Electronic transformer multi-physics field coupling insulation voltage resistance verification method

Abstract

The invention discloses an electronic mutual inductor multi-physics coupling insulation voltage-withstanding verification method, which relates to the field of electric power system engineering, and comprises the following steps: constructing a multi-physics coupling simulation model comprising an electromagnetic field, a temperature field and a mechanical stress field, and setting specific simulation parameters to operate the model; therefore, the insulation and voltage resistance of the electronic transformer under different working conditions can be evaluated. Meanwhile, a sensor is installed in the mutual inductor to collect working state data in real time, the data and a simulation result are subjected to fusion analysis, the insulation state trend is predicted through a machine learning algorithm, a maintenance plan is made accordingly, potential faults are early warned in time, and safe and stable operation of equipment is ensured. The safety, the reliability and the operation efficiency of the electronic transformer are remarkably improved, and insulation and voltage resistance can be efficiently verified.

Description

Technical Field

[0001] The invention relates to the field of power system engineering, and in particular to a method for verifying the insulation withstand voltage of an electronic mutual inductor using multi-physical field coupling. Background Art

[0002] As the power system develops towards higher voltage levels and larger capacities, traditional electromagnetic transformers have gradually revealed their limitations, such as large size, high cost, and limited dynamic range. At present, the research on the insulation performance of electronic transformers mostly uses single physical field analysis methods. However, under actual operating conditions, the environment in which electronic transformers are located often involves the coupling of multiple physical fields, and single physical field analysis is difficult to fully and accurately evaluate their insulation withstand voltage performance. Therefore, developing a simulation model that can comprehensively consider the effects of multi-physical field coupling has become the key to improving the reliability of electronic transformers.

[0003] Traditional insulation performance evaluation methods usually rely on laboratory tests and empirical judgments, which is not only time-consuming and laborious, but also difficult to achieve online monitoring and fault warning. At the same time, although electronic transformers have shown superiority in many aspects, their insulation withstand voltage performance under extreme environmental conditions is still uncertain. Summary of the invention

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides an electronic transformer multi-physical field coupling insulation withstand voltage verification method to solve the problems of inaccurate insulation withstand voltage performance evaluation of electronic transformers under multi-physical field coupling and lack of real-time monitoring and fault warning mechanism in the prior art.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, an embodiment of the present invention provides a method for verifying the insulation withstand voltage of an electronic transformer by multi-physical field coupling, which includes establishing a multi-physical field coupling simulation model of the electronic transformer, wherein the model includes an electromagnetic field model, a temperature field model, and a mechanical stress field model;

[0008] Set simulation parameters, input into the simulation model, run the simulation model, obtain insulation performance data under different working conditions, analyze simulation results, and evaluate the insulation withstand voltage performance of electronic transformers under multi-physical field coupling;

[0009] Install sensors and detection devices inside the electronic transformer to collect working status data of the electronic transformer in real time;

[0010] The collected data is transmitted to the central processing unit for real-time analysis and fusion to generate working status analysis results;

[0011] According to the working status analysis results, the working status of the electronic transformer is monitored in real time, and an early warning is issued in time when a potential fault is found;

[0012] Use machine learning algorithms to intelligently analyze the fused data and identify trends in the insulation status of electronic transformers;

[0013] Make plans based on trends in the insulation condition of electronic instrument transformers.

[0014] As a preferred solution of the multi-physical field coupling insulation withstand voltage verification method of the electronic transformer of the present invention, wherein: a multi-physical field coupling simulation model of the electronic transformer is established, and the model includes an electromagnetic field model, a temperature field model and a mechanical stress field model;

[0015] The electromagnetic field model is established as follows:

[0016]

[0017] in, represents the deviation of the dynamic behavior of the electromagnetic field, represents the deviation of the electromagnetic field constraint condition, H represents the magnetic field intensity, E represents the electric field intensity, j represents the current density, D represents the electric displacement vector, B represents the magnetic induction intensity vector, ρ represents the charge density, Ω e represents the electromagnetic field area, dΩ represents a small increment of the integral variable, Indicates the deviation of this additional current density, J th represents the current density vector caused by temperature change, J mech represents the current density vector caused by the change in mechanical stress, represents the energy functional of the integrated electromagnetic field model;

[0018] The expression for establishing the temperature field model is:

[0019]

[0020] in, represents the energy functional of the temperature field model, c p is the specific heat capacity, ρ t represents the material density in the temperature field, θ represents the temperature, k represents the thermal conductivity, τ th represents the heat source intensity vector caused by temperature change, τ el represents the heat source intensity vector caused by the change of electromagnetic field, q represents the intensity of other heat sources, Ω t represents the temperature field area;

[0021] The mechanical stress field model expression is:

[0022]

[0023] in, represents the energy functional of the mechanical stress field model, E represents Young's modulus, σ′ ij represents the mechanical stress tensor, ε′ ij represents the strain tensor, v represents the Poisson's ratio, δ′ ij represents the Kronecker delta function, α′ represents the thermal expansion coefficient, T′ represents the temperature, σ′ el,ij represents the mechanical stress tensor caused by the change of electromagnetic field, σ′ mech,ij represents the mechanical stress tensor caused by the change in mechanical stress, ε′ kk Indicates the degree of volume change of the material, Ω m represents the mechanical stress field area;

[0024] Combining the above three models, by finding a set of state variables to minimize the total energy functional, a multi-physics field coupling model is formed, which is expressed as:

[0025]

[0026] in, represents the integrated multi-physics field coupling model, H represents the magnetic field intensity vector, E represents the electric field intensity vector, J represents the current density vector, ρ represents the charge density, D represents the electric displacement vector, B represents the magnetic induction intensity vector, T′ represents the temperature, and u represents the displacement vector.

[0027] As a preferred solution of the multi-physical field coupling insulation withstand voltage verification method of the electronic transformer of the present invention, wherein: setting simulation parameters, including working voltage, current, frequency, temperature, humidity;

[0028] Define the working voltage of the electronic transformer as V, the working current as I, the working frequency as f, the ambient temperature as T0, and the relative humidity as RH;

[0029] Input the defined simulation parameters V, I, f, T0, RH into the simulation model as initial conditions;

[0030] Use simulation software to run the model to simulate the dynamic changes of electromagnetic field, temperature field and mechanical stress field under given working voltage V, working current I, working frequency f, ambient temperature T0 and relative humidity RH;

[0031] Obtain insulation performance data under different working conditions through simulation models;

[0032] The insulation performance data obtained is analyzed to determine the insulation performance of the insulation layer under different working conditions. The expression is:

[0033]

[0034] in, Indicates the insulation withstand voltage performance index, ε m Indicates the maximum electric field strength, E t represents the electric field strength threshold, T m Indicates the maximum temperature, T t represents the temperature threshold, σ m represents the maximum stress, σ t represents the stress threshold;

[0035] As a preferred solution of the multi-physical field coupling insulation withstand voltage verification method of the electronic transformer of the present invention, a temperature sensor and an electrical parameter monitoring device are installed inside the electronic transformer, and a vibration sensor and an environmental sensor are installed outside to collect the working status data of the electronic transformer in real time;

[0036] Preprocess the collected real-time data;

[0037] The pre-processed working status data is subjected to feature extraction and wavelet transform W is used to T and W V The time-frequency characteristics of the temperature signal and the vibration signal are extracted respectively, and their expressions are as follows:

[0038]

[0039] Among them, W T (T) represents the time-frequency characteristics of the temperature signal, W V (V) represents the time-frequency characteristics of the vibration signal, T represents the temperature, V represents the vibration signal, t represents the time variable, ψ * represents the conjugate of the wavelet basis function, u represents the translation parameter, and dt represents the small increment of the time variable t;

[0040] Calculate the effective value of voltage and current data within the sampling window, and the average value of ambient temperature and humidity data within the sampling window to obtain electrical and environmental characteristics. The expression is:

[0041]

[0042] Among them, Φ E (E) represents the effective value of voltage and current data within the sampling window, Φ Env (Env) represents the average value of ambient temperature and humidity data within the sampling window, E represents electrical parameter data, U i Represents the voltage value of the i-th sampling point, I i represents the current value of the i-th sampling point, n represents the number of sampling points, Env represents environmental data, Tempi Represents the ambient temperature value of the i-th sampling point, Humid i Represents the ambient humidity value of the i-th sampling point.

[0043] As a preferred solution of the multi-physical field coupling insulation withstand voltage verification method of the electronic transformer of the present invention, the simulation data and the collected real-time data are transmitted to the central processing unit for real-time analysis and fusion, and the expression is:

[0044]

[0045] Among them, F f Represents the fused simulation data and real-time monitoring data;

[0046] After analysis and fusion, the working status analysis result is generated, and its expression is:

[0047]

[0048] Among them, S represents the working status evaluation score of the equipment, m represents the number of sensors of different types, b represents the number of data points of each type of sensor, and x i Represents the value of the i-th data point.

[0049] As a preferred solution of the multi-physical field coupling insulation withstand voltage verification method of the electronic transformer of the present invention, the working state of the electronic transformer is monitored in real time according to the working state analysis result, and an early warning is issued in time when a potential fault is found. The expression is:

[0050]

[0051] Among them, A j represents the alarm signal of the jth sensor, S j represents the working status evaluation score of the j-th sensor, θ represents the threshold, and α represents the parameter for adjusting the steepness of the curve;

[0052] The setting range of the threshold is based on the four states of S. When S indicates that the device is in poor condition, an alarm signal will be triggered and the operation and maintenance personnel will be notified via email, SMS, or phone call.

[0053] As a preferred solution of the multi-physical field coupled insulation withstand voltage verification method of the electronic transformer of the present invention, wherein: a machine learning algorithm is used to perform intelligent analysis on the fused data to identify the trend of the insulation state of the electronic transformer;

[0054] The data after the simulation data and the collected real-time data are fused, the working status evaluation score of the equipment, and the alarm signal vector of the sensor are extracted to extract various factors related to the insulation status, and the dynamic trend analysis function G(X) is used to identify the trend of the insulation status. Its expression is:

[0055] X=(Ff,S,A);

[0056]

[0057] Among them, X represents the features after extracting insulation state related information, T represents the time series, t represents the time variable, t0 represents the current time point, β represents the attenuation coefficient, and dt represents the small increment of the time variable t;

[0058] Based on the dynamic trend analysis function G(X), a complex information filtering function H(G(X)) is used to predict the trend of the insulation state. This function will be combined with a machine learning algorithm to predict future state changes. The specific expression is as follows:

[0059]

[0060] Where Y represents the trend prediction value of the insulation state, k represents the number of different state categories, σ(x) represents the sigmoid function, μ j represents the mean of the j-th class of states, σ j represents the standard deviation of the j-th class state, ω j Represents the weight of the j-th class state.

[0061] As a preferred solution of the multi-physical field coupled insulation withstand voltage verification method of the electronic transformer of the present invention, wherein: a plan is formulated based on the trend of the insulation state of the electronic transformer;

[0062] Once it is identified that the insulation condition is deteriorating, the system should immediately activate the early warning mechanism, notify the operation and maintenance personnel to perform maintenance, and the system should increase the monitoring frequency, improve the frequency of data collection and analysis, and formulate a specific maintenance plan based on the insulation condition, including cleaning, replacing parts and performing necessary repairs. Maintenance and repair work should be performed according to the action plan, and the results of maintenance and repair should be fed back to the monitoring system, the equipment status should be updated, and equipment performance should continue to be monitored.

[0063] In a second aspect, an embodiment of the present invention provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program is executed by the processor, any step of the electronic transformer multi-physical field coupling insulation withstand voltage verification method as described in the first aspect of the present invention is implemented.

[0064] In a third aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, any step of the electronic transformer multi-physical field coupling insulation withstand voltage verification method as described in the first aspect of the present invention is implemented.

[0065] The beneficial effects of the present invention are as follows: by establishing a simulation model including electromagnetic fields, temperature fields and mechanical stress fields, an accurate simulation of the insulation performance of the transformer under complex working conditions is achieved. This process not only improves the accuracy of the evaluation, but also guides subsequent design optimization. By setting specific simulation parameters and running the model, the insulation performance under different working conditions can be quantitatively evaluated, which helps to discover potential design defects or operating risks and provide a scientific basis for maintenance plans. By installing sensors inside the transformer and collecting data in real time, continuous monitoring of the operating status is achieved, abnormal conditions are discovered in time, and the risk of failure is reduced. Intelligent analysis combined with machine learning algorithms can provide early warning of potential failures, reduce unplanned downtime, help formulate effective maintenance plans, extend equipment life and reduce maintenance costs. Overall, the present invention significantly improves the safety, reliability and operating efficiency of electronic transformers. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0067] Figure 1 This is a flow chart of the multi-physical field coupling insulation withstand voltage verification method for the electronic transformer in Example 1.

[0068] Figure 2 This is a determination diagram of the multi-physical field coupling insulation withstand voltage verification method for the electronic transformer in Example 1. DETAILED DESCRIPTION

[0069] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0070] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0071] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0072] Example 1, reference Figure 1 and Figure 2 , which is the first embodiment of the present invention, and provides a method for verifying the insulation withstand voltage of an electronic transformer using multi-physical field coupling, comprising the following steps:

[0073] S1. Establishing a multi-physical field coupling simulation model of an electronic mutual inductor, wherein the model includes an electromagnetic field model, a temperature field model and a mechanical stress field model;

[0074] An electromagnetic field model is established. The model includes a basic set of electromagnetic field equations, taking into account the interaction between the electromagnetic field and the current density, as well as the additional current density caused by changes in temperature and mechanical stress. The electromagnetic field model is established by:

[0075]

[0076] in, It represents the deviation of electromagnetic field dynamic behavior and is the energy functional of the electromagnetic field dynamic equation. represents the deviation of the electromagnetic field constraint condition, the energy functional of the electromagnetic field constraint equation, H represents the magnetic field intensity vector, W represents the electric field intensity, J represents the current density, D represents the electric displacement vector, B represents the magnetic induction intensity vector, ρ represents the charge density, Ω e represents the electromagnetic field area, dΩ represents a small increment of the integral variable, The energy functional of the additional current density caused by temperature change and mechanical stress change, J th represents the current density vector caused by temperature change, J mech represents the current density vector caused by the change in mechanical stress, represents the energy functional of the integrated electromagnetic field model;

[0077] To further explain, the energy functional is a mathematical tool used to describe the energy of a system in different states. In physics and engineering, the energy functional is often used to find the lowest energy state of the system, which usually corresponds to the stable state of the system. The electromagnetic field model refers to the description of the behavior of the electromagnetic field by mathematical expressions, and the energy functional is a tool used to solve these mathematical expressions.

[0078] The smaller the value is, the smaller the deviation of the electromagnetic field model is, that is, the interaction between the electromagnetic field and the current is closer to the real situation;

[0079] The expression for establishing the temperature field model is:

[0080]

[0081] in, represents the energy functional of the temperature field model, c p is the specific heat capacity, ρ t represents the material density in the temperature field, θ represents the temperature, k represents the thermal conductivity, τ th represents the heat source intensity vector caused by temperature change, τ el represents the heat source intensity vector caused by electromagnetic field changes, q represents the intensity of other heat sources, which are heat sources other than those caused by temperature changes and electromagnetic field changes, such as solar radiation and nearby heating equipment, and Ω t represents the temperature field area;

[0082] The mechanical stress field model expression is:

[0083]

[0084] in, represents the energy functional of the mechanical stress field model, E represents Young's modulus, σ′ j represents the mechanical stress tensor, ε′ ij represents the strain tensor, v represents the Poisson's ratio, δ′ ij represents the Kronecker delta function, which is defined as 1 when i = j and 0 otherwise, α′ represents the thermal expansion coefficient, T′ represents the temperature, and σ′ el,ij represents the mechanical stress tensor caused by the change of electromagnetic field, σ′ mech,ij represents the mechanical stress tensor caused by the change in mechanical stress, ε′ kk Indicates the degree of volume change of the material, Ω m represents the mechanical stress field area;

[0085] The Young's modulus E described above describes the ability of a material to resist elastic deformation. For example, suppose we have a material sample with a side length of L = 1m. When we apply a force of 1000N, the sample stretches by 1mm, 0.5mm, and 0.8mm in the x, y, and z directions, respectively. Then, the expression for Young's modulus is:

[0086]

[0087] Where E represents Young's modulus, FH represents the applied force, AS represents the cross-sectional area, ∈ represents the strain, L represents the original length, and ΔL represents the elongation;

[0088] In the x direction, Young's modulus E x The expression is:

[0089]

[0090] Where Ex is the Young's modulus in the x direction, 1000N is the applied force, 1m is the original length, 1mm is the elongation, and 10 6 Pa represents the calculated Young's modulus;

[0091] In the y direction, Young's modulus E y The expression is:

[0092]

[0093] Among them, E y represents the Young's modulus in the y direction, 1000N represents the applied force, 1m represents the original length, 0.5mm represents the elongation, 2×10 6 Pa represents the calculated Young's modulus;

[0094] In the z direction, Young's modulus E z The expression is:

[0095]

[0096] Among them, E z represents the Young's modulus in the z direction, 1000 N represents the applied force, 1 m represents the original length, 0.8 mm represents the elongation, and 1.25×10 6 Pa represents the calculated Young's modulus;

[0097] The strain tensor ε′ ij Describes the deformation of the material in all directions. In three-dimensional space, it is a 3×3 matrix, where the diagonal elements represent linear strain and the non-diagonal elements represent shear strain;

[0098] Strain tensor ε′ ij The expression is:

[0099]

[0100] Among them, ε′ ij represents the strain tensor, ∈′ xx represents the linear strain in the x direction, ∈′ yy represents the linear strain in the y direction, ∈′ zz represents the linear strain in the z direction, ∈′ xy ,∈′ yx represents the shear strain in the xy plane, ∈′ xz ,∈′zx represents the shear strain in the xz plane, ∈′ yz ,∈′ zy represents the shear strain in the yz plane;

[0101] Linear strain ∈′ in the x direction xx The expression is:

[0102]

[0103] Where, ΔL x represents the elongation in the x direction, L represents the original length, 1×10 -3 represents the calculated linear strain in the x-direction;

[0104] Linear strain ∈′ in the y direction yy The expression is:

[0105]

[0106] Where, ΔL y represents the elongation in the y direction, 5×10 -4 represents the calculated linear strain in the y direction;

[0107] Linear strain ∈′ in the z direction zz The expression is:

[0108]

[0109] Where, ΔL z Indicates the elongation in the z direction, 8×10 -4 represents the calculated linear strain in the z direction;

[0110] For shear strain, assuming that no shear force is applied, the shear strain is zero, that is, ε′ xy =∈′ yx =ε′ xz =ε′ zx =ε′ yz =ε′ zy = 0, therefore, the strain tensor ε′ ij The shear strain expression is:

[0111]

[0112] The above electromagnetic field model, temperature field model, and mechanical stress model are integrated into a unified framework, and the best solution of these models is found by solving an optimization problem. The goal of the optimization problem is to find a set of state variables such as magnetic field intensity H, electric field intensity E, current density J, temperature T, and displacement u, so that the total energy functional reaches the minimum value, thereby integrating and forming a multi-physics field coupling model, which is expressed as follows:

[0113]

[0114] in, represents the integrated multi-physics field coupling model, H represents the magnetic field intensity vector, E represents the electric field intensity vector, J represents the current density vector, ρ represents the charge density, D represents the electric displacement vector, B represents the magnetic induction intensity vector, T′ represents the temperature, and u represents the displacement vector.

[0115] A set of state variables found by the optimization problem is used to minimize the total energy functional of the electromagnetic field model, temperature field model and mechanical stress field model, thereby most accurately simulating the system behavior in the real world.

[0116] S2. Set simulation parameters, including operating voltage, current, frequency, temperature, and humidity;

[0117] The working voltage of the electronic transformer is set to V = 110V, which is based on the typical working range of the transformer;

[0118] The working current of the electronic transformer is set to I = 1A, which is based on the typical working current of the transformer;

[0119] The operating frequency of the electronic transformer is set to f = 50 Hz, which is based on the standard frequency of the power grid;

[0120] Set the ambient temperature to T0 = 25°C, which is the standard room temperature;

[0121] Set the relative humidity to RH = 50%, which is a typical indoor humidity level;

[0122] Input the defined simulation parameters V, I, f, T0, RH into the simulation model as initial conditions;

[0123] Use simulation software to run the model to simulate the dynamic changes of electromagnetic field, temperature field and mechanical stress field under given working voltage V, working current I, working frequency f, ambient temperature T0 and relative humidity RH;

[0124] The insulation performance data under different working conditions are obtained through simulation models, including the electric field distribution, temperature distribution, and stress distribution of the insulation layer;

[0125] The insulation performance data obtained are analyzed to determine the insulation performance of the insulation layer under different working conditions, such as the maximum electric field strength, the highest temperature point, and the maximum stress. The expression is:

[0126]

[0127] in, Indicates the insulation withstand voltage performance index, ε m Indicates the maximum electric field strength, E t represents the electric field strength threshold, T m Indicates the maximum temperature, T t represents the temperature threshold, σ m represents the maximum stress, σ t represents the stress threshold;

[0128] The threshold of P is [0, 1]. The closer the value of P is to 1, the better the insulation performance of the electronic transformer is, and it can withstand larger electric fields, temperatures and stresses. The closer the value of P is to 0, the worse the insulation performance is. When it is lower than 0.3, measures need to be taken to improve it.

[0129] S3. Install a temperature sensor and an electrical parameter monitoring device inside the electronic transformer, and install a vibration sensor and an environmental sensor outside to collect the working status data of the electronic transformer in real time;

[0130] The temperature sensor is used to collect the temperature data inside the transformer, including the temperature changes in the hot spot area;

[0131] The vibration data of the transformer housing is collected through the vibration sensor to reflect the vibration conditions of the internal components;

[0132] Collect the working electrical parameters of the transformer through the built-in electrical parameter monitoring device;

[0133] Collect environmental data such as ambient temperature and humidity through environmental sensors;

[0134] Filter and smooth the collected real-time data to eliminate noise interference;

[0135] The pre-processed working status data is subjected to feature extraction and wavelet transform W is used to T and W V The time-frequency characteristics of the temperature signal and the vibration signal are extracted respectively, and their expressions are as follows:

[0136]

[0137] Among them, W T (T) represents the time-frequency characteristics of the temperature signal, W V(V) represents the time-frequency characteristics of the vibration signal, T represents the temperature, V represents the vibration signal, t represents the time variable, ψ * represents the conjugate of the wavelet basis function, u represents the translation parameter, and dt represents the small increment of the time variable t;

[0138] Calculate the effective value of voltage and current data within the sampling window, and the average value of ambient temperature and humidity data within the sampling window to obtain electrical and environmental characteristics. The expression is:

[0139]

[0140]

[0141] Among them, Φ E (E) represents the effective value of voltage and current data within the sampling window, Φ Env (Env) represents the average value of ambient temperature and humidity data within the sampling window, E represents electrical parameter data, U i Represents the voltage value of the i-th sampling point, I i represents the current value of the i-th sampling point, n represents the number of sampling points, Env represents environmental data, Temp i Represents the ambient temperature value of the i-th sampling point, Humid i Represents the ambient humidity value of the i-th sampling point.

[0142] S4, transmitting the simulation data and the collected real-time data to the central processing unit for real-time analysis and fusion, the expression is:

[0143]

[0144] Among them, F f Represents the fused simulation data and real-time monitoring data;

[0145] F f Represents the fused eigenvalue, which ranges from 0 to 1. The closer to 1, the more important the eigenvalue;

[0146] After analysis and fusion, the working status analysis result is generated, and its expression is:

[0147]

[0148] Among them, S represents the working status evaluation score of the equipment, m represents the number of different types of sensors, n represents the number of data points of each type of sensor, and x i Represents the value of the i-th data point.

[0149] The value range of S is [0, 1],

[0150] When 0 < S ≤ 0.2, it indicates that the device is in an abnormal state;

[0151] When 0.2 < S ≤ 0.5, it indicates that the device is in a sub - optimal state;

[0152] When 0.5 < S ≤ 0.8, it indicates that the device is in a good state;

[0153] When 0.8 < S ≤ 1, it indicates that the device is in an excellent state.

[0154] S5. Design a threshold - based early warning mechanism for detecting potential faults. To achieve this goal, we will adopt an innovative method that uses a non - linear threshold function to determine whether the device state exceeds the normal range. According to the working state analysis results, the working state of the electronic current transformer is monitored in real - time, and an early warning is issued in a timely manner when potential faults are detected. Its expression is:

[0155]

[0156] Among them, A j represents the alarm signal of the j - th type of sensor, S j represents the working state evaluation score of the j - th type of sensor, θ represents the threshold, and α represents the parameter for adjusting the steepness of the curve;

[0157] The setting range of the threshold is based on the four states of S. When S is in the sub - optimal state of the device, that is, when the value of S is less than or equal to 0.5, an alarm signal will be triggered, and the operation and maintenance personnel will be notified by email, text message, or phone.

[0158] S6. Use machine learning algorithms to perform intelligent analysis on the fused data to identify the trend of the insulation state of the electronic current transformer;

[0159] After fusing the simulation data and the real - time data collected, extract features from the data, the working state evaluation score of the device, and the alarm signal vector of the sensor, extract various factors related to the insulation state, and use the dynamic trend analysis function G(X) to identify the trend of the insulation state. This function will consider the correlation between historical data and current data, as well as the change pattern of the time series. Its expression is:

[0160] X = (Ff, S, A);

[0161]

[0162] Among them, X represents the features related to the extracted insulation state, T represents the time series, t represents the time variable, t0 represents the current time point, β represents the attenuation coefficient, which is used to control the influence degree of historical data, and dt represents the small increment of the time variable t;

[0163] Based on the dynamic trend analysis function G(X), a complex information filtering function H(G(X)) is used to predict the trend of the insulation state. This function will be combined with a machine learning algorithm to predict future state changes. The specific expression is as follows:

[0164]

[0165] Where Y represents the trend prediction value of the insulation state, k represents the number of different state categories, σ(x) represents the sigmoid function, which is used to map the trend to the [0,1] interval, μ j represents the mean of the j-th class of states, σ j represents the standard deviation of the j-th class state, ω j Represents the weight of the j-th class state.

[0166] The threshold of Y is [0,1]. When 0≤Y<0.25, it indicates that the insulation status trend is seriously deteriorating. When 0.25≤Y<0.5, it indicates that the insulation status trend is deteriorating. When 0.5≤Y<0.75, it indicates that the insulation status trend is stable. When 0.75≤Y≤1, it indicates that the insulation status trend is improving.

[0167] S7. Make plans based on the trend of the insulation status of the electronic transformer;

[0168] Once it is identified that the insulation status is deteriorating, the system should immediately start the early warning mechanism and notify the operation and maintenance personnel. The notification content is the equipment identification to ensure that the operation and maintenance personnel know which transformer has a problem; the operation and maintenance personnel should also be notified of the current insulation status and deterioration trend of the equipment, and the system should increase the monitoring frequency, increase the frequency of data collection and analysis, and formulate a specific maintenance plan based on the insulation status, including cleaning, replacing parts and performing necessary repairs, and prepare spare parts that may need to be replaced in advance to shorten the maintenance time. Perform maintenance and repair work according to the action plan, and feedback the results of maintenance and repair to the monitoring system, update the equipment status, and continue to monitor equipment performance. Even after the maintenance or repair work is completed, it is necessary to continue to closely monitor the equipment status to ensure that the problem is resolved. Based on the experience of this incident, optimize the monitoring strategy and maintenance plan to prevent similar problems in the future.

[0169] This embodiment also provides a computer device, which is suitable for the case of the electronic transformer multi-physical field coupling insulation withstand voltage verification method, including: a memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement the electronic transformer multi-physical field coupling insulation withstand voltage verification method proposed in the above embodiment.

[0170] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0171] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, the method for realizing the multi-physical field coupling insulation withstand voltage verification of an electronic transformer as proposed in the above embodiment is implemented; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (Static Random Access Memory, referred to as SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, referred to as EEPROM), erasable programmable read-only memory (Erasable Programmable Read Only Memory, referred to as EPROM), programmable read-only memory (Programmable Red-Only Memory, referred to as PROM), read-only memory (Read-Only Memory, referred to as ROM), magnetic storage, flash memory, disk or optical disk.

[0172] In summary, the present invention realizes accurate simulation of the insulation performance of the transformer under complex working conditions by establishing a simulation model including electromagnetic field, temperature field and mechanical stress field. This process not only improves the accuracy of the evaluation, but also guides the subsequent design optimization. By setting specific simulation parameters and running the model, the insulation performance under different working conditions can be quantitatively evaluated, which helps to discover potential design defects or operating risks and provide a scientific basis for maintenance plans. By installing sensors inside the transformer and collecting data in real time, continuous monitoring of the operating status is achieved, abnormal conditions are discovered in time, and the risk of failure is reduced. Combined with machine learning algorithms for intelligent analysis, potential faults can be warned in advance, unplanned downtime can be reduced, effective maintenance plans can be formulated, equipment life can be extended, and maintenance costs can be reduced. In general, the present invention significantly improves the safety, reliability and operating efficiency of electronic transformers.

[0173] Example 2, referring to Table 1, is the second example of the present invention. To further verify the advancement of the present invention, experimental simulation data of the multi-physical field coupling insulation withstand voltage verification method of the electronic transformer are provided.

[0174] The experimental object is an electronic transformer with an operating voltage of 110V, an operating current of 1A, an operating frequency of 50Hz, an ambient temperature of 25℃, and a relative humidity of 50%.

[0175] First, a multi-physics field coupling simulation model of the electronic transformer was established, which includes an electromagnetic field model, a temperature field model, and a mechanical stress field model. Multi-physics field coupling analysis was performed using simulation software to obtain insulation performance data under different working conditions. Then, temperature sensors, electrical parameter monitoring devices, and other sensors were installed inside the transformer, and vibration sensors and environmental sensors were installed outside to collect the working status data of the transformer in real time. These data were then transmitted to the central processing unit for real-time analysis and fusion, generating working status analysis results. Based on these results, the system monitors the working status of the transformer in real time and issues warnings in a timely manner when potential faults are found. Finally, the fused data was intelligently analyzed using a machine learning algorithm, the trend of the transformer insulation status was identified, and a maintenance plan was formulated accordingly.

[0176] Data collection and analysis,

[0177] In order to more intuitively compare the effects of the prior art and the method of the present invention, we conducted a comparative test on the two methods under the same conditions. The test lasted for 72 hours, and data was recorded every 10 minutes, with a total of 432 sets of data recorded. The data includes the simulation results of the electromagnetic field model, temperature field model, and mechanical stress field model, as well as the actual collected temperature, vibration, voltage, current and other data. The statistical results of some data are given below, as shown in Table 1:

[0178] Table 1 Insulation Withstand Voltage Verification Results

[0179]

[0180]

[0181] Tabular data analysis,

[0182] It can be seen from the table data that, compared with the prior art, the method of the present invention has significant advantages in many aspects.

[0183] Electromagnetic field deviation: The average deviation of the method of the present invention is 0.0041, while the average deviation of the prior art is 0.0051. This shows that the method of the present invention has higher accuracy in electromagnetic field simulation and can more accurately predict the electromagnetic field behavior of the transformer.

[0184] Temperature field deviation: The average deviation of the method of the present invention is 0.0028, while the average deviation of the prior art is 0.0038. This means that the method of the present invention can simulate the influence of temperature changes more accurately, which is especially important for mutual inductors in high temperature environments.

[0185] Mechanical stress field deviation: The average deviation of the method of the present invention is 0.0039, while the average deviation of the prior art is 0.0049. This shows that the method of the present invention is more accurate in simulating mechanical stress changes and helps to better understand the impact of mechanical stress on insulation withstand voltage performance.

[0186] Working state score: The average score of the method of the present invention is 0.82, while the average score of the prior art is 0.72. A higher score means that the working state of the transformer under the method of the present invention is more stable and reliable, and the possibility of potential failure is lower.

[0187] Alarm signals: Although no alarm signals were triggered by either method during the test, considering the difference in working status scores, the method of the present invention can identify problems and take actions more quickly when encountering abnormal conditions in the future, thereby reducing the risk of failures.

[0188] In summary, the method of the present invention not only improves the simulation accuracy through the establishment of a multi-physical field coupling simulation model, but also enhances the system's monitoring capability and fault warning capability through intelligent analysis and fusion of real-time data, thereby significantly improving the safety and reliability of the electronic transformer.

[0189] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for verifying the insulation withstand voltage of an electronic transformer by multi-physical field coupling, characterized in that: include: Establishing a multi-physical field coupling simulation model of an electronic mutual inductor, wherein the model includes an electromagnetic field model, a temperature field model and a mechanical stress field model; Set simulation parameters, input into the simulation model, run the simulation model, obtain insulation performance data under different working conditions, analyze simulation results, and evaluate the insulation withstand voltage performance of electronic transformers under the coupling of multiple physical fields; Install sensors and detection devices inside the electronic transformer to collect working status data of the electronic transformer in real time; The collected data is transmitted to the central processing unit for real-time analysis and fusion to generate working status analysis results; According to the working status analysis results, the working status of the electronic transformer is monitored in real time, and an early warning is issued in time when a potential fault is found; Use machine learning algorithms to intelligently analyze the fused data and identify trends in the insulation status of electronic transformers; Make plans based on trends in the insulation condition of electronic instrument transformers.

2. The electronic transformer multi-physical field coupling insulation withstand voltage verification method according to claim 1, characterized in that: Establishing a multi-physical field coupling simulation model of an electronic mutual inductor, wherein the model includes an electromagnetic field model, a temperature field model and a mechanical stress field model; The electromagnetic field model is established as follows: in, represents the deviation of the dynamic behavior of the electromagnetic field, represents the deviation of the electromagnetic field constraint condition, H represents the magnetic field intensity, E represents the electric field intensity, J represents the current density, D represents the electric displacement vector, B represents the magnetic induction intensity vector, ρ represents the charge density, Ω e represents the electromagnetic field area, dΩ represents a small increment of the integral variable, Indicates the deviation of this additional current density, J th represents the current density vector caused by temperature change, J mech represents the current density vector caused by the change in mechanical stress, represents the energy functional of the integrated electromagnetic field model; The temperature field model is established as follows: in, represents the energy functional of the temperature field model, c p represents specific heat capacity, ρ t represents the material density in the temperature field, θ represents the temperature, k represents the thermal conductivity, τ th represents the heat source intensity vector caused by temperature change, τ e1 represents the heat source intensity vector caused by the change of electromagnetic field, q represents the intensity of other heat sources, Ω t represents the temperature field area; The mechanical stress field model expression is: in, represents the energy functional of the mechanical stress field model, E represents Young's modulus, σ′ ij represents the mechanical stress tensor, ε′ ij represents the strain tensor, v represents the Poisson's ratio, δ′ ij represents the Kronecker delta function, α′ represents the thermal expansion coefficient, T′ represents the temperature, σ′ el，ij represents the mechanical stress tensor caused by the change of electromagnetic field, σ′ mech，ij represents the mechanical stress tensor caused by the change in mechanical stress, ε′ kk Indicates the degree of volume change of the material, Ω m represents the mechanical stress field area; Combining the above three models, by finding a set of state variables to minimize the total energy functional, a multi-physics field coupling model is formed, which is expressed as: in, represents the integrated multi-physics field coupling model, H represents the magnetic field intensity vector, E represents the electric field intensity vector, J represents the current density vector, ρ represents the charge density, D represents the electric displacement vector, B represents the magnetic induction intensity vector, T′ represents the temperature, and u represents the displacement vector.

3. The electronic transformer multi-physical field coupling insulation withstand voltage verification method according to claim 2, characterized in that: Set simulation parameters, including operating voltage, current, frequency, temperature, and humidity; Define the working voltage of the electronic transformer as V, the working current as I, the working frequency as f, the ambient temperature as T0, and the relative humidity as RH; Input the defined simulation parameters V, I, f, T0, RH into the simulation model as initial conditions; Use simulation software to run the model to simulate the dynamic changes of electromagnetic field, temperature field and mechanical stress field under given working voltage V, working current I, working frequency f, ambient temperature T0 and relative humidity RH; Obtain insulation performance data under different working conditions through simulation models; The insulation performance data obtained is analyzed to determine the insulation performance of the insulation layer under different working conditions. The expression is: in, Indicates the insulation withstand voltage performance index, ε m Indicates the maximum electric field strength, E t represents the electric field strength threshold, T m Indicates the maximum temperature, T t represents the temperature threshold, σ m represents the maximum stress, σ t Represents the stress threshold.

4. The electronic transformer multi-physical field coupling insulation withstand voltage verification method according to claim 3, characterized in that: Install temperature sensors and electrical parameter monitoring devices inside the electronic transformer, and install vibration sensors and environmental sensors outside to collect working status data of the electronic transformer in real time; Preprocess the collected real-time data; The preprocessed working status data is used for feature extraction, and wavelet transform WT and W V The time-frequency characteristics of the temperature signal and the vibration signal are extracted respectively, and their expressions are as follows: Among them, W T (T) represents the time-frequency characteristics of the temperature signal, W V (V) represents the time-frequency characteristics of the vibration signal, T represents the temperature, V represents the vibration signal, t represents the time variable, ψ* represents the conjugate of the wavelet basis function, u represents the translation parameter, and dt represents the small increment of the time variable t; Calculate the effective value of voltage and current data within the sampling window, and the average value of ambient temperature and humidity data within the sampling window to obtain electrical and environmental characteristics. The expression is: Among them, Φ E (E) represents the effective value of voltage and current data within the sampling window, Φ Env (Env) represents the average value of ambient temperature and humidity data within the sampling window, E represents electrical parameter data, U i Represents the voltage value of the i-th sampling point, I i represents the current value of the i-th sampling point, n represents the number of sampling points, Emv represents environmental data, Temp i Represents the ambient temperature value of the i-th sampling point, Humid i Represents the ambient humidity value of the i-th sampling point.

5. The electronic transformer multi-physical field coupling insulation withstand voltage verification method according to claim 4, characterized in that: The simulation data and the collected real-time data are transmitted to the central processing unit for real-time analysis and fusion. The expression is: Among them, F f Represents the fused simulation data and real-time monitoring data; After analysis and fusion, the working status analysis result is generated, and its expression is: Among them, S represents the working status evaluation score of the equipment, m represents the number of different types of sensors, n represents the number of data points of each type of sensor, and x i Represents the value of the i-th data point.

6. The electronic transformer multi-physical field coupling insulation withstand voltage verification method according to claim 5, characterized in that: According to the working status analysis results, the working status of the electronic transformer is monitored in real time, and an early warning is issued in time when a potential fault is found. The expression is: Among them, A j represents the alarm signal of the jth sensor, S j represents the working status evaluation score of the j-th sensor, θ represents the threshold, and α represents the parameter for adjusting the steepness of the curve; The setting range of the threshold is based on the four states of S. When S indicates that the device is in poor condition, an alarm signal will be triggered and the operation and maintenance personnel will be notified via email, text message, or phone call.

7. The electronic transformer multi-physical field coupling insulation withstand voltage verification method according to claim 6, characterized in that: Use machine learning algorithms to intelligently analyze the fused data and identify trends in the insulation status of electronic transformers; The data after the simulation data and the collected real-time data are fused, the working status evaluation score of the equipment, and the alarm signal vector of the sensor are extracted to extract various factors related to the insulation status, and the dynamic trend analysis function G(X) is used to identify the trend of the insulation status. Its expression is: X = (Ff, S, A); Among them, X represents the features after extracting insulation state related information, T represents the time series, t represents the time variable, t0 represents the current time point, β represents the attenuation coefficient, and dt represents the small increment of the time variable t; Based on the dynamic trend analysis function G(X), a complex information filtering function H(G(X)) is used to predict the trend of the insulation state. This function will be combined with a machine learning algorithm to predict future state changes. The specific expression is as follows: Where Y represents the trend prediction value of the insulation state, k represents the number of different state categories, σ(x) represents the sigmoid function, μ j represents the mean of the j-th class of states, σ j represents the standard deviation of the j-th class state, ω j Represents the weight of the,th class state.

8. The method for verifying the insulation withstand voltage of an electronic transformer by multi-physical field coupling according to claim 7, characterized in that: Make plans based on trends in the insulation condition of electronic instrument transformers; Once it is identified that the insulation condition is deteriorating, the system should immediately activate the early warning mechanism, notify the operation and maintenance personnel to perform maintenance, and increase the monitoring frequency, improve the frequency of data collection and analysis, formulate a specific maintenance plan based on the insulation condition, perform maintenance and repair work according to the action plan, and feedback the results of maintenance and repair to the monitoring system, update the equipment status, and continue to monitor equipment performance.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the electronic transformer multi-physical field coupling insulation withstand voltage verification method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the electronic transformer multi-physical field coupling insulation withstand voltage verification method according to any one of claims 1 to 7 are implemented.

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