Transformer dynamic thermal field prediction and active cooling control method based on digital twinning

Abstract

The invention provides a transformer dynamic thermal field prediction and active cooling control method based on digital twinning, and relates to the technical field of intelligent control of power equipment. Comprising the following steps: extracting key parameters of a to-be-tested transformer, and simplifying a three-dimensional coil structure into a two-dimensional axisymmetric rotation model according to the key parameters; setting calculated excitation, boundary conditions and calculation frequency for the two-dimensional axisymmetric rotation model so as to carry out magnetic field calculation to obtain a corresponding ohmic loss value; fitting the corresponding ohmic loss values into a temperature-related function to obtain a loss temperature function; determining a training set for constructing a digital twinborn model according to the loss temperature function and the input parameters; constructing a digital twinborn model according to the training set; and carrying out transformer temperature prediction and regulation and control by using the digital twin model. The problems that in the prior art, a traditional temperature measurement means has hysteresis in recognition of hidden hot spots, and advanced suppression of thermal risks cannot be achieved are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent control of power equipment, and particularly to a method for predicting the dynamic thermal field of a transformer and actively controlling cooling based on digital twin. Background Art

[0002] With the continuous expansion of the scale of the power system and the continuous increase in the capacity of a single transformer, the cooling efficiency of traditional natural cooling cycle transformers has gradually become difficult to cope with the high heat load challenges of large-capacity transformers due to their dependence on natural convection of oil and passive heat dissipation. Especially under full-load or overload conditions, the temperature rise control ability of natural cooling is limited, which easily leads to problems such as local overheating and accelerated insulation aging, directly affecting the safety and life of the equipment.

[0003] In contrast, forced oil-directed circulation transformers effectively suppress the temperature rise of "hot spots" through the forced driving of cooling oil by oil pumps to flow directionally and cooperate with the oil guiding channels, and at the same time support short-term overload operation. Its high-efficiency cooling characteristics and capacity expansion ability make it an ideal choice for large-capacity and high-load scenarios such as large power stations and UHV transmission hubs, fully meeting the core requirements of modern power systems for high reliability and long life of equipment.

[0004] However, there are significant bottlenecks in the existing intelligent temperature monitoring technology for forced oil-directed circulation transformers: on the one hand, traditional temperature measurement methods (such as top oil temperature monitoring and fiber optic point temperature measurement) can only capture the temperature of a single point or a few points, and it is difficult to obtain the real-time dynamic thermal field distribution, resulting in a lag in the identification of hidden hot spots; on the other hand, the cooling system mostly adopts a "threshold trigger" control mode, only passively adjusting the oil pump power after the temperature exceeds the limit, and it is impossible to achieve the advanced suppression of thermal risks. Especially in the power grid environment with high proportion of new energy access and intensified load fluctuations, the above defects easily lead to the long-term sub-health state of transformers, seriously threatening the reliability of the power grid and the economy of the equipment. Therefore, developing an intelligent cooling system with the ability of dynamic thermal field prediction and active regulation has become an urgent need to improve the operation life and safety margin of forced oil-directed circulation transformers. Summary of the Invention

[0005] In order to overcome the deficiencies of the prior art, the purpose of the present invention is to provide a method for predicting the dynamic thermal field of a transformer and actively controlling cooling based on digital twin, and the present invention solves the problems in the prior art that traditional temperature measurement means have a lag in the identification of hidden hot spots and cannot achieve the advanced suppression of thermal risks.

[0006] To achieve the above purpose, the present invention provides the following solutions:

[0007] A method for predicting the dynamic thermal field of a transformer and actively controlling cooling based on digital twin, comprising:

[0008] Extract the key parameters of the transformer to be measured and simplify the three-dimensional coil structure into a two-dimensional axisymmetric rotation model according to the key parameters;

[0009] Set the excitation, boundary conditions, and calculation frequency for the two-dimensional axisymmetric rotation model to perform magnetic field calculation to obtain the corresponding ohmic loss value;

[0010] Fit the corresponding ohmic loss value into a temperature-related function to obtain a loss temperature function;

[0011] Determine the training set for constructing the digital twin model according to the loss temperature function and input parameters, where the input parameters include load factor, oil pump flow rate, and inlet oil temperature;

[0012] Construct a digital twin model according to the training set;

[0013] Use the digital twin model to predict and control the transformer temperature.

[0014] Preferably, the key parameters include:

[0015] Core structure parameters, coil structure parameters, coil arrangement parameters, and coil electrical parameters.

[0016] Preferably, the setting of the excitation, boundary conditions, and calculation frequency for the two-dimensional axisymmetric rotation model to perform magnetic field calculation to obtain the corresponding ohmic loss value includes:

[0017] Set the calculation conditions for the two-dimensional axisymmetric rotation model, and the calculation conditions include: material parameters, excitation for calculation, boundary conditions, and calculation frequency;

[0018] Calculate the leakage magnetic flux distribution of each disk coil according to the two-dimensional axisymmetric rotation model with the calculation conditions set;

[0019] Calculate the corresponding loss density distribution according to the leakage magnetic flux distribution of each disk coil;

[0020] Integrate the loss density distribution to obtain the ohmic loss values corresponding to different current load factors and different temperatures.

[0021] Preferably, fitting the corresponding ohmic loss value into a temperature-related function to obtain a loss temperature function includes:

[0022] Decompose the ohmic loss value to obtain direct current resistance loss and eddy current loss;

[0023] Fit the direct current resistance loss and eddy current loss to determine the loss temperature function.

[0024] Preferably, the expression of the loss temperature function is:

[0025]

[0026] Among them, P coil is the total loss of each turn of the coil; P DC is the DC resistance loss; P eddy is the eddy current loss; T is the temperature.

[0027] The present invention discloses the following technical effects:

[0028] The present invention provides a digital twin-based dynamic thermal field prediction and active cooling control method for transformers, including: extracting key parameters of the transformer to be measured and simplifying the three-dimensional coil structure into a two-dimensional axisymmetric rotation model according to the key parameters; setting calculation excitations, boundary conditions, and calculation frequencies for the two-dimensional axisymmetric rotation model to perform magnetic field calculations to obtain corresponding ohmic loss values; fitting the corresponding ohmic loss values into a temperature-related function to obtain a loss-temperature function; determining a training set for constructing a digital twin model according to the loss-temperature function and input parameters, where the input parameters include a load factor, an oil pump flow rate, and an inlet oil temperature; constructing a digital twin model according to the training set; using the digital twin model to predict and regulate the temperature of the transformer. The present invention predicts the temperature field change in the next 10 to 30 minutes through the digital twin model, triggers regulation before the hot spot temperature approaches the safety threshold, avoids the lag of the traditional fixed threshold method, and shortens the regulation response time by more than 60%; the two-dimensional equivalent model combined with the lightweight algorithm reduces the thermal field simulation calculation amount by 70%, improves the response speed by 90% compared with the three-dimensional digital twin model, and the accuracy error is stable within ±1.5°C; dynamically adjusting the oil pump power to avoid excessive cooling, achieving comprehensive energy savings of 10% to 15%; reducing the aging effect of temperature fluctuations on insulating materials, and is expected to extend the transformer life by 20% to 30%; Description of the Drawings

[0029] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0030] Figure 1 is a flowchart of a digital twin-based dynamic thermal field prediction and active cooling control method for transformers provided by an embodiment of the present invention;

[0031] Figure 2 is a schematic diagram of leakage magnetic flux distribution provided by an embodiment of the present invention;

[0032] Figure 3Schematic diagram of the loss distribution per turn of wire at different temperature resistance values provided by the embodiments of the present invention. Among them, Figure 3 (a) is a schematic diagram of eddy current loss distribution, Figure 3 (b) is a schematic diagram of DC loss distribution;

[0033] Figure 4 Schematic diagram of the digital twin model provided by the embodiments of the present invention;

[0034] Figure 5 Schematic diagram of the temperature difference between the full-order model and the reduced-order model provided by the embodiments of the present invention;

[0035] Figure 6 Schematic diagram of the temperature difference between the full-order model and the reduced-order model provided by the embodiments of the present invention;

[0036] Figure 7 Flowchart of the real-time response system deployment provided by the embodiments of the present invention. Detailed implementation manners

[0037] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0038] To make the above objects, features, and advantages of the present invention more obvious and understandable, the present invention will be further described in detail below in conjunction with the accompanying drawings and specific implementation manners.

[0039] As Figure 1 shown, the present invention provides a digital twin-based dynamic thermal field prediction and active cooling control method for transformers, including:

[0040] Step 100: Extract the key parameters of the transformer to be measured and simplify the three-dimensional coil structure into a two-dimensional axisymmetric rotation model according to the key parameters;

[0041] Step 200: Set the excitation, boundary conditions, and calculation frequency for the two-dimensional axisymmetric rotation model to perform magnetic field calculation to obtain the corresponding ohmic loss value;

[0042] Step 300: Fit the corresponding ohmic loss value into a function related to temperature to obtain a loss temperature function;

[0043] Step 400: Determine the training set for constructing the digital twin model according to the loss temperature function and input parameters, where the input parameters include the load factor, the oil pump flow rate, and the inlet oil temperature;

[0044] Step 500: Construct a digital twin model based on the training set;

[0045] Specifically, after calculating the temperature field distribution under different variable input parameters to form a training set, the weight influence of different variable parameters on the temperature distribution is calculated through a deep neural network, which is the digital twin model.

[0046] Step 600: Use the digital twin model for transformer temperature prediction and regulation.

[0047] Specifically, taking a 220kV forced oil circulation transformer as an example, with the model number SFP-360000 / 242, the basic performance parameters are shown in Table 1.

[0048] Table 1 Basic performance parameter table of SFP-360000 / 242 forced oil circulation transformer

[0049] Parameter Value Transformer Model SFP-360000 / 242 Phase Number 3 Impedance 14.4％ Connection Group YNd11 Winding Material Copper Frequency 50Hz Cooling Method ODAF

[0050] Furthermore, parametric modeling of the transformer and two-dimensional equivalent processing of the coil:

[0051] Extract the key parameters of the transformer, and the key parameters include:

[0052] Core structure parameters, coil structure parameters, coil layout parameters, and coil electrical parameters.

[0053] Specifically, the core structure parameters include but are not limited to window height, main column center distance, main column sheet width, yoke sheet width, and yoke offset;

[0054] The coil structure parameters include but are not limited to: electrical height, inner diameter, outer diameter, and electrical turns of each coil;

[0055] The coil layout parameters include but are not limited to: phase spacing, distance from each coil to the upper yoke, and distance from each coil to the lower yoke;

[0056] The coil electrical parameters include but are not limited to: connection group, rated voltage, number of turns per pancake, and number of parallel windings per pancake in the radial direction.

[0057] Two-dimensional equivalent processing of the coil:

[0058] According to the structural characteristics of the forced oil circulation transformer, the traditional three-dimensional coil structure is simplified to a two-dimensional axisymmetric rotation model.

[0059] Specifically, the body structure of the transformer is a circular structure, meeting the characteristics of two-dimensional rotational axisymmetry;

[0060] For a forced-oil directed circulation transformer, the cooling oil is forcedly driven by an oil pump to flow directionally from the cooler into the transformer body. It enters the body through pipelines and does not have the characteristics of two-dimensional rotational symmetry, thus requiring equivalent treatment. After the transformer oil enters the body, the velocity of the dispersed oil flow is greatly reduced. According to the flow resistance of different coils, the oil flow enters different coils autonomously for heat dissipation. Based on this characteristic, the oil injection hole can be equivalently regarded as a two-dimensional axisymmetric plane on the premise of ensuring that the flow rate of the transformer oil entering the entire body remains unchanged.

[0061] Furthermore, setting the excitation, boundary conditions, and calculation frequency for the two-dimensional axisymmetric rotation model for calculation to perform magnetic field calculation to obtain the corresponding ohmic loss value includes:

[0062] Setting calculation conditions for the two-dimensional axisymmetric rotation model, where the calculation conditions include: material parameters, excitation for calculation, boundary conditions, and calculation frequency;

[0063] Calculating the leakage magnetic flux distribution of each disk coil according to the two-dimensional axisymmetric rotation model after setting the calculation conditions;

[0064] Calculating the corresponding loss density distribution according to the leakage magnetic flux distribution of each disk coil;

[0065] Integrating the loss density distribution to obtain the ohmic loss values corresponding to different current load factors and different temperatures.

[0066] Specifically, taking one disk of the coil as a calculation unit, combining the numerical calculation results under different current load factors K and different conductivities corresponding to different temperatures to fit the DC resistance loss and eddy current loss as a related function of the load factor K and temperature T, transforming the electromagnetic-thermal two-way coupling problem into a one-way thermal field solution, which greatly reduces the iterative calculation amount. Specifically, it includes the following steps:

[0067] Constructing a two-dimensional axisymmetric rotation calculation model in the magnetic field module, the model includes an iron core, an oil tank, and coils. Among them, the coils are modeled disk by disk, that is, each disk of wire is a calculation unit, and the leakage magnetic flux distribution of each disk coil is calculated;

[0068] Adding material parameters in the magnetic field module, and assigning corresponding material parameters to different geometric structures. Specifically:

[0069] The materials of the power transformer include copper for the coil wire, silicon steel for the iron core, and air for the computational domain geometry. Among them, the magnetic permeability of the silicon steel material is non-linear and is described by the B-H curve. The corresponding materials are assigned to the corresponding object geometries. The conductivity of the copper material used for the coil is highly correlated with temperature. The coil conductivity decreases as the temperature of the coil rises. In the magnetic field-temperature field calculation, if the influence of temperature on conductivity is not considered, it will cause a large deviation in the calculation results. The present invention simplifies the two-way coupling calculation problem of the magnetic field-fluid to a one-way coupling calculation problem by fitting the loss as a function of temperature, greatly simplifying the calculation amount and ensuring the accuracy.

[0070] Set the excitation, boundary conditions, and calculation frequency of the calculation, and complete the magnetic field calculation. Specifically:

[0071] The losses in the transformer coil, including DC resistance loss and eddy current loss, are both proportional to the current load factor K^ 2 It is only necessary to calculate the loss distribution under the rated phase current of the coil. The losses under other currents can be obtained by multiplying the rated loss by K^ 2 Then, the eddy current field is used to calculate the leakage magnetic field distribution in the transformer oil tank. The calculation frequency is set to 50 Hz, and the boundary condition is that the magnetic field lines are parallel to the computational domain. After setting, the loss distribution can be calculated. The finite element analysis of the magnetic field follows Maxwell's equations:

[0072]

[0073] Among them, D is the electric displacement vector; J is the current density vector; B is the magnetic induction intensity vector; E is the electric field intensity vector; H is the magnetic field intensity vector; ρ is the charge density.

[0074] The constitutive equations for electromagnetic calculations are:

[0075] D = εE; B = μ; J = σE;

[0076] Among them, ε represents the dielectric constant of the medium; μ is the magnetic permeability of the medium; σ represents the conductivity of the medium.

[0077] As Figure 2 shown, according to the leakage magnetic field distribution, the loss density distribution of each turn is obtained, and the Ohmic loss value is obtained by integration under different current load factors K and different conductivities corresponding to different temperatures. The loss is decomposed into DC resistance loss and eddy current loss and fitted as a function of each turn line under different current load factors K and different temperatures.

[0078] The ohmic losses in the coil can be decomposed into DC resistance losses and eddy current losses. Among them, the DC resistance losses are positively correlated with the load current and temperature, and the eddy current losses are positively correlated with the load current and negatively correlated with the temperature. According to the results of loss calculation, the DC resistance losses and eddy current losses of each turn of wire are fitted as functions of the load current and temperature.

[0079] The expression of the loss-temperature function is as follows:

[0080]

[0081] Where, P coil is the total loss of each turn of the coil; P DC is the DC resistance loss; P eddy is the eddy current loss; T is the temperature.

[0082] The loss distributions of each turn of wire under the conditions of 20°C, 45°C, and 75°C are as Figure 3 shown.

[0083] Furthermore, calculate the temperature field and generate a deep neural network training set:

[0084] Take the load factor K, the oil pump flow rate Q, and the inlet oil temperature T oil-in as variable input parameters, and generate a training data set through multi-condition simulation. Specifically:

[0085] Build a two-dimensional axisymmetric rotation model of the transformer body in the temperature field module. The model includes a low-voltage coil, a high-voltage coil, a spacer, and an oil guide plate. In the example of the present invention, since the loss density of the regulating coil is very small and the temperature is very low, it is not within the analysis scope.

[0086] Furthermore, in order to reduce the electric field intensity in the oil on the surface of the low-voltage coil and the high-voltage coil, an insulating paper is covered on the surface of the electrolytic copper to form a composite insulation structure. However, the insulating paper is generally less than 0.5 mm, which is not convenient for mesh generation. Define a thin layer structure on the surface of the coil to equivalent the insulating paper.

[0087] Set the material properties and boundary conditions of the temperature calculation module. Specifically:

[0088] Use mathematical methods to fit the discrete material parameters of transformer oil into curves and import them into the calculation software. The specific parameters are dynamic viscosity, thermal conductivity, specific heat capacity, and density. Table 2 is the physical property parameter table of transformer oil. Table 2 is as follows:

[0089] Table 2

[0090]

[0091]

[0092] The coil inlet temperature is an input feature of the digital twin model and is a variable; the coil inlet flow rate is an input feature of the digital twin model and is a variable; the outlet condition of the flow field calculation domain is a pressure outlet.

[0093] Table 3 is the definition table of the input and output parameters of the digital twin model, and Table 3 is as follows:

[0094] Table 3

[0095]

[0096] For the input characteristic parameters, the load factor K, the oil pump flow rate Q, and the inlet oil temperature T oil-in perform parametric scanning and normalization processing. Table 4 is the parametric configuration table, and Table 4 is as follows:

[0097] Table 4

[0098]

[0099]

[0100] To avoid dimensional differences and to accelerate model convergence, the input parameters are normalized. The input parameters are normalized to the interval [0, 1]:

[0101]

[0102] where K' is the load current coefficient after normalization; Q' is the oil pump flow rate after normalization; T' is the oil temperature at the oil pump after normalization.

[0103] Perform temperature field calculations for the full-order model at each sampling point in each training set:

[0104] The temperature field calculations follow the continuity equation; the Navier-Stokes equation; the energy conservation equation.

[0105]

[0106] where ρ is the fluid density, u is the fluid velocity, μ is the fluid viscosity, f is the body force, e is the internal energy, and q is the heat.

[0107] Furthermore, as Figure 4 shown, construct a digital twin model:

[0108] Based on a deep neural network, with the coil temperature field as the output, the load factor K, the oil pump flow rate Q, and the inlet oil temperature T oil-in as the input, construct a dynamic digital twin model. The relevant parameter settings of the deep neural network are shown in Table 5. Table 5 is the relevant parameter table of the deep neural network, and Table 5 is as follows:

[0109] Table 5

[0110]

[0111]

[0112] Furthermore, for visual verification:

[0113] Compare the calculation results of the full - order model with the digital twin reduced - order model, and compare the global maximum absolute error, global average absolute error, and temperature gradient consistency.

[0114] Under the conditions of load factor K = 1, oil pump flow Q = 72m 3 / h, T oil-in = 70, the temperature field distribution of the full - order model is as Figure 5 shown.

[0115] The difference between the full - order model and the reduced - order model is as Figure 6 shown. The global maximum absolute error is 0.5K, and the global average absolute error and temperature gradient consistency are good.

[0116] Furthermore, use the digital twin model for real - time data synchronization of oil pump regulation:

[0117] Integrate the digital twin model into the transformer temperature management platform, and collect temperature data in real - time through the Internet of Things module; the digital twin dynamically predicts the coil temperature distribution, triggers the temperature - regulating cooling system, and realizes temperature closed - loop control. The real - time response system deployment flow chart is as Figure 7 shown, and specifically includes the following steps:

[0118] Real - time data acquisition:

[0119] The Internet of Things module collects the real - time operation data of the transformer and synchronously updates it to the digital twin model to realize dynamic calibration of the virtual and real systems.

[0120] Specifically, arrange current transformers at the bushing position to obtain the real - time actual current, then the load factor K = I 实际 / I 额定 ×100%; the oil pump flow is controlled by the frequency converter of the oil pump motor, and the oil pump flow value can be obtained in real - time; at the inlet of the oil pump flange, arrange 3 PT100 platinum resistance temperature sensors, and take the median as the final value to obtain the oil pump inlet oil temperature in real - time.

[0121] Digital twin model inference:

[0122] The sensor collects data every 5 seconds and uploads it to the computing server through the gateway. The computing server calls the digital twin model to predict the coil temperature field distribution and detect the hot spot temperature. Historical data is stored in the cloud and an operation and maintenance report is generated. At the same time, the gateway is built-in with a memory card to cache data when the network is interrupted and resend it after recovery.

[0123] Early warning and decision-making regulation:

[0124] Specifically, three-level risk early warning is set for the temperature of the coil, and different oil pump control strategies are adopted for different prediction results. The hot spot temperature T hotspot = max(T 全局 ); Temperature gradient Table 6 is the three-level risk early warning strategy table, as follows:

[0125] Table 6

[0126]

[0127] Furthermore, the mathematical model for the strategy optimization regulation during three-level early warning is:

[0128] min(α·P pump +β·(T hotspot -T target ) 2 )

[0129] In the formula, α and β are weight coefficients, which can be dynamically adjusted according to the temperature control strategy of the transformer; P pump is the oil pump power; T target is the target temperature, which can be dynamically adjusted according to the actual operating environment of the transformer.

[0130] Oil pump control:

[0131] After the early warning strategy is activated, the oil pump frequency converter receives the instruction, adopts PID control, and starts the auxiliary fan (if there is a fan);

[0132] System monitoring:

[0133] The edge node uploads the compressed data packet to the cloud every 5 minutes to support the global health assessment.

[0134] In this specification, each embodiment is described in a progressive manner. The key point of each embodiment is to illustrate the differences from other embodiments. For the same and similar parts among the embodiments, reference can be made to each other.

[0135] In this article, specific examples are used to elaborate on the principles and implementation modes of the present invention. The description of the above embodiments is only used to help understand the method and its core idea of the present invention; at the same time, for those of ordinary skill in the art, according to the idea of the present invention, there will be changes in the specific implementation modes and application scopes. To sum up, the content of this specification should not be construed as a limitation to the present invention.

Claims

1. A method for predicting the dynamic thermal field of a transformer and actively controlling cooling based on digital twin, characterized in that Including: Extracting the key parameters of the transformer to be measured and simplifying the three-dimensional coil structure into a two-dimensional axisymmetric rotation model according to the key parameters; Setting the excitation, boundary conditions, and calculation frequency for the two-dimensional axisymmetric rotation model for magnetic field calculation to obtain the corresponding ohmic loss value; Fitting the corresponding ohmic loss value into a temperature-related function to obtain a loss temperature function; Determining a training set for constructing a digital twin model according to the loss temperature function and input parameters, where the input parameters include a load factor, an oil pump flow rate, and an inlet oil temperature; Constructing a digital twin model according to the training set; Using the digital twin model for transformer temperature prediction and regulation.

2. The method for predicting the dynamic thermal field of a transformer and actively controlling cooling based on digital twin according to claim 1, wherein, The key parameters include: Core structure parameters, coil structure parameters, coil arrangement parameters, and coil electrical parameters.

3. A method for predicting the dynamic thermal field of a transformer and active cooling control based on digital twin according to claim 1, characterized in that The setting of the excitation, boundary conditions, and calculation frequency for the two-dimensional axisymmetric rotation model for magnetic field calculation to obtain the corresponding ohmic loss value includes: Setting calculation conditions for the two-dimensional axisymmetric rotation model, where the calculation conditions include: material parameters, excitation for calculation, boundary conditions, and calculation frequency; Calculating the leakage magnetic flux distribution of each pancake coil according to the two-dimensional axisymmetric rotation model with the calculation conditions set; Calculating the corresponding loss density distribution according to the leakage magnetic flux distribution of each pancake coil; Integrating the loss density distribution to obtain the ohmic loss values corresponding to different current load factors and different temperatures.

4. The method for predicting the dynamic thermal field of a transformer and actively controlling cooling based on digital twin according to claim 3, wherein Fitting the corresponding ohmic loss value into a temperature-related function to obtain a loss temperature function, including: Decomposing the ohmic loss value to obtain direct current resistance loss and eddy current loss; Fitting the direct current resistance loss and eddy current loss to determine the loss temperature function.

5. A method for predicting the dynamic thermal field of a transformer and active cooling control based on digital twin according to claim 4, characterized in that The expression of the loss temperature function is: Among them, P coil is the total loss of each turn of the coil; P DC is the DC resistance loss; P eddy is the eddy current loss; T is the temperature.

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