Cable state evaluation method and device and nonvolatile storage medium

Abstract

The invention discloses a cable state evaluation method and device and a nonvolatile storage medium. The method comprises the following steps: 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; target fault parameters are determined based on the actual measurement waveform and the simulation waveform of the fault point in the target cable, and the target fault parameters represent index data describing fault characteristics and states; determining waveform characteristics of the fault point according to the target fault parameter; and determining a state evaluation result of the target cable based on the waveform characteristics. The technical problem that cable assessment is not accurate enough due to the fact that early faults, especially transient fault features, of cable equipment are difficult to sensitively capture and accurately assess at present is solved.

Description

Technical Field

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

[0002] At present, most of the distribution network fault diagnosis technologies are based on the analysis of steady-state or long-time-scale fault waveforms, and it is difficult to sensitively capture the transient fault characteristics within less than one power frequency cycle; when dealing with random and intermittent arc faults, traditional models often ignore the sudden change of arc energy and the influence of multi-branch line coupling on the fault propagation process, resulting in insufficient simulation accuracy and fault location accuracy. At the same time, fault identification mostly relies on single-point or intermittent measurement, making it difficult to comprehensively obtain the dynamic waveform characteristics during the occurrence of faults, and it is easy to have missed detections or misjudgments. The trend analysis and predictive diagnosis of repetitive faults are also relatively weak, lacking continuous tracking and quantitative evaluation of the health state of equipment, and it is difficult to detect potential hidden dangers in a timely manner.

[0003] In view of the above problems, no effective solution has been proposed yet. Summary of the Invention

[0004] Embodiments of the present invention provide a method, device, and non-volatile storage medium for evaluating the state of a cable, so as to at least solve the technical problem that it is currently difficult to sensitively capture and accurately evaluate the early faults of cable equipment, especially transient fault characteristics, resulting in inaccurate evaluation of the cable.

[0005] According to one aspect of the embodiments of the present invention, a method for evaluating the state of a cable is provided, including: 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 a target fault parameter based on the measured waveform and the simulation waveform of the fault point in the target cable, where the target fault parameter represents index data describing the fault characteristics and state; determining the waveform characteristics of the fault point according to the target fault parameter; and determining the state evaluation result of the target cable based on the waveform characteristics.

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

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

[0008] Optionally, based on the target fault parameters, determine the waveform characteristics of the fault point, including: based on the target fault parameters, adjust the fault arc model and the line model to obtain the target fault arc model and the target line model; based on the target fault arc model and the target line model, obtain the target simulated waveform; extract the waveform characteristics of the fault point from the target simulated waveform, where the waveform characteristics include fault initial phase angle, peak current, duration, and waveform slope.

[0009] Optionally, based on the waveform characteristics, determine the state evaluation result of the target cable, including: according to the waveform characteristics, determine the waveform feature vector; input the waveform feature vector into a preset health mapping function to obtain the health index; based on the health index, determine the state evaluation result of the target cable.

[0010] Optionally, based on the waveform characteristics, determine the state evaluation result of the target cable, including: obtain the waveform characteristics corresponding to multiple faults of the target cable; based on the waveform characteristics corresponding to multiple faults, calculate the health indexes corresponding to multiple faults; based on the health indexes corresponding to multiple faults, determine the comprehensive health index corresponding to the target cable; based on a preset time series prediction model and the waveform characteristics corresponding to multiple faults, predict the future waveform characteristics corresponding to the target cable; based on the future waveform characteristics, determine the future health index corresponding to the target cable; based on the comprehensive health index and the future health index, determine the state evaluation result of the target cable.

[0011] According to another aspect of the embodiments of the present invention, there is also provided a device for evaluating the state of a cable, including: a construction module for constructing a fault arc model and a line model corresponding to the target cable; a generation module for generating a simulated waveform based on the fault arc model and the line model; a first determination module for determining the target fault parameters based on the measured waveform and the simulated waveform of the fault point in the target cable, where the target fault parameters represent index data describing fault characteristics and states; 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 state evaluation result of the target cable based on the waveform characteristics.

[0012] According to another aspect of the embodiments of the present invention, a non-volatile storage medium is further provided. The non-volatile storage medium includes a stored program, and when the program runs, it controls the device where the non-volatile storage medium is located to execute any one of the above cable state evaluation methods.

[0013] According to yet another aspect of the embodiments of the present invention, a computer device is further provided. The computer device includes a processor for running a program, and when the program runs, it executes any one of the above cable state evaluation methods.

[0014] According to yet another aspect of the embodiments of the present invention, a computer program product is further provided, including a computer program that implements any one of the above cable state evaluation methods when executed by a processor.

[0015] In the embodiments of the present invention, by adopting the cable state evaluation method, a fault arc model and a line model corresponding to the target cable are constructed; based on the fault arc model and the line model, a simulation waveform is generated; based on the measured waveform and the simulation waveform of the fault point in the target cable, target fault parameters are determined, where the target fault parameters represent index data describing the fault characteristics and states; according to the target fault parameters, the waveform characteristics of the fault point are determined; based on the waveform characteristics, the state evaluation result of the target cable is determined, 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 technical problem that it is currently difficult to sensitively capture and accurately evaluate the early faults of cable equipment, especially the transient fault characteristics, resulting in inaccurate cable evaluation. 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 schematic embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation to the present invention. In the drawings:

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

[0018] Figure 2 It is a flowchart of the cable state evaluation method provided according to the embodiments of the present invention;

[0019] Figure 3 It is a structure block diagram of the cable state evaluation device provided according to the embodiments of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0020] In order to enable those skilled in the art to better understand the solution of the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0021] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and do not necessarily have to be used to describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so that the embodiments of the present invention described here can be implemented in an order different from those illustrated or described here. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device that includes a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units 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 method for evaluating the state of a cable 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 the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order than here.

[0023] The method embodiment provided in the first embodiment of the present application can be executed on a mobile terminal, a computer terminal or a similar computing device. Figure 1 A hardware structure block diagram of a computer terminal for implementing a method for evaluating the state of a cable is shown. As Figure 1 shown, the computer terminal 10 may include one or more (shown as 102a, 102b,..., 102n in the figure) processors (the processor may include, but is not limited to, a processing device such as a microprocessor MCU or a programmable logic device FPGA), and a memory 104 for storing data. In addition, it may further include: a display, an input / output interface (I / O interface), a universal serial bus (USB) port (which can be included as one of the ports of the BUS bus), a network interface, a power supply, and / or a camera. Those of ordinary skill in the art can understand that Figure 1 the structure shown is only schematic and does not limit the structure of the above-mentioned electronic device. For example, the computer terminal 10 may further include more or fewer components than Figure 1 shown in the figure, or have the same asFigure 1 The different configurations shown.

[0024] It should be noted that one or more of the above-mentioned processors and / or other data processing circuits can generally be referred to as "data processing circuits" herein. The data processing circuit can be embodied in software, hardware, firmware, or any combination thereof, in whole or in part. In addition, the data processing circuit can be a single independent processing module, or be incorporated in whole or in part into any one of the other elements in the computer terminal 10. As involved in the embodiments of the present application, the data processing circuit is a processor control (such as the selection of a variable resistance terminal path connected to an interface).

[0025] The memory 104 can be used to store software programs and modules of application software, such as the program instructions / data storage devices corresponding to the cable status evaluation method in the embodiments of the present invention. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory 104, that is, implements the cable status evaluation method of the above-mentioned application program. The memory 104 can include high-speed random access memory, and can also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memories. In some instances, the memory 104 can further include memories remotely provided with respect to the processor, and these remote memories can be connected to the computer terminal 10 through a network. Examples of the above-mentioned network include but are not limited to the Internet, enterprise intranet, local area network, mobile communication network, and combinations thereof.

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

[0027] Figure 2 is a schematic flowchart of the cable status evaluation method provided according to the embodiments of the present invention, as Figure 2 shown, the method includes the following steps:

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

[0029] When a cable equipment fails, especially in the case of an arc fault, its electrical characteristics will change significantly. By constructing a fault arc model, the formation, development, and extinction processes of the arc can be accurately simulated, including the variation of key parameters such as the arc resistance, current, voltage, and energy release over time. This helps to understand the physical mechanism of the fault and more accurately predict and analyze the impact of the fault. As a medium for power transmission, the cable line's own characteristics, such as resistance, inductance, capacitance, and conductance, will affect the propagation and attenuation of the fault signal. Constructing a line model can analyze the propagation speed, path, attenuation degree, and phase change of the fault signal in the cable, which is crucial for locating the fault point, evaluating the scope of the fault impact, and predicting the fault propagation behavior.

[0030] An appropriate arc model can be selected according to the cable's operating environment, voltage level, and fault type. The Cassie model and the Mayr model are relatively common steady-state arc models. Among them, the Cassie model is applicable to low voltage and small current conditions, while the Mayr model is more suitable for high voltage and large current situations. A distributed parameter model can be used to describe the cable line. Considering 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. According to the actual parameters of the cable, such as conductor material, conductor cross-section, insulation type, and environmental conditions, the parameters of the line are determined. The fault arc model is used as a current source or voltage source and placed at a specific position in the line model, that is, the assumed fault point.

[0031] Before that, the fault waveform can be obtained first. High-sampling-rate measurement devices can be installed at the distribution network substation, switch station, or branch line. When an abnormal voltage / current mutation is detected, the data recording function is automatically triggered to record waveform segments for several milliseconds (e.g., 10 - 20 ms) before and after the fault to ensure capturing the complete process of the fault occurrence. The collected fault voltage / current waveforms are band-pass filtered (e.g., 20 Hz - 5 kHz) to remove DC drift and high-frequency noise. An effective time period is intercepted according to the sampling period and the fault duration, such as the waveform interval from 2 ms before the fault start to 2 ms after the fault end, and it is normalized for subsequent analysis.

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

[0033]

[0034] Among them, is the initial conduction degree, usually given by historical fault tests or manufacturer parameters, It is the energy constant (or time constant) maintained by the arc column, which is related to the arc radius, the temperature of the arc root area, etc. If the random extinction / relighting of the arc is considered, a random factor is set. The initial distribution or triggering condition (such as the arc extinguishes when the critical arc current is less than a certain threshold).

[0035] In PSCAD / EMTDC or other electromagnetic transient simulation platforms, a distributed parameter line module is used to model the fault line. According to the line length, conductor type, branch topology, and branch load, the parameters of each section of the line are set, and the arc model is injected at the fault point to simulate the waveforms of the fault voltage / current evolving with time.

[0036] Specifically, in a multi-branch distribution network line, the spatial distribution characteristics of voltage and current cannot be ignored. The voltage and current propagation equations of a single-phase line can be described by a distributed parameter model:

[0037]

[0038] Among them, respectively represent the resistance, inductance, capacitance, and conductance per unit length of the line, represents the position of the voltage, represents the position of the current, which changes with time . represents the spatial gradient of the voltage on the line, is the rate of change of the voltage with time, represents the spatial gradient of the current on the line, is the rate of change of the current with time.

[0039] For a multi-branch node coupling in a distribution network line with several branches, node equations need to be introduced at each branch node:

[0040]

[0041] Among them, there are m branch lines, node represents the current of the th branch line at the node, node 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, resulting in the mutual superposition and attenuation of the fault voltage and current among 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 the health assessment, fault diagnosis, and maintenance strategy of cable equipment.

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

[0044] In this step, the parameters of the fault arc model and line model can be set according to the physical characteristics and historical fault data of the target cable. This includes the arc resistance, time constant, random factor distribution characteristics, etc. of the arc model, as well as the resistance, inductance, capacitance, and conductance parameters per unit length of the line model. Build a simulation environment in the simulation software. This usually 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 supply, load, and other circuit components, as well as setting the simulation time window and sampling frequency. In the model, artificially trigger or simulate a fault event, such as introducing an arc at a certain point on the cable line. Different fault conditions can be set, such as the initial phase angle, peak current, and duration of the arc, to observe the influence of different parameters on the waveform. Run the model simulation to observe the propagation of the fault signal on the cable line. The simulation will consider the distributed parameter characteristics of the line and the attenuation, reflection, and refraction phenomena of the signal during propagation, generating a simulation waveform of voltage and current changing with time.

[0045] Step S206: Determine the target fault parameters based on the measured waveform and simulation waveform of the fault point in the target cable, where the target fault parameters are the index data characterizing the fault characteristics and states.

[0046] In this step, the measured waveform can be preprocessed, including denoising, filtering, and data normalization, to reduce measurement errors and interference from non-correlated signals, ensuring the accuracy and comparability of waveform data. Then, a target function is defined to measure the difference between the simulated waveform and the measured waveform. The target function usually includes the errors of voltage and current waveforms and can be in the form of a weighted average to ensure the matching degree of the two waveforms in key features. An optimization algorithm (such as the particle swarm algorithm, genetic algorithm, or least squares method) is used to search for and optimize the fault parameters of the target cable. The goal of optimization is to find a set of parameters that minimize the target function, that is, to make the simulated waveform as consistent as possible with the measured waveform. During the optimization process, the characteristics of the simulated and measured waveforms, such as the initial phase angle, peak value, duration, harmonic components, etc., are compared to ensure that these key features are accurately reflected in the simulated waveform. The optimal parameters found are the target fault parameters. Based on the above optimization process, index data that can best describe the fault characteristics and states are extracted, including but not limited to the resistance of the arc, the time constants of arc extinction and re-ignition, the probability distribution of random extinction / re-ignition, and the distributed parameters of the line. These parameters constitute the target fault parameters and are used for subsequent fault location, fault nature 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, providing more scientific and reliable data support for the health assessment of cable equipment, thus helping to improve the safety and reliability of the power system.

[0048] Step S208: Determine 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 fault arc model and line model to update the model parameters, ensuring that the model can more accurately reflect the actual electrical characteristics of a specific fault point. Using the updated model, rerun the simulation in the simulation software to simulate the dynamic changes of voltage and current in the cable under fault conditions. The simulation waveforms generated in this step will be closer to the waveform characteristics under actual fault conditions. Key waveform characteristics can be extracted from the waveforms obtained from the simulation. These characteristics may include: initial phase angle, the phase information at the start of the fault, which is crucial for fault location and nature identification; amplitude, the maximum value of the fault current or voltage, reflecting the magnitude of the fault energy; duration, the time length from the start to the end of the arc fault, helping to evaluate the severity and stability of the fault; slope, the speed at which the waveform rises or falls, reflecting the suddenness of the fault; harmonic components, frequency domain characteristics, analyzing the high-frequency fluctuations caused by the fault, which helps to identify the fault type; impedance mutation, impedance domain characteristics, by measuring the change in impedance at the fault point to assist in fault location. The extracted waveform characteristics can also be quantified. Statistical methods (such as mean, standard deviation) or signal processing techniques (such as wavelet transform, short-time Fourier transform) may be needed to further analyze and process these characteristics to ensure that they can accurately characterize the electrical state of the fault point.

[0050] In summary, by applying the target fault parameters to the simulation model, the waveform characteristics of the fault point can be accurately simulated and extracted, and then the health status of the cable equipment can be evaluated, providing a scientific basis for fault location, nature judgment, 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 and equipment management of the power system.

[0051] Step S210, determine the status evaluation result of the target cable based on the waveform characteristics.

[0052] In this step, determining the status evaluation result of the target cable based on waveform features is a process of converting the waveform features extracted from the fault arc model and the line model into a quantitative judgment of the health status of the cable equipment. The extracted waveform features can be standardized to ensure the comparability of features of different magnitudes in the evaluation. For example, the Z-score standardization method is used to convert features such as the initial phase angle, peak current, and duration to the same magnitude. The standardized waveform features are combined into a feature vector, and then a health function is defined to map the feature vector to a numerical value representing the health status of the equipment. The health function can be a linear weighted function, an exponential function, or a more complex machine learning model (such as a neural network). Then, a series of health thresholds are defined to map the calculated health function to specific status levels, such as normal, attention, severe, and critical. The threshold setting is based on historical fault data and equipment performance indicators. The calculation result of the health function is compared with the classification threshold to determine the current status level of the target cable. For example, if the value is higher than the threshold for the normal state, the cable is evaluated as being in the normal state; if it is lower than the threshold for the severe state, it may be in the severe or critical state. For multiple fault events, the feature vectors and health indices of all events can also be collected and analyzed, and the development trend of the equipment health status can be judged through trend analysis. A time series model (such as ARIMA or LSTM) can be constructed to predict the future health, or the slope of the change in health over time can be calculated to evaluate the change rate of the equipment status.

[0053] Through the above steps, the cable status evaluation based on waveform features can provide real-time and quantitative equipment health status information, providing scientific decision-making support for the operation and maintenance personnel of the power system, helping them timely discover potential problems of the equipment, take effective measures to prevent faults from occurring, and thus ensure the safe and stable operation of the power system.

[0054] Through the above steps, the purpose of jointly judging the status evaluation result based on the simulation waveform and the measured waveform is achieved, thereby realizing the technical effect of improving the accuracy of cable evaluation, and further solving the technical problem that it is currently difficult to sensitively capture and accurately evaluate the early faults of cable equipment, especially the transient fault characteristics, resulting in inaccurate cable evaluation.

[0055] As an optional embodiment, constructing a fault arc model corresponding to the target cable includes: based on the performance parameters of the target cable, constructing a stable arc model, where 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, where the intermittent frequency model is used to simulate the intermittent conduction situation of the target cable; and obtaining the fault arc model based on the stable arc model, the random factor, and the intermittent frequency model.

[0056] Optionally, a stable arc model can be constructed first. Based on the voltage level and fault characteristics of the target cable, a suitable arc model is selected. The Cassie model and the Mayr model are two common choices, which are respectively applicable to different descriptions of arc characteristics. The construction of the stable arc model needs to be based on the performance parameters of the target cable, especially the conductivity (the reciprocal 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 a specific differential equation, which is related to the arc current, the time constant, and the threshold conductivity for maintaining the arc. These equations describe the behavior of the arc in the steady state.

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

[0058]

[0059] where represents the arc conductivity ( is the arc resistance), is the arc current, is the time constant, is the threshold conductivity for maintaining the arc.

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

[0061] Among them, the value ranges of the key parameters are as follows:

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

[0063] Arc column maintenance energy constant (or time constant) : Related to the arc radius, arc root zone temperature, etc., and the range can be .

[0064] Maintaining conductivity threshold : Reflecting the arc maintenance condition, the range depends on the distribution network voltage level, generally between .

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

[0066] In actual operation, arc faults may exhibit characteristics of intermittent conduction and extinction. A random factor can be introduced to modulate the output of the arc model. The random factor can take a 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. Among them, the random factor modulating the output of the faulty arc model can be defined as:

[0067]

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

[0069] Based on historical fault data, analyze the intermittent frequency of arc faults, and identify the probability distribution of arc conduction and extinction within different time intervals. Then use statistical methods (such as Poisson process fitting) or machine learning techniques (such as Gaussian process regression) to establish an intermittent frequency model, which reflects the randomness and repeatability of arc faults on the time scale. To simulate the fault process with both continuity and intermittency, can be regarded as a random signal with a certain probability distribution. For example:

[0070]

[0071] Among them, can be fitted by combining the actual fault statistical characteristics (such as Poisson process or Gaussian process), which reflects the probability change of fault occurrence or recovery in different time periods.

[0072] Combining the steady arc model, random factor modulation, and intermittent frequency model forms a complete faulty arc model. This step needs to consider the interaction between the arc model and the intermittent conduction characteristics to ensure that the model can comprehensively reflect the cable fault scenario.

[0073] Constructing a complete faulty arc model is crucial for accurately evaluating the health status of cable equipment in the fault state. This model can not only capture the steady-state arc behavior but also reflect the 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 the power system.

[0074] As an alternative embodiment, based on the measured waveform and the simulated waveform of the fault point in the target cable, the target fault parameters are determined, including: based on the measured waveform and the simulated waveform, the objective function is determined; based on a preset optimization method, the fault parameters in the objective function are iteratively solved until the objective function converges, and the target fault parameters are obtained, where the fault parameters include arc resistance, time constant, random factor distribution characteristics, and line distribution parameter deviation.

[0075] Optionally, the process of determining the target fault parameters based on the measured waveform and the simulated waveform of the fault point in the target cable involves precise definition of the objective function and application of an efficient optimization algorithm. An objective function can be defined to measure the difference or the degree of fit between the simulated waveform and the measured waveform. Then, an appropriate optimization method is selected to iteratively solve the fault parameters in the objective function. Commonly used optimization methods include: genetic algorithm, particle swarm algorithm, least squares method, etc. Then, the parameter vector can be initialized, the simulation model can be run to obtain the simulated waveform; then, the value of the objective function can be calculated; updated according to the rules of the selected optimization method; repeat the simulation and the objective function calculation until the convergence condition is met (such as the change in the objective function value is less than a preset threshold, or the number of iterations reaches the upper limit).

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

[0077] For example, let the measured waveform data at the station end be , ,and the waveform obtained by simulation be , ,where represents the vector of parameters to be estimated of the fault model (including the arc model and line parameters), that is, the fault parameters.

[0078] Construct the objective function as follows:

[0079]

[0080] where, and are the weighting coefficients, used to balance the importance of voltage and current matching.

[0081] Using a global optimization method such as the least squares method, particle swarm algorithm or genetic algorithm, search for the that minimizes to obtain the estimated fault parameter ,including arc resistance ,time constant ,random factor distribution characteristics (such as arc extinction frequency), line distribution parameter deviation, etc. The waveform restoration near the fault point is determined in After that, the voltage and current waveforms near the fault point or fault node can be deduced in the simulation environment, so as to obtain more accurate time-domain characteristics of the fault disturbance.

[0082] As an optional embodiment, according to the target fault parameters, determine the waveform characteristics of the fault point, including: based on the target fault parameters, adjust the fault arc model and the line model to obtain the target fault arc model and the target line model; based on the target fault arc model and the target line model, obtain the target simulation waveform; extract the waveform characteristics of the fault point from the target simulation waveform, where the waveform characteristics include the fault initial phase angle, peak current, duration, and waveform slope.

[0083] Optionally, adjust the parameters of the fault arc model and the line model according to the above determined target fault parameters (including arc resistance, time constant, random factor distribution characteristics, line distribution parameter offset, etc.). This means updating the arc resistance (arc resistance) in the arc model, the time constant of the arc current, etc., and adjusting the resistance, inductance, capacitance, and conductance parameters in the line model to ensure that these models can more accurately reflect the electrical behavior of the target cable in the fault state. Using the adjusted parameters, reconstruct the fault arc model to reflect the actual characteristics of the arc in the target cable. By introducing random factors and intermittent frequency models, the random arc extinction / re-ignition characteristics of the arc and the dynamic changes of the arc within less than one power frequency cycle can be more realistically simulated. According to the optimized line distribution parameters, refine the distributed line model to ensure that the model can consider the voltage and current attenuation characteristics during the coupling of multi-branch lines and the fault propagation process. This step is crucial for accurately evaluating the fault impact in a complex distribution network environment.

[0084] Based on the adjusted fault arc model and line model, use simulation software for simulation to generate the target simulation waveform. This includes applying the fault arc model at the fault point and calculating the propagation and change of voltage and current in the cable network through the line model. Then extract key waveform characteristics from the target simulation waveform, such as multi-dimensional characteristics in the time domain, frequency domain, time-frequency domain, and impedance domain, such as fault peak value, duration, harmonic content, impedance mutation rate, etc.

[0085] Specifically, the extracted waveform characteristic quantities can include time-domain characteristics. Among them, for short-time dynamic fault disturbance waveforms (less than 1 power frequency cycle), the following types of characteristics are mainly concerned:

[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 of fault occurrence.

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

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

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

[0090] In the repetitive fault scenario, statistics can also be extracted for multiple fault events:

[0091]

[0092] Among them, is the peak value of the -th fault, is the standard deviation of the disturbance, is the total number of fault occurrences; similarly, statistics can also be performed on and other characteristics.

[0093] If it is necessary to capture high-frequency or transient components, the short-time Fourier transform (STFT) or wavelet transform can be performed 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 alternative embodiment, based on the waveform characteristics, determine the status evaluation result of the target cable, 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; based on the health index, determining the status evaluation result of the target cable.

[0095] Optionally, the waveform characteristics can be converted into a vector , for facilitating subsequent calculation of the health function. An exponential or linear function can be used to convert the feature quantity deviation into a health index, and the specific formula is as follows:

[0096]

[0097] If is higher than the preset threshold, it indicates that the device is temporarily in a relatively safe state; otherwise, it indicates a greater fault risk. According to 's numerical range, the device status is divided into four levels. If the evaluation level is attention or critical, it is recommended that the operation and maintenance personnel carry out inspection and troubleshooting. If it is attention, monitoring can be strengthened; if it is normal, it can be maintained according to the normal cycle.

[0098] Specifically, define the health level threshold to map the health degree H to the 0-1 interval, corresponding to the following levels:

[0099] 0.8 ≤ H ≤ 1.0 (Normal): The overall status of the equipment is normal, without obvious signs of failure, and the regular maintenance plan can be implemented as usual.

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

[0101] 0.4 ≤ H < 0.6 (Severe): The deviation degrees of multiple failure characteristics are relatively large, and obvious potential hazards of the equipment are present. Repairs or component replacements need to be arranged.

[0102] 0 ≤ H < 0.4 (Critical): The health of the equipment has significantly decreased and shows a trend of accelerating deterioration. It is recommended to immediately stop operation or perform emergency repairs.

[0103] As an alternative embodiment, based on waveform characteristics, determine the status evaluation result of the target cable, including: obtaining the waveform characteristics corresponding to multiple faults of 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 indices corresponding to each of the multiple faults; predicting the future waveform characteristics corresponding to the target cable based on the 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 evaluation result of the target cable based on the comprehensive health index and the future health index.

[0104] Optionally, when there are multiple fault disturbances in the same cable equipment within a certain time range, dynamic tracking and trend prediction of the equipment health status can be achieved by statistically analyzing the characteristic quantities of multiple faults and performing time series prediction.

[0105] During the operation of the cable equipment, keep the measuring device continuously online and automatically record once a fault waveform is detected; store the measured waveforms of each fault and the extracted characteristic quantities (such as in the historical database, and attach metadata such as time stamps and operating conditions. Normalize, align, and interpolate the waveforms collected for each fault to avoid analysis errors caused by sampling rates or data loss, and establish an index for multiple fault events for convenient subsequent retrieval and comparison.

[0106] For each fault calculate the health respectively, and associate and store it with the time stamp. If the waveform quality of some fault events is poor, a signal quality threshold can be set to eliminate or mark them.

[0107] For the recent faults (such as the last 5 or 10 times), perform average or weighted average processing, and define the comprehensive health

[0108]

[0109] Then observe the evolution trend over time. If it shows a continuous and significant decline, it may indicate that components such as equipment insulation or joints are gradually deteriorating. Based on the multiple fault feature quantity sequences , an appropriate time series prediction model can be selected, such as ARIMA, LSTM, GRU, or other statistical / machine learning methods; establish subsequence models for each component of (such as peak amplitude, duration, harmonic content, etc.), or use a multiple regression model for joint prediction. Predict the possible values of the next or future multiple fault feature quantities By comparing with the reference value , calculate the predicted fault health degree, that is, the future health index . If the prediction result shows that the key feature quantities (such as , ) continue to increase, it indicates that the subsequent fault risk increases, and the corresponding equipment health degree may further decline. On the basis of the historical health degree , superimpose the predicted next fault health degree , and define the weighted comprehensive index:

[0110]

[0111] where is the weight coefficient, reflecting the emphasis on historical data and future predictions.

[0112] According to value and its change rate, dynamically update the equipment health levels of normal, attention, serious, and critical, and form a trend chart on the operation and maintenance platform; if it is detected that the health degree drops sharply in the short term, an alarm can be given immediately and maintenance can be arranged preferentially. Key monitoring can be carried out on cable equipment in the attention state, a maintenance / replacement plan should be formulated for cable equipment in the serious state, and cables in the critical state need to be quickly shut down and urgently inspected; in practical applications, if repetitive arc faults occur (and the frequency is continuously increasing), it often means cable insulation damage, loose joints, or serious partial discharge, and the faulty equipment should be isolated in time or on-site testing should be carried out.

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

[0114] Through the description of the above embodiments, those skilled in the art can clearly understand that the cable state evaluation method according to the above embodiments can be implemented by means of software plus a necessary general hardware platform. Of course, it can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention, in essence or the part 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, magnetic disk, optical disk), and includes several instructions to enable a terminal device (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods described in various embodiments of the present invention.

[0115] According to an embodiment of the present invention, there is also provided a cable state evaluation device for implementing the above-mentioned cable state evaluation method. Figure 3 It is a structural block diagram of a cable state evaluation device provided according to an embodiment of the present invention. As shown in Figure 3, the cable state evaluation 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 following will explain the cable state evaluation device.

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

[0117] The generation module 304 is connected to the construction 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 simulation waveform of the fault point in the target cable, where the target fault parameters represent index data describing the fault characteristics and states.

[0119] The second determination module 308 is connected to the first determination module 306 and is used 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 state evaluation result of the target cable based on the waveform characteristics.

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

[0122] An embodiment of the present invention can provide a computer device. Optionally, in this embodiment, the above-mentioned computer device can be located in at least one network device among multiple network devices of a computer network. The computer device includes a memory and a processor.

[0123] Among them, the memory can be used to store software programs and modules, such as the program instructions / modules corresponding to the cable state evaluation method and device in the embodiment of the present invention. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory, that is, implements the above-mentioned cable state evaluation method. The memory can include a high-speed random access memory, and can also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memories. In some instances, the memory can further include a memory remotely set relative to the processor, and these remote memories can be connected to the computer terminal through a network. Examples of the above-mentioned network include but are not limited to the Internet, enterprise intranet, local area network, mobile communication network, and their combinations.

[0124] The processor can call the information and application programs stored in the memory through the transmission device to execute 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 simulation waveform of the fault point in the target cable, where the target fault parameters represent index data describing the fault characteristics and states; 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.

[0125] Optionally, the above-mentioned processor can 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, where the performance parameters include the conductivity and arc column maintenance energy constant of the target cable; setting a random factor; establishing an intermittent frequency model, where the intermittent frequency model is used to simulate the intermittent conduction situation of the target cable; and obtaining the fault arc model based on the stable arc model, the random factor, and the intermittent frequency model.

[0126] Optionally, the above-mentioned processor may also execute the program code of the following steps: Based on the measured waveform and the simulated waveform of the fault point in the target cable, determine the target fault parameters, including: based on the measured waveform and the simulated waveform, determine the objective function; based on a preset optimization method, perform iterative solution on the fault parameters in the objective function until the objective function converges to obtain the target fault parameters, where the fault parameters include arc resistance, time constant, random factor distribution characteristics, and line distribution parameter deviation.

[0127] Optionally, the above-mentioned processor may also execute the program code of the following steps: Based on the target fault parameters, determine the waveform characteristics of the fault point, including: based on the target fault parameters, adjust the fault arc model and the line model to obtain the target fault arc model and the target line model; based on the target fault arc model and the target line model, obtain the target simulated waveform; extract the waveform characteristics of the fault point from the target simulated waveform, where the waveform characteristics include fault initial phase angle, peak current, duration, and waveform slope.

[0128] Optionally, the above-mentioned processor may also execute the program code of the following steps: Based on the waveform characteristics, determine the state evaluation result of the target cable, including: according to the waveform characteristics, determine the waveform feature vector; input the waveform feature vector into a preset health mapping function to obtain the health index; based on the health index, determine the state evaluation result of the target cable.

[0129] Optionally, the above-mentioned processor may also execute the program code of the following steps: Based on the waveform characteristics, determine the state evaluation result of the target cable, including: obtain the waveform characteristics corresponding to multiple faults of the target cable respectively; based on the waveform characteristics corresponding to multiple faults respectively, calculate the health indices corresponding to multiple faults respectively; based on the health indices corresponding to multiple faults respectively, determine the comprehensive health index corresponding to the target cable; based on a preset time series prediction model and the waveform characteristics corresponding to multiple faults respectively, predict the future waveform characteristics corresponding to the target cable; based on the future waveform characteristics, determine the future health index corresponding to the target cable; based on the comprehensive health index and the future health index, determine the state evaluation result of the target cable.

[0130] By adopting the embodiment of the present invention, a method for evaluating the state of a cable is provided. A fault arc model and a line model corresponding to the target cable are constructed; based on the fault arc model and the line model, a simulation waveform is generated; based on the measured waveform and the simulation waveform of the fault point in the target cable, target fault parameters are determined, where the target fault parameters represent index data describing fault characteristics and states; according to the target fault parameters, waveform characteristics of the fault point are determined; based on the waveform characteristics, a state evaluation result of the target cable is determined, 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 technical problem that it is difficult to keenly capture and accurately evaluate early faults of cable equipment at present, especially transient fault characteristics, resulting in inaccurate cable evaluation.

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

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

[0133] Optionally, in this embodiment, the above non-volatile storage medium can 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 set to store program code for performing 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 simulation waveform of the fault point in the target cable, where the target fault parameters represent index data describing fault characteristics and states; determining waveform characteristics of the fault point according to the target fault parameters; determining a state evaluation 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, where 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, where the intermittent frequency model is used to simulate the intermittent conduction condition of the target cable; and obtaining the 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 performing the following steps: determining target fault parameters based on the measured waveform and the simulation waveform of the fault point in the target cable, including: determining an objective function based on the measured waveform and the simulation waveform; performing iterative solution on the fault parameters in the objective function based on a preset optimization method until the objective function converges to obtain the target fault parameters, where 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 according to the target fault parameters, including: adjusting the fault arc model and the line model based on the target fault parameters 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; and extracting the waveform characteristics of the fault point from the target simulation waveform, where the waveform characteristics include 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 the state evaluation result of the target cable based on the waveform characteristics, including: determining a 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 state evaluation 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 performing the following steps: determining a state evaluation result of the target cable based on waveform features, including: obtaining waveform features corresponding to multiple faults of the target cable; calculating health index values corresponding to the multiple faults based on the waveform features corresponding to the multiple faults; determining a comprehensive health index value corresponding to the target cable based on the health index values corresponding to the multiple faults; predicting future waveform features corresponding to the target cable based on a preset time series prediction model and the waveform features corresponding to the multiple faults; determining a future health index value corresponding to the target cable based on the future waveform features; and determining the state evaluation result of the target cable based on the comprehensive health index value and the future health index value.

[0140] An embodiment of the present invention further provides a computer program product, including a computer program. Optionally, in this embodiment, when the computer program is executed by a processor, it can implement: 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 simulation waveform of the fault point in the target cable, where the target fault parameters represent index data describing fault characteristics and states; determining the waveform features of the fault point according to the target fault parameters; and determining the state evaluation result of the target cable based on the waveform features.

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

[0142] In the above embodiments of the present invention, the descriptions of the respective embodiments have their own emphases. For parts not detailed in a certain embodiment, reference may be made to the relevant descriptions of other embodiments.

[0143] In 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 illustrative. For example, the division of the units can be a logical function division. In actual implementation, there may be other division methods. For example, 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 displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces. The indirect couplings or communication connections of the units or modules can be in an electrical or other form.

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

[0145] In addition, in each embodiment of the present invention, each functional unit can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of a software functional unit.

[0146] If the above-mentioned integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a non-volatile storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in each embodiment of the present invention. The foregoing storage medium includes: various media that can store program codes, such as USB flash drives, read-only memories (ROMs), random access memories (RAMs), mobile hard disks, magnetic disks, or optical discs.

[0147] The above are only the preferred embodiments of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the principle of the present invention, several improvements and refinements can be made, and these improvements and refinements should also be regarded as the protection scope of the present invention.

Claims

1. A method for evaluating the state of a cable, characterized in that, Including: 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 of the fault point in the target cable and the simulation waveform, where the target fault parameters characterize the index data describing the fault characteristics and states; Determining the waveform characteristics of the fault point according to the target fault parameters; Determining the state evaluation result of the target cable based on the waveform characteristics.

2. The method according to claim 1, wherein The constructing 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, where the performance parameters include the conductivity and the arc column maintenance energy constant of the target cable; Setting a random factor; Establishing an intermittent frequency model, where the intermittent frequency model is used to simulate the intermittent conduction condition of the target cable; Obtaining the fault arc model based on the stable arc model, the random factor, and the intermittent frequency model.

3. The method according to claim 1, wherein The determining of the target fault parameters based on the measured waveform of the fault point in the target cable and the simulation waveform includes: Determining an objective function based on the measured waveform and the simulation 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, where the fault parameters include arc resistance, time constant, random factor distribution characteristics, and line distribution parameter offset.

4. The method according to claim 1, wherein The determining of the waveform characteristics of the fault point according to the target fault parameters includes: Adjusting the fault arc model and the line model based on the target fault parameters 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; Extracting the waveform characteristics of the fault point from the target simulation waveform, where the waveform characteristics include fault initial phase angle, peak current, duration, and waveform slope.

5. The method according to claim 1, wherein The determining of the state evaluation result of the target cable based on the waveform characteristics includes: Determining a waveform feature vector according to the waveform characteristics; Inputting the waveform feature vector into a preset health mapping function to obtain a health index; Determining the state evaluation result of the target cable based on the health index.

6. The method according to claim 1, characterized in that, The determining of the state evaluation result of the target cable based on the waveform characteristics includes: Obtaining the waveform characteristics corresponding to multiple faults of the target cable; Calculating the health indexes corresponding to the multiple faults based on the waveform characteristics corresponding to the multiple faults; Determining the comprehensive health index corresponding to the target cable based on the health indexes corresponding to 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; Determining the state evaluation result of the target cable based on the comprehensive health index and the future health index.

7. A cable condition assessment device, characterized in that, Including: A building module for building a fault arc model and a line model corresponding to a target cable; A generating 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 of a fault point in the target cable and the simulation waveform, wherein the target fault parameters represent index data describing fault characteristics and states; A second determination module for determining the waveform characteristics of the fault point according to the target fault parameters; A result determination module for determining a state evaluation result of the target cable based on the waveform characteristics.

8. A non-volatile storage medium, characterized in that, The non-volatile storage medium includes a stored program, wherein when the program runs, it controls the device where the non-volatile storage medium is located to execute the cable state evaluation method according to any one of claims 1 to 6.

9. A computer device, characterized in that, Comprising: A memory and a processor, The memory stores a computer program; The processor is configured to execute the computer program stored in the memory, and when the computer program runs, it causes the processor to execute the cable state evaluation method according to any one of claims 1 to 6.

10. A computer program product comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the cable state evaluation method according to any one of claims 1 to 6.

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