Cable fault identification method and device based on multi-physics field influence, and medium

Abstract

The invention discloses a cable fault identification method and device based on multi-physical field influence and a medium, and relates to the technical field of cable broadband impedance spectroscopy fault identification, and the method comprises the steps: obtaining electrical parameters of each structural layer of a target cable, inputting the electrical parameters into an initial cable model, and obtaining a target cable model; establishing a numerical evaluation standard; based on the evaluation coefficients of the multiple physical field parameters and a target cable model, analyzing a first association relationship between the multiple physical field parameters and each distribution parameter and an association fault interval corresponding to each distribution parameter under different fault types; the distribution parameters are used for calculating to obtain a dynamic characteristic impedance factor, and then the dynamic characteristic impedance factor is substituted into the broadband impedance spectrum model to optimize the broadband impedance spectrum model. According to the method, the impedance spectrum characteristics output by the broadband impedance spectrum model are more comprehensive, and the accuracy of the cable fault identification degree is improved.

Description

Technical Field

[0001] This application generally relates to the field of cable broadband impedance spectrum fault identification technology, and specifically to a cable fault identification method, device and medium based on the influence of multiple physical fields. Background Technology

[0002] With the upgrading and transformation of urban and rural power grids and the rapid development of new energy power plants, the number of underground power cables laid and operated has increased dramatically. During the operation of these cables, the long-term transmission of high voltage and high current causes insulation damage and aging. In addition, the humid laying environment often leads to chemical corrosion, which in turn causes short circuits, open circuits, partial discharges and other faults in the cables before they reach their service life.

[0003] Broadband impedance spectroscopy, as a cable fault identification technique, collects impedance information to obtain impedance amplitude and phase spectra. By comparing the impedance amplitude and phase during a fault with those under normal conditions, the fault type can be identified. However, current research on cable faults often involves coupling of multiple fields such as electric, magnetic, and temperature fields. Traditional research based on fixed cable distributed parameters has certain limitations, leading to discrepancies between fault identification results and reality, making it impossible to accurately determine the cause of the fault. This, in turn, affects the power system's goal of rapid fault diagnosis and precise repair. Summary of the Invention

[0004] In view of the above-mentioned defects or deficiencies in the prior art, it is desirable to provide a cable fault identification method, device and medium based on the influence of multi-physics fields.

[0005] This application provides a cable fault identification method based on the influence of multiphysics, which includes the following steps: Obtain the electrical parameters of each structural layer of the target cable, and input the electrical parameters into the initial cable model to obtain the target cable model; A numerical evaluation standard is established, which includes evaluation coefficients related to multiple physical field parameters; the physical field parameters include at least: temperature field, magnetic field, electric field and magnetic induction intensity; Based on the evaluation coefficients of the aforementioned multiple physical field parameters and the target cable model, the first correlation between the multiple physical field parameters and each distributed parameter, as well as the associated fault intervals corresponding to each distributed parameter, are analyzed under different fault types; the distributed parameters are used to calculate the dynamic characteristic impedance factor. Based on the first correlation and the associated fault range, the variation range of the dynamic characteristic impedance factor under different fault types is confirmed, and the dynamic characteristic impedance factor is substituted into the broadband impedance spectrum model to optimize the broadband impedance spectrum model. The broadband impedance spectrum model is used to output cable fault analysis results.

[0006] Based on the technical solution provided in this application, the process of establishing a numerical evaluation standard is as follows: Sampling is performed under different fault type scenarios, with the fault initiation time as the sampling starting point, to obtain the sampled values ​​of various physical field parameters at the steady-state sampling point; The absolute logarithm of each of the sampled values ​​is taken as the vertical axis, and the standard value of each of the physical field parameters under normal conditions is taken as the horizontal axis to establish a numerical evaluation standard; the evaluation coefficient is the ratio of the sampled value to the standard value corresponding to the same physical field parameter.

[0007] According to the technical solution provided in this application, based on the evaluation coefficients of the multiple physical field parameters and the target cable model, the first correlation between the multiple physical field parameters and each distributed parameter, as well as the associated fault intervals corresponding to each distributed parameter, are analyzed under different fault types, including: Based on the performance of the target cable model under different fault type scenarios, the values ​​and distribution parameters of each evaluation coefficient are obtained in real time. Based on the values ​​and distribution parameters of the evaluation coefficients, the first correlation between the physical field parameters and each distribution parameter is analyzed. Based on the first correlation and the fluctuation range of physical field parameters under different fault type scenarios, the associated fault intervals corresponding to each distributed parameter are identified; the associated fault intervals are used to characterize the fluctuations of multiple distributed parameters caused by any change in physical field parameter.

[0008] According to the technical solution provided in this application, the expression for the dynamic characteristic impedance factor is as follows: (14) in, Represented as dynamic characteristic impedance factor; Represented as resistance; Represented as inductance; Represented as electrical conductance; Represented as capacitor; The angular frequency of the induced current; The unit is imaginary; the distributed parameters include at least: inductance, resistance, capacitance, and conductance.

[0009] According to the technical solution provided in this application, based on the first correlation relationship and the associated fault interval, the variation range of the dynamic characteristic impedance factor under different fault types is determined, including: Based on the expression of the dynamic characteristic impedance factor and the associated fault interval of the distributed parameters, the change value of the dynamic characteristic impedance factor under the action of multi-physics parameters is calculated to confirm the range of change of the dynamic characteristic impedance factor under different fault types.

[0010] According to the technical solution provided in this application, after substituting the dynamic characteristic impedance factor into the broadband impedance spectrum model and optimizing the broadband impedance spectrum model, the method includes: The broadband impedance spectrum model optimized by the dynamic characteristic impedance factor was used to conduct experiments under different fault type scenarios, and the impedance information after the experiments was collected. Based on the impedance information, the impedance amplitude spectrum and impedance phase spectrum corresponding to different fault types are obtained, and the cable fault analysis results are obtained by analyzing the impedance amplitude spectrum and impedance phase spectrum.

[0011] According to the technical solution provided in this application, the target cable model is a two-dimensional small air gap model of a 35kV three-core cable; The target cable model has at least a cable core, an inner insulation layer, and an outer insulation layer. The target cable model also has built-in correlation equations based on Maxwell's equations and the first law of thermodynamics to obtain multiple physical field parameters, which are used for comprehensive analysis of different fault type scenarios.

[0012] According to the technical solution provided in this application, when sampling under different fault type scenarios, sampling points are selected on the structural layer of the target cable model according to the fault type to be analyzed.

[0013] In summary, this technical solution specifically discloses a cable fault identification method, device, and medium based on the influence of multiple physics fields. The method includes: acquiring the electrical parameters of each structural layer of the target cable and inputting these parameters into an initial cable model to obtain the target cable model; establishing a numerical evaluation standard, which includes evaluation coefficients related to multiple physics field parameters; the physics field parameters include at least: temperature field, magnetic field, electric field, and magnetic induction intensity; based on the evaluation coefficients of the multiple physics field parameters and the target cable model, analyzing the first correlation between the multiple physics field parameters and each distributed parameter under different fault types, as well as the associated fault intervals corresponding to each distributed parameter; the distributed parameters are used to calculate the dynamic characteristic impedance factor; according to the first correlation and the associated fault intervals, confirming the variation range of the dynamic characteristic impedance factor under different fault types, and substituting the dynamic characteristic impedance factor into a broadband impedance spectrum model to optimize the broadband impedance spectrum model; the broadband impedance spectrum model is used to output the cable fault analysis results.

[0014] Traditional research based on fixed cable distributed parameters has certain limitations, leading to discrepancies between fault identification results and actual conditions, and failing to accurately determine the cause of the fault. This application breaks away from the traditional approach of using constant characteristic impedance. By quantifying the changes in distributed parameters caused by the coupling of multiple physical field parameters, the range of dynamic characteristic impedance factor variation under different faults is determined. Based on this, a broadband impedance spectrum model is optimized, enabling the optimized model to more accurately adapt to different fault scenarios. Ultimately, this results in more reliable and accurate cable fault analysis results, improving the overall accuracy, comprehensiveness, and reliability of cable fault diagnosis. Attached Figure Description

[0015] Other features, objects, and advantages of this application will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings: Figure 1 This is a flowchart illustrating a cable fault identification method based on the influence of multiphysics. Figure 2 This describes the relationship between resistivity and temperature. Figure 3 This relates to the relationship between electric field and conductance. Figure 4 To consider the impedance amplitude spectrum of normal and open-circuit fault cables with dynamic characteristic impedance factors; Figure 5 To consider the impedance phase spectrum of normal and open-circuit fault cables with dynamic characteristic impedance factors; Figure 6 To consider the impedance amplitude spectrum of cables under normal and short-circuit fault conditions with dynamic characteristic impedance factors; Figure 7 To consider the dynamic characteristic impedance factor of the cable impedance phase spectrum under normal and short-circuit fault conditions; Figure 8 To consider the impedance amplitude spectrum of normal and high-resistance fault cables with dynamic characteristic impedance factors; Figure 9 To consider the impedance phase spectrum of normal and high-resistance fault cables with dynamic characteristic impedance factors; Figure 10 To consider the impedance amplitude spectrum of normal and low-resistance fault cables with dynamic characteristic impedance factors. Figure 11 To consider the impedance phase spectrum of normal and low-resistance fault cables with dynamic characteristic impedance factors. Figure 12 This is a schematic diagram of a terminal device.

[0016] The following numbers are used in the diagram: 500, Terminal device; 501, CPU; 502, ROM; 503, RAM; 504, Bus; 505, I / O interface; 506, Input section; 507, Output section; 508, Storage section; 509, Communication section; 510, Driver; 511, Removable media. Detailed Implementation

[0017] The present application will now be described in further detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and not intended to limit it. Furthermore, it should be noted that, for ease of description, only the parts relevant to the invention are shown in the accompanying drawings.

[0018] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0019] Example 1 To make the technical solutions of the embodiments of this application clearer and easier to understand, the application background of the embodiments of this application is introduced below.

[0020] With the upgrading and transformation of urban and rural power grids and the rapid development of new energy power plants, the number of buried power cables has increased dramatically. During cable operation, the long-term transmission of high voltage and high current causes insulation damage and aging. Combined with the humid laying environment, chemical corrosion often occurs, leading to short circuits, open circuits, partial discharges, and other faults before the cables reach their service life. Wideband impedance spectroscopy, as a cable fault identification technology, collects impedance information to obtain impedance amplitude and phase spectra. By comparing the impedance amplitude and phase at fault with those under normal conditions, the fault type can be identified. This method has high accuracy in single-grid structures; simply comparing the magnitude, period, and initial phase of the impedance amplitude and phase spectra under normal and fault conditions is sufficient to determine the fault type. However, current grid structures are becoming increasingly complex, and traditional fault identification methods for single-grid structures are insufficient to meet the needs of complex power networks. Therefore, there is an urgent need to improve wideband impedance spectroscopy technology.

[0021] Most existing broadband impedance spectroscopy improvement techniques enhance accuracy by incorporating window functions, with Kaiser and Chebyshev windows being commonly used. While these methods improve accuracy to some extent, they also increase the difficulty of widespread application. Other researchers reconstruct the impedance using collected information, analyzing the attenuation and power transfer characteristics of the excitation voltage signal at the head end. This improves accuracy to some extent, but it doesn't consider the influence of physical fields. Cable faults are often accompanied by coupling from multiple fields, including electric, magnetic, and temperature fields. Traditional studies assume cable distributed parameters are constant, but multiple physical fields cause these parameters to change. Therefore, traditional research based on constant cable distributed parameters has limitations, leading to discrepancies between fault identification results and actual conditions.

[0022] In view of this, this application proposes a cable fault identification method based on the influence of multiple physics fields. The method includes: acquiring the electrical parameters of each structural layer of the target cable and inputting these parameters into an initial cable model to obtain the target cable model; establishing a numerical evaluation standard, which includes evaluation coefficients related to multiple physics field parameters; the physics field parameters include at least: temperature field, magnetic field, electric field, and magnetic induction intensity; based on the evaluation coefficients of the multiple physics field parameters and the target cable model, analyzing the first correlation between the multiple physics field parameters and each distributed parameter under different fault types, as well as the associated fault intervals corresponding to each distributed parameter; the distributed parameters are used to calculate the dynamic characteristic impedance factor; according to the first correlation and the associated fault intervals, confirming the variation range of the dynamic characteristic impedance factor under different fault types, and substituting the dynamic characteristic impedance factor into a broadband impedance spectrum model to optimize the broadband impedance spectrum model; the broadband impedance spectrum model is used to output the cable fault analysis results.

[0023] As can be seen, the fault identification method proposed in this application first constructs an accurate target cable model by acquiring the electrical parameters of each structural layer of the target cable. Simultaneously, it incorporates multi-physical field parameters such as temperature, magnetic field, electric field, and magnetic induction intensity into the analysis, using evaluation coefficients of the correlated multi-physical fields to cover the complex actual operating conditions of the cable. Based on this, by clarifying the correlation between multi-physical field parameters and distributed parameters under different fault types, and the fault intervals of each distributed parameter, it provides precise benchmarks for fault identification, including feature labels and interval definitions, significantly reducing the false positive rate of similar faults and the false negative rate of minor faults, and providing a set range for subsequent dynamic characteristic impedance factor experiments. Furthermore, by substituting the dynamic characteristic impedance factor (calculated from the distributed parameters) affected by multi-physical fields into the broadband impedance spectrum model, it optimizes the model to reflect the dynamic changes of the fault, thereby improving the broadband impedance spectrum model's ability to distinguish between different types and degrees of faults. This effectively solves the problems of traditional methods being disconnected from reality, having ambiguous features, and failing to accurately determine the fault cause, combining technical precision with engineering practicality.

[0024] To make the technical solution of this application clearer and easier to understand, the following description, in conjunction with the accompanying drawings, introduces a cable fault identification method based on the influence of multiphysics fields provided by an embodiment of this application. Figure 1 As shown in the figure, this is a flowchart of a cable fault identification method based on multiphysics field influence provided in an embodiment of this application. The method includes: S100. Obtain the electrical parameters of each structural layer of the target cable and input the electrical parameters into the initial cable model to obtain the target cable model; The principle behind the confirmation and establishment of the initial cable model is as follows: If air gap defects exist in the insulation of the cable core, electron polarization will occur under high voltage, resulting in glow discharge. At this stage, the defect does not develop rapidly and has not yet reached the breakdown voltage of the inner insulation. Subsequently, due to the intensification of molecular polarization, corona discharge and spark discharge will gradually occur. This tip discharge still does not reach the breakdown voltage, but only weakens the insulation level, thus reducing the breakdown voltage. As the insulation ages, the discharge current path becomes more and more significant, and the current increases. Accompanied by dielectric loss, it is dissipated in the form of heat, eventually forming a tip arc discharge, breaking down the inner insulation, and subsequently damaging the outer insulation, affecting other non-faulty phases.

[0025] In this embodiment, the target cable model is a two-dimensional small air gap model of a 35kV three-core cable. The 35kV three-core cable is the core equipment of the low-voltage distribution network in the power system and is widely used in urban distribution networks, industrial park power supply and other scenarios. Its operational stability directly affects the reliability of power supply. This target cable model is not a general-purpose model, but a specific model for a 35kV voltage level, three-core cable. Therefore, the actual electrical parameters of each structural layer of the cable (such as the resistivity of the copper conductor in the cable core, the dielectric constant and dielectric loss tangent of the inner insulation layer XLPE (cross-linked polyethylene), and the breakdown field strength of the outer insulation layer PVC (polyvinyl chloride)) or basic electrical parameters such as resistance, inductance, capacitance, and conductance are used to replace the general parameters in the initial cable model. In addition, the target cable model has built-in correlation equations for multiple physical field parameters based on Maxwell's equations and the first law of thermodynamics, which are used to comprehensively analyze different fault types and scenarios. This ensures that the electrical characteristics and structural dimensions of the model are completely matched with the actual 35kV three-core cable, providing a basis consistent with the physical object for subsequent multiphysics analysis and fault identification. Then, short-circuit, open-circuit, and partial discharge analyses are performed in conjunction with the target cable model.

[0026] Specifically, buried three-core cables have the following characteristics: they mainly consist of a cable core, inner insulation, and outer insulation. The inner insulation contains a copper shielding layer and a water-blocking conductive strip, while the outer insulation contains a steel armor layer, an inner liner, and an outer sheath. The space between the cable core and the inner liner is filled with a PVC filler strip that provides high-temperature resistance and support. A tiny air gap exists between the cable core and the inner insulation, approximately equivalent to small air bubbles. Because the air gap uses air as the dielectric, its breakdown voltage is lower than that of a solid dielectric. The uniform electric field strength near the inner insulation is distorted by the presence of these air bubbles, gradually forming a non-uniform electric field strength. At this point, the breakdown voltage of the air gap further decreases under the distorted electric field strength, leading to the first occurrence of spark discharge (intermittent breakdown), which gradually evolves into partial discharge.

[0027] Finally, the equivalent circuit diagram of the two-dimensional small air gap model of the 35kV three-core cable is as follows: Figure 2 As shown, the calculation formula is as follows: (1); (2); in, Represented as a fill layer The overall capacitance, This indicates that the capacitor is partially intact. Represented as the air gap equivalent capacitance; This is represented as the capacitance connected in series with the air gap; Represented as partial voltage; Overall voltage; It is expressed as the angular frequency of the current; It is represented as a time variable and is used to describe the change of voltage over time.

[0028] The Maxwell's equations and the first law of thermodynamics built into the target cable model are used to analyze different fault scenarios. Details are as follows: (1) Maxwell's equations for the normal operation of underground cables are as follows: (3); in, Represented as the Hamiltonian operator; H Expressed as magnetic field strength; J Expressed as current density; D It is represented as an electric displacement vector.

[0029] E Expressed as electric field strength; B Expressed as magnetic flux density; It is expressed as charge volume density.

[0030] It is expressed as the rate of change of the electric displacement vector over time.

[0031] As can be seen, Maxwell's equations are used to describe the coupling relationship between electric field, magnetic field and current during normal operation, and electromagnetic anomalies can be reflected by parameter mutations during faults.

[0032] (2) Air gap electric field By influencing current density J The final value of the interfering magnetic field is thus determined by the current density J shown below: (4);

[0033] in, This refers to the electrical conductivity of the pipe. Expressed as a scalar potential, Represented as magnetic vector potential, Expressed as angular frequency; It is the imaginary unit.

[0034] It can be seen that formula (4) can effectively quantify the combined influence of electric field and magnetic field on current density.

[0035] (3) Induced current generates eddy currents in the sheath and copper shielding layer. In the eddy current region, vector potential and scalar potential need to be combined to describe the magnetic field and electric field distribution, as follows: (5); (6); (7); in, It is expressed as permeability. Since the magnetic induction intensity depends on the vector potential, the electric field intensity is related to both the vector and scalar potentials. Represented as magnetic vector potential; Represented as the Hamiltonian operator; The imaginary unit; Expressed as conductivity; It is expressed as a scalar potential.

[0036] It can be seen that the relationship between magnetic vector potential and magnetic induction intensity, and scalar potential and electric field intensity can be directly established based on formulas (5)-(7), providing a basis for the calculation of field distribution in the eddy current region.

[0037] (4) The formula for temperature field loss is as follows: (8); Among them, c, s , a These represent the conductor, shielding layer, and armor layer, respectively. , , , , and This is represented as parameter adjustment using the least squares method; I Represented as conductor current, , , These represent the heat losses of the conductor, shielding layer, and armor layer, respectively. , , These represent the temperature of the conductor, the temperature of the shielding layer, and the temperature of the armor layer, respectively. It can be seen that formula (8) can effectively quantify the relationship between the change in current and the surge in heat loss during a fault.

[0038] (5) The differential equation for heat conduction under the first law of thermodynamics is as follows: (9); in, Expressed as thermal conductivity, Expressed as charge volume density, C Expressed as specific heat capacity, T Represented as temperature, Represented as time, x , y , z It is represented as a spatial coordinate system; it can be seen that formula (9) can describe the dynamic process of temperature change with time / space caused by heat loss during a fault, and is used to reflect the temperature rise rate and distribution characteristics.

[0039] Based on the description of formulas (1) to (9) above, the correlation of multi-physics parameters under different faults can be clearly defined, which facilitates subsequent coupling analysis. This allows the target cable model to accurately describe the electric field, magnetic field, temperature field and other field coupling phenomena that often accompany cable faults, thereby assisting the experiment in short circuit, open circuit and partial discharge analysis.

[0040] S200. Establish numerical evaluation standards, which include evaluation coefficients related to multiple physical field parameters; the physical field parameters include at least: temperature field, magnetic field, electric field and magnetic induction intensity; The purpose of establishing numerical evaluation standards is to visualize the deviation characteristics of each physical field under fault through coordinate system visualization and evaluation coefficients, namely the deviation characteristics of temperature field, magnetic field, electric field and magnetic induction intensity. In practical applications, these deviation characteristics are directly synchronized to the cloud platform to form data on the terminal interface. This design can not only be used for preliminary fault judgment, but also establish a relationship between the intuitive data changes of temperature field, magnetic field, electric field and magnetic induction intensity and the distribution parameters observed below.

[0041] Furthermore, the process of establishing numerical evaluation standards specifically includes the following: Step A1: Sample under different fault type scenarios, taking the fault initiation time as the sampling starting point, and obtain the sampled values ​​of various physical field parameters at the steady-state sampling point; Specifically, the method used to establish the numerical evaluation standard in this application embodiment is multiple sampling. First, sampling experiments need to be carried out in different fault type scenarios. Before the sampling experiment begins, sampling points are selected on the structural layer of the target cable model according to the fault type to be analyzed. In this embodiment, except for the sampling point of air gap breakdown at the breakdown point, the sampling points of other faults are all at the insulation surface.

[0042] Next, based on the target cable model, simulations of different fault scenarios were conducted. Taking the fault initiation time as the sampling starting point, five time sampling points were collected: 0, 1 / 4 steady-state t, 1 / 2 steady-state t, 3 / 4 steady-state t, and steady-state t. At different sampling points, the sampled values ​​of various physical field parameters (temperature field, magnetic field, electric field, and magnetic induction intensity) were obtained, resulting in a sampling data sequence for the fault scenario. From the sampling data sequence, the sampled values ​​of various physical field parameters at steady-state t were selected to participate in the establishment of subsequent numerical evaluation criteria. The sampling data sequence here serves two purposes: firstly, to accurately locate steady-state t, and secondly, to preserve the trend of the data, thereby verifying the accuracy of the data.

[0043] Step A2: Take the absolute logarithm of each sampled value as the vertical axis and the standard value of each physical field parameter under normal conditions as the horizontal axis to establish a numerical evaluation standard; the evaluation coefficient is the ratio of the sampled value to the standard value corresponding to the same physical field parameter.

[0044] Taking the absolute value of the logarithm of the sampled values ​​for different types yields the ordinate data formed by the temperature field, magnetic field, electric field, and magnetic induction intensity. These ordinates are denoted as follows: , , and ; Represented as the steady-state value of the magnetic field during the fault process. Represented as the steady-state value of magnetic flux density during the fault process. Represented as the steady-state value of the electric field during the fault process and This represents the steady-state temperature field value during the fault process; the horizontal axis is based on the normal values ​​of each physical field parameter, so the evaluation coefficients reflected in the numerical evaluation standard are as follows: Magnetic field evaluation coefficient ; Magnetic induction intensity evaluation coefficient ; Electric field evaluation coefficient ; Temperature field evaluation coefficient .

[0045] As can be seen, the intuitive performance of the evaluation coefficients can reflect the magnitude of the physical field parameter shift caused by the fault. At the same time, the experiment also found that the evaluation coefficients corresponding to different fault types also have different characteristics (see Table 1 below for details), which can be used as a preliminary judgment of the fault. The deviation characteristics can also be directly synchronized to the cloud platform to form data on the terminal interface.

[0046] Table 1 Examples of evaluation coefficients for different fault types

[0047] As shown in Table 1 above, the physical field parameters under each fault type have corresponding characteristics. For example, under air gap breakdown fault, the electric field evaluation coefficient will be doubled compared to other evaluation coefficients; under short circuit fault, the magnetic induction intensity evaluation coefficient will be doubled compared to other evaluation coefficients; while under open circuit fault, all evaluation coefficients are in a state of less than 1 or close to 1.

[0048] S300, based on the evaluation coefficients of multiple physical field parameters and the target cable model, analyzes the first correlation between multiple physical field parameters and each distributed parameter under different fault types, as well as the associated fault intervals corresponding to each distributed parameter; the distributed parameters are used to calculate the dynamic characteristic impedance factor; Specifically, the coupling effect of physical field parameters under different fault types directly affects the distributed parameters of the cable. These distributed parameters are inductance (L), resistance (R), capacitance (C), and conductance (G), which are distributed parameters in transmission line theory. These four parameters are uniformly distributed along the cable length and jointly determine the electromagnetic transmission characteristics and energy loss law of the cable. It is equivalent to treating the cable in normal operation as a uniform transmission line and establishing a transmission line system based on the distributed parameters. Based on these four distributed parameters, the state of the cable during a fault can be investigated. The dynamic characteristic impedance factor is also a factor for investigating the change law of distributed parameters under the coupling of physical field parameters. However, this dynamic characteristic impedance factor is not a rangeless and boundless fluctuating value. Therefore, it is necessary to conduct further experiments by combining the evaluation coefficients of multiple physical field parameters and the target cable model to confirm the associated fault interval corresponding to the distributed parameters.

[0049] The specific process of step S300 includes: based on the performance of the target cable model under different fault type scenarios, the values ​​and distribution parameters of each evaluation coefficient are obtained in real time; based on the values ​​and distribution parameters of the evaluation coefficients, the first correlation between the physical field parameters and each distribution parameter is analyzed; based on the first correlation and the fluctuation range of the physical field parameters under different fault type scenarios, the associated fault intervals corresponding to each distribution parameter are confirmed; the associated fault intervals are used to characterize the fluctuations of multiple distribution parameters caused by any change in physical field parameters.

[0050] When performing short-circuit, open-circuit, and partial discharge analyses on the target cable model, the first correlation between the physical field parameters and each distributed parameter can be obtained by acquiring the values ​​and distribution parameters of each evaluation coefficient under the established numerical evaluation standard in real time. For example, under short-circuit conditions, , , and The values ​​are 1.5, 4, 1.8, and 1.1 respectively. Assume that the fluctuations of the distributed parameters, namely inductance, resistance, and capacitance, are 25%. 20% -1% Based on current data analysis, it is known that when the magnetic field increases to 1.5 times the normal state, the magnetic induction intensity increases to 4 times the normal state, the electric field increases to 1.8 times the normal state, and the temperature field increases to 1.1 times the normal state, the inductance, resistance, and capacitance increase by a certain factor. This establishes the first correlation between multi-physics parameter coupling and distributed parameters. This first correlation is that when the increases in magnetic field, magnetic induction intensity, electric field, and temperature field are 1.5, 4, 1.8, and 1.1 respectively, the corresponding inductance, resistance, and capacitance are 25%. 20% -1% The relationship.

[0051] Furthermore, if we obtain , , and By determining the fluctuation range (from the fault occurrence point to the upper boundary point of the fault) under different fault scenarios, we can obtain the variation range of inductance, resistance, and capacitance. This allows us to identify the associated fault intervals for each distributed parameter. The associated fault intervals are the fault intervals of various related inductors, resistors, and capacitors generated under the coupling effects of multiple physical field parameters within the same fault scenario. It should be noted that, combined with... Figure 3 It can be seen that when the electric field strength is less than a certain value, the conductance G is approximately constant, because the conductance increases rapidly after the electric field increases to a certain value. At a voltage level of 35kV, the influence of the electric field on the conductance can be ignored.

[0052] The relationships between inductance, resistance, capacitance, and multiphysics parameters mentioned above can be established using the following formula: (1) First, in combination Figure 2 It can be seen that the temperature field affects the resistance R in the distributed parameters by influencing the resistivity, and thus the magnitude of the resistance. Figure 2 As shown, the relationship between resistance and resistivity is given by the following formula (10): (10); in, , Represented as the resistivity of the conductor and the copper shielding layer, and having ; Represented as the radius of the cable shielding layer; Expressed as vacuum permeability; It is expressed as the angular frequency of the current; It is expressed as the radius of the cable conductor.

[0053] (2) Secondly, the inductance L and the magnetic flux The relationship is related to the magnetic induction intensity B and the magnetic field strength H, and the relationship is shown in the following formulas (11) and (12): (11); (12); in, , Represented as the resistivity of the conductor and the copper shielding layer; , which represents the angular frequency of the current; It is expressed as the permeability of a conductor; and These represent the radius of the cable shield and the radius of the cable conductor, respectively. and These are the skin depths of the conductor and the copper shielding layer, respectively. I It is represented as conductor current.

[0054] (3) The capacitance C is mainly related to the relative permittivity. The relationship is as follows: its value first increases and then decreases with increasing temperature, and decreases with increasing frequency. The relationship is shown in the following formula (13): (13); in, Represented as the radius of the k-th layer of the cable; Represented as the radius of the (k+1)th layer of the cable; It is the vacuum permittivity. and It is the first k The real and imaginary parts of the constant of the layered medium are functions of frequency.

[0055] Based on the above formulas (10)-(13), it can be seen that the changes in multi-physical field parameters will affect various distributed parameters. For example, in short-circuit faults, a sudden increase in current corresponds to a significant increase in magnetic field and a sharp rise in temperature; in partial discharge, air gap breakdown corresponds to a sharp rise in electric field and a pulse-like increase in local magnetic field; in open-circuit faults, current interruption corresponds to a significant weakening of magnetic field, and if there is an electric arc, the local temperature will rise sharply but the overall temperature change is small. However, by performing short-circuit, open-circuit, and partial discharge analysis on the target cable model and obtaining the variation law of each distributed parameter when most physical field parameters change together, the influence of the coupling effect of multi-physical field parameters on each distributed parameter is fully considered, rather than the influence of a single multi-physical field parameter on each distributed parameter. This makes the variation range and law of the dynamic characteristic impedance factor under different fault types calculated later more accurate.

[0056] It is evident that by performing short-circuit, open-circuit, and partial discharge analyses on the target cable model, it is possible to obtain the range of distributed parameters that the physical field parameters of the cable can cause under different fault scenarios. This is because it is not the intention to investigate the fault after the cable is completely damaged. The key is the early detection and prevention of faults. Therefore, the changes and fluctuations of physical field parameters under different fault scenarios need to be determined based on the performance of the target cable model.

[0057] S400. Based on the first correlation and the associated fault interval, confirm the variation range of the dynamic characteristic impedance factor under different fault types, and substitute the dynamic characteristic impedance factor into the broadband impedance spectrum model to optimize the broadband impedance spectrum model. The broadband impedance spectrum model is used to output the cable fault analysis results.

[0058] Specifically, the process of determining the variation range of the dynamic characteristic impedance factor under different fault types based on the first correlation and the associated fault interval is as follows: Based on the expression of the dynamic characteristic impedance factor and the associated fault interval of the distributed parameters, the variation value of the dynamic characteristic impedance factor under the action of multi-physics parameters is calculated to determine the variation range of the dynamic characteristic impedance factor under different fault types.

[0059] Assuming the current fault type is a short circuit, the fault ranges for inductors, resistors, and capacitors are 0.22 to 0.26, respectively. 0.36~0.42 190~200 Within this fault range, the range of variation of the dynamic characteristic impedance factor can be determined by formula (14). Specifically, the expression for the dynamic characteristic impedance factor is as follows: formula (14): (14); in, Represented as dynamic characteristic impedance factor; Represented as resistance; Represented as inductance; Represented as electrical conductance; Represented as capacitor; The angular frequency of the induced current; The unit is imaginary; distributed parameters include at least: resistance, capacitance, and conductance.

[0060] Based on formula (14), the maximum and minimum values ​​of the dynamic characteristic impedance factor can be calculated by substituting the values ​​of the inductor, resistor, and capacitor according to their respective fault intervals. The maximum and minimum values ​​constitute the corresponding range of variation. This range of variation can show the influence of multi-physics parameters on the distributed parameters, and the dynamic characteristic impedance factor... The changing pattern of the dynamic characteristic impedance factor can be used to quantify the magnitude and trend of the impact on the distributed parameters. In actual experiments, the changes of the dynamic characteristic impedance factor under different fault types can be shown in Table 2 below. It should be noted that the dynamic characteristic impedance factor here... The increase and decrease of are relative to the traditional fixed values ​​of inductance, resistance, and capacitance. It can be seen that under the combined effect of multiple physical field parameters, the dynamic characteristic impedance factor... It is not a constant, and the dynamic characteristic impedance factor corresponds to different fault types. The range of variation is also different, and separate experiments are required for each.

[0061] Table 2. Variation of dynamic characteristic impedance factor under different fault types

[0062] In a preferred embodiment, after obtaining the range of the dynamic characteristic impedance factor, it can be substituted into the broadband impedance spectrum model as a constraint condition for the dynamic characteristic impedance factor. That is, after optimizing the broadband impedance spectrum model, the method includes: Step F1: Conduct experiments under different fault type scenarios using the broadband impedance spectrum model optimized by the dynamic characteristic impedance factor, and collect impedance information after the experiments; Step F2: Based on the impedance information, obtain the impedance amplitude spectrum and impedance phase spectrum corresponding to different fault types, and use the impedance amplitude spectrum and impedance phase spectrum to analyze the cable fault analysis results.

[0063] Ultimately, by using the dynamic characteristic impedance factor Substituting into the calculation formula (15) corresponding to the broadband impedance spectrum model, and controlling the broadband impedance spectrum model to conduct open circuit, short circuit, high resistance, and low resistance fault experiments, we can obtain different impedance amplitude spectra and impedance phase spectra output by the broadband impedance spectrum model under different fault tests. Using the current output impedance amplitude spectrum and impedance phase spectrum as a reference for identifying the cause of cable faults, we can better assist technicians in accurately identifying the cause of cable faults.

[0064] (15); in, This is expressed as a formula for calculating the amplitude spectrum; This is expressed as a formula for calculating the phase spectrum; It is expressed as the reflection coefficient after cable attenuation and phase shift, and has , It is expressed as the phase factor of the reflected wave, and has , Represented as the reflection coefficient, α It is expressed as the attenuation constant; β Expressed as a phase constant; Represented as the imaginary unit; This is expressed as the cable length from the measurement point to the load end; This represents the original reflection coefficient at the cable load end. It is a natural constant, which can be taken as 2.71828.

[0065] For example, the process of conducting open-circuit, short-circuit, high-resistance, and low-resistance fault experiments using a broadband impedance spectrum model can be as follows: Set the scanning frequency (MHz) of the sampling signal and the cable fault point; collect relevant data such as the amplitude, initial phase, and period of the spectrum after the experiment. See Table 3 below for comparison results. Figures 4-11 The results shown in Table 3 are as follows: the first two columns show the impedance amplitude spectrum and impedance phase spectrum under different fault type scenarios when considering the dynamic characteristic impedance factor, while the third column shows the impedance amplitude spectrum and impedance phase spectrum after considering the dynamic characteristic impedance factor.

[0066] Table 3 Comparison of impedance amplitude and impedance phase spectra before and after the experiment

[0067] It is evident that by analyzing the variations in temperature, magnetic field, electric field, and magnetic induction intensity under different faults as reflected in the constructed target cable model, numerical evaluation criteria for different faults can be derived. Then, by relating the physical quantities to the distributed parameters, the dynamic characteristic impedance factor considering multi-physics field variations when a fault is detected can be compared. It is no longer a constant. Next, consider the dynamic characteristic impedance factor. The changes were investigated by conducting open-circuit, short-circuit, high-resistance, and low-resistance fault experiments using a broadband impedance spectrum model. The experimental results were compared with those under normal conditions to identify the characteristics of the four types of cable faults, which filled the gaps in the fault characteristics in existing studies and provided a more comprehensive impedance spectrum profile.

[0068] The above text combined Figures 1-11 The active power control method for electrochemical energy storage power stations provided in the embodiments of this application has been described in detail below, and will be further explained in conjunction with the appendix. Figure 12 The devices and media provided in the embodiments of this application will be described.

[0069] The present invention also provides a terminal device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of a cable fault identification method based on multiphysics field influence as described in Embodiment 1.

[0070] In this embodiment, as Figure 12 As shown, the terminal device 500 includes a CPU (Central Processing Unit) 501, which can perform various appropriate actions and processes according to a program stored in ROM (Read-Only Memory) 502 or a program loaded from storage into RAM (Random Access Memory) 503. RAM 503 also stores various programs and data required for system operation. The CPU 501, ROM 502, and RAM 503 are interconnected via a bus 504. An I / O (Input / Output) interface 505 is also connected to the bus 504.

[0071] The following components are connected to I / O interface 505: an input section 506 including a keyboard, mouse, etc.; an output section 507 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and speakers, etc.; a storage section 508 including a hard disk, etc.; and a communication section 509 including a network interface card such as a LAN card, modem, etc. The communication section 509 performs communication processing via a network such as the Internet. A drive 510 is also connected to I / O interface 505 as needed. A removable medium 511, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on drive 510 as needed so that computer programs read from it can be installed into storage section 508 as needed.

[0072] In particular, according to embodiments of the present invention, the above-described flowchart is as follows. Figure 1The described process can be implemented as a computer software program. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowchart. In such embodiments, the computer program can be downloaded and installed from a network via a communication component, and / or installed from a removable medium. When the computer program is executed by CPU 501, it performs the functions defined above in the system of the present invention.

[0073] It should be noted that the computer-readable medium shown in this invention can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, RAM 503, ROM 502, an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In this invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In this invention, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media can also be any computer-readable medium other than computer-readable storage media, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0074] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0075] The units described in the embodiments of the present invention can be implemented in software or hardware, and the described units can also be located in a processor. The names of these units do not necessarily limit the specific unit itself. The described units or modules can also be located in a processor; for example, a processor can be described as including a first generation module, an acquisition module, a search module, a second generation module, and a merging module. The names of these units or modules do not necessarily limit the specific unit or module itself; for example, the acquisition module can also be described as "an acquisition module for acquiring multiple instances to be probed in the base table".

[0076] The present invention also provides a computer-readable medium, which may be included in the electronic device described in the above embodiments; or it may exist independently and not assembled into the electronic device. The computer-readable medium carries one or more programs, which, when executed by the electronic device, cause the electronic device to implement a cable fault identification method based on multiphysics field influence as described in the above embodiments.

[0077] The above description is merely a preferred embodiment of this application and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of the invention involved in this application is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the inventive concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features with similar functions disclosed in this application.

Claims

1. A cable fault identification method based on the influence of multiphysics, characterized in that, The method includes the following steps: Obtain the electrical parameters of each structural layer of the target cable, and input the electrical parameters into the initial cable model to obtain the target cable model; A numerical evaluation standard is established, which includes evaluation coefficients related to multiple physical field parameters; the physical field parameters include at least: temperature field, magnetic field, electric field and magnetic induction intensity; Based on the evaluation coefficients of the aforementioned multiple physical field parameters and the target cable model, the first correlation between the multiple physical field parameters and each distributed parameter, as well as the associated fault intervals corresponding to each distributed parameter, are analyzed under different fault types; the distributed parameters are used to calculate the dynamic characteristic impedance factor. Based on the first correlation and the associated fault range, the variation range of the dynamic characteristic impedance factor under different fault types is confirmed, and the dynamic characteristic impedance factor is substituted into the broadband impedance spectrum model to optimize the broadband impedance spectrum model. The broadband impedance spectrum model is used to output cable fault analysis results.

2. The cable fault identification method based on multiphysics field influence according to claim 1, characterized in that, The process of establishing numerical evaluation standards is as follows: Sampling is performed under different fault type scenarios, with the fault initiation time as the sampling starting point, to obtain the sampled values ​​of various physical field parameters at the steady-state sampling point; The absolute logarithm of each of the sampled values ​​is taken as the vertical axis, and the standard value of each of the physical field parameters under normal conditions is taken as the horizontal axis to establish a numerical evaluation standard; the evaluation coefficient is the ratio of the sampled value to the standard value corresponding to the same physical field parameter.

3. The cable fault identification method based on multiphysics field influence according to claim 2, characterized in that, Based on the evaluation coefficients of the aforementioned multiple physical field parameters and the target cable model, the first correlation between the multiple physical field parameters and each distributed parameter, as well as the associated fault intervals corresponding to each distributed parameter, are analyzed under different fault types, including: Based on the performance of the target cable model under different fault type scenarios, the values ​​and distribution parameters of each evaluation coefficient are obtained in real time. Based on the values ​​and distribution parameters of the evaluation coefficients, the first correlation between the physical field parameters and each distribution parameter is analyzed. Based on the first correlation and the fluctuation range of physical field parameters under different fault type scenarios, the associated fault intervals corresponding to each distributed parameter are identified; the associated fault intervals are used to characterize the fluctuations of multiple distributed parameters caused by any change in physical field parameter.

4. The cable fault identification method based on multiphysics field influence according to claim 1, characterized in that, The expression for the dynamic characteristic impedance factor is as follows: （14） in, Represented as dynamic characteristic impedance factor; Represented as resistance; Represented as inductance; Represented as electrical conductance; Represented as capacitor; The angular frequency of the induced current; The unit is imaginary; the distributed parameters include at least: inductance, resistance, capacitance, and conductance.

5. The cable fault identification method based on multiphysics field influence according to claim 4, characterized in that, Based on the first correlation and the associated fault range, the variation range of the dynamic characteristic impedance factor under different fault types is determined, including: Based on the expression of the dynamic characteristic impedance factor and the associated fault interval of the distributed parameters, the change value of the dynamic characteristic impedance factor under the action of multi-physics parameters is calculated to confirm the range of change of the dynamic characteristic impedance factor under different fault types.

6. The cable fault identification method based on multiphysics field influence according to claim 1, characterized in that, The method involves substituting the dynamic characteristic impedance factor into a broadband impedance spectrum model and optimizing the model. The broadband impedance spectrum model optimized by the dynamic characteristic impedance factor was used to conduct experiments under different fault type scenarios, and the impedance information after the experiments was collected. Based on the impedance information, the impedance amplitude spectrum and impedance phase spectrum corresponding to different fault types are obtained, and the cable fault analysis results are obtained by analyzing the impedance amplitude spectrum and impedance phase spectrum.

7. The cable fault identification method based on multiphysics field influence according to claim 1, characterized in that, The target cable model is a two-dimensional small air gap model of a 35kV three-core cable. The target cable model has at least a cable core, an inner insulation layer, and an outer insulation layer. The target cable model also has built-in correlation equations based on Maxwell's equations and the first law of thermodynamics to obtain multiple physical field parameters, which are used for comprehensive analysis of different fault type scenarios.

8. The cable fault identification method based on multiphysics field influence according to claim 2, characterized in that, When sampling under different fault type scenarios, sampling points are selected on the structural layer of the target cable model according to the fault type to be analyzed.

9. A terminal device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method as described in any one of claims 1 to 8.

10. A computer-readable storage medium storing a computer program thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1 to 8.

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