Online fault diagnosis method and system for cable and accessories thereof

Abstract

The invention relates to the technical field of electrical engineering, in particular to an online fault diagnosis method and system for a cable and accessories thereof, and the method comprises the steps: constructing a target function of an inversion model based on the distance from any unit in the inversion model to a measurement point of the cable and the accessories thereof; determining a sensitivity matrix of the inversion model based on the stiffness matrix corresponding to the temperature field of the cable and the accessory thereof; determining initial heat source distribution of the cable and accessories thereof based on the sensitivity matrix and the objective function; determining initial temperature distribution of the cable and accessories thereof based on the initial heat source distribution by adopting a forward modeling model; iteratively updating the initial heat source distribution based on a temperature difference value between the initial temperature distribution and the measured temperature distribution until target heat source distribution meeting a preset condition is obtained; and performing fault diagnosis on the cable and the accessories thereof based on the target heat source distribution and the forward model. According to the method, fault diagnosis of the cable and the accessories thereof can be accurately realized, and the accuracy of fault diagnosis of the cable and the accessories thereof is improved.

Description

Technical Field

[0001] The present invention relates to the field of electrical engineering technology, and in particular to an online fault diagnosis method and system for cables and accessories thereof in the field of electrical engineering technology. Background Art

[0002] To determine the operating status of a cable, including whether it is normal, faulty, or fully loaded, it is necessary to obtain the internal temperature field of the cable and its accessories and perform an assessment based on this temperature field. Inversion algorithms are currently used to determine whether a cable is fully loaded. Commonly used methods are mostly based on equivalent thermal circuit models. This method treats each layer of the cable and its accessories as a circuit element and infers the core temperature from the external temperature as a function of the internal and external temperatures. This approach is relatively simple in algorithm construction and easy to implement. However, real cables are three-dimensional structures, and these methods fail to account for the local nature of faults, resulting in significant deviations in the judgment results. Summary of the Invention

[0003] The purpose of the present invention is to provide an intelligent anticipatory control method for extreme stability, and the technical solutions adopted are as follows: In a first aspect, an embodiment of the present invention provides a method for online fault diagnosis of a cable and its accessories, the method comprising: Constructing an objective function of the inversion model based on the distance from any unit in the inversion model to the measurement point of the cable and its accessories; Determining a sensitivity matrix of the inversion model based on a stiffness matrix corresponding to the temperature field of the cable and its accessories; Determining an initial heat source distribution of the cable and its accessories based on the sensitivity matrix and the objective function; Determining the initial temperature distribution of the cable and its accessories based on the initial heat source distribution using a forward model; Iteratively updating the initial heat source distribution based on the temperature difference between the initial temperature distribution and the measured temperature distribution until a target heat source distribution that meets a preset condition is obtained; Based on the target heat source distribution and the forward model, fault diagnosis is performed on the cable and its accessories.

[0004] In a second aspect, an online fault diagnosis system for cables and their accessories is provided, the system comprising: A construction module for constructing an objective function of the inversion model based on the distance from any unit in the inversion model to a measurement point of the cable and its accessories; A first determining module is configured to determine a sensitivity matrix of the inversion model based on a stiffness matrix corresponding to a temperature field of the cable and its accessories; A second determination module is configured to determine an initial heat source distribution of the cable and its accessories based on the sensitivity matrix and the objective function; A forward modeling module, configured to determine an initial temperature distribution of the cable and its accessories based on the initial heat source distribution using a forward model; an iterative updating module, configured to iteratively update the initial heat source distribution based on a temperature difference between the initial temperature distribution and the measured temperature distribution, until a target heat source distribution meeting a preset condition is obtained; A diagnosis module is used to perform fault diagnosis on the cable and its accessories based on the target heat source distribution and the forward model.

[0005] In a third aspect, a computer program product is provided, comprising: a computer program code, which, when executed on a computer, enables the computer to execute the method in the first aspect or any one of the possible implementations of the first aspect.

[0006] In a fourth aspect, a computer-readable storage medium is provided, which stores a computer program code. When the computer program code runs on a computer, the computer executes the method in the first aspect or any possible implementation method described in the first aspect.

[0007] The present invention has the following beneficial effects: by considering the distance from any unit in the inversion model to the measurement point of the cable and its accessories, the objective function of the inversion model is constructed, which can suppress the multi-solution nature of the objective function of the inversion model. Afterwards, the sensitivity matrix of the inversion model is calculated through the stiffness matrix corresponding to the temperature field of the cable and its accessories, thereby accelerating the speed of the inversion calculation; and based on the sensitivity matrix and the objective function, the initial heat source distribution of the cable and its accessories is inversely calculated; through the forward model, based on the initial heat source distribution, the initial temperature distribution of the cable and its accessories is determined; the temperature difference between the initial temperature distribution and the measured temperature distribution is analyzed, and the initial heat source distribution is iteratively updated through the inversion module until a target heat source distribution that meets the preset conditions is obtained, so that the accuracy of the target heat source distribution is high. Finally, the temperature distribution corresponding to the target heat source distribution is calculated through the forward model, so that the fault diagnosis of the cable and its accessories can be accurately realized, thereby improving the accuracy of the fault diagnosis of the cable and its accessories. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] In order to more clearly illustrate the technical solutions and advantages of the embodiments of the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the prior art descriptions. 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 any creative work.

[0009] Figure 1 This is a schematic diagram of the implementation flow of a method for online fault diagnosis of cables and their accessories provided by an embodiment of the present invention; Figure 2 This is another implementation flow diagram of a method for online fault diagnosis of cables and their accessories provided by an embodiment of the present invention; Figure 3 This is another implementation flowchart of a method for online fault diagnosis of cables and their accessories provided by an embodiment of the present invention; Figure 4 This is a schematic diagram of the structure of an online fault diagnosis system for cables and their accessories provided by an embodiment of the present invention; Figure 5 It is a structural diagram of a computer device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0010] To further illustrate the technical means and effectiveness of the present invention in achieving its intended objectives, the following, in conjunction with the accompanying drawings and preferred embodiments, describes in detail the specific implementation, structure, features, and effectiveness of an online fault diagnosis method for cables and their accessories proposed by the present invention. In the following description, references to "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.

[0011] In the description of the embodiments of the present invention, unless otherwise specified, " / " means or, for example, A / B can mean A or B: "and / or" in the text is only a description of the association relationship of associated objects, indicating that there can be three relationships, for example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone. In addition, in the description of the embodiments of the present invention, "multiple" refers to two or more than two.

[0012] In the following, the terms "first" and "second" are used for descriptive purposes only and should not be understood to imply or suggest relative importance or implicitly indicate the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features.

[0013] Unless otherwise defined, all technical and scientific terms used in the embodiments of the present invention have the same meanings as commonly understood by one of ordinary skill in the art to which the present invention belongs.

[0014] In related technologies, few scholars have applied inversion algorithms to the measurement of internal heat source distribution in cables and their accessories. Most of them use thermal circuit models to invert the core temperature. Such algorithms make each layer of the cable equivalent, which is relatively simple, but not intuitive and cannot obtain the temperature value at any position of the cable. The device developed in the embodiment of the present invention obtains external temperature values ​​based on infrared or optical fiber, uses this as a convergence condition, and combines the finite element algorithm with regularization or singular value decomposition to invert its internal heat source distribution. At the same time, it can obtain the temperature distribution at any position inside the cable, clearly display the location of the heat source, and is more conducive to evaluating the operating status of the cable based on the field distribution to achieve online fault diagnosis.

[0015] The following describes in detail a method for online fault diagnosis of a cable and its accessories provided by the present invention in conjunction with the accompanying drawings. Figure 1 , which shows a schematic diagram of an implementation flow of a method for online fault diagnosis of cables and their accessories provided by one embodiment of the present invention, the method comprising: 101. Construct an objective function of the inversion model based on the distance from any unit in the inversion model to a measurement point of the cable and its accessories.

[0016] In the heat source inversion model, heat sources near the measurement point have a greater influence on the potential at that point. When performing heat source inversion across the entire space, the sensitivity near the measurement point is higher, causing the inverted heat source density to be concentrated on the surface, resulting in a "skin effect." Therefore, introducing a distance-weighted function into the inversion equation is essential for accurately calculating the heat source density distribution.

[0017] In some possible implementations, the above step 101 can be performed by Figure 2 The steps shown achieve: 201 : Determine a weighting coefficient based on the distance from any element in the inversion model to a measurement point of the cable and its accessories.

[0018] Here, the distance weighting factor is calculated by obtaining the maximum and minimum distances from any unit to the measurement points of the cable and its accessories, and the weighting coefficient is obtained by summing the distance weighting factors corresponding to each measurement point.

[0019] In some possible implementations, the above step 201 may be implemented by the following steps 211 to 213 (not shown): 211. Determine the maximum distance and the minimum distance from any one of the units to the measurement point of the cable and its accessories.

[0020] 212. Determine a weighting factor based on the maximum distance and the minimum distance.

[0021] 213 , fusing the distances from the multiple units to the measurement points of the cable and its accessories with the weighting factors to obtain the weighting coefficients.

[0022] In the above steps 211 to 213, In this way, the inversion model is divided into triangular grids. Assuming that the model has M units, the distance from the i-th unit to the measurement point is shown in formula (1): (1); in, , represents the coordinates of the center point of the i-th unit, Represents the coordinates of the measurement point. Weights are assigned based on the distance from the measurement point to the inner electrode to adjust the heat source's sensitivity matrix to the electric field. Distance weighting should suppress the sensitivity of locations close to the measurement point. The distance weighting factor (i.e., weighting coefficient) is shown in formula (2): (2); in, .

[0023] in, The definition of is shown in formula (3): (3); Among them, d iA,max Indicates the maximum distance from the inversion point to the electrode; d iA,min Indicates the minimum distance from the inversion point to the electrode. iA Close to d iA,min , the distance weighting factor is close to 1. When d iA Close to d iA,max When , the distance weighting factor is close to 0. Therefore, the distance weighting factor increases as it approaches the measurement point, and decreases as it moves away from the electrode. The comprehensive weighting factor w of the i-th unit in the inversion model is i It should be the weighted sum of the weights of K measurement points, with j representing the position of the measurement point. The comprehensive weighting factor is shown in formula (4): (4); 202. Construct an objective function of the inversion model based on the weighting coefficients.

[0024] Here, the objective function is obtained by obtaining the sensitivity matrix of the inversion model, the temperature of the measurement point, and the regularization factor; and adjusting the sensitivity matrix, the temperature of the measurement point, and the regularization factor through the weighting coefficient. In some possible implementations, the weighting coefficient in formula (4) is applied to the objective function of the inversion model, and the objective function of the inversion model is obtained as shown in formula (5): (5); Among them, W represents the matrix composed of distance weighted functions, represents the regularization factor, and u represents the temperature of the measurement point. represents the sensitivity matrix. Thus, solving formula (5) is equivalent to solving formula (6): (6); in, .

[0025] In the embodiment of the present invention, by introducing distance weighting, the multi-solution property of the objective function of the inversion model can be effectively suppressed.

[0026] 102. Determine a sensitivity matrix of the inversion model based on a stiffness matrix corresponding to the temperature field of the cable and its accessories.

[0027] Here, a temperature measurement platform is installed on the test cable or accessories to obtain the device's surface temperature. The sensitivity matrix of the surface temperature measurement deviation to the heat source distribution is calculated. A stiffness matrix is ​​introduced based on finite element analysis, and the sensitivity matrix is ​​calculated from the stiffness matrix. In some possible implementations, the temperature field of the cable and its accessories is first calculated using finite element analysis to obtain the stiffness matrix.

[0028] Here, when the finite element method is used to calculate the temperature field of the cable and its accessories, the corresponding stiffness matrix equation is shown in formula (7): (7); Among them, K represents the stiffness matrix, and each element in the matrix is ​​related to the relative dielectric constant of the model and the grid properties; V represents the temperature at the grid point; S represents the source term, which is related to the heat source density.

[0029] Secondly, based on the stiffness matrix, the derivative of the temperature at any measurement point with respect to the heat source density is determined.

[0030] Here, finding the sensitivity matrix can be reduced to finding the partial derivative of temperature with respect to the heat source. Therefore, taking the partial derivative of formula (7) with respect to the heat source density, we can obtain formula (8): (8); Among them, the stiffness matrix K, source term S and temperature V have been obtained in the forward modeling, and the derivative of the stiffness matrix with respect to the heat source is 0. Formula (8) can be transformed into formula (9): (9); The derivative D of the source term with respect to the heat source density can be directly obtained in the forward modeling, and D=[d1, d2, …d m ] T , write formula (9) into the form of formula (10): (10); Replace [1, 0, 0, …, 0] T Treated as a new source term, the partial derivative of the temperature at point M with respect to the heat source in formula (10) is converted to formula (11): (11); Where: V(k, M) represents the temperature at point M when a heat source is placed at node k. According to the reciprocity theorem, formula (11) can be transformed into formula (12): (12); Where V(M, k) represents the temperature at point k when a heat source is placed at node M. When the number of measurement points is much smaller than the inversion parameter, this method can greatly reduce the workload of constructing the sensitivity matrix.

[0031] Finally, based on the derivative of the temperature with respect to the heat source density at any measurement point, the sensitivity matrix of the inversion model is determined; thus, by combining the finite element method, obtaining the sensitivity matrix is ​​reduced to obtaining the partial derivative of the temperature with respect to the heat source density, which enables the sensitivity matrix of the inversion model to be constructed quickly and accurately.

[0032] 103. Determine the initial heat source distribution of the cable and its accessories based on the sensitivity matrix and the objective function.

[0033] Here, after constructing the sensitivity matrix, the objective function of the inversion model is substituted and the processed matrix is ​​obtained by transposing the sensitivity matrix. Then, based on the weighted coefficients in the objective function and the processed matrix, the initial heat source distribution of the cable and its accessories is calculated. As shown in formula (6), the processed matrix A T , weighting coefficient W and measured temperature u, the initial heat source distribution of the cable and its accessories can be calculated.

[0034] 104. Determine the initial temperature distribution of the cable and its accessories based on the initial heat source distribution using a forward model.

[0035] Here, after the initial heat source distribution is calculated, the initial heat source distribution is used as the input of the forward model, and the temperature distribution of the entire calculation domain is calculated by the forward model, that is, the initial temperature distribution is obtained.

[0036] 105 , iteratively updating the initial heat source distribution based on the temperature difference between the initial temperature distribution and the measured temperature distribution until a target heat source distribution that meets a preset condition is obtained.

[0037] Here, the temperature of any measurement point in the cable and its accessories is measured to obtain a measured temperature distribution. The temperature difference between the initial temperature distribution and the measured temperature distribution is calculated to determine whether the temperature difference is greater than a preset temperature difference threshold. If the temperature difference is greater than the preset temperature difference threshold, a regularization or singular value decomposition algorithm is used to inversely calculate a heat source correction value for the cable and its accessories. The initial heat source distribution is iteratively updated using the heat source correction value until a target heat source distribution that meets preset conditions is obtained.

[0038] In some possible implementations, after iteratively updating the initial heat source distribution, the forward model is used to calculate the target temperature distribution of the cable and its accessories based on the target heat source distribution; if the temperature difference between the target temperature distribution and the measured temperature distribution is less than or equal to the preset temperature difference threshold, it is determined that the target heat source distribution meets the preset condition.

[0039] In some possible implementations, this can be achieved by Figure 3 The steps shown implement the iterative update process of the target heat source distribution: 301 , based on the distance from the i-th unit of the inversion model to the measurement point, distance weighting is performed to construct an objective function of the inversion model.

[0040] 302. Build a temperature measurement platform on the cable or accessory to obtain the surface temperature of the device and calculate a sensitivity matrix of the surface temperature measurement value deviation to the charge density. 303, setting the initial heat source distribution in the device.

[0041] 304, the initial temperature distribution of the entire computational domain is calculated through the forward model.

[0042] 305 , determining whether the deviation between the temperature at the measurement point obtained by inversion and the measured value is less than a requirement. If the requirement is met, the inversion is terminated.

[0043] Here, it is determined whether the deviation between the temperature at the measurement point obtained by inversion and the measured value is less than the requirement, that is, whether the temperature difference between the target temperature distribution and the measured temperature distribution is less than or equal to the preset temperature difference threshold, wherein the preset temperature difference threshold can be a custom smaller value.

[0044] 306 , if the temperature difference does not meet the requirement, the initial heat source distribution is corrected by regularization or singular value decomposition algorithm, and the process returns to step 303 .

[0045] 307, if the temperature difference meets the requirements, the inversion ends and the target heat source distribution is output.

[0046] In some possible implementations, the heat source distribution in the solution domain is obtained by inverting the temperature at the measurement point. It is necessary to determine the functional relationship between the measured temperature and the heat source density. Suppose the relationship between the temperature at the measurement point and the heat source density at the location to be inverted is , ignoring the first-order infinitesimal, the first-order Taylor expansion of the function at the measurement point a is shown in formula (13): (13); If the inversion parameter is the heat source density in N cells, then: (14); On the basis of the establishment of the forward FEM model, the quantitative relationship between the deviation between the measured and calculated surface temperature at point j and the increment of the heat source density of the tth unit is expressed as shown in formula (15): (15); Among them, u cej represents the temperature measurement value at point j; u mj represents the calculated temperature value at point j; It represents the correction value of the heat source density at unit t in the inversion model; It represents the partial derivative of the deviation between the measured and calculated temperature at point j with respect to the heat source density at unit t.

[0047] The temperature residual corresponding to the measurement point j is shown in formula (16): (j=1, 2, …, M) (16); According to formula (15), the sensitivity of the temperature deviation (i.e., temperature difference) at point j to the heat source density at unit t is defined as shown in formula (17): (17); Extending formula (15) to the entire structural unit model, the linear equations of the heat source distribution inversion model are obtained as shown in formula (18): (18); in, d It represents the residual between the measured value and the theoretical calculated value. ; ρ represents the heat source density increment to be corrected in the model, ; A Represents the sensitivity matrix of the model ; u ce Represents measurement data; u Indicates the calculated data. The dimension and d The dimensions of are different, so formula (18) is an ill-conditioned equation.

[0048] In mathematics, the inversion problem of heat source density can be viewed as the solution of an inverse problem, that is, the deviation between the temperature measurement result and the calculated result is taken as input, and the heat source density correction value is taken as output, resulting in formula (19): (19); In order to obtain an electric field FEM calculation model that is consistent with the measurement results, it is necessary to continuously update the heat source distribution in the FEM model to obtain formula (20): (20); in, represents the updated heat source density on the t-th unit; Represents the heat source density before correction on the t-th unit. After multiple iterative corrections, the heat source density distribution that meets the minimum temperature deviation is obtained, that is, the target heat source distribution is obtained.

[0049] 106. Perform fault diagnosis on the cable and its accessories based on the target heat source distribution and the forward model.

[0050] Here, after the target heat source distribution is obtained by inversion calculation, the target heat source distribution is input into a forward model (for example, a forward FEM model), and the target temperature distribution at each position is obtained by forward calculation; the cable and its accessories are diagnosed for faults based on the target temperature distribution and the location information of the target temperature distribution.

[0051] Here, the determined fault type is sent to the customer via infrared / optical fiber. For example, two infrared sensors and multiple optical fibers are required, which are laid outside the device. The target temperature distribution and the location information of the target temperature distribution are uploaded to the cloud. The cloud determines the fault type and sends it to the user.

[0052] In some possible implementations, the equipment developed using the embodiments of the present invention can take into account the three-dimensional structure of the cable, invert the internal heat source distribution through the external temperature conditions, calculate the internal temperature distribution using the forward model, intuitively display the area of ​​abnormal heating, and judge the fault type based on the degree and location of heating, as shown in Table 1.

[0053] Table 1 Correspondence between abnormal heat source distribution characteristics and fault types

[0054] In an embodiment of the present invention, by considering the distance from any unit in the inversion model to the measurement point of the cable and its accessories, the objective function of the inversion model is constructed, which can suppress the multi-solution nature of the objective function of the inversion model. Afterwards, the sensitivity matrix of the inversion model is calculated by the stiffness matrix corresponding to the temperature field of the cable and its accessories, thereby accelerating the speed of the inversion calculation; and based on the sensitivity matrix and the objective function, the initial heat source distribution of the cable and its accessories is inversely calculated; based on the initial heat source distribution, the initial temperature distribution of the cable and its accessories is determined by the forward model; by analyzing the temperature difference between the initial temperature distribution and the measured temperature distribution, the initial heat source distribution is iteratively updated until a target heat source distribution that meets the preset conditions is obtained, so that the accuracy of the target heat source distribution is high. Finally, the temperature distribution corresponding to the target heat source distribution is calculated by the forward model, so that the fault diagnosis of the cable and its accessories can be accurately realized, thereby improving the accuracy of the fault diagnosis of the cable and its accessories.

[0055] The embodiment of the present invention provides an online fault diagnosis system for cables and their accessories. Figure 4 , which shows a schematic structural diagram of a cable and its accessories online fault diagnosis system provided by one embodiment of the present invention. The system 400 includes: A construction module 401 is used to construct an objective function of the inversion model based on the distance from any unit in the inversion model to the measurement point of the cable and its accessories; a first determination module 402 is used to determine the sensitivity matrix of the inversion model based on the stiffness matrix corresponding to the temperature field of the cable and its accessories; a second determination module 403 is used to determine the initial heat source distribution of the cable and its accessories based on the sensitivity matrix and the objective function; a forward modeling module 404 is used to determine the initial temperature distribution of the cable and its accessories based on the initial heat source distribution using the forward model; an iterative update module 405 is used to iteratively update the initial heat source distribution based on the temperature difference between the initial temperature distribution and the measured temperature distribution until a target heat source distribution that meets preset conditions is obtained; a diagnosis module 406 is used to perform fault diagnosis on the cable and its accessories based on the target heat source distribution and the forward model.

[0056] In some possible implementations, the construction module 401 is further configured to determine a weighting coefficient based on the distance from any unit in the inversion model to a measurement point of the cable and its accessories; and construct an objective function of the inversion model based on the weighting coefficient.

[0057] In some possible implementations, the construction module 401 is further used to determine the maximum distance and the minimum distance from any unit to the measurement point of the cable and its accessories; determine a weighting factor based on the maximum distance and the minimum distance; and fuse the distances from multiple units to the measurement points of the cable and its accessories with the weighting factor to obtain the weighting coefficient.

[0058] In some possible implementations, the construction module 401 is also used to determine the sensitivity matrix of the inversion model, the temperature of the measurement point and the regularization factor; based on the weighting coefficient, the sensitivity matrix, the temperature of the measurement point and the regularization factor are adjusted to obtain the objective function.

[0059] In some possible implementations, the first determination module 402 is further used to calculate the temperature field of the cable and its accessories based on finite elements to obtain the stiffness matrix; based on the stiffness matrix, determine the derivative of the temperature at any measuring point with respect to the heat source density; based on the derivative of the temperature at any measuring point with respect to the heat source density, determine the sensitivity matrix of the inversion model.

[0060] In some possible implementations, the second determination module 403 is further configured to transpose the sensitivity matrix to obtain a processed matrix; and calculate the initial heat source distribution of the cable and its accessories based on the weighted coefficients in the objective function and the processed matrix.

[0061] In some possible implementations, the iterative update module 405 is also used to determine whether the temperature difference is greater than a preset temperature difference threshold; if the temperature difference is greater than the preset temperature difference threshold, a regularization or singular value decomposition algorithm is used to inversely calculate the heat source correction value of the cable and its accessories; and based on the heat source correction value, the initial heat source distribution is iteratively updated until a target heat source distribution that meets the preset conditions is obtained.

[0062] In some possible implementations, the iterative update module 405 is also used to calculate the target temperature distribution of the cable and its accessories based on the target heat source distribution using the forward model; if the temperature difference between the target temperature distribution and the measured temperature distribution is less than or equal to the preset temperature difference threshold, it is determined that the target heat source distribution meets the preset condition.

[0063] In some possible implementations, the diagnostic module 406 is further configured to calculate a target temperature distribution corresponding to the target heat source distribution using the forward model; and perform fault diagnosis on the cable and its accessories based on the target temperature distribution and location information of the target temperature distribution.

[0064] Optionally, the transmission medium may be a wired link (such as, but not limited to, coaxial cable, optical fiber, and digital subscriber line (DSL)) or a wireless link (such as, but not limited to, wireless Fidelity (WIFI), Bluetooth, and mobile device network). It should be noted that the system provided in the above embodiment is only illustrated by the division of the above functional modules. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the computer device can be divided into different functional modules to complete all or part of the functions described above. In addition, the method embodiments provided in the above embodiments belong to the same concept. The specific implementation process is detailed in the method embodiments and will not be repeated here.

[0065] Figure 5 FIG. 1 is a schematic diagram of the structure of a computer device provided by an embodiment of the present invention. For example, Figure 5 As shown, the computer device 500 includes: a memory 501, a processor 502, and a computer program 503 stored in the memory 501 and running on the processor 502, wherein when the processor 502 executes the computer program 503, the computer device can execute any one of the above-mentioned online fault diagnosis methods for cables and their accessories.

[0066] In addition, an embodiment of the present invention also protects a system, which may include a memory and a processor, wherein the memory stores an executable program code, and the processor is used to call and execute the executable program code to perform an online fault diagnosis method for cables and their accessories provided by an embodiment of the present invention. This embodiment can divide the system into functional modules according to the above method example. For example, it can correspond to each functional module, or two or more functions can be integrated into one processing module. The above integrated module can be implemented in the form of hardware. It should be noted that the division of modules in this embodiment is schematic and is only a logical function division. There may be other division methods in actual implementation. It should be noted that all relevant contents of each step involved in the above method embodiment can be referred to the functional description of the corresponding functional module, and will not be repeated here.

[0067] It should be understood that the system provided in this embodiment is used to perform the above-mentioned online fault diagnosis method for cables and their accessories, and thus can achieve the same effect as the above-mentioned implementation method. In the case of an integrated unit, the system may include a processing module and a storage module. Specifically, when the system is applied to a device, the processing module can be used to control and manage the actions of the device. The storage module can be used to support the device in executing mutual program codes, etc. Specifically, the processing module can be a processor or a controller, which can implement or execute the various exemplary logic blocks, modules and circuits described in conjunction with the contents disclosed in the present invention. The processor can also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of digital signal processing (DSP) and a microprocessor, etc. The storage module can be a memory.

[0068] In addition, the system provided by the embodiments of the present invention can be specifically a chip, component, or module. The chip may include a connected processor and memory; the memory is used to store instructions. When the processor calls and executes the instructions, the chip can execute the online fault diagnosis method for cables and their accessories provided by the above embodiment. This embodiment also provides a computer-readable storage medium, which stores computer program code. When the computer program code is executed on a computer, it causes the computer to execute the relevant method steps described above to implement the online fault diagnosis method for cables and their accessories provided by the above embodiment.

[0069] This embodiment also provides a computer program product. When the computer program product is executed on a computer, it causes the computer to execute the above-mentioned steps to implement the online fault diagnosis method for cables and accessories provided in the above embodiment. The system, computer-readable storage medium, computer program product, or chip provided in this embodiment are all used to perform the corresponding method provided above. Therefore, the beneficial effects achieved by the system can refer to the beneficial effects of the corresponding method provided above and will not be repeated here. Through the description of the above embodiments, those skilled in the art will understand that for the convenience and simplicity of description, only the division of the above-mentioned functional modules is used as an example. In actual application, the above-mentioned functions can be distributed to different functional modules as needed, that is, the internal structure of the system can be divided into different functional modules to complete all or part of the functions described above. In the embodiments provided by the present invention, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the system embodiments described above are merely illustrative. For example, the division of modules or units is only a logical functional division. In actual implementation, other division methods can be used. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not implemented. On the other hand, the mutual coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection through some interface, system or unit, which may be electrical, mechanical or other forms.

[0070] It should be noted that the above-mentioned order of the embodiments of the present invention is for description only and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multi-task processing and parallel processing are also possible or may be advantageous. The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other. Each embodiment focuses on the differences from other embodiments. The above content is only a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of changes or replacements within the technical scope disclosed by the present invention, which should be covered within the scope of protection of the present invention.

Claims

1. A method for online fault diagnosis of cables and their accessories, characterized in that: The method comprises: Constructing an objective function of the inversion model based on the distance from any unit in the inversion model to the measurement point of the cable and its accessories; Determining a sensitivity matrix of the inversion model based on a stiffness matrix corresponding to the temperature field of the cable and its accessories; Determining an initial heat source distribution of the cable and its accessories based on the sensitivity matrix and the objective function; Determining the initial temperature distribution of the cable and its accessories based on the initial heat source distribution using a forward model; Iteratively updating the initial heat source distribution based on the temperature difference between the initial temperature distribution and the measured temperature distribution until a target heat source distribution that meets a preset condition is obtained; Based on the target heat source distribution and the forward model, fault diagnosis is performed on the cable and its accessories.

2. The method for online fault diagnosis of a cable and its accessories according to claim 1, characterized in that: The objective function of the inversion model is constructed based on the distance from any unit in the inversion model to the measurement point of the cable and its accessories, including: Determining a weighting coefficient based on the distance from any element in the inversion model to a measurement point of the cable and its accessories; Based on the weighting coefficients, an objective function of the inversion model is constructed.

3. The online fault diagnosis method for cables and their accessories according to claim 2, characterized in that: The determining of the weighting coefficient based on the distance from any unit in the inversion model to the measurement point of the cable and its accessories includes: Determine the maximum and minimum distances from any of the units to the measurement points of the cable and its accessories; determining a weighting factor based on the maximum distance and the minimum distance; The distances from the multiple units to the measurement points of the cable and its accessories are fused with the weighting factors to obtain the weighting coefficients.

4. The online fault diagnosis method for cables and their accessories according to claim 2, characterized in that: The objective function of the inversion model is constructed based on the weighting coefficient, comprising: Determining a sensitivity matrix, a temperature of a measurement point, and a regularization factor of the inversion model; Based on the weighting coefficients, the sensitivity matrix, the temperature of the measurement point and the regularization factor are adjusted to obtain the objective function.

5. The online fault diagnosis method for cables and their accessories according to claim 1, characterized in that: The determining of the sensitivity matrix of the inversion model based on the stiffness matrix corresponding to the temperature field of the cable and its accessories includes: Calculating the temperature field of the cable and its accessories based on finite elements to obtain the stiffness matrix; Determining the derivative of the temperature at any measurement point with respect to the heat source density based on the stiffness matrix; The sensitivity matrix of the inversion model is determined based on the derivative of the temperature of any measurement point with respect to the heat source density.

6. The online fault diagnosis method for cables and their accessories according to claim 1, characterized in that: The determining of the initial heat source distribution of the cable and its accessories based on the sensitivity matrix and the objective function includes: performing transposition processing on the sensitivity matrix to obtain a processed matrix; Based on the weighted coefficients in the objective function and the processed matrix, an initial heat source distribution of the cable and its accessories is calculated.

7. The method for online fault diagnosis of a cable and its accessories according to claim 1, characterized in that: The iterative updating of the initial heat source distribution based on the temperature difference between the initial temperature distribution and the measured temperature distribution until a target heat source distribution meeting a preset condition is obtained includes: Determining whether the temperature difference is greater than a preset temperature difference threshold; If the temperature difference is greater than the preset temperature difference threshold, a regularization or singular value decomposition algorithm is used to inversely calculate a heat source correction value of the cable and its accessories; The initial heat source distribution is iteratively updated based on the heat source correction value until a target heat source distribution that meets preset conditions is obtained.

8. The online fault diagnosis method for cables and their accessories according to claim 7, characterized in that: The method further comprises: Calculating a target temperature distribution of the cable and its accessories based on the target heat source distribution using the forward model; If the temperature difference between the target temperature distribution and the measured temperature distribution is less than or equal to the preset temperature difference threshold, it is determined that the target heat source distribution meets the preset condition.

9. The method for online fault diagnosis of a cable and its accessories according to claim 1, characterized in that: The performing fault diagnosis on the cable and its accessories based on the target heat source distribution and the forward model includes: Calculating a target temperature distribution corresponding to the target heat source distribution using the forward model; Based on the target temperature distribution and the location information of the target temperature distribution, fault diagnosis is performed on the cable and its accessories.

10. An online fault diagnosis system for cables and their accessories, characterized in that: The system comprises: A construction module for constructing an objective function of the inversion model based on the distance from any unit in the inversion model to a measurement point of the cable and its accessories; A first determining module is configured to determine a sensitivity matrix of the inversion model based on a stiffness matrix corresponding to a temperature field of the cable and its accessories; A second determination module is configured to determine an initial heat source distribution of the cable and its accessories based on the sensitivity matrix and the objective function; A forward modeling module, configured to determine an initial temperature distribution of the cable and its accessories based on the initial heat source distribution using a forward model; an iterative updating module, configured to iteratively update the initial heat source distribution based on a temperature difference between the initial temperature distribution and the measured temperature distribution, until a target heat source distribution meeting a preset condition is obtained; A diagnosis module is used to perform fault diagnosis on the cable and its accessories based on the target heat source distribution and the forward model.

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