Cable status assessment method, device and non-volatile storage medium

Abstract

The present invention discloses a cable status assessment method, device, and non-volatile storage medium. The method comprises: constructing a fault arc model and a line model corresponding to a target cable; generating a simulation waveform based on the fault arc model and the line model; determining target fault parameters based on the measured waveform and the simulation waveform at the fault point in the target cable, wherein the target fault parameters represent indicator data describing the fault characteristics and status; determining the waveform characteristics of the fault point based on the target fault parameters; and determining the target cable status assessment result based on the waveform characteristics. The present invention solves the current technical problem of difficulty in sensitively capturing and accurately assessing early-stage faults in cable equipment, particularly transient fault characteristics, resulting in inaccurate cable assessments.

Description

Technical Field

[0001] The present invention relates to the field of power electronics technology, and in particular to a cable status assessment method and device, and a non-volatile storage medium. Background Art

[0002] Current distribution network fault diagnosis technologies are mostly based on steady-state or long-timescale fault waveform analysis, making it difficult to sensitively capture transient fault characteristics of less than one power frequency cycle. When dealing with random and intermittent arc faults, traditional models often ignore the impact of sudden changes in arc energy and multi-branch line coupling on the fault propagation process, resulting in insufficient simulation accuracy and fault location accuracy. Furthermore, fault identification often relies on single-point or intermittent measurements, making it difficult to fully capture the dynamic waveform characteristics of the fault, making it prone to missed detections or misjudgments. Trend analysis and predictive diagnosis of repetitive faults are also weak, and the lack of continuous tracking and quantitative assessment of equipment health makes it difficult to promptly identify potential hazards.

[0003] To address the above-mentioned problems, no effective solutions have been proposed so far. Summary of the Invention

[0004] The embodiments of the present invention provide a cable status assessment method, device and non-volatile storage medium to at least solve the current technical problem that it is difficult to keenly capture and accurately assess early faults of cable equipment, especially transient fault characteristics, resulting in inaccurate cable assessment.

[0005] According to one aspect of an embodiment of the present invention, a cable status assessment method is provided, comprising: constructing a fault arc model and a line model corresponding to a target cable; generating a simulation waveform based on the fault arc model and the line model; determining target fault parameters based on the measured waveform and the simulation waveform of the fault point in the target cable, wherein the target fault parameters represent indicator data describing the fault characteristics and status; determining the waveform characteristics of the fault point according to the target fault parameters; and determining the status assessment result of the target cable based on the waveform characteristics.

[0006] Optionally, a fault arc model corresponding to the target cable is constructed, including: constructing a stable arc model based on the performance parameters of the target cable, wherein the performance parameters include the conductivity of the target cable and the arc column maintenance energy constant; setting random factors; establishing an intermittent frequency model, wherein the intermittent frequency model is used to simulate the intermittent conductivity of the target cable; and obtaining a fault arc model based on the stable arc model, the random factors and the intermittent frequency model.

[0007] Optionally, target fault parameters are determined based on the measured waveform and simulated waveform of the fault point in the target cable, including: determining a target function based on the measured waveform and the simulated waveform; iteratively solving the fault parameters in the target function based on a preset optimization method until the target function converges to obtain target fault parameters, wherein the fault parameters include arc resistance, time constant, random factor distribution characteristics, and line distribution parameter offset.

[0008] Optionally, the waveform characteristics of the fault point are determined according to the target fault parameters, including: adjusting the fault arc model and the line model based on the target fault parameters to obtain the target fault arc model and the target line model; obtaining the target simulation waveform based on the target fault arc model and the target line model; extracting the waveform characteristics of the fault point from the target simulation waveform, wherein the waveform characteristics include the fault initial phase angle, peak current, duration and waveform slope.

[0009] Optionally, based on the waveform characteristics, the status assessment result of the target cable is determined, including: determining a waveform feature vector based on the waveform characteristics; inputting the waveform feature vector into a preset health mapping function to obtain a health index; and determining the status assessment result of the target cable based on the health index.

[0010] Optionally, based on the waveform characteristics, the status assessment result of the target cable is determined, including: obtaining the waveform characteristics corresponding to multiple faults corresponding to the target cable; calculating the health index corresponding to each of the multiple faults based on the waveform characteristics corresponding to each of the multiple faults; determining the comprehensive health index corresponding to the target cable based on the health index corresponding to each of the multiple faults; predicting the future waveform characteristics corresponding to the target cable based on a preset time series prediction model and the waveform characteristics corresponding to the multiple faults; determining the future health index corresponding to the target cable based on the future waveform characteristics; and determining the status assessment result of the target cable based on the comprehensive health index and the future health index.

[0011] According to another aspect of an embodiment of the present invention, a cable status assessment device is also provided, including: a construction module for constructing a fault arc model and a line model corresponding to a target cable; a generation module for generating a simulation waveform based on the fault arc model and the line model; a first determination module for determining target fault parameters based on the measured waveform and the simulated waveform of the fault point in the target cable, wherein the target fault parameters represent indicator data describing the fault characteristics and status; a second determination module for determining the waveform characteristics of the fault point according to the target fault parameters; and a result determination module for determining the status assessment result of the target cable based on the waveform characteristics.

[0012] According to another aspect of an embodiment of the present invention, a non-volatile storage medium is provided. The non-volatile storage medium includes a stored program, wherein when the program is running, the device where the non-volatile storage medium is located is controlled to execute any of the above-mentioned cable status assessment methods.

[0013] According to another aspect of the embodiments of the present invention, a computer device is provided. The computer device includes a processor, and the processor is used to run a program. When the program is run, any one of the above-mentioned cable status assessment methods is executed.

[0014] According to yet another aspect of an embodiment of the present invention, a computer program product is provided, including a computer program, which implements any one of the above-mentioned cable status assessment methods when executed by a processor.

[0015] In an embodiment of the present invention, a cable status assessment method is adopted, by constructing a fault arc model and a line model corresponding to the target cable; generating a simulation waveform based on the fault arc model and the line model; determining the target fault parameters based on the measured waveform and the simulation waveform of the fault point in the target cable, wherein the target fault parameters represent indicator data describing the fault characteristics and status; determining the waveform characteristics of the fault point based on the target fault parameters; and determining the status assessment result of the target cable based on the waveform characteristics, thereby achieving the purpose of jointly judging the status assessment result based on the simulation waveform and the measured waveform, thereby realizing the technical effect of improving the accuracy of cable assessment, and further solving the current technical problem that it is difficult to keenly capture and accurately assess early faults of cable equipment, especially transient fault characteristics, resulting in inaccurate cable assessment. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0017] Figure 1 A hardware structure block diagram of a computer terminal for implementing a cable status assessment method is shown;

[0018] Figure 2 is a schematic flow chart of a cable status assessment method according to an embodiment of the present invention;

[0019] Figure 3 4 is a structural block diagram of a cable status assessment device provided according to an embodiment of the present invention. DETAILED DESCRIPTION

[0020] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0021] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0022] According to an embodiment of the present invention, a method embodiment of a cable status assessment method is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0023] The method embodiment provided in the first embodiment of the present application can be executed in a mobile terminal, a computer terminal or a similar computing device. Figure 1 The hardware structure block diagram of a computer terminal for implementing a cable status assessment method is shown in FIG. Figure 1 As shown, the computer terminal 10 may include one or more processors (illustrated as 102a, 102b, ..., 102n in the figure) (the processor may include, but is not limited to, a microprocessor MCU or a programmable logic device FPGA, etc.), a memory 104 for storing data. In addition, it may also include: a display, an input / output interface (I / O interface), a universal serial bus (USB) port (which may be included as one of the ports of the BUS), a network interface, a power supply, and / or a camera. Those skilled in the art will understand that Figure 1 The structure shown is only for illustration and does not limit the structure of the above electronic device. Figure 1 More or fewer components than shown, or with Figure 1 Different configurations shown.

[0024] It should be noted that the one or more processors and / or other data processing circuits described above may generally be referred to herein as "data processing circuitry." The data processing circuitry may be embodied in whole or in part as software, hardware, firmware, or any other combination thereof. Furthermore, the data processing circuitry may be a single, independent processing module, or may be fully or partially integrated into any of the other components of the computer terminal 10. As discussed in the embodiments of the present application, the data processing circuitry functions as a processor control (e.g., the selection of a variable resistor terminal path connected to an interface).

[0025] Memory 104 can be used to store software programs and modules for application software, such as the program instructions / data storage device corresponding to the cable condition assessment method in the embodiments of the present invention. The processor executes the software programs and modules stored in memory 104 to execute various functional applications and data processing, thereby implementing the cable condition assessment method for the aforementioned application. Memory 104 can include high-speed random access memory (RAM) and non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some embodiments, memory 104 can further include memory remotely located from the processor, which can be connected to the computer terminal 10 via a network. Examples of such networks include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0026] The display may be, for example, a touch screen liquid crystal display (LCD) that enables a user to interact with a user interface of the computer terminal 10 .

[0027] Figure 2 FIG. 1 is a flow chart of a method for evaluating a cable state according to an embodiment of the present invention. Figure 2 As shown, the method includes the following steps:

[0028] Step S202: constructing a fault arc model and a line model corresponding to the target cable.

[0029] When a cable device fails, especially an arc fault, its electrical characteristics change significantly. By constructing a fault arc model, we can accurately simulate the arc's formation, development, and extinction process, including the temporal changes in key parameters such as arc resistance, current, voltage, and energy release. This helps us understand the physical mechanism of the fault and more accurately predict and analyze its impact. As a medium for power transmission, cable lines have inherent characteristics such as resistance, inductance, capacitance, and conductance, which affect the propagation and attenuation of fault signals. Constructing a line model allows us to analyze the propagation speed, path, attenuation, and phase changes of fault signals in the cable, which is crucial for locating the fault point, assessing the fault impact range, and predicting fault propagation behavior.

[0030] The appropriate arc model can be selected based on the operating environment, voltage level and fault type of the cable. The Cassie model and the Mayr model are relatively commonly used steady-state arc models. The Cassie model is suitable for low voltage and low current, while the Mayr model is more suitable for high voltage and high current. A distributed parameter model can be used to describe the cable line. Taking into account the length and structure of the cable, the resistance, inductance, capacitance and conductance parameters per unit length are set. In the cable line model, multi-branch nodes need to be considered, and node equations are introduced to reflect the distribution and mutual influence of the fault current among the branches. The parameters of the line are determined based on the actual parameters of the cable, such as conductor material, conductor cross-section, insulation type and environmental conditions. The fault arc model is placed as a current source or voltage source at a specific position in the line model, that is, the assumed fault point.

[0031] Before this, the fault waveform can be acquired. High-sampling-rate measurement devices can be installed at distribution network substations, switch stations, or branch lines. When an abnormal voltage / current change is detected, the data recording function is automatically triggered to record waveform fragments for several milliseconds (e.g., 10-20 ms) before and after the fault to ensure that the entire fault process is captured. The collected fault voltage / current waveform is bandpass filtered (e.g., 20 Hz to 5 kHz) to remove DC drift and high-frequency noise. The valid time period is intercepted based on the sampling period and fault duration, such as the waveform interval from 2 ms before the fault starts to 2 ms after the fault ends. This time period is normalized to facilitate subsequent analysis.

[0032] Then, according to the voltage level of the distribution line and historical fault experience, select the appropriate arc model (Cassie or Mayr model) initial value to initialize the fault arc model, such as:

[0033]

[0034] in, is the initial conductivity, usually given by historical fault tests or manufacturer parameters. The energy constant (or time constant) for maintaining the arc column is related to the arc radius, arc root temperature, etc. If the arc is randomly extinguished / reignited, a random factor is set. The initial distribution or triggering conditions (such as arc extinction when the critical arc current is less than a certain threshold).

[0035] In PSCAD / EMTDC or other electromagnetic transient simulation platforms, the distributed parameter line module is used to model the fault line. The fault parameters of each line are set according to the line length, conductor type, branch topology and branch load. Parameters, an arc model is injected at the fault point, and the waveform of the fault voltage / current evolving over time is simulated.

[0036] Specifically, in multi-branch distribution lines, the spatial distribution characteristics of voltage and current cannot be ignored. A distributed parameter model can be used to describe the voltage and current propagation equations of a single-phase line:

[0037]

[0038] in, Represent the resistance, inductance, capacitance and conductance per unit length of the line, Indicates location The voltage at Indicates location The current at change. represents the spatial gradient of voltage on the line, is the rate of change of voltage with time, represents the spatial gradient of the current on the line, is the rate of change of current with time.

[0039] For a distribution network line with several branches, it is necessary to introduce a node equation at each branch node:

[0040]

[0041] Among them, there are m branch lines, node Indicates the The current of the branch line at the node, node This is the fault current or the current input from the main line to this node. When a fault disturbance occurs at a branch point or somewhere in the main line, a coupling effect will occur between the branches, causing the fault voltage and current to overlap and attenuate between the branch lines.

[0042] Through the above steps, the fault arc model and line model of the target cable constructed can more accurately reflect the electrical characteristics of the cable under fault conditions, providing a scientific basis for health assessment, fault diagnosis and maintenance strategies of cable equipment.

[0043] Step S204: Generate a simulation waveform based on the fault arc model and the line model.

[0044] In this step, the parameters of the fault arc model and line model are set based on the physical characteristics of the target cable and historical fault data. This includes the arc resistance, time constant, and random factor distribution characteristics of the arc model, as well as the resistance, inductance, capacitance, and conductance per unit length parameters of the line model. A simulation environment is set up in the simulation software. This typically involves creating a circuit model, adding the fault arc model as a current source or voltage source to the cable line model, setting the power source, load, and other circuit components, and setting the simulation time window and sampling frequency. In the model, a fault event is manually triggered or simulated, such as introducing an arc at a specific point in the cable line. Different fault conditions can be set, such as the arc's initial phase angle, peak current, and duration, to observe the effects of different parameters on the waveform. The model simulation is run to observe the propagation of the fault signal along the cable line. The simulation takes into account the distributed parameter characteristics of the line, as well as the attenuation, reflection, and refraction of the signal during propagation, to generate simulated waveforms of voltage and current varying over time.

[0045] Step S206 , determining target fault parameters based on the measured waveform and the simulated waveform of the fault point in the target cable, wherein the target fault parameters represent indicator data describing the fault characteristics and status.

[0046] In this step, the measured waveform can be preprocessed, including denoising, filtering, and data normalization, to reduce measurement errors and interference from irrelevant signals, ensuring the accuracy and comparability of the waveform data. An objective function is then defined to measure the difference between the simulated and measured waveforms. This objective function typically includes the errors in the voltage and current waveforms and can be a weighted average to ensure that the two waveforms match each other in key features. An optimization algorithm (such as a particle swarm optimization algorithm, a genetic algorithm, or a least squares method) is used to search for and optimize the fault parameters of the target cable. The optimization goal is to find a set of parameters that minimizes the objective function, ensuring that the simulated and measured waveforms are as consistent as possible. During the optimization process, the simulated and measured waveforms are compared for features such as initial phase angle, peak value, duration, and harmonic content to ensure that these key features are accurately reflected in the simulated waveform. The optimal parameters found are the target fault parameters. Based on this optimization process, the indicators that best describe the fault characteristics and status are extracted, including but not limited to the arc resistance, the time constants for arc extinction and reignition, the probability distribution of random extinction / reignition, and the distribution parameters of the line. These parameters constitute the target fault parameters, which are used for subsequent fault location, property judgment and health status assessment.

[0047] Through the above process, the determined target fault parameters can more accurately reflect the characteristics of actual cable faults, provide more scientific and reliable data support for the health assessment of cable equipment, and thus help improve the safety and reliability of the power system.

[0048] Step S208: determining the waveform characteristics of the fault point according to the target fault parameters.

[0049] In this step, the determined target fault parameters can be input into the previously constructed arc fault model and line model to update the model parameters and ensure that the model more accurately reflects the actual electrical characteristics of the specific fault point. Using the updated model, the simulation software is rerun to simulate the dynamic changes in voltage and current in the cable under fault conditions. The simulated waveform generated in this step will more closely resemble the waveform characteristics under actual fault conditions. Key waveform features can be extracted from the simulated waveform. These features may include: initial phase angle, the phase information at the onset of the fault, which is crucial for fault location and identification; amplitude, the maximum value of the fault current or voltage, which reflects the magnitude of the fault energy; duration, the length of time from the start to the end of the arc fault, which helps assess the severity and stability of the fault; slope, the speed at which the waveform rises or falls, which reflects the suddenness of the fault; harmonic components, a frequency domain feature that analyzes the high-frequency fluctuations caused by the fault and helps identify the fault type; impedance mutation, an impedance domain feature that assists in fault location by measuring the change in impedance at the fault point. The extracted waveform features can also be quantified, and statistical methods (such as mean value, standard deviation) or signal processing techniques (such as wavelet transform, short-time Fourier transform) may be needed to further analyze and process these features to ensure that they can accurately represent the electrical status of the fault point.

[0050] In summary, by applying target fault parameters to the simulation model, we can accurately simulate and extract the waveform characteristics of the fault point, thereby assessing the health of the cable equipment and providing a scientific basis for fault location, fault nature assessment, and predictive maintenance. This process not only improves the accuracy and response speed of fault diagnosis, but also provides strong support for the safe operation of the power system and equipment management.

[0051] Step S210: determining a status assessment result of the target cable based on the waveform characteristics.

[0052] In this step, determining the target cable's health assessment result based on waveform features converts waveform features extracted from the arc fault model and line model into a quantitative assessment of the cable equipment's health. The extracted waveform features can be normalized to ensure comparability among features of different magnitudes during the assessment. For example, the Z-score normalization method can be used to convert features such as initial phase angle, peak current, and duration to the same magnitude. The normalized waveform features are combined into a feature vector, and a health function is then defined to map the feature vector to a numerical value representing the equipment's health status. The health function can be a linear weighted function, an exponential function, or a more complex machine learning model (such as a neural network). A series of health thresholds are then defined to map the calculated health function to specific status levels, such as normal, caution, severe, and critical. The thresholds are set based on historical fault data and equipment performance indicators. The calculated health function result is compared with the classification threshold to determine the target cable's current health level. For example, if the value is above the normal threshold, the cable is assessed as normal; if it is below the severe threshold, the cable may be in severe or critical status. For multiple failure events, you can also collect and analyze the feature vectors and health index of all events, and use trend analysis to determine the development trend of equipment health. You can build time series models (such as ARIMA or LSTM) to predict future health, or calculate the slope of health changes over time to assess the rate of change of equipment status.

[0053] Through the above steps, cable status assessment based on waveform characteristics can provide real-time, quantitative information on equipment health status, providing scientific decision-making support for power system operation and maintenance personnel, helping them to promptly identify potential equipment problems and take effective measures to prevent failures, thereby ensuring the safe and stable operation of the power system.

[0054] Through the above steps, the purpose of jointly judging the status assessment results based on the simulated waveform and the measured waveform is achieved, thereby achieving the technical effect of improving the accuracy of cable assessment, and further solving the current technical problem of difficulty in sensitively capturing and accurately assessing early faults of cable equipment, especially transient fault characteristics, resulting in inaccurate cable assessment.

[0055] As an optional embodiment, a fault arc model corresponding to the target cable is constructed, including: constructing a stable arc model based on the performance parameters of the target cable, wherein the performance parameters include the conductivity of the target cable and the arc column maintenance energy constant; setting a random factor; establishing an intermittent frequency model, wherein the intermittent frequency model is used to simulate the intermittent conductivity of the target cable; and obtaining a fault arc model based on the stable arc model, the random factor and the intermittent frequency model.

[0056] Alternatively, a stable arc model can be constructed first, and then an appropriate arc model can be selected based on the voltage level and fault characteristics of the target cable. The Cassie model and the Mayr model are two common choices, each suitable for describing different arc characteristics. The construction of a stable arc model needs to be based on the performance parameters of the target cable, especially the conductivity (the inverse of the arc resistance) and the arc column maintenance energy constant. These parameters can be obtained from historical fault data, equipment specifications, or laboratory tests. For the Cassie model, the arc conductivity satisfies specific differential equations and is related to the arc current, time constant, and the threshold conductivity required to maintain the arc. These equations describe the behavior of the arc in steady state.

[0057] For example, the present invention adopts the classic Cassie or Mayr model to describe the steady-state arc. Under the Cassie model, the arc conductivity Satisfies the following differential equation:

[0058]

[0059] in, Indicates arc conductivity ( is the arc resistance), is the arc current, is the time constant, To maintain the threshold conduction of the arc.

[0060] In steady-state conditions (i.e. ), the arc resistance can be obtained and arc current The approximate functional relationship of is used to evaluate the steady-state characteristics of the arc. The steady-state characteristics of typical distribution network equipment under steady-state arc faults are obtained through experiments or simulations. 、 The value range of key parameters such as .

[0061] The value ranges of key parameters are as follows:

[0062] Initial conductivity :Given by historical fault tests or manufacturer parameters, usually between.

[0063] Arc column maintains energy constant (or time constant) : It is related to arc radius, arc root temperature, etc., and the range can be .

[0064] Maintaining continuity threshold : Reflects the arc maintenance conditions, the range depends on the distribution network voltage level, generally .

[0065] By calibrating the above parameters in a steady-state arc, the foundation can be laid for the subsequent simulation and analysis of transient and intermittent fault characteristics.

[0066] In actual operation, arc faults may exhibit intermittent conduction and extinction. The introduction of random factors can modulate the output of the arc model. The random factor can take the value of 0 or 1, or a continuous random process between 0 and 1, which is used to simulate the random extinction and reignition of the arc. The modulated arc fault model output can be defined as:

[0067]

[0068] in, represents the arc current calculated by the steady-state arc model, It is 0 or 1 (or a random process between 0 and 1), which is used to reflect the random extinction / reignition of the fault arc.

[0069] Based on historical fault data, the intermittent frequency of arc faults is analyzed to identify the probability distribution of arc conduction and extinction in different time intervals. Then, statistical methods (such as Poisson process fitting) or machine learning techniques (such as Gaussian process regression) are used to establish an intermittent frequency model to reflect the randomness and repeatability of arc faults on a time scale. In order to simulate the coexistence of continuous and intermittent fault processes, Considered as a random signal with a certain probability distribution, for example:

[0070]

[0071] in, The actual statistical characteristics of faults (such as Poisson process or Gaussian process) can be combined for fitting to reflect the changes in the probability of fault occurrence or recovery in different time periods.

[0072] The stable arc model, random factor modulation, and intermittent frequency model are combined to form a complete fault arc model. This step requires considering the interaction between the arc model and the intermittent conduction characteristics to ensure that the model can fully reflect the cable fault scenario.

[0073] Building a complete arc fault model is crucial for accurately assessing the health of cable equipment under fault conditions. This model not only captures steady-state arc behavior but also reflects transient and random arc characteristics, thereby improving the accuracy of fault diagnosis and prediction, and providing a scientific basis for the maintenance and safe operation of power systems.

[0074] As an optional embodiment, target fault parameters are determined based on the measured waveform and simulated waveform of the fault point in the target cable, including: determining the objective function based on the measured waveform and the simulated waveform; iteratively solving the fault parameters in the objective function based on a preset optimization method until the objective function converges to obtain the target fault parameters, wherein the fault parameters include arc resistance, time constant, random factor distribution characteristics, and line distribution parameter offset.

[0075] Optionally, determining the target fault parameters based on the measured and simulated waveforms at the target cable fault point requires precise objective function definition and the application of an efficient optimization algorithm. An objective function can be defined to measure the difference or degree of fit between the simulated and measured waveforms. An appropriate optimization method is then selected to iteratively solve for the fault parameters in the objective function. Common optimization methods include genetic algorithms, particle swarm optimization, and least squares methods. The parameter vector is then initialized, and the simulation model is run to obtain the simulated waveform. The objective function is then calculated and updated according to the rules of the selected optimization method. The simulation and objective function calculation are repeated until convergence conditions are met (e.g., the objective function value changes by less than a preset threshold, or the number of iterations reaches an upper limit).

[0076] When the objective function converges, the current parameter vector is the target fault parameter.

[0077] For example, suppose the waveform data measured at the station is , The waveform obtained by simulation is , ,in Represents the parameter vector to be estimated of the fault model (including arc model and line parameters), that is, the fault parameters.

[0078] The objective function is constructed as follows:

[0079]

[0080] in, and is a weighting factor used to balance the importance of voltage and current matching.

[0081] Use global optimization methods such as least squares method, particle swarm optimization or genetic algorithm to search for smallest , get the estimated fault parameters , including arc resistance , time constant , random factor distribution characteristics (such as arc extinction frequency), line distribution parameter offset, etc. The waveform restoration near the fault point is determined After that, the voltage and current waveforms near the fault point or fault node can be calculated in the simulation environment, thereby obtaining more accurate time domain characteristics of the fault disturbance.

[0082] As an optional embodiment, the waveform characteristics of the fault point are determined according to the target fault parameters, including: adjusting the fault arc model and the line model based on the target fault parameters to obtain the target fault arc model and the target line model; obtaining the target simulation waveform based on the target fault arc model and the target line model; extracting the waveform characteristics of the fault point from the target simulation waveform, wherein the waveform characteristics include the fault initial phase angle, peak current, duration and waveform slope.

[0083] Optionally, the parameters of the fault arc model and line model are adjusted based on the target fault parameters determined above (including arc resistance, time constant, random factor distribution characteristics, and line distribution parameter offsets). This involves updating the arc resistance (arc resistance) and arc current time constant in the arc model, as well as adjusting the resistance, inductance, capacitance, and conductance parameters in the line model to ensure that these models more accurately reflect the electrical behavior of the target cable under fault conditions. Using the adjusted parameters, the fault arc model is reconstructed to reflect the actual characteristics of the arc in the target cable. By introducing random factors and an intermittent frequency model, the random extinction / reignition characteristics of the arc, as well as the dynamic changes of the arc within less than one power frequency cycle, can be more realistically simulated. Based on the optimized line distribution parameters, the distributed line model is refined to ensure that the model accounts for the voltage and current decay characteristics of multi-branch line coupling and fault propagation. This step is crucial for accurately assessing the impact of faults in complex distribution network environments.

[0084] Based on the adjusted arc fault model and line model, simulation software is used to generate the target simulation waveform. This involves applying the arc fault model at the fault point and calculating the propagation and changes of voltage and current in the cable network using the line model. Key waveform features are then extracted from the target simulation waveform, including multi-dimensional features in the time, frequency, time-frequency, and impedance domains, such as fault peak value, duration, harmonic content, and impedance mutation rate.

[0085] Specifically, the extracted waveform features may include time domain features. For short-term dynamic fault disturbance waveforms (less than one power frequency cycle), the following features are particularly important:

[0086] Initial phase angle : Refers to the phase angle of the fault current / voltage relative to the power frequency reference (such as 0°) at the moment the fault occurs.

[0087] Peak amplitude : The maximum amplitude of the fault current / voltage during this short-term disturbance

[0088] Fault duration : The time interval from the start of the fault disturbance to the extinction of the arc or significant attenuation.

[0089] Fault slope : The slope of the waveform rising or falling in a short period of time, used to measure the sudden change of fault current / voltage.

[0090] In repetitive fault scenarios, you can also extract statistics for multiple fault events:

[0091]

[0092] in, For the Second fault peak, is the standard deviation of the disturbance, is the total number of faults; similarly, The other characteristics were also statistically analyzed.

[0093] If you need to capture high-frequency or transient components, you can perform short-time Fourier transform (STFT) or wavelet transform on the waveform to obtain the typical high-frequency energy bandwidth. and characteristic frequency components, etc., to quantify the random discharge characteristics of the fault arc.

[0094] As an optional embodiment, the status assessment result of the target cable is determined based on the waveform characteristics, including: determining a waveform feature vector based on the waveform characteristics; inputting the waveform feature vector into a preset health mapping function to obtain a health index; and determining the status assessment result of the target cable based on the health index.

[0095] Optionally, the waveform features can be converted into vectors , in order to facilitate the subsequent calculation of the health function. An exponential or linear function can be used to convert the characteristic deviation into a health index. The specific formula is as follows:

[0096]

[0097] like If the value is higher than the preset threshold, it indicates that the device is temporarily in a relatively safe state; otherwise, it indicates that there is a greater risk of failure. The equipment status is divided into four levels according to the numerical range. If the assessment level is caution or critical, the operation and maintenance personnel are advised to carry out inspection and investigation. If it is caution, monitoring can be strengthened; if it is normal, maintenance can be carried out according to the normal cycle.

[0098] Specifically, the health level threshold is defined to map the health level H to the range of 0-1, corresponding to the following levels:

[0099] 0.8≤H≤1.0 (Normal): The overall condition of the equipment is normal, with no obvious signs of failure, and the regular maintenance plan can be followed.

[0100] 0.6≤H<0.8 (Note): Early, minor fault characteristics are detected, but they are not yet sufficient to pose a significant risk. The inspection cycle can be appropriately shortened or monitoring can be strengthened.

[0101] 0.4≤H<0.6 (Severe): The deviation of multiple fault characteristics is large, and the equipment has obvious hidden dangers. Maintenance or component replacement is required.

[0102] 0≤H<0.4 (Critical): The equipment health has significantly deteriorated and is showing an accelerating deterioration trend. It is recommended to immediately shut down the equipment or perform emergency maintenance.

[0103] As an optional embodiment, based on the waveform characteristics, the status assessment result of the target cable is determined, including: obtaining the waveform characteristics corresponding to multiple faults corresponding to the target cable; calculating the health index corresponding to each of the multiple faults based on the waveform characteristics corresponding to each of the multiple faults; determining the comprehensive health index corresponding to the target cable based on the health index corresponding to each of the multiple faults; predicting the future waveform characteristics corresponding to the target cable based on a preset time series prediction model and the waveform characteristics corresponding to the multiple faults; determining the future health index corresponding to the target cable based on the future waveform characteristics; and determining the status assessment result of the target cable based on the comprehensive health index and the future health index.

[0104] Optionally, when the same cable equipment experiences multiple fault disturbances within a certain time range, the health status of the equipment can be dynamically tracked and trend predicted by performing statistics and time series prediction on the characteristic quantities of multiple faults.

[0105] During the operation of the cable equipment, the measuring device is kept online continuously, and once a fault waveform is detected, it is automatically recorded; the measured waveform of each fault and the extracted characteristic quantities (such as The data is stored in a historical database with metadata such as timestamps and operating condition information. The waveforms collected for each fault are normalized, aligned, and interpolated to avoid analysis errors caused by sampling rate or data loss. An index of multiple fault events is established to facilitate subsequent retrieval and comparison.

[0106] For each fault Calculate health separately , and store them in association with the timestamp. If the waveform quality of some fault events is poor, a signal quality threshold can be set to eliminate or mark them.

[0107] Close The average or weighted average of the failures (such as the last 5 or 10) is used to define the overall health

[0108]

[0109] Then observe If the evolution trend over time shows a continuous decline with a large amplitude, it is possible that the insulation or joints of the equipment are gradually deteriorating. , you can choose a suitable time series forecasting model, such as ARIMA, LSTM, GRU or other statistical / machine learning methods; Each component (such as peak amplitude, duration, harmonic content, etc.) is separately modeled as a subsequence, or a multivariate regression model is used to jointly predict them. Predict the possible values ​​of the next or multiple fault characteristic quantities in the future. By comparing with the reference value Compare and calculate the predicted fault health, that is, the future health index If the prediction results show that the key feature quantity (such as 、 ) continues to rise, it indicates that the risk of subsequent failures is increasing, and the health of the corresponding equipment may further decline in the historical health level. On this basis, the predicted health of the next fault is superimposed , define the weighted comprehensive index:

[0110]

[0111] in is the weight coefficient, which reflects the emphasis on historical data and future forecasts.

[0112] according to The value and rate of change of the equipment health status (normal, caution, serious, and critical) are dynamically updated, and a trend chart is generated on the operation and maintenance platform. If a sharp decline in health status is detected in a short period of time, an alarm can be immediately triggered and maintenance can be prioritized. Cable equipment in a caution state can be monitored closely, while cable equipment in a serious state should be planned for maintenance / replacement. Cables in a critical state must be quickly shut down and urgently inspected. In actual applications, if repetitive arc faults occur (with increasing frequency), they often indicate damaged cable insulation, loose joints, or severe partial discharges. The faulty equipment should be isolated or tested on-site.

[0113] It should be noted that for the aforementioned method embodiments, for simplicity of description, they are all expressed as a series of action combinations. However, those skilled in the art should be aware that the present invention is not limited by the order of the actions described, because according to the present invention, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in this specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present invention.

[0114] Through the description of the above embodiments, those skilled in the art will clearly understand that the cable condition assessment method according to the above embodiments can be implemented by software plus a necessary general-purpose hardware platform. Of course, it can also be implemented by hardware, but in many cases the former is a more preferred embodiment. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, a magnetic disk, or an optical disk) and includes a number of instructions for enabling a terminal device (which can be a mobile phone, a computer, a server, or a network device, etc.) to execute the methods described in various embodiments of the present invention.

[0115] According to an embodiment of the present invention, a cable status assessment device for implementing the above cable status assessment method is also provided. Figure 3 : is a structural block diagram of a cable status assessment device provided according to an embodiment of the present invention. Figure 3 As shown, the cable status assessment device includes: a construction module 302, a generation module 304, a first determination module 306, a second determination module 308 and a result determination module 310. The cable status assessment device is described below.

[0116] The construction module 302 is used to construct a fault arc model and a line model corresponding to the target cable.

[0117] The generating module 304 is connected to the constructing module 302 and is used to generate a simulation waveform based on the fault arc model and the line model.

[0118] The first determination module 306 is connected to the generation module 304 and is used to determine target fault parameters based on the measured waveform and the simulated waveform of the fault point in the target cable, wherein the target fault parameters represent indicator data describing the fault characteristics and status.

[0119] The second determining module 308 is connected to the first determining module 306 and is configured to determine the waveform characteristics of the fault point according to the target fault parameters.

[0120] The result determination module 310 is connected to the second determination module 308 and is used to determine the status assessment result of the target cable based on the waveform characteristics.

[0121] It should be noted that the construction module 302, generation module 304, first determination module 306, second determination module 308, and result determination module 310 described above correspond to steps S202 to S210 in the embodiment. The examples and application scenarios implemented by these modules and corresponding steps are the same, but are not limited to the contents disclosed in the above embodiment. It should be noted that the above modules, as part of the device, can be run in the computer terminal 10 provided in the embodiment.

[0122] An embodiment of the present invention may provide a computer device. Optionally, in this embodiment, the computer device may be located in at least one of a plurality of network devices in a computer network. The computer device includes a memory and a processor.

[0123] The memory can be used to store software programs and modules, such as the program instructions / modules corresponding to the cable condition assessment method and apparatus in the embodiments of the present invention. The processor executes the software programs and modules stored in the memory to perform various functional applications and data processing, thereby implementing the aforementioned cable condition assessment method. The memory can include high-speed random access memory (RAM) and can also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some embodiments, the memory can further include memory remotely located from the processor, and such remote memory can be connected to the computer terminal via a network. Examples of such networks include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0124] The processor can call the information and application programs stored in the memory through the transmission device to perform the following steps: construct a fault arc model and a line model corresponding to the target cable; generate a simulation waveform based on the fault arc model and the line model; determine the target fault parameters based on the measured waveform and the simulated waveform of the fault point in the target cable, wherein the target fault parameters represent the indicator data describing the fault characteristics and status; determine the waveform characteristics of the fault point according to the target fault parameters; and determine the status assessment result of the target cable based on the waveform characteristics.

[0125] Optionally, the processor may also execute the program code of the following steps: constructing a fault arc model corresponding to the target cable, including: constructing a stable arc model based on the performance parameters of the target cable, wherein the performance parameters include the conductivity of the target cable and the arc column maintenance energy constant; setting a random factor; establishing an intermittent frequency model, wherein the intermittent frequency model is used to simulate the intermittent conductivity of the target cable; and obtaining a fault arc model based on the stable arc model, the random factor and the intermittent frequency model.

[0126] Optionally, the processor may also execute the program code of the following steps: determining the target fault parameters based on the measured waveform and the simulated waveform of the fault point in the target cable, including: determining the objective function based on the measured waveform and the simulated waveform; iteratively solving the fault parameters in the objective function based on a preset optimization method until the objective function converges to obtain the target fault parameters, wherein the fault parameters include arc resistance, time constant, random factor distribution characteristics, and line distribution parameter offset.

[0127] Optionally, the processor may also execute the program code of the following steps: determining the waveform characteristics of the fault point according to the target fault parameters, including: adjusting the fault arc model and the line model based on the target fault parameters to obtain the target fault arc model and the target line model; obtaining the target simulation waveform based on the target fault arc model and the target line model; extracting the waveform characteristics of the fault point from the target simulation waveform, wherein the waveform characteristics include the fault initial phase angle, peak current, duration and waveform slope.

[0128] Optionally, the processor may also execute the program code of the following steps: determining the status assessment result of the target cable based on the waveform characteristics, including: determining the waveform feature vector according to the waveform characteristics; inputting the waveform feature vector into a preset health mapping function to obtain a health index; and determining the status assessment result of the target cable based on the health index.

[0129] Optionally, the processor may also execute the program code of the following steps: determining the status assessment result of the target cable based on the waveform characteristics, including: obtaining the waveform characteristics corresponding to each of the multiple faults corresponding to the target cable; calculating the health index corresponding to each of the multiple faults based on the waveform characteristics corresponding to each of the multiple faults; determining the comprehensive health index corresponding to the target cable based on the health index corresponding to each of the multiple faults; predicting the future waveform characteristics corresponding to the target cable based on a preset time series prediction model and the waveform characteristics corresponding to each of the multiple faults; determining the future health index corresponding to the target cable based on the future waveform characteristics; and determining the status assessment result of the target cable based on the comprehensive health index and the future health index.

[0130] An embodiment of the present invention provides a method for evaluating the state of a cable, by constructing a fault arc model and a line model corresponding to a target cable; generating a simulation waveform based on the fault arc model and the line model; determining target fault parameters based on the measured waveform and the simulation waveform of the fault point in the target cable, wherein the target fault parameters represent indicator data describing the fault characteristics and state; determining the waveform characteristics of the fault point according to the target fault parameters; and determining the state evaluation result of the target cable based on the waveform characteristics, thereby achieving the purpose of jointly judging the state evaluation result based on the simulation waveform and the measured waveform, thereby realizing the technical effect of improving the accuracy of cable evaluation, and further solving the current technical problem that it is difficult to keenly capture and accurately evaluate early faults of cable equipment, especially transient fault characteristics, resulting in inaccurate cable evaluation.

[0131] A person skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing the hardware related to the terminal device through a program, and the program can be stored in a non-volatile storage medium, which may include: a flash drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0132] The embodiment of the present invention further provides a non-volatile storage medium. Optionally, in this embodiment, the non-volatile storage medium can be used to store the program code executed by the cable status assessment method provided in the above embodiment.

[0133] Optionally, in this embodiment, the non-volatile storage medium may be located in any computer terminal in a computer terminal group in a computer network, or in any mobile terminal in a mobile terminal group.

[0134] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for executing the following steps: constructing a fault arc model and a line model corresponding to the target cable; generating a simulation waveform based on the fault arc model and the line model; determining target fault parameters based on the measured waveform and the simulated waveform of the fault point in the target cable, wherein the target fault parameters represent indicator data describing the fault characteristics and status; determining the waveform characteristics of the fault point based on the target fault parameters; and determining the status assessment result of the target cable based on the waveform characteristics.

[0135] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: constructing a fault arc model corresponding to the target cable, including: constructing a stable arc model based on the performance parameters of the target cable, wherein the performance parameters include the conductivity of the target cable and the arc column maintenance energy constant; setting a random factor; establishing an intermittent frequency model, wherein the intermittent frequency model is used to simulate the intermittent conductivity of the target cable; and obtaining a fault arc model based on the stable arc model, the random factor and the intermittent frequency model.

[0136] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for executing the following steps: determining target fault parameters based on the measured waveform and the simulated waveform of the fault point in the target cable, including: determining the objective function based on the measured waveform and the simulated waveform; iteratively solving the fault parameters in the objective function based on a preset optimization method until the objective function converges to obtain the target fault parameters, wherein the fault parameters include arc resistance, time constant, random factor distribution characteristics, and line distribution parameter offset.

[0137] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: determining the waveform characteristics of the fault point based on the target fault parameters, including: adjusting the fault arc model and the line model based on the target fault parameters to obtain the target fault arc model and the target line model; obtaining the target simulation waveform based on the target fault arc model and the target line model; extracting the waveform characteristics of the fault point from the target simulation waveform, wherein the waveform characteristics include the fault initial phase angle, peak current, duration and waveform slope.

[0138] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: determining a status assessment result of the target cable based on the waveform characteristics, including: determining a waveform feature vector based on the waveform characteristics; inputting the waveform feature vector into a preset health mapping function to obtain a health index; and determining a status assessment result of the target cable based on the health index.

[0139] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for executing the following steps: determining a status assessment result of the target cable based on waveform characteristics, including: obtaining waveform characteristics corresponding to multiple faults corresponding to the target cable; calculating a health index corresponding to each of the multiple faults based on the waveform characteristics corresponding to each of the multiple faults; determining a comprehensive health index corresponding to the target cable based on the health index corresponding to each of the multiple faults; predicting future waveform characteristics corresponding to the target cable based on a preset time series prediction model and the waveform characteristics corresponding to each of the multiple faults; determining a future health index corresponding to the target cable based on the future waveform characteristics; and determining a status assessment result of the target cable based on the comprehensive health index and the future health index.

[0140] An embodiment of the present invention also provides a computer program product, including a computer program. Optionally, in this embodiment, when the computer program is executed by a processor, it can achieve: constructing a fault arc model and a line model corresponding to the target cable; generating a simulation waveform based on the fault arc model and the line model; determining the target fault parameters based on the measured waveform and the simulated waveform of the fault point in the target cable, wherein the target fault parameters represent indicator data describing the fault characteristics and status; determining the waveform characteristics of the fault point according to the target fault parameters; and determining the status assessment result of the target cable based on the waveform characteristics.

[0141] The serial numbers of the above embodiments of the present invention are for description only and do not represent the advantages or disadvantages of the embodiments.

[0142] In the above embodiments of the present invention, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0143] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only exemplary. For example, the division of the units can be a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0144] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0145] In addition, the functional units in the various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0146] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a non-volatile storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes various media that can store program code, such as a USB flash drive, read-only memory (ROM), random access memory (RAM), a mobile hard drive, a magnetic disk, or an optical disk.

[0147] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as within the scope of protection of the present invention.

Claims

1. A method for evaluating the condition of a cable, characterized in that: include: Construct the fault arc model and line model corresponding to the target cable; generating a simulation waveform based on the arc fault model and the line model; Determining target fault parameters based on the measured waveform of the fault point in the target cable and the simulated waveform, wherein the target fault parameters represent indicator data describing the fault characteristics and status; determining waveform characteristics of the fault point according to the target fault parameters; Determining a status assessment result of the target cable based on the waveform characteristics; Among them, the construction of the fault arc model corresponding to the target cable includes: constructing a stable arc model based on the performance parameters of the target cable, wherein the performance parameters include the conductivity of the target cable and the arc column maintenance energy constant; setting random factors; establishing an intermittent frequency model, wherein the intermittent frequency model is used to simulate the intermittent conductivity of the target cable; and obtaining the fault arc model based on the stable arc model, the random factors and the intermittent frequency model.

2. The method according to claim 1, characterized in that The determining of target fault parameters based on the measured waveform of the fault point in the target cable and the simulated waveform includes: Determining an objective function based on the measured waveform and the simulated waveform; Based on a preset optimization method, the fault parameters in the objective function are iteratively solved until the objective function converges to obtain the target fault parameters, wherein the fault parameters include arc resistance, time constant, random factor distribution characteristics, and line distribution parameter offset.

3. The method according to claim 1, characterized in that The determining of the waveform characteristics of the fault point according to the target fault parameter includes: Based on the target fault parameters, adjusting the fault arc model and the line model to obtain a target fault arc model and a target line model; Obtaining a target simulation waveform based on the target fault arc model and the target line model; The waveform characteristics of the fault point are extracted from the target simulation waveform, wherein the waveform characteristics include the fault initial phase angle, peak current, duration and waveform slope.

4. The method according to claim 1, wherein Determining a status assessment result of the target cable based on the waveform characteristics includes: Determining a waveform feature vector according to the waveform feature; Inputting the waveform feature vector into a preset health mapping function to obtain a health index; Based on the health index, a status assessment result of the target cable is determined.

5. The method according to claim 1, wherein Determining a status assessment result of the target cable based on the waveform characteristics includes: Obtain waveform characteristics corresponding to multiple faults corresponding to the target cable; Calculating the health index corresponding to each of the multiple faults based on the waveform characteristics corresponding to each of the multiple faults; Determining a comprehensive health index corresponding to the target cable based on the health indexes corresponding to the multiple faults; Predicting future waveform characteristics corresponding to the target cable based on a preset time series prediction model and waveform characteristics corresponding to each of the multiple faults; Determining a future health index corresponding to the target cable based on the future waveform characteristics; A status assessment result of the target cable is determined based on the comprehensive health index and the future health index.

6. A cable status assessment device, characterized in that: include: A construction module, used to construct a fault arc model and a line model corresponding to the target cable; A generating module, configured to generate a simulation waveform based on the fault arc model and the line model; A first determining module is configured to determine target fault parameters based on the measured waveform of the fault point in the target cable and the simulated waveform, wherein the target fault parameters represent indicator data describing the fault characteristics and status; A second determining module is used to determine the waveform characteristics of the fault point according to the target fault parameter; A result determination module, configured to determine a status assessment result of the target cable based on the waveform characteristics; The construction module is further used to construct a stable arc model based on the performance parameters of the target cable, wherein the performance parameters include the conductivity of the target cable and the arc column maintenance energy constant; set random factors; establish an intermittent frequency model, wherein the intermittent frequency model is used to simulate the intermittent conductivity of the target cable; and obtain the fault arc model based on the stable arc model, the random factors and the intermittent frequency model.

7. A non-volatile storage medium, characterized in that: The non-volatile storage medium includes a stored program, wherein when the program is executed, the device where the non-volatile storage medium is located is controlled to execute the cable status assessment method according to any one of claims 1 to 5.

8. A computer device, characterized in that: include: memory and processor, The memory stores a computer program; The processor is configured to execute a computer program stored in the memory, and when the computer program is run, the processor is enabled to execute the cable condition assessment method according to any one of claims 1 to 5.

9. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the cable condition assessment method according to any one of claims 1 to 5 is implemented.

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