A real-time online simulation method and related device for temperature distribution of power transmission and transformation equipment

Abstract

The present application discloses a real-time online simulation method and related device for the temperature distribution of power transmission and transformation equipment, which combines the dimension reduction of the data set with the data fitting mapping technology, obtains a finite set of precise results through finite element simulation based on the conditions that the structural model of the already put into operation power transmission and transformation equipment is fixed and the extreme operating state is known, and a mapping relationship is constructed on this basis to achieve the second-level output of multiple physical field quantities under the corresponding conditions. The number of grid node data is reduced in dimension, which effectively reduces the number of input data required to be calculated in the mapping relationship construction stage, and further speeds up the calculation speed of data-driven simulation. The empirical orthogonal function analysis method adopted can quickly and effectively reconstruct the data set corresponding to the original grid node after dimensionality reduction and truncation, and has high accuracy and fast calculation speed, and effectively restores the temperature field distribution of the power transmission and transformation equipment after mapping calculation. Thereby solving the problem that the existing finite element method is not suitable for online real-time simulation in seconds.

Description

Technical Field

[0001] The present application relates to the field of electric power digitalization technology, and in particular to a real-time online simulation method and related device for temperature distribution of power transmission and transformation equipment. Background Art

[0002] The digitization of power transmission and transformation equipment requires the real-time visualization of its internal multi-physical field status in order to determine the equipment's operating status and arrange inspection and maintenance. The visualization of multi-physical field status depends on the simulation analysis of field quantity distribution. Currently, finite element simulation software such as ANSYS and COMSOL are mostly used. However, the calculation of equipment temperature field distribution based on the finite element method has the characteristics of a large number of grid nodes and strong nonlinearity of fluid calculation, which requires its simulation time to reach hours and is slow. It is only suitable for application in the design and verification analysis stage. When parameters such as the external environment and current carrying capacity change, operation and maintenance personnel need to obtain the temperature field distribution calculation results in real time. At this time, if the finite element method is used for calculation, it cannot meet the needs of digital real-time evaluation and analysis.

[0003] Therefore, in order to solve the problems of slow speed and poor convergence of temperature field distribution simulation in the application of digital equipment of power transmission and transformation equipment based on finite element method, it is necessary to further develop a data-driven digital real-time simulation method of temperature field distribution, while reducing the size of the driving data set, speeding up the rapid analysis of the temperature field of power transmission and transformation equipment while meeting the accuracy requirements, and realizing the online simulation calculation of the temperature field of power transmission and transformation equipment. Summary of the invention

[0004] The present application provides a real-time online simulation method and related devices for temperature distribution of power transmission and transformation equipment, which are used to solve the problem that the existing finite element method is not suitable for online real-time simulation at the second level, and provide a direction for the visualization and digital evaluation of the internal characteristics of power transmission and transformation equipment.

[0005] In view of this, the first aspect of the present application provides a real-time online simulation method for temperature distribution of power transmission and transformation equipment, the method comprising:

[0006] According to the digital requirements of the temperature distribution of power transmission and transformation equipment, a three-dimensional electric-magnetic-thermal-fluid multi-physics field simulation calculation model of power transmission and transformation equipment is established to determine the main variable X of the temperature field distribution of power transmission and transformation equipment;

[0007] Using the equal interval scanning method, we can obtain multiple sets of data sets P consisting of different main variables X = {X1, X2, X3, ..., X N}, and the corresponding temperature field distribution data set T = {U1, U2, U3, ..., U N};

[0008] The empirical orthogonal function analysis method is used to reduce the dimension of the grid node data in the data set T to obtain the reduced-dimensional data set B;

[0009] Construct a mapping relationship between data set P and data set B to obtain a dimension reduction calculation model for real-time online simulation of temperature distribution of power transmission and transformation equipment;

[0010] The main variable X to be simulated f Input into the dimension reduction calculation model of the real-time online simulation for simulation, and output the result B x , according to the output result B x The actual temperature field distribution U is obtained by temperature field reconstruction method f .

[0011] Optionally, the main variables include: current carrying capacity, contact resistance of plug contacts, ambient temperature, ambient wind speed, convection heat transfer coefficient of equipment housing-air interface, and solar radiation.

[0012] Optionally, the equal-interval scanning method is used to obtain multiple sets of data sets P consisting of different main variables X = {X1, X2, X3, ..., X N}, and the corresponding temperature field distribution data set T = {U1, U2, U3, ..., U N}, followed by:

[0013] The format of (x, y, z, temperature) is adopted. The (x, y, z) coordinates of the grid nodes correspond to the temperature one by one. When the main variable X is different, the grid used in the finite element calculation remains unchanged. All exported data are arranged from large to small according to x, y, and z.

[0014] Optionally, the empirical orthogonal function analysis method is used to perform dimensionality reduction processing on the grid node data in the data set T, specifically including:

[0015] The data set T = {U1, U2, U3, ..., U N} decomposed into n orthogonal eigenvectors whose corresponding eigenvalues ​​are greater than or equal to 0 And its corresponding coefficient column vector set

[0016] Based on the orthogonal eigenvector t n and coefficient column vector set b n Construct the original temperature distribution expression formula;

[0017] Wherein, the original temperature distribution expression formula is:

[0018]

[0019] The upper limit of the cumulative term in the original temperature distribution expression formula is taken as m, and the data set used to characterize the reduced dimension is obtained. The vector [b i1 ,b i2 ,…,b im ] has a one-to-one correspondence with the element X in the data set P.

[0020] Optionally, the mapping relationship between the dataset P and the dataset B is constructed as follows:

[0021] A data analysis method is used to construct a mapping relationship between each row in the data set B and the corresponding element X in the data set P, wherein the data analysis method includes: one of a response surface method, a kriging method and a RBF neural network method.

[0022] Optionally, the output result B x The temperature field distribution U under actual conditions is obtained by reconstruction method f , specifically including:

[0023] Based on the temperature field distribution formula, the output result B x Reconstruct and obtain the main variable X to be simulated f Temperature field distribution data;

[0024]

[0025] Where m is the upper limit of the cumulative term in the original temperature distribution expression formula, is an orthogonal eigenvector, b ij is a vector consisting of each row in dataset B.

[0026] A second aspect of the present application provides a real-time online simulation system for temperature distribution of power transmission and transformation equipment, the system comprising:

[0027] Establishing a unit for establishing a three-dimensional electric-magnetic-thermal-fluid multi-physics field simulation calculation model of the power transmission and transformation equipment according to the digital requirements of the temperature distribution of the power transmission and transformation equipment, and determining the main variable X of the temperature field distribution of the power transmission and transformation equipment;

[0028] The acquisition unit is used to obtain multiple sets of data sets P consisting of different main variables X by using an equal interval scanning method, which are P={X1, X2, X3, ..., X N}, and the corresponding temperature field distribution data set T = {U1, U2, U3, ..., U N};

[0029] A dimension reduction unit is used to perform dimension reduction processing on the grid node data in the data set T by using an empirical orthogonal function analysis method to obtain a dimension-reduced data set B;

[0030] A construction unit, used to construct a mapping relationship between the data set P and the data set B, and obtain a dimension reduction calculation model for real-time online simulation of temperature distribution of power transmission and transformation equipment;

[0031] The simulation unit is used to convert the main variable X to be simulated f Input into the dimension reduction calculation model of the real-time online simulation for simulation, and output the result B x , according to the output result B x The actual temperature field distribution U is obtained by temperature field reconstruction method f .

[0032] Optionally, the dimension reduction unit is specifically used to:

[0033] The data set T = {U1, U2, U3, ..., U N} decomposed into n orthogonal eigenvectors whose corresponding eigenvalues ​​are greater than or equal to 0 And its corresponding coefficient column vector set

[0034] Based on the orthogonal eigenvector t n and coefficient column vector set b n Construct the original temperature distribution expression formula;

[0035] Wherein, the original temperature distribution expression formula is:

[0036]

[0037] The upper limit of the cumulative term in the original temperature distribution expression formula is taken as m, and the data set used to characterize the reduced dimension is obtained. The vector [b i1 ,b i2 ,…,b im ] has a one-to-one correspondence with the element X in the data set P.

[0038] A third aspect of the present application provides a real-time online simulation device for temperature distribution of power transmission and transformation equipment, the device comprising a processor and a memory:

[0039] The memory is used to store program code and transmit the program code to the processor;

[0040] The processor is used to execute the steps of the real-time online simulation method of temperature distribution of power transmission and transformation equipment as described in the first aspect according to the instructions in the program code.

[0041] A fourth aspect of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium is used to store program code, and the program code is used to execute the real-time online simulation method of temperature distribution of power transmission and transformation equipment described in the first aspect above.

[0042] Compared with the prior art, the present invention has the following beneficial effects:

[0043] The real-time online simulation method of the temperature distribution of power transmission and transformation equipment of the present invention combines the dimension reduction technology of the data set with the data fitting mapping technology. According to the conditions that the structural model of the power transmission and transformation equipment that has been put into operation is fixed and the extreme operating state is known, a finite set of accurate results is obtained through finite element simulation, and on this basis, a data-driven method is used to construct a mapping relationship, which can effectively achieve the second-level output of multiple physical field quantities under corresponding conditions. The number of grid node data is reduced in dimension, which effectively reduces the number of input data required to be calculated in the mapping relationship construction stage, and further accelerates the calculation speed of data-driven simulation. The empirical orthogonal function analysis method used can quickly and effectively reconstruct the data set corresponding to the original grid node after dimensionality reduction and truncation, and has high accuracy and fast calculation speed, and can effectively restore the temperature field distribution of the power transmission and transformation equipment after mapping calculation. Thereby solving the problem that the existing finite element method is not suitable for online real-time simulation at the second level, and providing a direction for the visualization and digital evaluation of the internal characteristics of power transmission and transformation equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 A schematic flow chart of a real-time online simulation method for temperature distribution of power transmission and transformation equipment provided in an embodiment of the present application;

[0045] Figure 2 The distribution cloud diagram of the three-phase GIS / GIL equipment calculated by finite element and the real-time simulation output result of this application B x 1. Contrast

[0046] Figure 3 This is the error distribution diagram of the online simulation results and finite element simulation results of the three-phase common box GIS / GIL equipment in this application;

[0047] Figure 4 A schematic diagram of the structure of a real-time online simulation system for temperature distribution of power transmission and transformation equipment provided in an embodiment of the present application. DETAILED DESCRIPTION

[0048] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0049] See also Figure 1 , a real-time online simulation method for temperature distribution of power transmission and transformation equipment provided in an embodiment of the present application includes:

[0050] Step 101: According to the digital requirements of the temperature distribution of the power transmission and transformation equipment, a three-dimensional electric-magnetic-thermal-fluid multi-physics field simulation calculation model of the power transmission and transformation equipment is established to determine the main variable X of the temperature field distribution of the power transmission and transformation equipment;

[0051] It should be noted that it is understandable that according to the digital requirements of the temperature distribution of the power transmission and transformation equipment, a three-dimensional electric-magnetic-thermal-fluid multi-physical field simulation calculation model of the power transmission and transformation equipment is established using finite element simulation software such as ANSYS and COMSOL, and the main variables X of the temperature field distribution of the power transmission and transformation equipment are determined. The main variables X of this embodiment include: current carrying capacity, contact resistance of plug contacts, ambient temperature, ambient wind speed, and convective heat transfer coefficient of the equipment housing-air interface. Those skilled in the art can select according to actual conditions and are not limited here.

[0052] The power transmission and transformation equipment is determined to be, for example, a three-phase common box GIS / GIL, where GIS electrical equipment is a gas-insulated switchgear and GIL is the abbreviation of a gas-insulated metal-enclosed transmission line.

[0053] Step 102: Using an equal-interval scanning method, obtain multiple sets of data sets P consisting of different main variables X = {X1, X2, X3, ..., X N}, and the corresponding temperature field distribution data set T = {U1, U2, U3, ..., U N};

[0054] It should be noted that, firstly, the equal interval scanning method is adopted, and the upper and lower limits of each main variable X need to be determined according to the operating conditions. The interval size of the ambient temperature and the convection heat transfer coefficient is not less than 2% of the upper limit minus the lower limit. In addition, the interval size of other main variables is not less than 5% of the upper limit minus the lower limit, so as to obtain multiple groups of data sets P composed of different main variables X = {X1, X2, X3, ..., X N}, it should be noted that X is a vector, which contains many elements, such as X1 = (ambient temperature 20 ° C, current carrying capacity 3000A, ..., ambient wind speed 1m / s); and the corresponding temperature field distribution data set T = {U1, U2, U3, ..., U N}, it should be noted that U is in vector form, indicating that U i =[T i1 T i2 …

[0055] T ip ] T (i=1,2,3,…,N) contains multiple variables.

[0056] Furthermore, in an optional embodiment, the format of (x, y, z, temperature) is adopted, the (x, y, z) coordinates of the grid nodes correspond to the temperature one by one, and when the main variable X is different, the grid used in the finite element calculation remains unchanged, and all the derived data are arranged in descending order according to x, y, z; it can be understood that U i =[T i1 T i2 …T ip ] T The standardized format used in (i=1,2,3,…,N), namely T ip The format of (x, y, z, temperature) is adopted, the (x, y, z) coordinates of the grid nodes correspond to the temperature one by one, and the grid used in the finite element calculation remains unchanged when the main variable X is different, and all the exported data are arranged from large to small according to x, y, z. Where p represents the node number in the finite element calculation.

[0057] Step 103: Using the empirical orthogonal function analysis method, perform dimensionality reduction processing on the grid node data in the data set T to obtain a data set B after dimensionality reduction;

[0058] It should be noted that, in one embodiment, step 103 specifically includes:

[0059] The empirical orthogonal function analysis method is used to transform the data set T = {U1, U2, U3, ..., U N} decomposed into n orthogonal eigenvectors whose corresponding eigenvalues ​​are greater than or equal to 0 And its corresponding coefficient column vector set

[0060] Based on the orthogonal eigenvector t n and coefficient column vector set b n Construct the original temperature distribution expression formula;

[0061] Wherein, the original temperature distribution expression formula is:

[0062]

[0063] The upper limit of the cumulative term in the original temperature distribution expression formula is taken as m, that is, it is truncated at m to obtain the data set used to characterize the reduced dimension. The vector [b i1 b i2 …b im ] has a one-to-one correspondence with the element X in the data set P. The determination of the truncation order m should be carried out through mathematical experiments, and the reconstruction error should not exceed 0.2%. In general, m can be selected according to the actual situation.

[0064] The following is an explanation of the principle of empirical orthogonal function analysis:

[0065] Assume that the three-phase integrated GIL current carrying capacity, ambient temperature, N2 gas content and other calculation parameters constitute the overall model input parameter set P = {P (1) ,P (2) ,…,P (i＝N)}, and the corresponding temperature field distribution is T(P)={T (1) ,T (2) ,…,T (i＝N)}, N represents the number of different working condition combinations of the three-phase integrated GIL set during calculation. (i) It is a q-row and 1-column matrix. Each element in the matrix represents the parameter value of different input state parameters such as different current carrying capacity and ambient temperature. (i) =[P i1 P i2 …P iq ] T , the subscript q represents the number of different input state parameters; the matrix T in T (i) is a p-row and 1-column matrix, T (i) =[T i1 T i2 …T ip ] T , p represents the node number in the finite element calculation. Then the sample matrix U used to construct the EOF basis vector and eigenvalue can be expressed as:

[0066]

[0067] Through the empirical orthogonal function analysis method (EOF), the data set T = {T (1) ,T (2) ,…,T (i＝N)} is decomposed into a set of n orthogonal characteristic column vectors whose corresponding eigenvalues ​​are greater than or equal to 0 Combine the corresponding coefficient column vector set The original temperature field distribution can be expressed as:

[0068]

[0069] in Arrange from large to small according to the eigenvalue, that is, the eigenvalue λ1≥λ2≥…≥λ n .

[0070] Among them, the characteristic column vector matrix t n It can be obtained by solving the eigenvectors and eigenvalues ​​of the sample covariance matrix of the temperature field distribution node data in formula (5-3).

[0071] UU T t n =t n diag{λ1 λ2 … λ n} (5-3)

[0072] Since the vector obtained after decomposition are orthogonal, then t n t n T =I; According to the definition of formula (5-2), the coefficient matrix Through T (i) and To solve:

[0073]

[0074] The temperature distribution T corresponding to the i-th input parameter set (i) , the current m principal component coefficients [b i1 b i2 …b im ] When its cumulative contribution can reach a high level, the original data can be fully calculated through the first m principal component coefficients, that is:

[0075]

[0076] The following is an explanation of the advantages of using grid node dataset dimensionality reduction:

[0077] In order to simplify the calculation data and amount of calculation while ensuring a certain accuracy, only the eigenvector set corresponding to the first 30 eigenvalues ​​can be selected as the input of the fitting prediction model.

[0078] When the input data size n = 100, the sample matrix U originally used directly for prediction is transformed into an indirect matrix through b nmFor prediction, the size of the operation matrix is ​​reduced from 23993×100 to m×100 (m∈[1~100], m=2 already contains most of the information), realizing the low-dimensional expression of grid node data.

[0079] Furthermore, the cost is only increased by one inverse matrix transformation, and the computational resources sacrificed are small.

[0080] Step 104: construct a mapping relationship between the data set P and the data set B to obtain a dimension reduction calculation model for real-time online simulation of temperature distribution of power transmission and transformation equipment;

[0081] It should be noted that, in one embodiment, step 104 uses a data analysis method to construct a mapping relationship between each row in the data set B and the corresponding element X in the data set P, wherein the data analysis method includes: response surface method, kriging method and RBF neural network method; specifically, when the scale of the set P is small, the response surface method is used, when the scale of the set P is large, the kriging method is used, and when the accuracy requirement is high and the time allowed is long, the RBF neural network method is used.

[0082] Furthermore, the method of constructing the mapping relationship may also include: machine learning, neural network, support vector machine, nonlinear regression, etc., with the maximum relative error of all grid node data as an indicator, and the above method is selected according to the characteristics of different equipment structures, without specific limitation.

[0083] Step 105: Set the main variable X to be simulated f Input to the real-time online simulation dimensionality reduction calculation model for simulation, and output the result B x , according to the output result B x The actual temperature field distribution U is obtained by temperature field reconstruction method f .

[0084] It should be noted that, first, the main variable to be simulated X f Input into the dimensionality reduction calculation model of digital real-time online simulation for simulation, and then output the result B x ; Among them, the main variable to be simulated is X f The values ​​measured by the actual sensors are used to achieve a one-to-one correspondence between the online simulation system results and the real physical conditions.

[0085] Next, in one embodiment, according to the output result B x The temperature field distribution U under actual conditions is obtained by reconstruction method f , specifically including:

[0086] Based on the temperature field distribution formula, the output result B x Reconstruct and obtain the main variable X to be simulated f Temperature field distribution data;

[0087]

[0088] Where m is the upper limit of the cumulative term in the original temperature distribution expression formula, is an orthogonal eigenvector, b ij is a vector consisting of each row in dataset B.

[0089] The following are simulation experiment figures and tables of this application.

[0090] See also Figure 2 and 3 , Figure 2 The distribution cloud diagram of the three-phase GIS / GIL equipment calculated by finite element and the real-time simulation output result of this application B x 1. Contrast Figure 3 This is the error distribution diagram of the online simulation results and the finite element simulation results of the three-phase common box GIS / GIL equipment in this application.

[0091] Please refer to Table 1, which shows the relationship between the temperature field distribution error and the dimensionality reduction order of the three-phase common box GIS / GIL equipment embodiment in this application.

[0092] Table 1

[0093] Truncation order m Maximum absolute error / ℃ Maximum relative error / % Average error / % 3 2.11 6.65 <![CDATA[2.30×10 -2 ]]> 10 1.44 4.48 <![CDATA[1.60×10 -3 ]]> 20 0.62 1.63 <![CDATA[2.07×10 -4 <!-- 6 -->]]> 30 <![CDATA[7.03×10 -2 ]]> <![CDATA[2.22×10 -1 ]]> <![CDATA[-1.56×10 -4 ]]> 40 <![CDATA[9.40×10 -3 ]]> <![CDATA[2.45×10 -2 ]]> <![CDATA[-1.67×10 -5 ]]> 50 <![CDATA[1.90×10 -3 ]]> <![CDATA[5.40×10 -3 ]]> <![CDATA[-3.99×10 -6 ]]> 60 <![CDATA[3.09×10 -4 ]]> <![CDATA[9.91×10 -4 ]]> <![CDATA[2.69×10 -7 ]]> 70 <![CDATA[3.44×10 -4 ]]> <![CDATA[1.12×10 -4 ]]> <![CDATA[1.16×10 -7 ]]> 80 <![CDATA[3.62×10 -5 ]]> <![CDATA[1.09×10 -4 ]]> <![CDATA[1.86×10 -8 ]]>

[0094] The real-time online simulation method for the temperature distribution of power transmission and transformation equipment provided in the embodiment of the present application combines the dimension reduction technology of the data set with the data fitting mapping technology. According to the conditions that the structural model of the power transmission and transformation equipment that has been put into operation is fixed and the extreme operating state is known, a finite set of accurate results is obtained through finite element simulation, and on this basis, a data-driven method is applied to construct a mapping relationship, which can effectively achieve the second-level output of multiple physical field quantities under corresponding conditions. The number of grid node data is reduced in dimension, which effectively reduces the number of input data that needs to be calculated in the mapping relationship construction stage, and further speeds up the calculation speed of data-driven simulation. The empirical orthogonal function analysis method adopted can quickly and effectively reconstruct the data set corresponding to the original grid node after dimensionality reduction and truncation, and has high accuracy and fast calculation speed, and can effectively restore the temperature field distribution of the power transmission and transformation equipment after mapping calculation.

[0095] The above is a real-time online simulation method for temperature distribution of power transmission and transformation equipment provided in an embodiment of the present application, and the following is a real-time online simulation system for temperature distribution of power transmission and transformation equipment provided in an embodiment of the present application.

[0096] See also Figure 4 , a real-time online simulation system for temperature distribution of power transmission and transformation equipment provided in an embodiment of the present application includes:

[0097] Establishing unit 201, used to establish a three-dimensional electric-magnetic-thermal-fluid multi-physics field simulation calculation model of the power transmission and transformation equipment according to the digital requirements of the temperature distribution of the power transmission and transformation equipment, and determine the main variable X of the temperature field distribution of the power transmission and transformation equipment;

[0098] The acquisition unit 202 is used to obtain multiple sets of data sets P consisting of different main variables X by using an equal interval scanning method, which are P={X1, X2, X3, ..., X N}, and the corresponding temperature field distribution data set T = {U1, U2, U3, ..., U N} and export them in a standardized format;

[0099] A dimension reduction unit 203 is used to perform dimension reduction processing on the grid node data in the data set T by using an empirical orthogonal function analysis method to obtain a data set B after dimension reduction;

[0100] A construction unit 204 is used to construct a mapping relationship between the data set P and the data set B to obtain a dimension reduction calculation model for real-time online simulation of temperature distribution of power transmission and transformation equipment;

[0101] The simulation unit 205 is used to convert the main variable X to be simulated f Input into the dimensionality reduction calculation model of digital real-time online simulation for simulation, and then output the result B x , according to the output result B x The temperature field distribution U under actual conditions is obtained by reconstruction method f .

[0102] Furthermore, in an embodiment of the present application, a real-time online simulation device for temperature distribution of power transmission and transformation equipment is provided, the device comprising a processor and a memory:

[0103] The memory is used to store program code and transmit the program code to the processor;

[0104] The processor is used to execute the steps of the real-time online simulation method of temperature distribution of power transmission and transformation equipment as described in the above method embodiment according to the instructions in the program code.

[0105] Furthermore, a computer-readable storage medium is provided in an embodiment of the present application, and the computer-readable storage medium is used to store program code, and the program code is used to execute the real-time online simulation method of temperature distribution of power transmission and transformation equipment described in the above method embodiment.

[0106] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0107] The terms "first", "second", "third", "fourth", etc. (if any) in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein, for example. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0108] It should be understood that in the present application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the objects associated before and after are in an "or" relationship. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0109] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0110] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0111] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0112] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (full name in English: Read-Only Memory, English abbreviation: ROM), random access memory (full name in English: Random Access Memory, English abbreviation: RAM), disk or optical disk and other media that can store program codes.

[0113] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A real-time online simulation method for temperature distribution of power transmission and transformation equipment, characterized in that: include: According to the digital requirements of the temperature distribution of power transmission and transformation equipment, a three-dimensional electric-magnetic-thermal-fluid multi-physics field simulation calculation model of power transmission and transformation equipment is established to determine the main variable X of the temperature field distribution of power transmission and transformation equipment; Using the equal interval scanning method, we can obtain multiple sets of data sets P consisting of different main variables X = {X1, X2, X3, ..., X N }, and the corresponding temperature field distribution data set T = {U1, U2, U3, ..., U N }; The empirical orthogonal function analysis method is used to reduce the dimension of the grid node data in the data set T to obtain the reduced-dimensional data set B; Construct a mapping relationship between data set P and data set B to obtain a dimension reduction calculation model for real-time online simulation of temperature distribution of power transmission and transformation equipment; The main variable X to be simulated f Input into the dimension reduction calculation model of the real-time online simulation for simulation, and output the result B x , according to the output result B x The actual temperature field distribution U is obtained by temperature field reconstruction method f .

2. The real-time online simulation method for temperature distribution of power transmission and transformation equipment according to claim 1, characterized in that: The main variables include: current carrying capacity, contact resistance of plug contacts, ambient temperature, ambient wind speed, convection heat transfer coefficient of equipment housing-air interface, and solar radiation.

3. The real-time online simulation method for temperature distribution of power transmission and transformation equipment according to claim 1, characterized in that: The equal interval scanning method is used to obtain multiple sets of data sets P consisting of different main variables X = {X1, X2, X3, ..., X N }, and the corresponding temperature field distribution data set T = {U1, U2, U3, ..., U N }, followed by: The format of (x, y, z, temperature) is adopted. The (x, y, z) coordinates of the grid nodes correspond to the temperature one by one. When the main variable X is different, the grid used in the finite element calculation remains unchanged. All exported data are arranged from large to small according to x, y, and z.

4. The real-time online simulation method for temperature distribution of power transmission and transformation equipment according to claim 1, characterized in that: The empirical orthogonal function analysis method is used to perform dimensionality reduction processing on the grid node data in the data set T, specifically including: The data set T = {U1, U2, U3, ..., U N } decomposed into n orthogonal eigenvectors whose corresponding eigenvalues ​​are greater than or equal to 0 And its corresponding coefficient column vector set Based on the orthogonal eigenvector t n and coefficient column vector set b n Construct the original temperature distribution expression formula; Wherein, the original temperature distribution expression formula is: The upper limit of the cumulative term in the original temperature distribution expression formula is taken as m, and the data set used to characterize the reduced dimension is obtained. The vector [b i1 ,b i2 ,…,b im ] has a one-to-one correspondence with the element X in the data set P.

5. The real-time online simulation method for temperature distribution of power transmission and transformation equipment according to claim 1, characterized in that: The mapping relationship between the dataset P and the dataset B is constructed as follows: A data analysis method is used to construct a mapping relationship between each row in the data set B and the corresponding element X in the data set P, wherein the data analysis method includes: one of a response surface method, a kriging method and a RBF neural network method.

6. The real-time online simulation method for temperature distribution of power transmission and transformation equipment according to claim 1, characterized in that: According to the output result B x The temperature field distribution U under actual conditions is obtained by reconstruction method f , specifically including: Based on the temperature field distribution formula, the output result B x Reconstruct and obtain the main variable X to be simulated f Temperature field distribution data; Where m is the upper limit of the cumulative term in the original temperature distribution expression formula, is an orthogonal eigenvector, b ij is a vector consisting of each row in dataset B.

7. A real-time online simulation system for temperature distribution of power transmission and transformation equipment, characterized in that: include: Establishing a unit for establishing a three-dimensional electric-magnetic-thermal-fluid multi-physics field simulation calculation model of the power transmission and transformation equipment according to the digital requirements of the temperature distribution of the power transmission and transformation equipment, and determining the main variable X of the temperature field distribution of the power transmission and transformation equipment; The acquisition unit is used to obtain multiple sets of data sets P consisting of different main variables X by using an equal interval scanning method, which are P={X1, X2, X3, ..., X N }, and the corresponding temperature field distribution data set T = {U1, U2, U3, ..., U N }; A dimension reduction unit is used to perform dimension reduction processing on the grid node data in the data set T by using an empirical orthogonal function analysis method to obtain a dimension-reduced data set B; A construction unit, used to construct a mapping relationship between the data set P and the data set B, and obtain a dimension reduction calculation model for real-time online simulation of temperature distribution of power transmission and transformation equipment; The simulation unit is used to convert the main variable X to be simulated f Input into the dimension reduction calculation model of the real-time online simulation for simulation, and output the result B x , according to the output result B x The actual temperature field distribution U is obtained by temperature field reconstruction method f .

8. The real-time online simulation system for temperature distribution of power transmission and transformation equipment according to claim 7, characterized in that: The dimension reduction unit is specifically used for: The data set T = {U1, U2, U3, ..., U N } decomposed into n orthogonal eigenvectors whose corresponding eigenvalues ​​are greater than or equal to 0 And its corresponding coefficient column vector set Based on the orthogonal eigenvector t n and coefficient column vector set b n Construct the original temperature distribution expression formula; Wherein, the original temperature distribution expression formula is: The upper limit of the cumulative term in the original temperature distribution expression formula is taken as m, and the data set used to characterize the reduced dimension is obtained. The vector [b i1 ,b i2 ,…,b im ] has a one-to-one correspondence with the element X in the data set P.

9. A real-time online simulation device for temperature distribution of power transmission and transformation equipment, characterized in that: The device comprises a processor and a memory: The memory is used to store program code and transmit the program code to the processor; The processor is used to execute the real-time online simulation method of temperature distribution of power transmission and transformation equipment according to any one of claims 1-6 according to the instructions in the program code.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium is used to store program codes, and the program codes are used to execute the real-time online simulation method for temperature distribution of power transmission and transformation equipment according to any one of claims 1-6.

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