Method and device for determining winding temperature field of oil-immersed transformer

Abstract

The invention provides an oil-immersed transformer winding temperature field determination method and device, and belongs to the field of transformers. The method comprises the steps of determining a plurality of discrete point temperatures of an oil-immersed transformer winding under a target working condition according to the target working condition and a pre-established discrete point temperature model; wherein the discrete point temperature model is determined according to different types of sampling working conditions and a plurality of discrete point temperatures corresponding to the interior of a field domain under each sampling working condition; determining the temperature field distribution of the oil-immersed transformer winding according to the temperatures of the plurality of discrete points and a pre-established winding temperature field analysis model; wherein the winding temperature field analysis model is determined according to the temperature field distribution of the oil-immersed transformer under the first preset number of different types of sampling working conditions and the temperature of the second preset number of discrete points in the field domain under the target working condition. According to the invention, the temperature distribution of the transformer winding can be rapidly and accurately obtained.

Description

Technical Field

[0001] The present invention relates to the technical field of transformers, and in particular to a method and a device for determining the temperature field of an oil-immersed transformer winding. Background Art

[0002] Oil-immersed power transformers are the core hub equipment for power system to achieve power transmission and voltage conversion. When the transformer suffers from a sudden short circuit or severe overload, the winding temperature may rise, which will not only shorten the life of the internal insulation material, but also reduce the mechanical strength of the wire, making it more susceptible to damage or deformation under the action of electric force. Therefore, the winding temperature and hot spot temperature are important indicators reflecting the operating stability and safety of oil-immersed transformers. Rapidly obtaining the winding temperature and hot spot temperature during the operation of the transformer is of great significance for real-time understanding of its operating status and early fault warning.

[0003] Due to the limitations of transformer structure and field application, winding temperature is difficult to measure directly. Multi-physics field coupling modeling and simulation calculation are the main technical means to obtain transformer winding temperature distribution. Generally speaking, the boundary value problems corresponding to each physical field can be converted into corresponding discrete equations for solution through different numerical methods. However, due to the high-order and strong nonlinear characteristics of the discrete equations in the temperature field calculation process of large power transformers, solving these high-order nonlinear equations requires a lot of time and data storage costs, which is difficult to meet the needs of engineering applications.

[0004] In the related technology, the solution can be obtained by introducing a reduced-order algorithm, that is, by using a calculation order far less than the original numerical system to obtain a more accurate mathematical description, while limiting the calculation error within a certain range. However, the error of the proxy model established in the solution process is large, resulting in a large error in the final temperature field distribution. Summary of the invention

[0005] The embodiment of the present invention provides a method and device for determining the temperature field of an oil-immersed transformer winding, so as to improve the determination accuracy of the temperature field of the oil-immersed transformer winding and quickly and accurately obtain the temperature distribution of the transformer winding.

[0006] In a first aspect, an embodiment of the present invention provides a method for determining a temperature field of an oil-immersed transformer winding, comprising:

[0007] According to the target working condition and the pre-established discrete point temperature model, a plurality of discrete point temperatures of the oil-immersed transformer winding under the target working condition are determined; wherein the discrete point temperature model is determined according to different types of sampling working conditions and a plurality of discrete point temperatures corresponding to the field under each sampling working condition;

[0008] The temperature field distribution of the oil-immersed transformer winding is determined according to the multiple discrete point temperatures and a pre-established winding temperature field analysis model; wherein the winding temperature field analysis model is determined based on the temperature field distribution of the oil-immersed transformer under a first preset number of different types of sampling conditions, and a second preset number of discrete point temperatures within the field under the target condition.

[0009] In a possible implementation, before determining the temperature field distribution of the oil-immersed transformer winding according to the multiple discrete point temperatures and the pre-established winding temperature field analysis model, the method further includes:

[0010] Acquire temperature field distribution under a first preset number of different types of sampling conditions;

[0011] According to the temperature field distribution under all sampling conditions, a snapshot matrix is ​​established;

[0012] Based on the snapshot matrix, determining a reduced-order modal matrix and a modal coefficient matrix;

[0013] Selecting a second preset number of discrete points;

[0014] Constructing a discrete point temperature matrix according to the discrete point temperatures corresponding to the discrete points under the target working conditions;

[0015] Determining an inversion matrix according to the discrete point temperature matrix, the reduced-order modal matrix and the modal coefficient matrix;

[0016] A winding temperature field analysis model is established based on the discrete point temperature matrix, the inversion matrix and the reduced-order modal matrix.

[0017] In a possible implementation, determining a reduced-order modal matrix and a modal coefficient matrix based on the snapshot matrix includes:

[0018] Performing singular value decomposition on the snapshot matrix to obtain a singular value representation of the snapshot matrix;

[0019] Performing intrinsic orthogonal decomposition according to the singular value representation of the snapshot matrix to obtain a reduced-order modal representation of the snapshot matrix;

[0020] Based on the reduced-order modal representation of the snapshot matrix, a reduced-order modal matrix and a modal coefficient matrix are determined.

[0021] In a possible implementation, determining an inversion matrix according to the discrete point temperature matrix, the reduced-order modal matrix, and the modal coefficient matrix includes:

[0022] Determine the node number of each discrete point according to the discrete point temperature matrix;

[0023] According to the discrete point temperature matrix, the node number of each discrete point and the modal coefficient matrix, a row vector consistent with the node number corresponding to the discrete point is selected from the reduced-order modal matrix to obtain an inversion matrix.

[0024] In a possible implementation, before determining a plurality of discrete point temperatures of the oil-immersed transformer winding under the target working condition according to the target working condition and a pre-established discrete point temperature model, the method further includes:

[0025] Obtain a variety of different types of sampling conditions and the corresponding discrete point temperatures inside the field under each sampling condition;

[0026] Based on multiple sampling conditions, the discrete point temperature corresponding to each sampling condition and the response surface model, a discrete point temperature model between the sampling conditions and discrete point temperatures of the oil-immersed transformer winding is established.

[0027] In a possible implementation, before determining the temperature field distribution of the oil-immersed transformer winding according to the multiple discrete point temperatures and the pre-established winding temperature field analysis model, the method further includes:

[0028] Under target working conditions, the measured temperatures of multiple discrete points of the oil-immersed transformer winding are obtained through multiple sensors arranged at the oil-immersed transformer winding;

[0029] Determine the temperature difference between the measured temperature corresponding to each discrete point and the temperature of the discrete point;

[0030] Determining whether the temperature difference is greater than a preset threshold;

[0031] If the temperature difference is greater than the preset threshold, the discrete point temperature model is adjusted according to the measured temperature.

[0032] In a possible implementation, according to the discrete point temperature matrix, the node number of each discrete point and the modal coefficient matrix, a row vector consistent with the node number corresponding to the discrete point is selected from the reduced-order modal matrix to obtain an inversion matrix, including:

[0033] According to the expression: Determine the inversion matrix;

[0034] Where, T D represents the discrete point temperature matrix, p j represents the row vector corresponding to the jth discrete point in the inversion matrix, α j represents the modal coefficient corresponding to the jth discrete point in the modal coefficient matrix, u' krepresents the row vector of the kth node number corresponding to the discrete point in the reduced-order modal matrix, j represents the jth discrete point, d represents the total number of discrete points, k represents the kth node number, T Dj represents the row vector corresponding to the jth discrete point in the discrete point temperature matrix, index(T Dj ) represents the node number corresponding to the jth discrete point, k = index(T Dj ) indicates that the k-th node number of the reduced-order modal matrix corresponds to the node number of the j-th discrete point.

[0035] In a possible implementation, the expression of the winding temperature field analysis model is:

[0036] T=U'P -1 T D ;

[0037] Where T is the snapshot matrix, U' is the reduced-order modal matrix, P is the inversion matrix, and T D Represents a discrete point temperature matrix.

[0038] In a possible implementation, the discrete point temperature model is expressed as:

[0039] T Dx =Xβ;

[0040] Where, T Dx represents the temperature of the xth discrete point, X represents the parameter matrix corresponding to the sampling condition of the winding, and β represents the polynomial coefficient matrix.

[0041] In a second aspect, an embodiment of the present invention provides a device for determining a temperature field of an oil-immersed transformer winding, comprising:

[0042] A first determination module is used to determine a plurality of discrete point temperatures of the oil-immersed transformer winding under the target working condition according to the target working condition and a pre-established discrete point temperature model; wherein the discrete point temperature model is determined according to different types of sampling working conditions and a plurality of discrete point temperatures corresponding to the field under each sampling working condition;

[0043] The second determination module is used to determine the temperature field distribution of the oil-immersed transformer winding according to the multiple discrete point temperatures and a pre-established winding temperature field analysis model; wherein the winding temperature field analysis model is determined based on the temperature field distribution of the oil-immersed transformer under a first preset number of different types of sampling conditions, and a second preset number of discrete point temperatures within the field under the target condition.

[0044] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0045] The embodiment of the present invention establishes a discrete point temperature model through different types of sampling conditions of the oil-immersed transformer and multiple discrete point temperatures corresponding to the field under each sampling condition, so as to establish the relationship between the sampling conditions of the oil-immersed transformer and the discrete point temperatures; and establishes a winding temperature field analysis model through the temperature field distribution under different types of sampling conditions of the oil-immersed transformer and the discrete point temperatures within the field under the target conditions, considering that there is a large correlation between the discrete point temperature and the external conditions, and there is a large correlation between the discrete point temperature and the temperature field distribution; then, the temperature field distribution of the oil-immersed transformer winding is determined through the multiple discrete point temperatures of the oil-immersed transformer winding under the target conditions and the pre-established winding temperature field analysis model, so that the temperature of the entire field can be quickly and accurately derived from the discrete point temperature, thereby improving the accuracy of the obtained oil-immersed transformer winding temperature field distribution. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0047] Figure 1 is a schematic structural diagram of an oil-immersed transformer winding provided in an embodiment of the present invention;

[0048] Figure 2 is a flow chart of an implementation of a method for determining a temperature field of an oil-immersed transformer winding provided in an embodiment of the present invention;

[0049] Figure 3 is a schematic diagram of an intrinsic orthogonal decomposition process provided by an embodiment of the present invention;

[0050] Figure 4 is a schematic diagram of the composition of the inversion matrix provided by an embodiment of the present invention;

[0051] Figure 5 is a schematic diagram of singular value distribution under different modal orders provided by an embodiment of the present invention;

[0052] Figure 6 is a schematic diagram of energy ratios at different modal orders provided by an embodiment of the present invention;

[0053] Figure 7 is a schematic diagram of the temperature field distribution result provided by an embodiment of the present invention, Figure 7 A in the figure is a schematic diagram of the temperature field distribution results when the modal order is 1. Figure 7 B in the figure is a schematic diagram of the temperature field distribution results when the modal order is 5. Figure 7C in the figure is a schematic diagram of the temperature field distribution results when the modal order is 10. Figure 7 D in the figure is a schematic diagram of the temperature field distribution results when the modal order is 15;

[0054] Figure 8 is a schematic diagram of discrete point temperature calculation error provided by an embodiment of the present invention;

[0055] Fig. 9 is a schematic diagram of error distribution under different sampling conditions provided by an embodiment of the present invention;

[0056] Fig.10 is a comparative schematic diagram of temperature field distribution provided by an embodiment of the present invention;

[0057] Fig.11 is a comparative schematic diagram of hot spot temperature calculation results provided by an embodiment of the present invention;

[0058] Fig.12 is a schematic diagram of hot spot temperature error provided by an embodiment of the present invention;

[0059] Fig.13 It is a structural schematic diagram of a device for determining the temperature field of an oil-immersed transformer winding provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0060] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present invention. However, it should be clear to those skilled in the art that the present invention may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present invention.

[0061] In order to make the purpose, technical solutions and advantages of the present invention more clear, specific embodiments will be described below in conjunction with the accompanying drawings.

[0062] The inventors have found that in order to analyze and determine the temperature field of the oil-immersed transformer winding, it is usually necessary to solve a high-order nonlinear equation group, which consumes a lot of time and data storage costs. Generally, two ways are used to improve this problem: one is to use parallel computing technology to achieve high-performance solution of large-scale equation groups by improving the hardware computing level; the other is to introduce a reduced-order algorithm, that is, to obtain a more accurate mathematical description by using a calculation order far less than the original numerical system. However, the former has high hardware requirements and is costly when performing large-scale engineering-level simulation calculations; in addition, the current parallel technology is already quite mature, and it is very difficult to obtain additional efficiency improvements under the same hardware conditions. When the latter performs reduced-order calculations for nonlinear problems, its nonlinear terms still need to be formed in the original space, which makes the computational efficiency of the Proper Orthogonal Decomposition (POD) method (POD) not significantly improved when facing large-scale strong nonlinear problems.

[0063] In the fast solution of complex nonlinear parameterized partial differential equations, there is also the concept of non-intrusive POD, that is, making full use of the reduced-order algorithm to use low-dimensional data to approximate the characteristics of the original full-order system, using the reduced-order model as a carrier to establish a proxy connection between external monitoring data and the full-order system, thereby skipping the complex nonlinear calculation process and achieving efficient calculation. Although external factors affect the temperature field distribution and thus cause changes in the POD modal coefficients, there is generally no clear correlation between the POD modal coefficients and external influencing factors, and the proxy model error between the two is large, resulting in large errors in the temperature field distribution obtained by combining the reduced-order mode even under relatively ideal example conditions. Therefore, it is necessary to consider a technology for determining the temperature field distribution of oil-immersed transformer windings.

[0064] In order to improve the accuracy of determining the temperature field of the oil-immersed transformer winding, in the embodiment of the present invention, a discrete point temperature model is established to establish the relationship between the working condition of the oil-immersed transformer and the discrete point temperature, and a winding temperature field analysis model is used to establish the relationship between the discrete point temperature and the temperature field distribution. Among them, there is a significant correlation between the working condition of the transformer and the discrete point temperature, and there is also a significant correlation between the discrete point temperature and the temperature field distribution. Through the working condition of the transformer to the discrete point temperature, and then to the temperature field distribution, it can be gradually advanced to quickly and accurately derive the temperature of the entire field, thereby improving the accuracy of the obtained oil-immersed transformer winding temperature field distribution.

[0065] Figure 1 A schematic diagram of the structure of an oil-immersed transformer winding provided in an embodiment of the present invention. Figure 1 A 110kV oil-immersed transformer winding is shown in the figure, where from left to right are low voltage, medium voltage and high voltage windings, and the transformer as a whole adopts natural oil circulation heat dissipation.

[0066] Figure 2 The implementation flow chart of the method for determining the temperature field of the oil-immersed transformer winding provided by the embodiment of the present invention is described in detail as follows:

[0067] Step S201, determining multiple discrete point temperatures of the oil-immersed transformer winding under the target operating condition according to the target operating condition and a pre-established discrete point temperature model; wherein the discrete point temperature model is determined according to different types of sampling conditions and multiple discrete point temperatures corresponding to the field under each sampling condition.

[0068] In this embodiment, in order to obtain the temperature of discrete points in the field, considering that there is a significant correlation between the working condition of the oil-immersed transformer winding and the temperature of the discrete points, a proxy model between the sampled working condition of the oil-immersed transformer winding and its temperature can be constructed. Through this proxy model, the discrete point temperature under the target working condition can be accurately determined.

[0069] Step S202, determining the temperature field distribution of the oil-immersed transformer winding according to multiple discrete point temperatures and a pre-established winding temperature field analysis model; wherein the winding temperature field analysis model is determined based on the temperature field distribution of the oil-immersed transformer under a first preset number of different types of sampling conditions, and a second preset number of discrete point temperatures within the field under the target condition.

[0070] In this embodiment, the discrete point temperature is a part of the field domain of the winding temperature field. Through the correlation between multiple discrete point temperatures and the temperature field distribution, the global temperature field distribution can be derived from the multiple discrete point temperatures. That is, by using the winding temperature field analysis model, multiple discrete point temperatures are analyzed to obtain the temperature field distribution of the oil-immersed transformer winding.

[0071] The embodiment of the present invention establishes a discrete point temperature model through different types of sampling conditions of the oil-immersed transformer and multiple discrete point temperatures corresponding to the field under each sampling condition, so as to establish the relationship between the sampling conditions of the oil-immersed transformer and the discrete point temperatures; and establishes a winding temperature field analysis model through the temperature field distribution under different types of sampling conditions of the oil-immersed transformer and the discrete point temperatures within the field under the target conditions, considering that there is a large correlation between the discrete point temperature and the external conditions, and there is a large correlation between the discrete point temperature and the temperature field distribution; then, the temperature field distribution of the oil-immersed transformer winding is determined through the multiple discrete point temperatures of the oil-immersed transformer winding under the target conditions and the pre-established winding temperature field analysis model, so that the temperature of the entire field can be quickly and accurately derived from the discrete point temperature, thereby improving the accuracy of the obtained oil-immersed transformer winding temperature field distribution.

[0072] In some embodiments, before determining the temperature field distribution of the oil-immersed transformer winding according to multiple discrete point temperatures and a pre-established winding temperature field analysis model, the temperature field distribution under a first preset number of different types of sampling conditions can be obtained; a snapshot matrix can be established based on the temperature field distribution under all sampling conditions; and based on the snapshot matrix, a reduced-order modal matrix and a modal coefficient matrix can be determined; then a second preset number of discrete points can be selected; a discrete point temperature matrix can be constructed according to the discrete point temperatures corresponding to the discrete points under the target conditions; and an inversion matrix can be determined based on the discrete point temperature matrix, the reduced-order modal matrix and the modal coefficient matrix; finally, a winding temperature field analysis model can be established based on the discrete point temperature matrix, the inversion matrix and the reduced-order modal matrix.

[0073] In this embodiment, a plurality of different types of sampling conditions may be selected to obtain corresponding temperature field distribution results, and the temperature field distributions of all sampling conditions may be arranged to form a matrix, namely, a snapshot matrix.

[0074] For example, select s different types of sampling conditions, and the temperature field distribution corresponding to the i-th condition is T i , then the snapshot matrix can be expressed as T = {T1, T2, ..., T i ,……,T s}.

[0075] By performing eigenorthogonal decomposition on the snapshot matrix, we can obtain the reduced-order orthogonal basis of the snapshot matrix in the reduced-order subspace, i.e., the reduced-order mode, and the orthogonal basis coefficients, i.e., the modal coefficients, thereby obtaining the reduced-order modal matrix and the modal coefficient matrix, thereby achieving the order reduction of the snapshot matrix.

[0076] The winding temperature field analysis model established in this embodiment is the relationship between multiple discrete point temperatures and temperature field distributions. It is necessary to reverse the discrete point temperatures to the temperature field distribution. Therefore, the relationship between the discrete point temperature matrix, the reduced-order modal matrix and the modal coefficient matrix is ​​first established to obtain the inversion matrix. Then, the discrete point temperature matrix, the reduced-order modal matrix and the inversion matrix are used to establish the winding temperature field analysis model to achieve the reverse inference from the discrete point temperatures to the temperature field distribution.

[0077] Here, the temperature field distribution under the first preset number of different types of sampling conditions can be obtained by calculation using the finite element method. Specifically, as follows:

[0078] The calculation of oil-immersed transformer winding temperature is a multi-field calculation process involving flow field and temperature field. According to the law of conservation of energy, the expression of its control equation can be determined as:

[0079]

[0080] In the formula, represents the heat convection term, ρ represents the medium density, the unit is kg / m 3 , C p represents the specific heat capacity at constant pressure, in J / (kg·K), λ represents the thermal conductivity, in W / (m·K), U represents the fluid velocity, T represents the temperature, represents the heat conduction term, the term on the right represents the heat source term, S T Indicates the heat source density in W / m 3 .

[0081] Combining the boundary conditions of the equation and using the Galerkin finite element discretization method, the discrete control equation can be obtained:

[0082] KT = B;

[0083] Where K represents the finite element stiffness matrix, K∈R n×n , T represents the temperature field solution vector, T∈R n×1 , B represents the right-hand side of the discrete control equation, B∈R n×1 .

[0084] By combining the above formula with appropriate numerical methods, the transformer winding temperature can be calculated and the temperature field distribution results under multiple different types of working conditions can be obtained, thereby establishing a winding temperature field analysis model for the non-intrusive POD algorithm.

[0085] In addition, it can be seen from the above formula that the form of the equation group is complex and the calculation scale is huge. When it is directly applied to the temperature calculation of the winding of a large power transformer, it takes a lot of time and storage costs. Therefore, the POD method is introduced in this embodiment to reduce the calculation scale of the equation group and thus improve the calculation efficiency.

[0086] Optionally, this embodiment determines the reduced-order modal matrix and modal coefficient matrix based on the snapshot matrix, and may first perform singular value decomposition on the snapshot matrix to obtain a singular value representation of the snapshot matrix; then perform intrinsic orthogonal decomposition based on the singular value representation of the snapshot matrix to obtain a reduced-order modal representation of the snapshot matrix; finally, based on the reduced-order modal representation of the snapshot matrix, determine the reduced-order modal matrix and modal coefficient matrix.

[0087] In this embodiment, see Figure 3 The schematic diagram of the intrinsic orthogonal decomposition process shown in FIG. 1 is to perform singular value decomposition on the snapshot matrix. The left singular matrix, the right singular matrix and the singular value matrix can be used to represent the snapshot matrix. The expression can be: T = U∑V T Where T represents the snapshot matrix, U represents the left singular matrix, ∑ represents the singular value matrix, V represents the right singular matrix, and T represents the transpose of the matrix.

[0088] The left singular matrix and the right singular matrix are both in orthogonal matrix form. The singular value matrix is ​​in diagonal matrix form, and its diagonal elements are the singular values ​​of the snapshot matrix T arranged from large to small.

[0089] Generally, the first d singular values ​​in the singular value matrix ∑ are much larger than the subsequent singular values. Therefore, the snapshot matrix T can be approximately represented by the first d singular values ​​in the singular value matrix ∑ and its corresponding left and right singular value matrices.

[0090] That is, T = U∑V T ≈U′∑′V′ T . Where U′ represents an approximate left singular matrix, U′∈R n×d ,∑′ represents the approximate singular value matrix,∑′∈R d×d , V′ represents an approximate right singular matrix, V′∈R n×d .

[0091] Here, since the first d singular values ​​are much larger than the latter singular values, that is, d<<n, the approximate left singular matrix U′ can be regarded as a set of orthogonal bases of the snapshot matrix T in the reduced-order subspace, then

[0092]

[0093] Where U′ represents the reduced-order modal matrix, or the reduced-order orthogonal basis matrix, i.e., the approximate left singular matrix mentioned above; α represents the modal coefficient matrix, i.e., the orthogonal basis coefficient, α∈R d×1 .u' i represents the row vector corresponding to the node number in the reduced-order modal matrix, α i Represents the vector corresponding to the i-th node number in the reduced modal matrix. The reduced modal matrix and the modal coefficient matrix together constitute the expression of the solution vector in the reduced subspace, so that the order of the snapshot matrix can be reduced from n to d.

[0094] In some embodiments, the inversion matrix is ​​determined according to the discrete point temperature matrix, the reduced-order modal matrix and the modal coefficient matrix. The node number of each discrete point can be first determined according to the discrete point temperature matrix; then, according to the discrete point temperature matrix, the node number of each discrete point and the modal coefficient matrix, the row vector consistent with the node number corresponding to the discrete point is selected from the reduced-order modal matrix to obtain the inversion matrix.

[0095] In this embodiment, assuming that the temperatures of m discrete points in the field are known, the discrete point temperature matrix can be expressed as T D =[T D1 ,T D2 ,…,T Dj ,…,T Dm], where 1 to m are the serial numbers of the discrete points. The discrete points need to be linked to the global temperature field. Therefore, the node numbers of the discrete points in the global temperature field need to be determined.

[0096] Afterwards, see Figure 4 The schematic diagram of the inversion matrix is ​​shown in FIG. 1 . In order to represent the discrete point temperature matrix, the row vectors consistent with the node numbers of the discrete points can be extracted from the reduced-order modal matrix to form a new matrix, namely the inversion matrix P, which can be expressed as P = [p1, p2, …, p j ,…,p m ].

[0097] Here, in the extraction process, the discrete point temperature matrix can be substituted into the formula represented by the above-mentioned reduced-order modal matrix and modal coefficient matrix to determine the specific expression of the inversion matrix.

[0098] Optionally, this embodiment selects a row vector with the same node number corresponding to the discrete point from the reduced-order modal matrix according to the discrete point temperature matrix, the node number of each discrete point and the modal coefficient matrix to obtain an inversion matrix, which can be based on the expression: Determine the inversion matrix; where T D represents the discrete point temperature matrix, p j represents the row vector corresponding to the jth discrete point in the inversion matrix, α j Indicates the modal coefficient corresponding to the jth discrete point in the modal coefficient matrix, u' k represents the row vector of the kth node number corresponding to the discrete point in the reduced modal matrix, j represents the jth discrete point, m represents the total number of discrete points, k represents the kth node number, T Dj Represents the row vector corresponding to the jth discrete point in the discrete point temperature matrix, index(T Dj ) represents the node number corresponding to the jth discrete point, k = index(T Dj ) indicates that the k-th node number of the reduced modal matrix corresponds to the node number of the j-th discrete point.

[0099] In this embodiment, the row vector corresponding to each discrete point in the inversion matrix is ​​selected from the reduced-order modal matrix through the above expression, and then the row vectors are combined to form an inversion matrix.

[0100] Optionally, if the number m of discrete points selected here is equal to the number d of singular values ​​selected in the reduced modal matrix, that is, m=d, then the matrix P is a reversible matrix, such as Figure 4 As shown, a 3×3 matrix is ​​shown. Then, the expression of the winding temperature field analysis model can be obtained as follows: T=U'α=U'P -1 T D; Where T represents the snapshot matrix, U' represents the reduced-order modal matrix, α represents the modal coefficient matrix, P represents the inversion matrix, T D Represents a discrete point temperature matrix.

[0101] In this embodiment, the modal coefficient matrix is ​​represented by the discrete point temperature matrix and the inversion matrix, so as to construct a winding temperature field analysis model. The reduced-order model is used as a carrier to establish a proxy relationship between the discrete point temperature inside the field and the temperature field distribution of the entire domain, thereby skipping the complex nonlinear calculation process and achieving efficient calculation.

[0102] Considering that the position and number of discrete points have a significant impact on the accuracy of temperature field distribution, a greedy algorithm can be used to select discrete points in this embodiment. The search idea can be:

[0103] The reduced-order modal matrix in the established winding temperature field analysis model is input; the reduced-order modal matrix includes a plurality of first reduced-order modes, namely, selected singular values.

[0104] The node number of the element with the largest absolute value in the first reduced-order mode u'1 of the first group in the reduced-order mode matrix is ​​determined as the position of the first discrete point, and the first column p1 of the inversion matrix is ​​established.

[0105] For the second group and each subsequent group of first reduced-order modes, the discrete points and inversion matrix determined by the previous group of the first reduced-order modes of the group are used to calculate the second reduced-order mode corresponding to the group, and the residual corresponding to each element in the second reduced-order mode is calculated, and the node number of the element with the largest residual is determined as the position of the discrete point corresponding to the first reduced-order mode of the group.

[0106] Discrete points are selected based on the positions of the discrete points corresponding to the first reduced-order modes of each group.

[0107] Here, the larger the residual is, the more important the point is. Selecting this point as a discrete point can significantly improve the accuracy of the final temperature field distribution.

[0108] The above article introduces the process of establishing the winding temperature field analysis model and the process of selecting discrete points. However, to determine the temperature field distribution, it is also necessary to obtain the temperature of the discrete points. The following will continue to introduce the process of establishing the discrete point temperature model.

[0109] In some embodiments, before determining multiple discrete point temperatures of the oil-immersed transformer winding under the target operating condition based on the target operating condition and a pre-established discrete point temperature model, it is also possible to first obtain a variety of different types of sampling conditions and the corresponding discrete point temperatures inside the field under each sampling condition; then, based on the multiple sampling conditions, the discrete point temperatures corresponding to each sampling condition and the response surface model, a discrete point temperature model between the sampling conditions and discrete point temperatures of the oil-immersed transformer winding is established.

[0110] In this embodiment, the discrete point temperature is obtained by using a response surface method. The relationship between the sampling working conditions of the winding and the discrete point temperature is established by using a response surface model.

[0111] Here, the response surface model is a functional expression that characterizes the relationship between the influencing factors and the target solution quantity. The polynomial response surface function has a simple structure and is easy to calculate. The following is an introduction using the polynomial response surface function as an example.

[0112] The polynomial response surface model is generally based on quadratic, so the expression of the quadratic polynomial response surface function that reflects the parameters of the sampling conditions of n windings and the temperature of discrete points can be:

[0113]

[0114] Where, T Dx represents a discrete point temperature under the parameter x of the sampling condition, x represents the parameter of the sampling condition of the winding, x i The parameter representing the sampling condition of the winding, x j The parameters representing the sampling conditions of the winding, β0, β i , β ii and β ij etc. represent polynomial coefficients, n represents the set of the number of sampling conditions of different types of windings, i represents the i-th sampling condition, and j represents the j-th sampling condition.

[0115] Assuming that s tests are required to obtain each coefficient, the above formula can be written in the following matrix form:

[0116]

[0117] Or:

[0118] T Dx =Xβ+e;

[0119] Where, T Dx1 represents the temperature of the xth discrete point in the first test, and Indicates the parameters of the sampling conditions in the first test, β0, β1, β n , β 11 , β nn , β 12 and β (n-1)n etc. represent polynomial coefficients, e1, e2, and e s etc. represent the error between the calculated value and the actual value in each test, T Dxrepresents the matrix corresponding to the temperature of the x-th discrete point formed in each experiment, X represents the parameter matrix corresponding to the parameters of the sampling working conditions of the winding, β represents the coefficient matrix of the polynomial coefficients, and e represents the error matrix between the calculated value and the actual value.

[0120] In order to obtain the polynomial coefficients, it is necessary to minimize the sum of squares of the errors in the above formula, that is:

[0121] mine T e=min(T Dx -Xβ) T (T Dx -Xβ);

[0122] Taking partial derivative of the above formula with respect to the coefficient matrix β, we can obtain:

[0123] 2XXβ-2XT Dx =0;

[0124] It can be seen that 2XXβ-2XT Dx =0 is the least squares solution:

[0125] β=(X T X) -1 X T T Dx .

[0126] That is, the polynomial coefficients in the quadratic polynomial response surface function are obtained. Therefore, the corresponding surface model of each discrete point temperature with respect to the winding working condition parameters, namely the discrete point temperature model, can be established, and the discrete point temperature can be quickly obtained using the parameters of the winding sampling working condition.

[0127] Optionally, the expression of the discrete point temperature model in this embodiment is: T Dx =Xβ; where T Dx represents the temperature of the xth discrete point, X represents the parameter matrix corresponding to the sampling condition of the winding, and β represents the polynomial coefficient matrix. .

[0128] In this embodiment, the corresponding polynomial coefficient matrix can be solved for each discrete point in the above manner, so that a discrete point temperature model corresponding to each discrete point can be established.

[0129] In some embodiments, before determining the temperature field distribution of the oil-immersed transformer winding based on multiple discrete point temperatures and a pre-established winding temperature field analysis model, it is also possible to obtain the measured temperatures of multiple discrete points of the oil-immersed transformer winding under the target operating conditions through multiple sensors set at the oil-immersed transformer winding; then determine the temperature difference between the measured temperature corresponding to each discrete point and the discrete point temperature; determine whether the temperature difference is greater than a preset threshold; if the temperature difference is greater than the preset threshold, adjust the discrete point temperature model according to the measured temperature.

[0130] In this embodiment, sensors can be set at discrete points of the oil-immersed transformer to measure the temperature corresponding to the discrete points to obtain the measured temperature, which is then compared with the temperature calculated by the discrete point temperature model. If the difference between the two is greater than a preset threshold, it indicates that the currently used discrete point temperature model may be inaccurate, and the device may be worn out due to long-term use.

[0131] Therefore, in order to reduce the inaccuracy of the discrete point temperature model caused by wear changes during the long-term use of the oil-immersed transformer, the accuracy of the discrete point temperature model can be improved by adjusting the polynomial coefficients in the model, and then the discrete point temperature and temperature field distribution can be determined.

[0132] The above describes the process of determining the discrete point temperature corresponding to the discrete point through the discrete point temperature model, as well as the process of establishing the relevant model. In addition, the discrete point temperature corresponding to the discrete point can also be directly obtained by setting the sensor.

[0133] In other embodiments, sensors can be set at positions corresponding to discrete points in the winding, and the measured temperatures of the corresponding discrete points can be obtained through the sensors, which can be used as the discrete point temperatures to directly determine the temperature field distribution, thereby further improving the accuracy and precision of the determined temperature field distribution.

[0134] In some possible embodiments, Figure 1 The oil-immersed transformer shown in the figure is used as an example for the experiment. The model of the oil-immersed transformer, from left to right, is the low-voltage, medium-voltage and high-voltage windings. The modeling method without turns is adopted, and the equivalent thermal conductivity is set to simplify the effect of the insulating paper on the heat dissipation of the winding. The low-voltage, medium-voltage and high-voltage windings contain 80, 96 and 90 coils respectively, and the transformer as a whole adopts the natural oil circulation heat dissipation method.

[0135] The non-intrusive reduced-order model is established by using the methods in the above embodiments, and the load rate and ambient temperature, two factors that have a significant impact on the winding temperature, are selected to form a sample space, where the load rate ranges from 50% to 140%, and the ambient temperature ranges from 278K to 313K.

[0136] 125 sample points are selected in the sample space, and the transformer temperature field distribution corresponding to these 125 sample points is calculated. The snapshot matrix is ​​formed and a POD reduction calculation model is established to obtain the transformer winding temperature field distribution model.

[0137] The order of reduction can be determined by the following expression: Where ε(d) represents the percentage of physical system energy that the selected d-order mode can represent, and σ irepresents the i-th singular value of the diagonal elements of the singular value matrix, d represents the number of reduction orders, and n represents the set of selected sample points, that is, the set of sampling conditions.

[0138] When the selected reduced order d makes ε(d)>99.9%, it can be considered that the reduced order model covers most of the features of the full order model and can be used for reduced order calculation.

[0139] Different modal orders correspond to different singular values ​​and energy ratios, see Figure 5 The schematic diagram of the singular value distribution under different modal orders is shown in , where the horizontal axis represents the reduced order and the vertical axis represents the singular value size. Figure 5 It can be seen that the singular values ​​of the snapshot matrix gradually decrease with the increase of the modal order, and the singular value corresponding to the first-order mode is 1.5×10 6 , and the corresponding singular values ​​after the 10th order mode are all less than 236.

[0140] as well as Figure 6 The schematic diagram of the energy ratio under different modal orders is shown in the figure, where the horizontal axis represents the reduced order and the vertical axis represents the energy ratio. Figure 6 It can be seen that when the reduced modal order is greater than 10, the energy ratio is greater than 99.9%, which meets the accuracy requirements of the reduced-order calculation model.

[0141] To further illustrate the feasibility of the number of reduced-order modes, taking a sampling condition as an example, the temperature field distribution results of different reduced-order modes inversion are shown in Figure 7 As shown, Figure 7 A in the figure is a schematic diagram of the temperature field distribution results when the modal order is 1. Figure 7 B in the figure is a schematic diagram of the temperature field distribution results when the modal order is 5. Figure 7 C in the figure is a schematic diagram of the temperature field distribution results when the modal order is 10. Figure 7 D in FIG. 1 is a schematic diagram of the temperature field distribution result with a modal order of 15, where the left side is the temperature field distribution and the right side is the error of the temperature field distribution.

[0142] Depend on Figure 7 It can be seen that with the increase of reduced-order modes, the error of temperature field inversion also decreases further: the maximum calculation error under the 1st-order mode is about 7.1K; the maximum calculation error under the 5th-order mode is reduced to about 4.2K; when the modal order is greater than 10, the maximum reduced-order error is reduced to below 2K and gradually stabilizes. Therefore, the 10th-order mode can be selected to ensure the overall calculation accuracy of the reduced-order calculation.

[0143] In other feasible embodiments, the effectiveness of the discrete point temperature model established by the response surface method may also be verified.

[0144] 100 test conditions can be selected as sampling conditions in the sample space to verify the fitting of the response surface model to 10 discrete point temperatures under the 100 test conditions. The error of the discrete point under each test condition is:

[0145]

[0146]

[0147] In the formula, T represents the simulated value of discrete point temperature, which is used as the reference value, T RSM represents the discrete point temperature determined by the discrete point temperature model established by the response surface method, N D Indicates the total number of sampling conditions.

[0148] See also Figure 8 The schematic diagram of the discrete point temperature calculation error shown in FIG. 1 shows the horizontal axis representing different test conditions, the left vertical axis representing the average temperature error, and the right vertical axis representing the average relative error. Figure 8 It can be seen that the discrete point temperature model determined by the polynomial response surface method is used to calculate the temperature of each discrete point under the test conditions. The maximum average temperature error is 1.29K, and the maximum average relative error is 3.37%, both of which meet the error requirements for temperature calculation under general circumstances, indicating the accuracy of the polynomial response surface model established by the present invention.

[0149] After establishing the response surface model between discrete point temperature and winding working condition, the winding temperature field distribution under the corresponding working condition can be quickly calculated. To verify the calculation accuracy of this method, 100 groups of test working conditions in the sample space are randomly selected as sampling working conditions, and the Fluent simulation software and the method in the above embodiment of the present invention are used to calculate the winding temperature field distribution corresponding to the 100 test points. To quantify the temperature field calculation error, the following error analysis indicators are set:

[0150]

[0151]

[0152] Where, T ROM represents the temperature field distribution corresponding to the reduced-order calculation result obtained by the method in the above embodiment of the present invention, T FOM represents the temperature field distribution corresponding to the full-order calculation results obtained by Fluent simulation software, N node Indicates the number of nodes in the farm.

[0153] The average absolute error and average relative error under 100 test conditions are calculated respectively. The results are as follows Fig. 9As shown, the horizontal axis represents different test conditions, the left vertical axis represents the average temperature error, and the right vertical axis represents the average relative error. Fig. 9 It can be seen that the maximum average absolute error of the temperature field distribution calculation is 0.49K, and the maximum average relative error is 2.69%. The former occurs when the load rate is 50% and the ambient temperature is 303K, and the latter occurs when the load rate is =80% and the ambient temperature is 283K. In addition, the average absolute error is less than 0.03K and the average relative error is less than 3% in most other working conditions, which shows that the full-field temperature calculation performed by the method provided by the present invention has a high calculation accuracy.

[0154] The advantage of the method provided by the present invention over the traditional fast algorithm is that it can obtain the winding temperature distribution with higher accuracy. Taking the working condition with the maximum mean absolute error as an example, the winding temperature field analysis model determined by the non-invasive POD algorithm provided by the present invention is compared with the temperature field distribution obtained by Fluent simulation calculation. Fig.10 As shown, from left to right are the calculation results of the winding temperature field analysis model provided by the present invention, the calculation results of the Fluent full-order model, and the error cloud diagram between the two. Fig.10 It can be seen that the maximum calculation error of each node in the two fields does not exceed 3K, which meets the requirements of engineering applications, and further illustrates the effectiveness of the method provided by the present invention for transformer winding temperature field calculation.

[0155] Considering that the hot spot temperature of the winding plays a very critical role in the safe operation of the transformer, the hot spot temperature calculation results of the winding under various test conditions are compared, such as Fig.11 As shown, the horizontal axis represents different test conditions and the vertical axis represents the hotspot temperature; and the hotspot temperature error is calculated, such as Fig.12 As shown, the horizontal axis represents different test conditions, and the vertical axis represents the hotspot temperature error. It can be seen that under 100 test conditions, the hotspot temperature results of the windings obtained by full-order calculation are approximately the same as the hotspot temperature results calculated by the winding temperature field analysis model determined by the reduced order provided by this method, and the maximum error is 1.72K, which meets the engineering calculation error requirements. Therefore, this method still has good accuracy when calculating the hotspot temperature of the windings.

[0156] In summary, no matter whether the whole field calculation or the winding hot spot calculation is considered, the method provided by the present invention has high calculation accuracy.

[0157] In addition, compared with traditional algorithms, the most significant advantage of the method provided in the embodiment of the present invention is that it has extremely high computational efficiency, performing full-order calculations (using Fluent software) and reduced-order calculations provided by the present invention under 100 sets of test conditions.

[0158] The calculation time of the method provided in the embodiment of the present invention is 0.78 s, and the calculation time of the full-order calculation using Fluent software is 3675 s.

[0159] It can be seen that the non-intrusive POD order reduction method can reduce the calculation time to 0.78s for calculating 100 sets of test conditions, which is about a thousand times more efficient than the full-order calculation method. At the same time, it can realize real-time or quasi-real-time monitoring of winding temperature under different winding conditions, meeting the requirements of digital online monitoring of power equipment.

[0160] The embodiment of the present invention establishes a discrete point temperature model through different types of sampling conditions of the oil-immersed transformer and multiple discrete point temperatures corresponding to the field under each sampling condition, so as to establish the relationship between the sampling conditions of the oil-immersed transformer and the discrete point temperatures; and establishes a winding temperature field analysis model through the temperature field distribution under different types of sampling conditions of the oil-immersed transformer and the discrete point temperatures within the field under the target conditions, considering that there is a large correlation between the discrete point temperature and the external conditions, and there is a large correlation between the discrete point temperature and the temperature field distribution; then, the temperature field distribution of the oil-immersed transformer winding is determined through the multiple discrete point temperatures of the oil-immersed transformer winding under the target conditions and the pre-established winding temperature field analysis model, so that the temperature of the entire field can be quickly and accurately derived from the discrete point temperature, thereby improving the accuracy of the obtained oil-immersed transformer winding temperature field distribution. Among them, by establishing a discrete point temperature model through the response surface model, the temperature calculation for discrete points can be realized, and the discrete point temperature can be directly used for subsequent analysis; then the winding temperature field analysis model is established through the reduced-order model, and the relationship between the discrete point temperature and the global temperature field can be obtained by reducing the order, and the global temperature field distribution can be obtained quickly and accurately from the discrete point temperature, thereby improving the efficiency of calculation. In addition, the discrete point temperature of the winding can be obtained through the sensor, and the temperature field distribution can be determined, further improving the accuracy of the winding temperature field distribution.

[0161] It should be understood that the order of execution of the steps in the above embodiment does not necessarily mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present invention.

[0162] The following is an embodiment of the device of the present invention. For details not described in detail therein, reference may be made to the corresponding method embodiment described above.

[0163] Fig.13 The structure diagram of the oil-immersed transformer winding temperature field determination device provided by the embodiment of the present invention is shown. For the convenience of description, only the part related to the embodiment of the present invention is shown, which is described in detail as follows:

[0164] like Fig.13 As shown, the oil-immersed transformer winding temperature field determination device 130 includes:

[0165] The first determination module 1301 is used to determine multiple discrete point temperatures of the oil-immersed transformer winding under the target working condition according to the target working condition and a pre-established discrete point temperature model; wherein the discrete point temperature model is determined according to different types of sampling working conditions and multiple discrete point temperatures corresponding to the field under each sampling working condition;

[0166] The second determination module 1302 is used to determine the temperature field distribution of the oil-immersed transformer winding according to multiple discrete point temperatures and a pre-established winding temperature field analysis model; wherein the winding temperature field analysis model is determined based on the temperature field distribution of the oil-immersed transformer under a first preset number of different types of sampling conditions, and a second preset number of discrete point temperatures within the field under the target condition.

[0167] In a possible implementation, the second determining module 1302 is further configured to:

[0168] Acquire temperature field distribution under a first preset number of different types of sampling conditions;

[0169] According to the temperature field distribution under all sampling conditions, a snapshot matrix is ​​established;

[0170] Based on the snapshot matrix, determine the reduced-order modal matrix and modal coefficient matrix;

[0171] Selecting a second preset number of discrete points;

[0172] According to the discrete point temperatures corresponding to the discrete points under the target working conditions, a discrete point temperature matrix is ​​constructed;

[0173] Determine the inversion matrix according to the discrete point temperature matrix, the reduced-order mode matrix and the mode coefficient matrix;

[0174] The winding temperature field analysis model is established based on the discrete point temperature matrix, inversion matrix and reduced-order modal matrix.

[0175] In a possible implementation, the second determining module 1302 is specifically configured to:

[0176] Perform singular value decomposition on the snapshot matrix to obtain the singular value representation of the snapshot matrix;

[0177] According to the singular value representation of the snapshot matrix, the intrinsic orthogonal decomposition is performed to obtain the reduced-order modal representation of the snapshot matrix;

[0178] Based on the reduced modal representation of the snapshot matrix, a reduced modal matrix and a modal coefficient matrix are determined.

[0179] In a possible implementation, the second determining module 1302 is specifically configured to:

[0180] According to the discrete point temperature matrix, determine the node number of each discrete point;

[0181] According to the discrete point temperature matrix, the node number of each discrete point and the modal coefficient matrix, the row vectors with the same node number corresponding to the discrete point are selected from the reduced-order modal matrix to obtain the inversion matrix.

[0182] In a possible implementation manner, the first determining module 1301 is further configured to:

[0183] Obtain a variety of different types of sampling conditions and the corresponding discrete point temperatures inside the field under each sampling condition;

[0184] Based on multiple sampling conditions, the discrete point temperature corresponding to each sampling condition and the response surface model, a discrete point temperature model between the sampling conditions and discrete point temperatures of the oil-immersed transformer winding is established.

[0185] In a possible implementation manner, the first determining module 1301 is further configured to:

[0186] Under target working conditions, the measured temperatures of multiple discrete points of the oil-immersed transformer winding are obtained through multiple sensors arranged at the oil-immersed transformer winding;

[0187] Determine the temperature difference between the measured temperature corresponding to each discrete point and the temperature of the discrete point;

[0188] Determine whether the temperature difference is greater than a preset threshold;

[0189] If the temperature difference is greater than the preset threshold, the discrete point temperature model is adjusted according to the measured temperature.

[0190] In a possible implementation, the second determining module 1302 is specifically configured to:

[0191] According to the expression: Determine the inversion matrix;

[0192] Where, T D represents the discrete point temperature matrix, p j represents the row vector corresponding to the jth discrete point in the inversion matrix, α j Indicates the modal coefficient corresponding to the jth discrete point in the modal coefficient matrix, u' k represents the row vector of the kth node number corresponding to the discrete point in the reduced modal matrix, j represents the jth discrete point, d represents the total number of discrete points, k represents the kth node number, T Dj Represents the row vector corresponding to the jth discrete point in the discrete point temperature matrix, index(TDj ) represents the node number corresponding to the jth discrete point, k = index(T Dj ) indicates that the k-th node number of the reduced modal matrix corresponds to the node number of the j-th discrete point.

[0193] In a possible implementation, the expression of the winding temperature field analysis model is:

[0194] T=U'P -1 T D ;

[0195] Where T is the snapshot matrix, U' is the reduced-order modal matrix, P is the inversion matrix, and T D Represents a discrete point temperature matrix.

[0196] In one possible implementation, the discrete point temperature model is expressed as:

[0197] T Dx =Xβ;

[0198] Where, T Dx represents the temperature of the xth discrete point, X represents the parameter matrix corresponding to the sampling condition of the winding, and β represents the polynomial coefficient matrix.

[0199] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0200] Those of ordinary skill in the art will appreciate that the templates, units, and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0201] If the module / 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 present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. Computer-readable media may include: any entity or device that can carry computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory, random access memory, electrical carrier signal, telecommunication signal and software distribution medium, etc.

[0202] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such 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 invention, and should be included in the protection scope of the present invention.

Claims

1. A method for determining the temperature field of an oil-immersed transformer winding, characterized in that: include: According to the target working condition and the pre-established discrete point temperature model, a plurality of discrete point temperatures of the oil-immersed transformer winding under the target working condition are determined; wherein the discrete point temperature model is determined according to different types of sampling working conditions and a plurality of discrete point temperatures corresponding to the field under each sampling working condition; The temperature field distribution of the oil-immersed transformer winding is determined according to the multiple discrete point temperatures and a pre-established winding temperature field analysis model; wherein the winding temperature field analysis model is determined based on the temperature field distribution of the oil-immersed transformer under a first preset number of different types of sampling conditions, and a second preset number of discrete point temperatures within the field under the target condition.

2. The method for determining the temperature field of oil-immersed transformer windings according to claim 1, characterized in that: Before determining the temperature field distribution of the oil-immersed transformer winding according to the multiple discrete point temperatures and the pre-established winding temperature field analysis model, the method further includes: Acquire temperature field distribution under a first preset number of different types of sampling conditions; According to the temperature field distribution under all sampling conditions, a snapshot matrix is ​​established; Based on the snapshot matrix, determining a reduced-order modal matrix and a modal coefficient matrix; Selecting a second preset number of discrete points; Constructing a discrete point temperature matrix according to the discrete point temperatures corresponding to the discrete points under the target working conditions; Determining an inversion matrix according to the discrete point temperature matrix, the reduced-order modal matrix and the modal coefficient matrix; A winding temperature field analysis model is established based on the discrete point temperature matrix, the inversion matrix and the reduced-order modal matrix.

3. The method for determining the temperature field of oil-immersed transformer windings according to claim 2, characterized in that: Based on the snapshot matrix, determining a reduced-order modal matrix and a modal coefficient matrix includes: Performing singular value decomposition on the snapshot matrix to obtain a singular value representation of the snapshot matrix; Performing intrinsic orthogonal decomposition according to the singular value representation of the snapshot matrix to obtain a reduced-order modal representation of the snapshot matrix; Based on the reduced-order modal representation of the snapshot matrix, a reduced-order modal matrix and a modal coefficient matrix are determined.

4. The method for determining the temperature field of oil-immersed transformer windings according to claim 2, characterized in that: Determining an inversion matrix according to the discrete point temperature matrix, the reduced-order modal matrix and the modal coefficient matrix includes: Determine the node number of each discrete point according to the discrete point temperature matrix; According to the discrete point temperature matrix, the node number of each discrete point and the modal coefficient matrix, a row vector consistent with the node number corresponding to the discrete point is selected from the reduced-order modal matrix to obtain an inversion matrix.

5. The method for determining the temperature field of oil-immersed transformer windings according to any one of claims 1 to 4, characterized in that: Before determining the multiple discrete point temperatures of the oil-immersed transformer winding under the target working condition according to the target working condition and the pre-established discrete point temperature model, the method further includes: Obtain a variety of different types of sampling conditions and the corresponding discrete point temperatures inside the field under each sampling condition; Based on multiple sampling conditions, the discrete point temperature corresponding to each sampling condition and the response surface model, a discrete point temperature model between the sampling conditions and discrete point temperatures of the oil-immersed transformer winding is established.

6. The method for determining the temperature field of oil-immersed transformer windings according to any one of claims 1 to 4, characterized in that: Before determining the temperature field distribution of the oil-immersed transformer winding according to the multiple discrete point temperatures and the pre-established winding temperature field analysis model, the method further includes: Under target working conditions, the measured temperatures of multiple discrete points of the oil-immersed transformer winding are obtained through multiple sensors arranged at the oil-immersed transformer winding; Determine the temperature difference between the measured temperature corresponding to each discrete point and the temperature of the discrete point; Determining whether the temperature difference is greater than a preset threshold; If the temperature difference is greater than the preset threshold, the discrete point temperature model is adjusted according to the measured temperature.

7. The method for determining the temperature field of oil-immersed transformer windings according to claim 4, characterized in that: According to the discrete point temperature matrix, the node number of each discrete point and the modal coefficient matrix, a row vector consistent with the node number corresponding to the discrete point is selected from the reduced-order modal matrix to obtain an inversion matrix, including: According to the expression: Determine the inversion matrix; Where, T D represents the discrete point temperature matrix, p j represents the row vector corresponding to the jth discrete point in the inversion matrix, α j represents the modal coefficient corresponding to the jth discrete point in the modal coefficient matrix, u' k represents the row vector of the kth node number corresponding to the discrete point in the reduced-order modal matrix, j represents the jth discrete point, d represents the total number of discrete points, k represents the kth node number, T Dj represents the row vector corresponding to the jth discrete point in the discrete point temperature matrix, index(T Dj ) represents the node number corresponding to the jth discrete point, k = index(T Dj ) indicates that the k-th node number of the reduced-order modal matrix corresponds to the node number of the j-th discrete point.

8. The method for determining the temperature field of oil-immersed transformer windings according to any one of claims 2 to 4, characterized in that: The expression of the winding temperature field analysis model is: T=U'P -1 T D ; Where T is the snapshot matrix, U' is the reduced-order modal matrix, P is the inversion matrix, and T D Represents a discrete point temperature matrix.

9. The method for determining the temperature field of oil-immersed transformer windings according to claim 5, characterized in that: The expression of the discrete point temperature model is: T Dx =Xβ; Where, T Dx represents the temperature of the xth discrete point, X represents the parameter matrix corresponding to the sampling condition of the winding, and β represents the polynomial coefficient matrix.

10. A device for determining the temperature field of an oil-immersed transformer winding, characterized in that: include: A first determination module is used to determine a plurality of discrete point temperatures of the oil-immersed transformer winding under the target working condition according to the target working condition and a pre-established discrete point temperature model; wherein the discrete point temperature model is determined according to different types of sampling working conditions and a plurality of discrete point temperatures corresponding to the field under each sampling working condition; The second determination module is used to determine the temperature field distribution of the oil-immersed transformer winding according to the multiple discrete point temperatures and a pre-established winding temperature field analysis model; wherein the winding temperature field analysis model is determined based on the temperature field distribution of the oil-immersed transformer under a first preset number of different types of sampling conditions, and a second preset number of discrete point temperatures within the field under the target condition.

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