Method for locating axial temperature hot spots in intermediate connections of low temperature self-healing cables based on simulation

Abstract

The application belongs to the technical field of simulation analysis, and particularly relates to a low-temperature self-fusion cable intermediate connection axial temperature hotspot positioning method based on simulation, and steps include: establishing a finite element simulation model with different heat sources; selecting a temperature extraction point; determining a temperature monitoring point of the cable intermediate connection through normalized sensitivity analysis; performing multi-heat source separation calculation on the influence of the temperature distribution curve based on measured temperature and position data and a thermal characteristic database; identifying a hotspot area using a dynamic threshold; obtaining the hotspot temperature and position of the cable intermediate connection by using a cubic spline interpolation method for a defect area and a quadratic polynomial fitting method for a normal area; and further calculating the conductor or insulation layer temperature at the corresponding position based on the calculated hotspot temperature and position according to a transient thermal circuit model. The application can obtain cable intermediate connection temperature hotspot positioning and can further calculate the surface temperature of each layer structure under the condition that the cable intermediate connection is put into operation.

Description

Technical Field

[0001] This invention belongs to the field of simulation analysis technology, specifically relating to a simulation-based method for locating axial temperature hotspots in intermediate connections of low-temperature self-fluxing cables. Background Technology

[0002] Currently, power cables have gradually become a major component of power transmission and distribution systems. Cable intermediate connections, as an important accessory of power cables, are a weak link in the current cable operation process. In order to promptly detect faults or abnormalities in cable intermediate connections, it is necessary to monitor and assess their operational status. Current methods for fault diagnosis of cable intermediate connections mainly include regular manual inspections, partial discharge signal monitoring, and fiber optic temperature measurement, but each method has its own advantages and disadvantages.

[0003] Unlike traditional cold-shrink joints, cryogenic self-fluxing cable intermediate connections feature good adhesion and no moving interfaces. They achieve connection between two cable segments by gradually restoring the cable structure layer by layer. After constructing a radial heat conduction model for cryogenic self-fluxing cable intermediate joints to accommodate various defects, it is difficult to calculate the conductor temperature at every location based on the radial thermal path model due to economic costs and practical engineering considerations. Therefore, it is necessary to consider reverse temperature analysis along the axis to determine hotspot locations, thereby initially identifying areas where faults may occur. Subsequently, the internal temperature at the corresponding location of the cable intermediate joint is accurately calculated based on the radial heat conduction model, reducing unnecessary temperature measurement points. Summary of the Invention

[0004] In view of the shortcomings of the prior art, the purpose of this invention is to provide a simulation-based method for locating axial temperature hotspots in the intermediate connection of a low-temperature self-fluxing cable. This method can locate the temperature hotspots in the intermediate connection of the cable while it is in operation and can calculate the surface temperature of each layer of the structure, thus providing a basis for timely detection of faults in the intermediate connection of the cable.

[0005] To achieve the above objectives, this invention provides a simulation-based method for locating axial temperature hotspots in intermediate connections of low-temperature self-fluxing cables, comprising the following steps:

[0006] S1. Based on the intermediate cable connections and fault types under actual working conditions, establish finite element simulation models with different heat sources, and build an experimental platform to verify the finite element simulation models.

[0007] S2. Based on the finite element simulation model, the temperature distribution curves of intermediate connections of cables with multiple heat sources and heat sources at different locations are calculated, and a thermal characteristic database of the influence of heat sources on the surface temperature of cold shrink tubes is established by comparison, and temperature extraction points are selected accordingly.

[0008] S3. Based on the location and temperature of the temperature extraction point during simulation, and with the temperature gradient as a reference, the temperature monitoring point of the cable intermediate connection is determined through normalized sensitivity analysis, which serves as the placement location of the built-in temperature sensor in actual working conditions.

[0009] S4. Based on the determined temperature monitoring points, place the built-in temperature sensor under actual working conditions to obtain the measured temperature and location data. Based on the thermal feature database, for different types of heat sources in the middle connection of the cable, use the spatial pattern recognition heat source separation method to perform multi-heat source separation calculation on the influence of the temperature distribution curve formation.

[0010] S5. Based on multi-heat source separation calculation, determine the actual heat source type in the intermediate connection of the cable and determine the corresponding heat source ratio. Use the first-order difference formula based on Fourier's law to calculate the axial temperature gradient, determine the temperature change area, and use dynamic threshold to identify hot spot area. Areas exceeding the dynamic threshold are judged as defect areas, and areas not exceeding the dynamic threshold are normal areas.

[0011] S6. A cubic spline interpolation method is used for defective areas, and a quadratic polynomial fitting method is used for normal areas. A calculation model is established using Matlab to obtain the hot spot temperature and location of the intermediate connection of the cable.

[0012] S7. Based on the calculated hotspot temperature and location, the temperature of the conductor or insulation layer at the corresponding location is further calculated according to the transient thermal circuit model.

[0013] As a preferred embodiment of the present invention, in S1, the fault types include fault types that have occurred in the historical records and potential fault types under actual operating conditions.

[0014] As a preferred embodiment of the present invention, in step S1, a test platform is constructed including a cable, a cable intermediate connection, an optical fiber temperature measuring device, a current transformer, a built-in temperature sensor, a wired communication device, a wireless communication device, and a processor. During the operation of the cable intermediate connection, the temperature distribution curve is obtained through the optical fiber temperature measuring device, the current flowing through the cable and the cable intermediate connection is obtained through the current transformer, the built-in temperature sensor is installed outside the cold shrink tube of the cable intermediate connection, the wired communication device and the wireless communication device are used for signal transmission, and finally the processor is used to obtain the temperature distribution curve of the conductor and the surface of the cold shrink tube and the hot spot temperature of the cable intermediate connection. Combined with various artificially created defects, test data under various working conditions are obtained to verify the effectiveness of the finite element simulation model. If the verification fails, the finite element simulation model is readjusted.

[0015] As a preferred embodiment of the present invention, in S2, the thermal feature database contains curve features of the influence of heat source on the surface temperature of cold shrink tube, including heat source location, heat source size, conductor temperature distribution curve, cold shrink tube surface temperature distribution curve, and fitting function of each curve.

[0016] As a preferred embodiment of the present invention, in S3, the temperature gradient is calculated using the central difference method, combined with the strategy of forward and backward difference at the edge. Specifically, the geometric center and the edge both use the first-order central difference, while the remaining areas use the third-order central difference. Then, the temperature monitoring point is determined through normalized sensitivity analysis.

[0017] The formula for calculating the temperature gradient is:

[0018] ;

[0019] ;

[0020] ;

[0021] In the formula, Let i be the temperature gradient at the intermediate temperature extraction point, i = 2, 3, ..., n-1, where n is the number of temperature extraction points; The temperature gradient at the first temperature extraction point; The temperature gradient at the final temperature extraction point; , , , , , , These are the temperatures of the 1st, 2nd, i-1th, ith, i+1th, n-1th, and nth temperature extraction points, respectively. , , , , , , These are the positions of the 1st, 2nd, i-1st, i, n-1st, and nth temperature extraction points, respectively.

[0022] The influence of the heat source on each temperature node is discretized, and the normalized sensitivity is calculated. Based on the influence of the j-th heat source on the i-th temperature extraction point, the temperature monitoring point is determined. The formulas for calculating the normalized sensitivity before and after discretization are as follows:

[0023] ;

[0024] ;

[0025] In the formula, Let be the internal heat source intensity of the j-th heat source; This indicates the sensitivity of the j-th heat source to the temperature extraction point; It is the step size of the heat source intensity perturbation; T is the temperature at the temperature extraction point.

[0026] As a preferred embodiment of the present invention, the normalized sensitivity is calculated using Matlab, and the temperature extraction point with the highest normalized sensitivity is selected sequentially as the temperature monitoring point.

[0027] As a preferred embodiment of the present invention, in S4, the heat source separation method using spatial pattern recognition to perform multi-heat source separation calculation on the influence of temperature distribution curve formation specifically involves approximating the multi-heat source temperature rise effect as a linear superposition, and calculating fitting formulas for different heat source characteristics, and linear superposition formulas. as follows:

[0028] ;

[0029] In the formula, Temperature distribution curve caused by conductor heating; The temperature rise distribution caused by the heat source in the pressurized pipe; The abnormal temperature rise is caused by a defective heat source.

[0030] As a preferred embodiment of the present invention, in S5, the dynamic threshold The calculation formula is:

[0031] ;

[0032] In the formula, This represents the mean of the temperature gradient; denoted as the standard deviation of the temperature gradient.

[0033] As a preferred embodiment of the present invention, in step S6, a cubic spline interpolation method is used for the defective region, and a quadratic polynomial fitting method is used for the normal region. The constraints satisfied by the cubic function, quadratic function construction formula, extrema points, and fitting formula are as follows:

[0034] ;

[0035] ;

[0036] ;

[0037] ;

[0038] ;

[0039] ;

[0040] ;

[0041] In the formula, x represents the hotspot location; y represents the location of the temperature monitoring point; and y represents the temperature value of the temperature monitoring point. , , These are the locations of temperature monitoring points a, a+1, and a+2, respectively. , These are the temperatures at temperature monitoring points a and a+1, respectively. , , These are the fitting coefficients of the quadratic function; , , , These are the fitting coefficients of the cubic function; Indicates the interval Cubic spline interpolation function on; , They represent exist , The function value at that location; , They represent At the right end The first and second derivatives at point ; , They represent the intervals respectively. The cubic spline interpolation function on the left endpoint The first and second derivatives at point .

[0042] As a preferred embodiment of the present invention, in S7, a transient thermal circuit model is established based on the actual working conditions and the temperature and location measured by the temperature monitoring point, and taking into account the intermediate connection structure of the cable at the corresponding location.

[0043] The algorithms and simulations involved in this invention can be executed by electronic devices, which include a memory, a processor, and a computer program stored in the memory and capable of running on the processor. The processor executes the software to realize the above-mentioned algorithm calculation and simulation process.

[0044] The beneficial effects of this invention are:

[0045] This invention combines multiple technologies such as finite element simulation, Matlab calculation, and thermal circuit model. By establishing a thermal characteristic database and heat source separation technology, it can effectively separate multiple heat sources and accurately locate temperature hotspots. It uses spatial pattern recognition technology to separate heat sources and combines normalized sensitivity analysis to determine the optimal temperature monitoring point, ensuring high accuracy and high reliability of the measurement.

[0046] This invention enables real-time monitoring and analysis of the temperature of cable intermediate connections while they are in operation, allowing for timely detection of potential faults, reducing power outages, and improving the stability and reliability of the power system. Through precise positioning and calculation, it reduces unnecessary temperature measurement points, lowers the cost of temperature measurement equipment, and saves power companies significant maintenance costs and time. Attached Figure Description

[0047] Figure 1 This is a flowchart illustrating the principle of this invention;

[0048] Figure 2 This is a schematic diagram of the distribution of normalized sensitivity in an embodiment of the present invention;

[0049] Figure 3 This is a schematic diagram of the surface temperature distribution curves of the cable and intermediate connection under different heat sources in an embodiment of the present invention. Detailed Implementation

[0050] The embodiments of the present invention will be further described below with reference to the accompanying drawings:

[0051] like Figure 1 As shown, the simulation-based method for locating axial temperature hotspots in the intermediate connection of a cryogenic self-fluxing cable includes the following steps:

[0052] S1. Based on the actual working conditions of the cable intermediate connection (this embodiment mainly focuses on the intermediate connection of low temperature self-fusion cable) and the fault type, establish a finite element simulation model with different heat sources (e.g., based on ANSYS or COMSOL), and build an experimental platform to verify the finite element simulation model.

[0053] S2. Based on the finite element simulation model, the temperature distribution curves of cable intermediate connections with multiple heat sources and heat sources at different locations are calculated. A thermal characteristic database of the influence of heat sources on the surface temperature of the cold shrink tube (one of the components of the cable intermediate connection) is established by comparison, and temperature extraction points are selected accordingly.

[0054] S3. Based on the location and temperature of the temperature extraction point during simulation, and with the temperature gradient as a reference, the temperature monitoring point of the cable intermediate connection is determined through normalized sensitivity analysis, which serves as the placement location of the built-in temperature sensor in actual working conditions.

[0055] S4. Based on the determined temperature monitoring points, place the built-in temperature sensor under actual working conditions to obtain the measured temperature and location data. Based on the thermal feature database, for different types of heat sources in the middle connection of the cable, use the spatial pattern recognition heat source separation method to perform multi-heat source separation calculation on the influence of the temperature distribution curve formation (which can be calculated based on Matlab).

[0056] S5. Based on multi-heat source separation calculation, determine the actual heat source type in the intermediate connection of the cable and determine the corresponding heat source ratio (weighting each heat source). Use the first-order difference formula based on Fourier's law to calculate the axial temperature gradient, determine the temperature change area, and use dynamic threshold to identify hot spot areas. Areas exceeding the dynamic threshold are judged as defect areas, and areas not exceeding the dynamic threshold are normal areas.

[0057] S6. Use cubic spline interpolation for defective areas and quadratic polynomial fitting for normal areas. Establish a calculation model using Matlab (write code to run using Matlab software) to obtain the hot spot temperature and location of the intermediate connection of the cable.

[0058] S7. Based on the calculated hotspot temperature and location, the temperature of the conductor or insulation layer at the corresponding location is further calculated according to the transient thermal circuit model.

[0059] In S1, the fault types include fault types that have occurred in the historical records, as well as potential (theoretically possible) fault types under actual operating conditions.

[0060] An experimental platform was constructed, comprising a cable, a cable intermediate connection, a fiber optic temperature measuring device, a current transformer, a built-in temperature sensor, a wired communication device, a wireless communication device, and a processor. During the operation of the cable intermediate connection, the temperature distribution curve was obtained through the fiber optic temperature measuring device, the current flowing through the cable and the cable intermediate connection was obtained through the current transformer, the built-in temperature sensor was installed on the outside of the cold shrink tubing of the cable intermediate connection, and the wired and wireless communication devices were used for signal transmission. Finally, the processor was used to obtain the temperature distribution curves of the conductor and the surface of the cold shrink tubing, as well as the hot spot temperature of the cable intermediate connection. Combined with various artificially created defects, test data under multiple working conditions were obtained. Based on the measured temperature distribution curve data from multiple tests, the corresponding finite element simulation model was verified to ensure the effectiveness and feasibility of the simulation design. If the verification failed, the finite element simulation model was readjusted.

[0061] In S2, the thermal feature database contains curve features showing the influence of heat sources on the surface temperature of the cold shrink tubing, including the location and size of the heat source, the conductor temperature distribution curve, the surface temperature distribution curve of the cold shrink tubing, and the fitting function for each curve. After selecting the temperature extraction points, corresponding finite element simulation models can be established based on theoretically possible faults in cable intermediate connections or new faults that occur in actual operation of cable intermediate connections, continuously improving and enriching the thermal feature database.

[0062] In S3, the temperature gradient is calculated using the central difference method, combined with the strategy of forward and backward difference at the edge. The geometric center and the edge both use the first-order central difference, while the remaining area uses the third-order central difference. Then, the temperature monitoring point is determined through normalized sensitivity analysis.

[0063] The formula for calculating the temperature gradient is:

[0064] ;

[0065] ;

[0066] ;

[0067] In the formula, Let i be the temperature gradient at the intermediate temperature extraction point, i = 2, 3, ..., n-1, where n is the number of temperature extraction points; The temperature gradient at the first temperature extraction point; The temperature gradient at the final temperature extraction point; , , , , , , These are the temperatures of the 1st, 2nd, i-1th, ith, i+1th, n-1th, and nth temperature extraction points, respectively. , , , , , , These are the positions of the 1st, 2nd, i-1st, i, n-1st, and nth temperature extraction points, respectively.

[0068] The influence of the heat source on each temperature node is discretized, and the normalized sensitivity is calculated. Based on the influence of the j-th heat source on the i-th temperature extraction point, the temperature monitoring point is determined. The formulas for calculating the normalized sensitivity before and after discretization are as follows:

[0069] Normalization calculation of heat source contribution before discretization can quickly identify the heat source with the greatest impact on the temperature of the measurement point:

[0070] ;

[0071] After discretization:

[0072] ;

[0073] In the formula, Let be the internal heat source intensity of the j-th heat source; This indicates the sensitivity of the j-th heat source to the temperature extraction point; It is the step size of the heat source intensity perturbation; T is the temperature at the temperature extraction point.

[0074] The normalized sensitivity was calculated using Matlab, and the temperature extraction points with the highest normalized sensitivity were selected as the temperature monitoring points.

[0075] In performing normalized sensitivity analysis, to reduce the non-uniformity of temperature measurement point distribution and obtain comprehensive temperature gradient information, two temperature extraction points with the second highest normalized sensitivity were selected as temperature monitoring points. The distribution of normalized sensitivity is as follows: Figure 2 As shown, based on the normalized sensitivity, nine temperature monitoring points were finally selected (referred to as monitoring points in the figure).

[0076] In S4, the heat source separation method using spatial pattern recognition is employed to perform multi-heat source separation calculations on the influence of temperature distribution curve formation. Specifically, the temperature rise effect of multiple heat sources is approximated as a linear superposition, and fitting formulas for different heat source characteristics are calculated separately, along with the linear superposition formula. as follows:

[0077] ;

[0078] In the formula, Temperature distribution curve caused by conductor heating; The temperature rise distribution caused by the heat source in the pressurized pipe; This refers to the abnormal temperature rise caused by a defective heat source. The crimped connector heat source is a major heat source at the intermediate connection of a cable, referring to the Joule heat generated at the crimped connector section of the cable connection due to the flow of current.

[0079] Examples of surface temperature distribution curves for cables and intermediate connections under different heat sources are shown below. Figure 3 As shown. Figure 3 The distribution characteristics of the heat source in the temperature distribution curve can be seen from the data, as shown in Table 1.

[0080] Table 1. Distribution characteristics of heat sources on temperature distribution curves

[0081]

[0082] , , It can be obtained using well-known methods, or by calculating it in the following ways:

[0083] ;

[0084] ;

[0085] ;

[0086] In the formula, x represents the location of the temperature monitoring point; Here is the location of temperature monitoring point a; f, b, c, and d are the fitting parameters of the temperature distribution curve caused by conductor heating, respectively; k is the Gaussian term index; K is the number of Gaussian terms, which is the number of local abnormal temperature rise regions in the temperature distribution curve; Let be the temperature rise of the k-th Gaussian term; Let be the center position of the pressure pipe for the k-th Gaussian term; Let be the thermal diffusion radius of the k-th Gaussian term; , , , The fitting parameters for the temperature rise distribution curve caused by the heat source of the pressurized pipe;

[0087] In S5, the dynamic threshold The calculation formula is:

[0088] ;

[0089] In the formula, This represents the mean of the temperature gradient; denoted as the standard deviation of the temperature gradient.

[0090] In S6, cubic spline interpolation is used for defective regions, and quadratic polynomial fitting is used for normal regions. The constraints satisfied by the cubic and quadratic function construction formulas, extrema points, and fitting formulas are as follows:

[0091] ;

[0092] ;

[0093] ;

[0094] ;

[0095] ;

[0096] ;

[0097] ;

[0098] In the formula, y represents the hotspot location; y represents the temperature value of the temperature monitoring point. , These are the locations of temperature monitoring points a, a+1, and a+2, respectively. , These are the temperatures at temperature monitoring points a and a+1, respectively. , , These are the fitting coefficients of the quadratic function; , , , These are the fitting coefficients of the cubic function; Indicates the interval Cubic spline interpolation function on; , They represent exist , The function value at that location; , They represent At the right end The first and second derivatives at point ; , They represent the intervals respectively. The cubic spline interpolation function on the left endpoint The first and second derivatives at point .

[0099] In S7, a transient thermal circuit model is established based on actual operating conditions, the temperature and location measured by temperature monitoring points, and considering the intermediate cable connection structure at the corresponding locations. The specific process of establishing the transient thermal circuit model can employ known techniques, such as the transient thermal circuit model design method in CN118261016B.

Claims

1. A method for locating axial temperature hotspots in an intermediate connection of a simulated cryogenic self-healing cable, characterized in that Includes the following steps: S1. Based on the intermediate cable connections and fault types under actual working conditions, establish finite element simulation models with different heat sources, and build an experimental platform to verify the finite element simulation models. S2. Based on the finite element simulation model, the temperature distribution curves of intermediate connections of cables with multiple heat sources and heat sources at different locations are calculated, and a thermal characteristic database of the influence of heat sources on the surface temperature of cold shrink tubes is established by comparison, and temperature extraction points are selected accordingly. S3. Based on the location and temperature of the temperature extraction point during simulation, and with the temperature gradient as a reference, the temperature monitoring point of the cable intermediate connection is determined through normalized sensitivity analysis, which serves as the placement location of the built-in temperature sensor in actual working conditions. S4. Based on the determined temperature monitoring points, place the built-in temperature sensor under actual working conditions to obtain the measured temperature and location data. Based on the thermal feature database, for different types of heat sources in the middle connection of the cable, use the spatial pattern recognition heat source separation method to perform multi-heat source separation calculation on the influence of the temperature distribution curve formation. S5. Based on multi-heat source separation calculation, determine the actual heat source type in the intermediate connection of the cable and determine the corresponding heat source ratio. Use the first-order difference formula based on Fourier's law to calculate the axial temperature gradient, determine the temperature change area, and use dynamic threshold to identify hot spot area. Areas exceeding the dynamic threshold are judged as defect areas, and areas not exceeding the dynamic threshold are normal areas. S6. A cubic spline interpolation method is used for defective areas, and a quadratic polynomial fitting method is used for normal areas. A calculation model is established using Matlab to obtain the hot spot temperature and location of the intermediate connection of the cable. S7. Based on the calculated hotspot temperature and location, the temperature of the conductor or insulation layer at the corresponding location is further calculated according to the transient thermal circuit model. In S3, the temperature gradient is calculated using the central difference method, combined with the strategy of forward and backward difference at the edge. The geometric center and the edge both use the first-order central difference, while the remaining area uses the third-order central difference. Then, the temperature monitoring point is determined through normalized sensitivity analysis. The formula for calculating the temperature gradient is: ； ； ； In the formula, Let i be the temperature gradient at the intermediate temperature extraction point, i = 2, 3, ..., n-1, where n is the number of temperature extraction points; The temperature gradient at the first temperature extraction point; The temperature gradient at the final temperature extraction point; , , , , , , These are the temperatures of the 1st, 2nd, i-1th, ith, i+1th, n-1th, and nth temperature extraction points, respectively. , , , , , , These are the positions of the 1st, 2nd, i-1st, i, n-1st, and nth temperature extraction points, respectively. The influence of the heat source on each temperature node is discretized, and the normalized sensitivity is calculated. Based on the influence of the j-th heat source on the i-th temperature extraction point, the temperature monitoring point is determined. The formulas for calculating the normalized sensitivity before and after discretization are as follows: ； ； In the formula, Let be the internal heat source intensity of the j-th heat source; This indicates the sensitivity of the j-th heat source to the temperature extraction point; It is the step size of the heat source intensity perturbation; T is the temperature at the temperature extraction point.

2. The simulated, low-temperature, self-fusing cable mid-joint axial temperature hot spot location method of claim 1, wherein, In S1, the fault types include fault types that have occurred in the historical records, as well as potential fault types under actual operating conditions.

3. The simulation-based method for locating axial temperature hotspots in intermediate connections of low-temperature self-fluxing cables according to claim 1, characterized in that: In S1, an experimental platform is constructed, including a cable, a cable intermediate connection, a fiber optic temperature measuring device, a current transformer, a built-in temperature sensor, a wired communication device, a wireless communication device, and a processor. During the operation of the cable intermediate connection, the temperature distribution curve is obtained through the fiber optic temperature measuring device, the current flowing through the cable and the cable intermediate connection is obtained through the current transformer, the built-in temperature sensor is installed outside the cold shrink tube of the cable intermediate connection, and the wired and wireless communication devices are used for signal transmission. Finally, the processor is used to obtain the temperature distribution curve of the conductor and the surface of the cold shrink tube and the hot spot temperature of the cable intermediate connection. Combined with various artificially created defects, test data under various working conditions are obtained to verify the effectiveness of the finite element simulation model. If the verification fails, the finite element simulation model is readjusted.

4. The simulated, low temperature, self-fusing cable mid-joint axial temperature hot spot location method of claim 1, wherein: In S2, the thermal feature database contains curve features of the influence of heat sources on the surface temperature of the cold shrink tube, including the location of the heat source, the size of the heat source, the conductor temperature distribution curve, the surface temperature distribution curve of the cold shrink tube, and the fitting function of each curve.

5. The simulated, low temperature, self-fusing cable mid-span axial temperature hot spot location method according to claim 1, characterized in that: The normalized sensitivity was calculated using Matlab, and the temperature extraction points with the highest normalized sensitivity were selected as the temperature monitoring points.

6. The simulated, low temperature, self-fusing cable mid-span axial temperature hot spot location method according to claim 1, characterized in that: The influence of the temperature distribution curve formed by the space mode recognition heat source separation method in S4 is calculated in detail. The multi-heat source temperature rise effect is approximated as linear superposition, and different heat source characteristic fitting formulas are calculated respectively, and the linear superposition formula is as follows: as follows: ； In the formula, Temperature distribution curve caused by conductor heating; The temperature rise distribution caused by the heat source in the pressurized pipe; The abnormal temperature rise is caused by a defective heat source.

7. The simulation-based method for locating axial temperature hotspots in intermediate connections of low-temperature self-fluxing cables according to claim 1, characterized in that, In S4, the dynamic threshold The calculation formula is: ； In the formula, This represents the mean of the temperature gradient; denoted as the standard deviation of the temperature gradient.

8. The simulated, low temperature, self-fusing cable mid-joint axial temperature hot spot location method of claim 1, wherein: In step S5, cubic spline interpolation is used for defective regions, and quadratic polynomial fitting is used for normal regions. The constraints satisfied by the cubic and quadratic function construction formulas, extrema points, and fitting formulas are as follows: ； ； ； ； ； ； ； In the formula, x represents the hotspot location; y represents the location of the temperature monitoring point; and y represents the temperature value of the temperature monitoring point. , , These are the locations of temperature monitoring points a, a+1, and a+2, respectively. , These are the temperatures at temperature monitoring points a and a+1, respectively. , , These are the fitting coefficients of the quadratic function; , , , These are the fitting coefficients of the cubic function; Indicates the interval Cubic spline interpolation function on; , They represent exist , The function value at that location; , They represent At the right end The first and second derivatives at point ; , They represent the intervals respectively. The cubic spline interpolation function on the left endpoint The first and second derivatives at point .

9. The simulated, low temperature, self-fusing cable mid-span axial temperature hot spot location method according to claim 1, wherein: In S6, a transient thermal circuit model is established based on the actual working conditions, the temperature and location measured by the temperature monitoring point, and considering the intermediate connection structure of the cable at the corresponding location.

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