to the actual transformer can be obtained first. According to the working state, the target interpolation function group is determined. Here, the target interpolation function group includes multiple interpolation functions, which respectively characterize the mapping relationship between a certain thermal property parameter and temperature. The number of interpolation functions depends on the number of thermal property parameters of the insulating oil that need to be set. If the number of thermal property parameters that need to be set is 3, the target interpolation function group includes 3 interpolation functions. The data used to determine a certain interpolation function are multiple temperatures of the insulating oil and the corresponding insulating oil parameter data when the heat exchange system is in this working state, that is, the thermal property parameter value of the insulating oil.

[0061] In specific implementation, the actual heat exchange system can be turned on and off in turn, and then the thermal property parameter values ​​of the actual transformer insulating oil at multiple temperatures are collected to obtain the thermal property parameter table of the insulating oil shown in Table 2 below. It is assumed that when the heat exchange system is turned off, the transformer insulating oil temperature is maintained above 55°C; when the heat exchange system is started, the transformer begins to cool down, and the transformer insulating oil temperature is maintained at 35°C to 50°C. Exemplarily, if the working state of the heat exchange system is closed, the following three data points are obtained for the target thermal property parameter of dynamic viscosity: (60, 0.0060), (70, 0.0046), (80, 0.0036). The built-in interpolation function of the simulation software can be used to fit these data points to obtain the target fitting function. The target fitting function for each thermal property parameter constitutes a target interpolation function group.

[0062] Table 2 Thermal property parameters of insulating oil

[0063]

[0064] After obtaining the target interpolation function group, the material property setting window can be opened to find the thermal property parameters related to the insulating oil. For each parameter, the corresponding target interpolation function is input to complete the setting of the thermal property parameters for the insulating oil, and finally the first transformer model is obtained.

[0065] The disclosed embodiment considers the influence of the working state of the heat exchange system on the thermal property parameters of the internal components of the transformer, and sets the thermal property parameters of the metal material and insulating oil in the transformer. Specifically, for metal materials that are less affected by temperature, the corresponding thermal property parameters are set to constants; for insulating oils that are more affected by temperature, the corresponding target interpolation function group is determined according to the working state of the heat exchange system, wherein the interpolation function characterizes the relationship between a certain thermal property parameter and temperature. Since the data used to determine the interpolation function is the temperature of the insulating oil and the corresponding thermal property parameter value when the heat exchange system is in this working state, the target function group is used to set multiple thermal property parameters of the insulating oil, so that the thermal property parameter values ​​in subsequent simulation calculations can be more accurate and closer to the actual operating state of the actual transformer, thereby greatly improving the accuracy of the subsequent first simulation oil domain temperature, and then improving the accuracy of the correction result. In addition, setting the thermal property parameters corresponding to the metal material to constants can also simplify the transformer model, greatly reduce the amount of simulation calculations, and improve the speed of simulation calculations.

[0066] During the operation of the transformer, load and no-load losses are the main heat sources. Therefore, after obtaining the first transformer model, the load and no-load losses of the actual transformer can be calculated based on the electrical performance parameter data, and the heat generated by each component of the transformer (such as windings and core) can be estimated, and applied to the model as a heat load to obtain the second transformer model. Specifically, for example, with the help of the solid heat transfer module in the COMSOL software, each heat generated can be applied to the specified position in the model in the form of an average heat source as a heat load of the temperature field. Here, the specified position is consistent with the actual transformer.

[0067] After obtaining the second transformer model, the thermal convection boundary conditions of the second transformer model can be set according to the thermal convection parameter data to obtain the third transformer model. Specifically, for example, the thermal convection boundary conditions of the second transformer model can be set by first identifying the fluid boundary in the simulation software; adding the convection boundary conditions; setting the convection heat transfer coefficient, and the like.

[0068] In actual application scenarios, if the working state of the heat exchange system is taken into consideration, in a possible implementation of the present disclosure, the boundary conditions of the second transformer model are set according to the heat convection parameter data to obtain the third transformer model, including:

[0069] Step A41: using a first convection heat transfer coefficient, setting a boundary condition for a first boundary where the heat load contacts the insulating oil.

[0070] Step A42: For the oil flow side of the second transformer model, using the second convection heat transfer coefficient, boundary conditions are set for the second boundary where the oil tank wall and the insulating oil in the second transformer model are in contact.

[0071] Step A43: For the air side of the second transformer model, boundary conditions are set for the third boundary where the oil tank wall and the air are in contact using the third convection heat transfer coefficient; wherein the first convection heat transfer coefficient, the second convection heat transfer coefficient, and the third convection heat transfer coefficient are determined based on the thermal convection parameter data.

[0072] It should be noted that the embodiment of the present disclosure does not limit the execution order of steps A41, A42, and A43.

[0073] In specific implementation, for example, simulation software can be used to first select the first boundary where the heat load contacts the insulating oil, and then, in the boundary setting, enter the first convective heat transfer coefficient formula. In a possible implementation, the first convective heat transfer coefficient is calculated by the following formula:

[0074]

[0075] Wherein, h is the first convection heat transfer coefficient, v is the flow rate of the insulating oil, Cp is the constant pressure specific heat capacity of the insulating oil, ρ is the density of the insulating oil, k is the thermal conductivity of the first boundary, and Δh is the height of the insulating oil raised along the heat dissipation path. In implementation, Δh can be, for example, 0.2 m.

[0076] Then, the second boundary where the oil tank wall and the insulating oil are in contact in the second transformer model can be selected, and then, in the boundary setting, the second convection heat transfer coefficient formula can be entered. In a possible implementation, the second convection heat transfer coefficient is calculated by the following formula:

[0077] h o =c(G o ·Pr o ) 3 / 2 λ o / L

[0078] Among them, G o =β o ·g·(θ top -θ a )·L 3 / μ 2 , P o =ρ o·μ o ·c p / λ o ;

[0079] The h o is the second convection heat transfer coefficient, c is the empirical coefficient, G o is the mass flow rate corresponding to the insulating oil, the Pr o is the Trump number of the insulating oil, the λ o is the thermal conductivity of the insulating oil, L is the oil flow path length of the insulating oil, βo is the thermal expansion coefficient of the insulating oil, g is the gravitational acceleration, and θ top is the top oil temperature of the insulating oil, the θ a is the ambient temperature, μ is the viscosity of the insulating oil, and P o is the Trump number of the insulating oil, the ρ o is the density of the insulating oil, the μ o is the viscosity of the insulating oil, the c p is the constant-pressure specific heat capacity of the insulating oil.

[0080] Then, the third boundary where the oil tank wall and the air are in contact may be selected, and then, in the boundary setting, the third convective heat transfer coefficient formula may be input. In a possible implementation, the third convective heat transfer coefficient may be calculated by the following formula:

[0081] h a =Nu a λ a / L

[0082] Among them, the h a is the third convective heat transfer coefficient, the Nu a is the Noorse number, the a is the thermal conductivity of air, and L is the path length of the insulating oil.

[0083] In implementation, when the heat exchange system is turned off, the convective heat exchange mode on the air side is natural convection; when the heat exchange system is turned off, the convective heat exchange mode on the air side is similar to forced convection. Based on this, in a possible implementation, if the working state is turned off, the Nourse number is calculated by the following formula:

[0084]

[0085] Among them, R a1 =G a +Pr a1 , the Nu a1 is the Noorse number under natural convection, R a1 is the Rayleigh number under natural convection, and Ga is the Grashof number, the Pr a1 is the Trump number of air under natural convection;

[0086] If the working state is on, the Nourse number is calculated by the following formula:

[0087]

[0088] Among them, R a2 =λ·V·u, where m, n, and λ are empirical constants, V is the wind speed, and Nu a2 is the Noorse number under forced convection, R a2 is the Rayleigh number under forced convection; u is the flow velocity of air, and Pr a2 is the Trump number of air under forced convection.

[0089] Finally, the excitation source parameter data is used to set the excitation source for the third transformer model and perform mesh division to obtain the transformer model. Specifically, the excitation source of the third transformer model can be set according to the current load and electromagnetic field input of the actual transformer; then, the model is meshed to obtain the final transformer model.

[0090] In a possible implementation manner, after the step of performing excitation source setting and meshing processing on the third transformer model according to the excitation source parameter data to obtain the transformer model, the method further includes:

[0091] Step A6: When the heat exchange system is in the working state, the insulating oil temperature of the actual transformer is sampled to obtain a standard oil domain temperature sequence; and a second temperature field simulation calculation is performed on the transformer model to obtain a second simulated oil domain temperature sequence.

[0092] Step A7: According to preset conditions, determine whether the second simulation oil domain temperature sequence is consistent with the standard oil domain temperature sequence.

[0093] Step A8: If the second simulated oil domain temperature sequence is inconsistent with the standard oil domain temperature sequence, the thermal property parameters of the components in the transformer model are adjusted to obtain a new transformer model, and the process returns to the step of performing a second temperature field simulation calculation on the transformer model to obtain a second simulated oil domain temperature sequence, until the second simulated oil domain temperature sequence is consistent with the standard oil domain temperature sequence.

[0094] In this embodiment, when the heat exchange system is in the working state, the temperature of the actual transformer insulating oil can be continuously sampled at a certain frequency to obtain a real standard oil domain temperature sequence; the temperature field simulation calculation can also be performed on the previously constructed transformer model to obtain a second simulated oil domain temperature sequence; according to preset conditions, it is determined whether the second simulated oil domain temperature sequence is consistent with the standard oil domain temperature sequence; if the second simulated oil domain temperature sequence is consistent with the standard oil domain temperature sequence, it indicates that the simulation effect of the pre-constructed transformer model is relatively good, and the transformer model can be used to calculate relatively real oil temperature data in the future; if the second simulated oil domain temperature sequence is inconsistent with the standard oil domain temperature sequence, it indicates that the simulation effect of the pre-constructed transformer model is relatively poor, and the oil temperature data calculated by the transformer model in the future is greatly different from the real data. Therefore, the thermal property parameters of the components in the transformer model can be adjusted to obtain a new transformer model, and return to the step of performing a second temperature field simulation calculation on the transformer model to obtain the second simulated oil domain temperature sequence until the second simulated oil domain temperature sequence is consistent with the standard oil domain temperature sequence.

[0095] During implementation, the preset condition can be set according to actual needs, and the embodiment of the present disclosure is not limited. Exemplarily, the preset condition can be: if the Euclidean distance between the second simulation oil domain temperature sequence and the standard oil domain temperature sequence is greater than a preset threshold, then the second simulation oil domain temperature sequence is judged to be inconsistent with the standard oil domain temperature sequence; if the Euclidean distance between the second simulation oil domain temperature sequence and the standard oil domain temperature sequence is less than or equal to the preset threshold, then the second simulation oil domain temperature sequence is judged to be consistent with the standard oil domain temperature sequence.

[0096] The disclosed embodiment utilizes the collected real transformer insulating oil temperature sequence to verify the simulation results of the transformer model. If the verification fails, the thermal property parameters in the transformer model are adjusted until the simulation results of the transformer model are close to the actual oil temperature data, thereby ensuring the correctness of the transformer model, and further ensuring the validity of the subsequent first simulation oil temperature sequence and the correctness of the correction results.

[0097] The embodiment of the present disclosure also provides a method for correcting abnormal data, including the following steps S1 to S17:

[0098] Step S1: Acquire the actual oil field temperature sequence with abnormal data.

[0099] Step S2: Obtain transformer operating parameters of an actual transformer corresponding to the actual oil domain temperature sequence, the transformer operating parameters including: geometric parameter data, thermal property parameter data, electrical performance parameter data, thermal convection parameter data, and excitation source parameter data.

[0100] Step S3: construct an initial transformer model according to the geometric parameter data. The initial transformer model includes: winding, iron core, and insulating oil; the primary winding in the winding is connected to the power supply, the secondary winding is connected to the load, the winding is wound on the iron core, and the insulating oil is filled between the winding and the iron core; wherein the winding is a cylinder; and the iron core is a cylinder.

[0101] Step S4: setting a plurality of thermal property parameters of the metal material in the initial transformer model according to the metal parameter data in the thermal property parameter data.

[0102] Step S5: Obtain the working status of the heat exchange system corresponding to the actual transformer.

[0103] Step S6: determining a target interpolation function group according to the working state; wherein the target interpolation function group includes a plurality of interpolation functions, and the interpolation function is determined according to the insulating oil parameter data in the thermal property parameter data within the target temperature range corresponding to the working state, and is used to characterize the functional relationship between the thermal property parameter and the temperature.

[0104] Step S7: using the objective function group, setting multiple thermal property parameters of the insulating oil in the initial transformer model to obtain a first transformer model.

[0105] Step S8: Determine the load loss and no-load loss of at least one component in the first transformer model according to the electrical performance parameter data, and add a thermal load at a specified position of the first transformer model to obtain a second transformer model.

[0106] Step S9: Using the first convection heat transfer coefficient, a boundary condition is set for the first boundary where the heat load contacts the insulating oil. The first convection heat transfer coefficient is calculated by the following formula:

[0107]

[0108] Among them, h is the first convective heat transfer coefficient, v is the flow rate of the insulating oil, Cp is the constant-pressure specific heat capacity of the insulating oil, ρ is the density of the insulating oil, k is the thermal conductivity of the first boundary, and Δh is the height of the insulating oil raised along the heat dissipation path.

[0109] Step S10: For the oil flow side of the second transformer model, the boundary condition is set for the second boundary where the oil tank wall and the insulating oil contact in the second transformer model using the second convection heat transfer coefficient. The second convection heat transfer coefficient is calculated by the following formula:

[0110] h o =c(G o ·Pr o ) 3 / 2 λ o / L

[0111] Among them, G o =β o ·g·(θ top -θ a )·L 3 / μ 2 , P o =ρ o ·μ o ·c p / λ o ;

[0112] h o is the second convection heat transfer coefficient, c is the empirical coefficient, G o is the mass flow rate of insulating oil, Pr o is the Trump number of the insulating oil, λ o is the thermal conductivity of the insulating oil, L is the oil flow path length of the insulating oil, βo is the thermal expansion coefficient of the insulating oil, g is the gravitational acceleration, θ top is the top oil temperature of the insulating oil, θ a is the ambient temperature, μ is the viscosity of the insulating oil, P o is the Trump number of the insulating oil, ρ o is the density of insulating oil, μ o is the viscosity of the insulating oil, c p is the constant pressure specific heat capacity of insulating oil.

[0113] Step S11: For the air side of the second transformer model, the third convection heat transfer coefficient is used to set the boundary condition for the third boundary where the oil tank wall and the air are in contact. The third convection heat transfer coefficient is calculated by the following formula:

[0114] h a =Nu a λ a / L

[0115] Among them, h a is the third convection heat transfer coefficient, Nu a is the Nourse number, λ a is the thermal conductivity of air, and L is the path length of the insulating oil. If the working state is closed, the Nourse number is calculated by the following formula:

[0116]

[0117] Among them, R a1 =G a +Pr a1 , Nu a1 is the Nurse number under natural convection, R a1 is the Rayleigh number under natural convection, G a is the Grashof number, Pr a1is the Trump number of air under natural convection; if the working state is on, the Nourse number is calculated by the following formula:

[0118]

[0119] Among them, R a2 =λ·V·u, m, n, λ are empirical constants, V is wind speed, Nu a2 is the Noorse number under forced convection, R a2 is the Rayleigh number under forced convection; u is the air flow velocity, Pr a2 is the Trump number of air under forced convection.

[0120] Step S12: According to the excitation source parameter data, the excitation source is set and the mesh is divided for the third transformer model to obtain the transformer model.

[0121] Step S13: when the heat exchange system is in operation, the insulating oil temperature of the actual transformer is sampled to obtain a standard oil temperature sequence; and a second temperature field simulation calculation is performed on the transformer model to obtain a second simulation oil temperature sequence.

[0122] Step S14: According to preset conditions, it is determined whether the second simulation oil domain temperature sequence is consistent with the standard oil domain temperature sequence.

[0123] Step S15: If the second simulated oil domain temperature sequence is inconsistent with the standard oil domain temperature sequence, the thermal property parameters of the components in the transformer model are adjusted to obtain a new transformer model, and the process returns to the step of performing a second temperature field simulation calculation on the transformer model to obtain a second simulated oil domain temperature sequence, until the second simulated oil domain temperature sequence is consistent with the standard oil domain temperature sequence.

[0124] Step S16: performing a first temperature field simulation calculation on the pre-built transformer model to obtain a first simulated oil domain temperature sequence.

[0125] Step S17: According to the first simulated oil domain temperature sequence and the actual oil domain temperature sequence, the abnormal elements in the actual oil domain temperature sequence are corrected to obtain a correction result.

[0126] Those skilled in the art will appreciate that, in the above method of the specific implementation manner, the writing order of the steps does not mean a strict execution order, but does not constitute any limitation on the implementation process. The specific execution order of the steps should be determined by their functions and possible internal logic.

[0127] It should be noted that, in practical applications, all possible implementations described above can be combined in any manner to form possible embodiments of the present disclosure, which will not be described one by one here.

[0128] Based on the same concept, the embodiment of the present disclosure also provides an abnormal data correction device. Figure 3 FIG. 1 shows a schematic diagram of the structure of the abnormal data correction device provided by the embodiment of the present disclosure, see FIG. Figure 3 As shown, the abnormal data correction device 300 provided in the embodiment of the present disclosure includes:

[0129] The abnormal data acquisition module 301 is used to acquire the actual oil field temperature sequence with abnormal data;

[0130] Model acquisition model 302, used to acquire a pre-built transformer model; the pre-built transformer model is established using simulation software according to transformer operating parameters of an actual transformer corresponding to the actual oil domain temperature sequence;

[0131] A simulation module 303 is used to perform a first temperature field simulation calculation on the pre-built transformer model to obtain a first simulation oil domain temperature sequence;

[0132] The correction module 304 is used to correct abnormal elements in the actual oil domain temperature sequence according to the first simulated oil domain temperature sequence and the actual oil domain temperature sequence to obtain a correction result.

[0133] In a possible implementation manner, the transformer operating parameters include: geometric parameter data, thermal property parameter data, electrical performance parameter data, thermal convection parameter data, and excitation source parameter data; the device also includes a construction module for:

[0134] constructing an initial transformer model according to the geometric parameter data;

[0135] According to the thermal property parameter data, multiple thermal property parameters of each component in the initial transformer model are set to obtain a first transformer model;

[0136] Determine the load loss and no-load loss of at least one component in the first transformer model according to the electrical performance parameter data, so as to add a thermal load at a specified position of the first transformer model to obtain a second transformer model;

[0137] According to the thermal convection parameter data, setting the boundary conditions of the second transformer model to obtain a third transformer model;

[0138] According to the excitation source parameter data, the excitation source is set and meshed for the third transformer model to obtain the transformer model.

[0139] In a possible implementation manner, the thermal property parameter data includes metal parameter data and insulating oil parameter data; and the construction module is used to:

[0140] According to the metal parameter data, multiple thermal property parameters of the metal material in the initial transformer model are set;

[0141] Obtaining the working status of the heat exchange system corresponding to the actual transformer;

[0142] According to the working state, a target interpolation function group is determined; wherein the target interpolation function group includes a plurality of interpolation functions, and the interpolation functions are determined according to the insulating oil parameter data within the target temperature range corresponding to the working state, and are used to characterize the functional relationship between the thermal property parameter and the temperature;

[0143] The objective function group is used to set a plurality of thermal property parameters of the insulating oil in the initial transformer model to obtain the first transformer model.

[0144] In a possible implementation manner, the building block is used to:

[0145] Using a first convection heat transfer coefficient, setting a boundary condition for a first boundary where the heat load contacts the insulating oil;

[0146] For the oil flow side of the second transformer model, using a second convection heat transfer coefficient, setting a boundary condition for a second boundary where the oil tank wall and the insulating oil in the second transformer model are in contact;

[0147] For the air side of the second transformer model, the boundary condition is set for the third boundary where the oil tank wall and the air are in contact using the third convection heat transfer coefficient; wherein the first convection heat transfer coefficient, the second convection heat transfer coefficient, and the third convection heat transfer coefficient are determined based on the thermal convection parameter data.

[0148] In a possible implementation manner, in the above device, the first convection heat transfer coefficient is calculated by the following formula:

[0149]

[0150] Among them, h is the first convection heat transfer coefficient, v is the flow rate of the insulating oil, Cp is the constant-pressure specific heat capacity of the insulating oil, ρ is the density of the insulating oil, k is the thermal conductivity of the first boundary, and Δh is the height of the insulating oil raised along the heat dissipation path.

[0151] In a possible implementation manner, in the above device, the second convective heat transfer coefficient is calculated by the following formula:

[0152] h o =c(G o ·Pr o ) 3 / 2λ o / L

[0153] Among them, G o =β o ·g·(θ top -θ a )·L 3 / μ 2 , P o =ρ o ·μ o ·c p / λ o ;

[0154] The h o is the second convective heat transfer coefficient, c is the empirical coefficient, G o is the mass flow rate corresponding to the insulating oil, the Pr o is the Trump number of the insulating oil, the λ o is the thermal conductivity of the insulating oil, L is the oil flow path length of the insulating oil, βo is the thermal expansion coefficient of the insulating oil, g is the gravitational acceleration, and θ top is the top oil temperature of the insulating oil, the θ a is the ambient temperature, μ is the viscosity of the insulating oil, and P o is the Trump number of the insulating oil, the ρ o is the density of the insulating oil, the μ o is the viscosity of the insulating oil, the c p is the constant-pressure specific heat capacity of the insulating oil.

[0155] In a possible implementation manner, in the above device, the third convective heat transfer coefficient is calculated by the following formula:

[0156] h a =Nu a λ a / L

[0157] Among them, the h a is the third convective heat transfer coefficient, the Nu a is the Noorse number, the a is the thermal conductivity of air, and L is the path length of the insulating oil.

[0158] In a possible implementation manner, in the above device, if the working state is closed, the Nourse number is calculated by the following formula:

[0159]

[0160] Among them, R a1 =G a+Pr a1 , the Nu a1 is the Noorse number under natural convection, R a1 is the Rayleigh number under natural convection, and G a is the Grashof number, the Pr a1 is the Trump number of air under natural convection;

[0161] If the working state is on, the Nourse number is calculated by the following formula:

[0162]

[0163] Among them, R a2 =λ·V·u, where m, n, and λ are empirical constants, V is the wind speed, and Nu a2 is the Noorse number under forced convection, R a2 is the Rayleigh number under forced convection; u is the flow velocity of air, and Pr a2 is the Trump number of air under forced convection.

[0164] In a possible implementation manner, the above device further includes a testing module, which is used to:

[0165] When the heat exchange system is in the working state, the insulating oil temperature of the actual transformer is sampled to obtain a standard oil domain temperature sequence; and a second temperature field simulation calculation is performed on the transformer model to obtain a second simulation oil domain temperature sequence;

[0166] According to a preset condition, determining whether the second simulation oil domain temperature sequence is consistent with the standard oil domain temperature sequence;

[0167] If the second simulated oil domain temperature sequence is inconsistent with the standard oil domain temperature sequence, the thermal property parameters of the components in the transformer model are adjusted to obtain a new transformer model, and the process returns to the step of performing a second temperature field simulation calculation on the transformer model to obtain a second simulated oil domain temperature sequence until the second simulated oil domain temperature sequence is consistent with the standard oil domain temperature sequence.

[0168] In a possible implementation, in the above-mentioned device, the initial transformer model includes: a winding, an iron core, and insulating oil; the primary winding of the winding is connected to a power supply, and the secondary winding is connected to a load, the winding is wound on the iron core, and the insulating oil is filled between the winding and the iron core; wherein the winding is a cylinder; and the iron core is a cylinder.

[0169] It should be noted that any of the above-mentioned abnormal data correction devices can implement the above-mentioned abnormal data correction method in a one-to-one correspondence, which will not be repeated here.

[0170] Figure 4 FIG. 1 shows a schematic diagram of the structure of an electronic device provided by an embodiment of the present disclosure. Figure 4 As shown, at the hardware level, the electronic device includes a processor, and optionally also includes an internal bus, a network interface, and a memory. Among them, the memory may include a memory, such as a high-speed random access memory (RAM), and may also include a non-volatile memory (non-volatile memory), such as at least one disk storage, etc. Of course, the electronic device may also include hardware required for other services.

[0171] The processor, network interface and memory can be interconnected through an internal bus, which can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 4 Only one bidirectional arrow is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0172] The memory is used to store programs. Specifically, the program may include program code, and the program code includes computer operation instructions. The memory may include internal memory and non-volatile memory, and provides instructions and data to the processor.

[0173] The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs it, forming an abnormal data correction news recommendation device at the logical level. The processor executes the program stored in the memory and is specifically used to execute the above method.

[0174] The processor may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by an integrated logic circuit of hardware in the processor or an instruction in the form of software. The above processor may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it may also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The disclosed methods, steps and logic block diagrams in the embodiments of the present disclosure may be implemented or executed. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The steps of the method disclosed in conjunction with the embodiments of the present disclosure may be directly embodied as being executed by a hardware decoding processor, or may be executed by a combination of hardware and software modules in a decoding processor. The software module may be located in a mature storage medium in the field such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory, and the processor reads the information in the memory and completes the steps of the above method in combination with its hardware.

[0175] The electronic device can execute the abnormal data correction method provided by the multiple embodiments of the present disclosure, and realize the abnormal data correction device in Figure 3 The functions of the illustrated embodiments will not be described in detail in the embodiments of the present disclosure.

[0176] The embodiments of the present disclosure also provide a computer-readable storage medium, which stores one or more programs, and the one or more programs include instructions, which, when executed by an electronic device including multiple application programs, can enable the electronic device to execute the abnormal data correction method provided by multiple embodiments of the present disclosure.

[0177] Those skilled in the art will appreciate that the embodiments of the present disclosure may be provided as methods, systems, or computer program products. Therefore, the present disclosure may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present disclosure may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0178] The present disclosure is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present disclosure. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0179] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0180] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0181] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0182] The memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0183] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0184] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, commodity or device including the element.

[0185] It will be appreciated by those skilled in the art that the embodiments of the present disclosure may be provided as methods, systems or computer program products. Therefore, the present disclosure may take the form of a complete hardware embodiment, a complete software embodiment or an embodiment combining software and hardware. Moreover, the present disclosure may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0186] The above are only embodiments of the present disclosure and are not intended to limit the present disclosure. For those skilled in the art, the present disclosure may have various modifications and variations. Any modification, equivalent substitution, improvement, etc. made within the spirit and principle of the present disclosure shall be included in the scope of the claims of the present disclosure.

Claims

1. A method for correcting abnormal data, characterized in that: The method comprises: Obtain the actual oil field temperature series with abnormal data; Obtaining a pre-built transformer model; the pre-built transformer model is established using simulation software according to transformer operating parameters of an actual transformer corresponding to the actual oil domain temperature sequence; Performing a first temperature field simulation calculation on the pre-built transformer model to obtain a first simulated oil domain temperature sequence; According to the first simulated oil domain temperature sequence and the actual oil domain temperature sequence, abnormal elements in the actual oil domain temperature sequence are corrected to obtain a correction result.

2. The method according to claim 1, characterized in that The transformer operation parameters include: geometric parameter data, thermal property parameter data, electrical performance parameter data, thermal convection parameter data, and excitation source parameter data; the pre-built transformer model is generated according to the following method: constructing an initial transformer model according to the geometric parameter data; According to the thermal property parameter data, multiple thermal property parameters of each component in the initial transformer model are set to obtain a first transformer model; Determine the load loss and no-load loss of at least one component in the first transformer model according to the electrical performance parameter data, so as to add a thermal load at a specified position of the first transformer model to obtain a second transformer model; According to the thermal convection parameter data, setting the boundary conditions of the second transformer model to obtain a third transformer model; According to the excitation source parameter data, the excitation source is set and meshed for the third transformer model to obtain the transformer model.

3. The method according to claim 2, characterized in that The thermal property parameter data includes metal parameter data and insulating oil parameter data; and according to the thermal property parameter data, a plurality of thermal property parameters of each component in the initial transformer model are set to obtain a first transformer model, including: According to the metal parameter data, multiple thermal property parameters of the metal material in the initial transformer model are set; Obtaining the working status of the heat exchange system corresponding to the actual transformer; According to the working state, a target interpolation function group is determined; wherein the target interpolation function group includes a plurality of interpolation functions, and the interpolation functions are determined according to the insulating oil parameter data within the target temperature range corresponding to the working state, and are used to characterize the functional relationship between the thermal property parameter and the temperature; The objective function group is used to set a plurality of thermal property parameters of the insulating oil in the initial transformer model to obtain the first transformer model.

4. The method according to claim 3, characterized in that: The step of setting the boundary conditions of the second transformer model according to the thermal convection parameter data to obtain a third transformer model comprises: Using a first convection heat transfer coefficient, setting a boundary condition for a first boundary where the heat load contacts the insulating oil; For the oil flow side of the second transformer model, using a second convection heat transfer coefficient, setting a boundary condition for a second boundary where the oil tank wall and the insulating oil in the second transformer model are in contact; For the air side of the second transformer model, the boundary condition is set for the third boundary where the oil tank wall and the air are in contact using the third convection heat transfer coefficient; wherein the first convection heat transfer coefficient, the second convection heat transfer coefficient, and the third convection heat transfer coefficient are determined based on the thermal convection parameter data.

5. The method according to claim 4, characterized in that The first convection heat transfer coefficient is calculated by the following formula: Among them, h is the first convection heat transfer coefficient, v is the flow rate of the insulating oil, Cp is the constant-pressure specific heat capacity of the insulating oil, ρ is the density of the insulating oil, k is the thermal conductivity of the first boundary, and Δh is the height of the insulating oil raised along the heat dissipation path.

6. The method according to claim 4, characterized in that The second convective heat transfer coefficient is calculated by the following formula: h o =c(G o ·Pr o , 3 / 2 λ o / L Among them, G o =b o ·g·(θ top -θ a )·L 3 / m 2 ,P o =ρ o ·m o ·c p / l o ; The h o is the second convective heat transfer coefficient, c is the empirical coefficient, G o is the mass flow rate corresponding to the insulating oil, the Pr o is the Trump number of the insulating oil, the λ o is the thermal conductivity of the insulating oil, L is the oil flow path length of the insulating oil, βo is the thermal expansion coefficient of the insulating oil, g is the gravitational acceleration, and θ top is the top oil temperature of the insulating oil, the θ a is the ambient temperature, μ is the viscosity of the insulating oil, and P o is the Trump number of the insulating oil, the ρ o is the density of the insulating oil, the μ o is the viscosity of the insulating oil, the c p is the constant-pressure specific heat capacity of the insulating oil.

7. The method according to claim 4, characterized in that The third convective heat transfer coefficient is calculated by the following formula: h a =No a λ a / IT Among them, the h a is the third convective heat transfer coefficient, the Nu a is the Noorse number, the a is the thermal conductivity of air, and L is the path length of the insulating oil.

8. An abnormal data correction device, characterized in that: The device comprises: An abnormal data acquisition module, used to obtain the actual oil field temperature sequence with abnormal data; A model acquisition model is used to acquire a pre-built transformer model; the pre-built transformer model is established by using simulation software according to transformer operating parameters of an actual transformer corresponding to the actual oil domain temperature sequence; A simulation module, used for performing a first temperature field simulation calculation on the pre-built transformer model to obtain a first simulation oil domain temperature sequence; The correction module is used to correct abnormal elements in the actual oil domain temperature sequence according to the first simulated oil domain temperature sequence and the actual oil domain temperature sequence to obtain a correction result.

9. An electronic device, comprising: processor; as well as A memory arranged to store computer executable instructions, wherein the executable instructions, when executed, cause the processor to perform the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium storing one or more programs, characterized in that: When the one or more programs are executed by an electronic device including a plurality of application programs, the electronic device executes the steps of the method as claimed in any one of claims 1 to 7.

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