Method for processing impact aging test data of resistance sheet and storage medium

Abstract

Embodiments of the present application provide a kind of resistance sheet's impact aging test data processing method and storage medium.The method comprises: obtaining multiple lightning arrester simulation impact current waveform data;Determine test loop;Obtain the impact aging test data of multiple resistance sheet test products;According to impact aging test data, construct test database;According to the test database of each resistance sheet test product, aging characterization processing is carried out, and the aging judgment result of each resistance sheet test product is obtained;According to the impact aging test data of multiple resistance sheet test products and initial intelligent prediction model, model training is carried out, and target intelligent prediction model is obtained.Through the process of data preprocessing, logarithmic linearization, regression calculation, dynamic prediction, the accuracy of the life prediction of the aging process of resistance sheet in lightning arrester is effectively improved.

Description

Technical Field

[0001] This application relates to the field of surge arrester research, and in particular to a method for processing and storing data from an impact aging test of a resistor element. Background Technology

[0002] Gapless metal oxide surge arresters, characterized by nonlinear properties, are widely used in power systems as a primary suppression device for power system operation and lightning overvoltage. The reliability of a surge arrester directly depends on the performance of its core component, the zinc oxide resistive element. Current criteria for analyzing surge arrester status include a DC reference voltage change rate of ≤5% for the resistive element U1mA and a leakage current I0.75U1mA ≤50μA.

[0003] Both the rate of change and leakage current are threshold-based criteria, which can only trigger alarms when aging accumulates to a critical value, and cannot reflect the gradual degradation process of the surge arrester throughout its entire life cycle. In addition, lightning and switching impulses are key factors in the aging and failure of surge arresters, but existing single-parameter analysis methods ignore the differences in surge arrester impulse aging rate and failure mode caused by waveform differences.

[0004] In existing technologies, static DC parameters are used to replace the dynamic multiphysics aging process, which leads to inaccurate analysis results of the aging status of the resistor elements in the surge arrester, resulting in inaccurate prediction results of the surge arrester's lifespan. Summary of the Invention

[0005] This application provides a method for processing and storing data from the impact aging test of resistor elements, in order to improve the accuracy of surge arrester life prediction.

[0006] In a first aspect, embodiments of this application provide a method for processing impact aging test data of a resistor, including:

[0007] Based on a pre-built simulation model, simulated impulse current waveform data of various surge arresters were obtained;

[0008] Based on the simulated impulse current waveform data of the various surge arresters, the test circuit was determined;

[0009] Impact aging test data of multiple resistor sample specimens were obtained based on the test circuit.

[0010] Based on the impact aging test data of each resistor sample, a test database for each resistor sample under multiple different impact current waveforms under different surge arrester simulated impact currents was constructed.

[0011] Aging characterization was performed on each resistor sample based on the test database to obtain the aging judgment results of each resistor sample.

[0012] Based on the impact aging test data of the multiple resistor sample specimens and the initial intelligent prediction model, the model is trained to obtain the target intelligent prediction model. The input of the target intelligent prediction model is a variety of impact current waveform parameters and the number of impact groups corresponding to each impact current waveform parameter. The output is the rate of change of electrical parameters of the resistor under various impact current waveforms to determine the degree of aging.

[0013] In one possible implementation, the step of obtaining multiple surge arrester simulated impulse current waveform data based on a pre-built simulation model includes: building an electromagnetic transient simulation analysis model of internal overvoltage; performing simulation analysis operations according to the electromagnetic transient simulation analysis model to obtain surge arrester impulse current waveform parameters and ranges under various preset lightning overvoltage conditions, thereby obtaining multiple surge arrester simulated impulse current waveform data, wherein each surge arrester simulated impulse current waveform data corresponds to a preset lightning overvoltage condition.

[0014] In one possible implementation, determining the test circuit based on the simulated impulse current waveform data of the various surge arresters includes: determining the impulse current generation principle based on the simulated impulse current waveform data of the various surge arresters; and determining the electrical components, component connection relationships, and resistance-capacitance-inductance parameters according to the impulse current generation principle, so as to determine the test circuit.

[0015] In one possible implementation, acquiring impact aging test data for multiple resistor sample specimens based on the test circuit includes: conducting impact tests on multiple resistor sample specimens based on the test circuit to acquire forward and reverse DC reference voltage, AC reference voltage, and residual voltage variation data for each resistor sample under different impact currents using measuring instruments; performing coefficient calculation processing on the impact current and DC reference voltage in the impact aging test data of each resistor sample to determine the nonlinear coefficient of each resistor sample; and determining the forward and reverse DC reference voltage, AC reference voltage, residual voltage variation data, and nonlinear coefficient of each resistor sample under different impact currents as the impact aging test data for multiple resistor sample specimens.

[0016] In one possible implementation, the formula for determining the nonlinear coefficient of each resistor sample by performing coefficient calculation processing based on the impact current and DC reference voltage in the impact aging test data of each resistor sample is as follows:

[0017]

[0018] In the formula, α is the nonlinear coefficient of the resistance sample, lg is the logarithmic function, U1 is the DC reference voltage under the impulse current I1, and U2 is the DC reference voltage under the impulse current I2.

[0019] In one possible implementation, aging characterization processing is performed based on the test database of each resistor sample to obtain the aging judgment result of each resistor sample. This includes: performing linear fitting based on the test database of each resistor sample to obtain the average change curve of multiple data change rates corresponding to each resistor sample, and determining a set of fitting linear relationships based on the average change curve of each data change rate; performing classification analysis processing based on the average change curve of multiple data change rates corresponding to each resistor sample to obtain the characteristic parameter change rate threshold corresponding to each data; matching the average change curve of a set of data change rates corresponding to each data according to the different impact currents suffered by each resistor sample to obtain the characteristic parameter change rate estimate, and determining the current change rate estimate corresponding to each data based on the characteristic parameter change rate estimate; when it is detected that the current change rate estimate corresponding to each data reaches the characteristic parameter change rate threshold, the aging judgment result of the resistor sample is obtained.

[0020] In one possible implementation, the step of training the target intelligent prediction model based on the impact aging test data of the plurality of resistor sample specimens and the initial intelligent prediction model includes: performing data preprocessing based on the impact aging test data of the plurality of resistor sample specimens to obtain test data to be extracted; performing feature extraction processing based on the test data to be extracted to obtain a dataset to be input; and dividing the dataset to be input into a training set and a test set to train the initial intelligent prediction model to obtain the target intelligent prediction model.

[0021] In one possible implementation, the method further includes: responding to a user's input operation to obtain input waveform data to be predicted; inputting the input waveform data to be predicted into the target intelligent prediction model to obtain the rate of change of feature parameters.

[0022] Secondly, embodiments of this application provide a data processing device for impact aging tests of resistor sheets, comprising:

[0023] The acquisition module is used to acquire various surge arrester simulation impulse current waveform data based on pre-built simulation models;

[0024] The determination module is used to determine the test circuit based on the simulated impulse current waveform data of the various surge arresters;

[0025] The acquisition module is also used to acquire impact aging test data of multiple resistor sample specimens based on the test circuit;

[0026] The module is used to build a test database for each resistor sample under multiple different impulse current waveforms under different surge arrester simulated impulse currents, based on the impulse aging test data of each resistor sample.

[0027] The processing module is used to perform aging characterization processing based on the test database of each resistor sample to obtain the aging judgment results of each resistor sample.

[0028] The training module is used to train the model based on the impact aging test data of the multiple resistor sample specimens and the initial intelligent prediction model to obtain the target intelligent prediction model. The input of the target intelligent prediction model is a variety of impact current waveform parameters and the number of impact groups corresponding to each impact current waveform parameter. The output is the rate of change of electrical parameters of the resistor under various impact current waveform impacts to determine the degree of aging.

[0029] In one possible implementation, the acquisition module is specifically used for: building an electromagnetic transient simulation analysis model of internal overvoltage; performing simulation analysis operations based on the electromagnetic transient simulation analysis model to obtain the surge arrester impulse current waveform parameters and range under various preset lightning overvoltage conditions, and to obtain multiple surge arrester simulated impulse current waveform data, wherein each surge arrester simulated impulse current waveform data corresponds to a preset lightning overvoltage condition.

[0030] In one possible implementation, the determining module is specifically used to: determine the impulse current generation principle based on the simulated impulse current waveform data of the various surge arresters; and determine the electrical components, component connection relationships, and resistance-capacitance-inductance parameters according to the impulse current generation principle, so as to determine the test circuit.

[0031] In one possible implementation, the acquisition module is specifically used for: conducting impact tests on multiple resistor sample specimens based on the test circuit, to acquire the forward and reverse DC reference voltage, AC reference voltage, and residual voltage change data of each resistor sample under different impact currents using measuring instruments; performing coefficient calculation processing on the impact current and DC reference voltage in the impact aging test data of each resistor sample to determine the nonlinear coefficient of each resistor sample; and determining the forward and reverse DC reference voltage, AC reference voltage, residual voltage change data, and nonlinear coefficient of each resistor sample under different impact currents as the impact aging test data of multiple resistor sample specimens.

[0032] In one possible implementation, the determining module is further configured to: calculate the nonlinear coefficient of each resistance element sample by performing coefficient calculation processing based on the impact current and DC reference voltage in the impact aging test data of each resistance element sample, wherein the calculation formula is as follows:

[0033]

[0034] In the formula, α is the nonlinear coefficient of the resistance sample, lg is the logarithmic function, U1 is the DC reference voltage under the impulse current I1, and U2 is the DC reference voltage under the impulse current I2.

[0035] In one possible implementation, the processing module is specifically configured to: perform linear fitting based on the test database of each resistor sample to obtain the average change curve of multiple data change rates corresponding to each resistor sample, and determine a set of fitting linear relationships based on the average change curve of each data change rate; perform classification analysis processing based on the average change curve of multiple data change rates corresponding to each resistor sample to obtain the characteristic parameter change rate threshold corresponding to each data; match a set of average change curves of data change rates corresponding to each data according to the different impact currents suffered by each resistor sample to obtain the characteristic parameter change rate estimate, and determine the current change rate estimate corresponding to each data based on the characteristic parameter change rate estimate; when it is detected that the current change rate estimate corresponding to each data reaches the characteristic parameter change rate threshold, the aging judgment result of the resistor sample is obtained.

[0036] In one possible implementation, the training module is specifically used for: performing data preprocessing based on the impact aging test data of the plurality of resistor sheet samples to obtain test data to be extracted; performing feature extraction processing based on the test data to be extracted to obtain a dataset to be input; dividing the dataset to be input into a training set and a test set to train the initial intelligent prediction model to obtain the target intelligent prediction model.

[0037] In one possible implementation, a prediction module is further included, specifically configured to: obtain input waveform data to be predicted in response to user input operations; and input the input waveform data to be predicted into the target intelligent prediction model to obtain the rate of change of feature parameters.

[0038] Thirdly, embodiments of this application provide an electronic device, including: a memory and a processor;

[0039] The memory stores computer-executed instructions;

[0040] The processor executes computer execution instructions stored in the memory, causing the processor to perform the first aspect and / or various possible implementations of the first aspect as described above.

[0041] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the first aspect and / or various possible implementations of the first aspect.

[0042] Fifthly, embodiments of this application provide a computer program product, including a computer program that, when executed by a processor, implements the first aspect and / or various possible implementations of the first aspect.

[0043] The data processing method and storage medium for impulse aging tests of resistor elements provided in this application embodiment acquire various simulated surge current waveform data of surge arresters based on a pre-built simulation model; determine the test circuit based on the simulated surge current waveform data of various surge arresters; acquire impulse aging test data of multiple resistor element samples based on the test circuit; construct a test database for each resistor element sample under multiple different impulse current waveforms under different surge arrester simulated impulse currents based on the impulse aging test data of each resistor element sample; perform aging characterization processing on each resistor element sample's test database to obtain the aging judgment result of each resistor element sample; and train the model based on the impulse aging test data of multiple resistor element samples and the initial intelligent prediction model to obtain the target intelligent prediction model. The input of the target intelligent prediction model is various impulse current waveform parameters and the number of impulse groups corresponding to each impulse current waveform parameter, and the output is the rate of change of electrical parameters of the resistor element used to judge the degree of aging under various impulse current waveforms. Through the process of data preprocessing, logarithmic linearization, regression calculation, and dynamic prediction, the accuracy of predicting the life of resistor elements in surge arresters during the aging process is effectively improved. Attached Figure Description

[0044] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0045] Figure 1 A flowchart illustrating the data processing method for the impact aging test of the resistor provided in this application;

[0046] Figure 2 A schematic diagram illustrating the wiring method of a DC power transmission project, as exemplified in the embodiments provided in this application;

[0047] Figure 3 A circuit diagram of the impulse current test circuit provided in the embodiments of this application;

[0048] Figure 4 The output waveform diagram of the test circuit in the embodiments provided in this application;

[0049] Figure 5 A schematic diagram illustrating the linear fitting of the average value of the DC reference voltage change rate, as illustrated in the embodiments provided in this application;

[0050] Figure 6 A schematic diagram of linear fitting of the average rate of change of leakage current resistive component and its fundamental and third harmonic amplitudes in the embodiments provided in this application;

[0051] Figure 7 A schematic diagram of linear fitting of the average value of the AC reference voltage change rate in the embodiments provided in this application;

[0052] Figure 8 A schematic diagram of linear fitting of the average value of the rate of change of DC nonlinear coefficient in the embodiments provided in this application;

[0053] Figure 9 A schematic diagram of linear fitting of the average value of the rate of change of the AC nonlinear coefficient in the embodiments provided in this application;

[0054] Figure 10 This is a schematic diagram of linear fitting of the average value of the residual pressure ratio change rate in the embodiments provided in this application;

[0055] Figure 11 Linear fitting plots of the rate of change of each feature parameter in the embodiments provided in this application;

[0056] Figure 12 A schematic diagram of the structure of the impact aging test data processing device for the resistor sheet provided in this application;

[0057] Figure 13 A schematic diagram of the structure of the electronic device provided in this application.

[0058] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0059] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0060] First, let me explain the terms used in this application:

[0061] Impact aging test: This refers to testing the performance degradation of a resistor under long-term stress by simulating high-frequency impact currents from lightning or switching overvoltages.

[0062] Depolarization effect: refers to the phenomenon that the performance of a resistor temporarily decreases due to charge accumulation under a high electric field;

[0063] DC reference voltage: refers to the reference voltage at which the resistor reaches a specific voltage value under DC current, and is often used to calibrate nonlinear characteristics;

[0064] Characteristic parameter change rate: refers to the rate of degradation of key parameters of a resistor (such as DC voltage and leakage current) over time.

[0065] Existing criteria for analyzing surge arrester condition include a DC reference voltage change rate of ≤5% for the resistor element U1mA and a leakage current I0.75U1mA ≤50μA. Both the change rate and leakage current are threshold-based criteria, triggering alarms only when aging accumulates to critical values, failing to reflect the gradual degradation process of the surge arrester throughout its entire lifespan. Furthermore, lightning strikes and switching impulses are key factors in surge arrester aging and failure, but existing single-parameter analysis methods neglect the differences in surge arrester impact aging rates and failure modes caused by waveform variations.

[0066] In existing technologies, static DC parameters are used to replace the dynamic multiphysics aging process, which leads to inaccurate results in the analysis of the aging status of the resistor elements in the surge arrester, resulting in inaccurate predictions of the surge arrester's lifespan.

[0067] The data processing method for the impact aging test of resistor elements provided in this application effectively improves the accuracy of life prediction for the aging process of resistor elements in surge arresters through data preprocessing, logarithmic linearization, regression calculation, and dynamic prediction.

[0068] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0069] Figure 1 This is a flowchart illustrating the data processing method for the impact aging test of the resistor element provided in this application, as shown below. Figure 1 As shown in the figure, this embodiment provides a method for processing impact aging test data of resistors. The process includes the following steps:

[0070] Step S101: Obtain simulation impulse current waveform data of various surge arresters based on the pre-built simulation model.

[0071] Specifically, a pre-built simulation model is used to simulate and analyze the typical impulse current waveforms flowing through surge arresters under various operational and lightning overvoltage conditions, such as the electromagnetic transient simulation of a ±800kV DC system. Internal overvoltage simulations can be conducted by combining actual engineering topology and parameters, using, for example, the CIGRE recommended benchmark control model, to simulate and analyze grounding and switching overvoltages within typical converter stations and along lines, analyzing the typical impulse current-limiting waveforms of each surge arrester in the converter station during this process. In lightning overvoltage simulations, high-frequency models are established based on the overhead line structure and parameters, including lightning current, towers, overhead lines, and insulator flashover criteria, to calculate the impulse current waveform parameters and range of gapped DC line surge arresters when the line is subjected to backflashover and backflashover. This provides a basis for test circuit design, covering current waveforms that may be encountered in actual operating conditions. The simulation model can simulate different lightning overvoltage conditions; by adjusting the voltage amplitude and waveform parameters, diverse impulse current waveforms can be generated according to requirements.

[0072] Step S102: Determine the test circuit based on the simulated impulse current waveform data of various surge arresters.

[0073] Specifically, the surge arrester's impulse current loop is an RLC series circuit. A corresponding loop is built using a PSCAD electromagnetic transient simulation model, and the RLC parameters in the loop are modified. The amplitude, wavefront time, and wavetail time of the output impulse current are read to ensure the accuracy of the test loop. Based on the amplitude and leading-edge steepness of the simulated waveform, the topology of the test loop (e.g., capacitor discharge type, inductive oscillation type) and component parameters (RLC values) are determined. This ensures that the test loop can reproduce the simulated current waveform, verifying the simulation accuracy.

[0074] Step S103: Obtain impact aging test data of multiple resistor sample specimens based on the test circuit.

[0075] Specifically, different impulse currents are applied to multiple resistor samples, and parameters such as forward and reverse DC reference voltage, AC reference voltage, and residual voltage are measured and the changes in electrical characteristics are recorded.

[0076] Step S104: Based on the impact aging test data of each resistor sample, construct a test database for each resistor sample under multiple different impact current waveforms under different surge arrester simulated impact currents.

[0077] Specifically, the test data of various resistor samples under multiple impact current waveforms (such as voltage, residual voltage, and nonlinear coefficient) are integrated, classified, and stored to build a database, providing structured data support for aging characterization and model training.

[0078] The following example illustrates the process of establishing a test database of complete aging cycles for resistors under different impact current waveforms:

[0079] Define database entities, with the test object as the main table, containing attributes such as test sample ID and test sample number.

[0080] The measurement results of characteristic parameters for impact aging tests with different waveforms include specimen number, test ID, number of impact groups, AC / DC reference voltages U1mA and U0.1mA, nonlinear coefficient, residual voltage, total leakage current and harmonics, resistive component of leakage current and harmonics, etc. There is a one-to-one relationship between the test object and these different waveform impact aging tests; that is, one test sample corresponds to one waveform impact aging test. Since the number of impact groups is greater than one, there is a one-to-many relationship between the test and the results.

[0081] Logical design is carried out based on the determined database entities, and inter-table constraints are established for each table. The test sample ID is used as the primary key of the test object, and the test sample ID is used as the foreign key of the different waveform impact aging test tables, which are associated with the test object in the main table.

[0082] Create a new table in the database. In the table creation interface, set the table structure according to the attribute parameters mentioned above. Add and set attributes for each field to ensure the table design meets the storage requirements of the experimental data to complete the creation of the new table.

[0083] For the acquired test data of the test samples, select the target table for import. Import using EXCEL file format, and ensure that the field mappings are correct during the import process.

[0084] Step S105: Perform aging characterization processing based on the test database of each resistor sample to obtain the aging judgment results of each resistor sample.

[0085] Specifically, aging characterization processes transform experimental data into quantifiable aging indicators, and by combining the change rates of parameters such as DC, AC, and residual voltage, the accuracy of aging assessment is effectively improved. In addition to the change rates of AC and DC residual voltage parameters, the data transformation process involves fitting these parameters to the corresponding number of impacts to obtain the corresponding rate of change. An accelerating trend of change is evidence of aging. By comprehensively analyzing the change rate trends of multiple parameters, the speed and severity of aging can be predicted more accurately. Different formulations of resistors exhibit different performance characteristics; by comprehensively evaluating multiple characteristic parameters, identifying the fastest-changing characteristic quantity can significantly improve the accuracy of aging assessment for different resistors.

[0086] Step S106: Based on the impact aging test data of multiple resistor samples and the initial intelligent prediction model, the model is trained to obtain the target intelligent prediction model. The input of the target intelligent prediction model is a variety of impact current waveform parameters and the number of impact groups corresponding to each impact current waveform parameter. The output is the rate of change of electrical parameters of the resistor sample under various impact current waveforms to determine the degree of aging.

[0087] Specifically, data preprocessing reduces the interference of outliers and noise on the model, improves the model's generalization ability, and combines electrical aging mechanisms with data-driven approaches to ensure prediction accuracy.

[0088] The data processing method for the impact aging test of resistors provided in this invention, through data preprocessing, logarithmic linearization, regression calculation, and dynamic prediction, enables a more accurate prediction of the lifespan of resistors in surge arresters during the aging process.

[0089] This embodiment provides a detailed description of the process for obtaining simulated impulse current waveform data of various surge arresters based on a pre-built simulation model as described in the above embodiments. The specific implementation of this process includes the following steps:

[0090] Step a1: Build an electromagnetic transient simulation analysis model for internal overvoltage.

[0091] Specifically, the model can realistically reflect the electrical environment of surge arresters in complex power grids, including voltage distribution and current surge waveforms.

[0092] This will be explained in detail through examples: such as Figure 2 As shown, an electromagnetic transient simulation analysis model for internal overvoltage is built to simulate the surge arrester current wavefront time and half-peak time under various typical operating conditions. For example, the surge arrester impulse waveform wavefront time is 15µs and the half-peak time is 35µs during filter switching, while it can be taken as 2 / 20µs during lightning impulse. According to the relevant provisions of IEC 60099-4 for repetitive charge transfer, resistance impulse aging tests can be carried out using an 8 / 20µs waveform under rated impulse current and a 15 / 35µs impulse current waveform under equivalent repetitive charge transfer and energy, respectively, to compare and analyze the influence of the same charge and different impulse current waveforms on the aging characteristics of the surge arrester. Table 1 shows the current waveforms and amplitudes of two typical impulse aging tests. For actual operating conditions, experimental comparison schemes with different impulse waveforms and amplitudes can be proposed based on the simulation results.

[0093] Table 1. Waveforms and amplitudes of the impact aging test current

[0094]

[0095] Step a2: Perform simulation analysis based on the electromagnetic transient simulation analysis model to obtain the surge current waveform parameters and range of the surge arrester under various preset lightning overvoltage conditions, and obtain various surge arrester simulated surge current waveform data, where each surge arrester simulated surge current waveform data corresponds to a preset lightning overvoltage condition.

[0096] Specifically, in the established simulation model, transient simulation calculations are run by pre-setting different lightning overvoltage conditions (such as amplitude, polarity, and waveform parameters). The current waveform, voltage waveform, and key parameters (such as peak current, leading edge steepness, and energy density) at both ends of the surge arrester are recorded to form impulse current data corresponding to the actual operating conditions, reflecting the response characteristics of the surge arrester under diverse stresses.

[0097] This invention, through the construction of a simulation model, accurately simulates the physical process of a surge arrester under lightning overvoltage, improving data accuracy and reliability and providing data support for subsequent applications.

[0098] This embodiment provides a detailed description of the process of determining the test circuit based on simulated impulse current waveform data of various surge arresters in the above embodiments. The specific implementation of this process includes the following steps:

[0099] Step b1: Determine the principle of impulse current generation based on the simulated impulse current waveform data of various surge arresters.

[0100] Specifically, by analyzing the simulated impulse current waveform data of various surge arresters (such as waveform leading edge steepness, peak value, attenuation characteristics, etc.), and combining the generation mechanism of the electromagnetic transient inverse impulse current, a basis is provided for the design of the test circuit.

[0101] Step b2: Based on the principle of impulse current generation, determine the electrical components, component connections, and resistance-capacitance-inductance parameters to determine the test circuit.

[0102] Specifically, referring to the examples in the above embodiments, based on the impulse current waveform proposed by the simulation results in the above embodiments, a test circuit is designed and built, and an impulse test is carried out. Specifically, based on the principle of impulse current generation, such as... Figure 3 As shown, design the RLC parameters of the test circuit and output the required impulse current waveforms, such as 15 / 35µs, 8 / 20µs, 2 / 20µs and 1 / 4µs.

[0103] Figure 3 In the diagram, L is an inductor, R, R1, and R2 are resistors, SG is a ball gap switch, RVMS is an impulse current measurement system, T.0 is the experimental object (resistive sample), and C1, C n CT is a capacitor, and AS is a current sensor.

[0104] Impact aging tests were conducted on the resistor plates using the constructed impact current test platform. The first, second, third and fourth waveform impact aging tests were carried out on a group of 10 valve plates.

[0105] Each test consists of 2 / n tests (adjusted according to actual energy), with an interval of 50-60 seconds between each test. After each test, the sample is cooled to room temperature before measuring various characteristic parameters until the rate of change of the DC reference voltage U1mA of the resistor is ≤5% or the leakage current I0.75U1mA is ≤50μA. Samples damaged prematurely during the impact test due to side flashover, pinholes, or cracking are not considered in this embodiment. Figure 4 The output waveform shown is a simulation based on the data in the loop parameter table. Loop parameters:

[0106]

[0107] This invention, through the determination of the test circuit, achieves a complete transformation from simulation data to the test circuit, providing fundamental support for subsequent steps.

[0108] This embodiment provides a detailed description of the process for obtaining impact aging test data of multiple resistance sample specimens based on the test circuit in the above embodiment. The specific implementation of this process includes the following steps:

[0109] Step c1 involves conducting impact tests on multiple resistor sample specimens based on the test circuit, using measuring instruments to obtain data on the forward and reverse DC reference voltage, AC reference voltage, and residual voltage changes of each resistor sample under different impact currents.

[0110] Specifically, the nonlinear current-voltage characteristic (UI curve) of the resistor element undergoes irreversible changes under the action of impulse current, manifested as a decrease in the forward and reverse DC reference voltage, AC reference voltage, and residual voltage. Impulse currents of different amplitudes / waveforms are applied through a test circuit to simulate actual overvoltage scenarios, triggering resistor element aging; changes in electrical parameters are recorded in real time using high-precision measuring instruments. Forward and reverse DC reference voltages reflect the degradation of the resistor element's varistor characteristics under DC operating conditions; AC reference voltage characterizes the attenuation of insulation performance under power frequency or switching overvoltages; residual voltage reflects a decrease in energy absorption capacity (increased residual voltage after impulse).

[0111] Referring to the examples in the above embodiments, this step involves measuring the forward and reverse DC reference voltages U1mA and U0.1mA of the resistance element sample during the impact aging test: The DC reference voltage of the resistance element sample is measured after each group of impact tests. During the test, the surface where the impact current is injected is considered the front side of the resistance element sample, and the grounding terminal is considered the back side. DC voltages are applied to both sides of the resistance element sample, and the voltage across the valve plate is measured when the current passing through the sample equals the reference currents of 1mA and 0.1mA. Due to the polarity effect, the lower value is taken as its DC reference voltage. The changes in the DC reference voltage after each group of impact tests are recorded.

[0112] Step c2 involves calculating the nonlinear coefficients of each resistor sample based on the impact current and DC reference voltage from the impact aging test data.

[0113] Specifically, by processing the data of inrush current and DC voltage, the nonlinear coefficient of the resistor can be determined efficiently and accurately, providing a basis for analyzing its protection performance, aging status, and optimizing its design.

[0114] Step c3: The forward and reverse DC reference voltage, AC reference voltage, residual voltage variation data and nonlinear coefficient of each resistor sample under different impact currents are determined as the impact aging test data of multiple resistor samples.

[0115] Specifically, the electrical parameters of each test specimen are correlated with the corresponding impulse current waveform parameters to form a multi-dimensional dataset, outputting a structured test database that clarifies the correspondence between different impulse currents and changes in electrical parameters. The test database for each resistive element test specimen includes the DC reference voltage, AC reference voltage, leakage current, resistive component of leakage current, and harmonic variation rate for each resistive element test specimen.

[0116] For example: Measuring the changes in AC reference voltages u1mA and u0.1mA of a resistive element sample during an impact aging test: Measure the AC reference voltage of the resistive element sample after each impact test. Apply a power frequency voltage to the resistive element sample and measure the effective value of the voltage across it when the resistive current passing through the sample equals the power frequency reference currents of 1mA and 0.1mA. Record the changes in AC reference voltage after each impact test, and further analyze the leakage current (total current I and resistive component I). R FFT analysis was performed to obtain the fundamental frequency I of the resistive component of the leakage current of the valve plate under initial u1mA and 0.8u1mA. R1 and third harmonic I R3 Amplitude. Measurement of residual voltage change of the resistance element sample during the impact aging test: Measure the residual voltage of the resistance element sample under an 8 / 20μs impact current after each impact test. Record the residual voltage change after each impact test.

[0117] This invention quantifies the degree of aging and correlates the electrical parameters of each test sample with the corresponding impact current waveform parameters to form a multi-dimensional dataset, outputting a structured test database that provides a direct basis for subsequent analysis.

[0118] Specifically, by processing the data of inrush current and DC voltage, the nonlinear coefficient of the resistor can be determined efficiently and accurately, providing a basis for analyzing its protection performance, aging status, and optimizing its design.

[0119] In some optional implementations, the nonlinear coefficient of each resistor sample is determined by calculating the impact current and DC reference voltage from the impact aging test data of each resistor sample, as follows:

[0120]

[0121] In the formula, α is the nonlinear coefficient of the resistance sample, lg is the logarithmic function, U1 is the DC reference voltage under the impulse current I1, and U2 is the DC reference voltage under the impulse current I2.

[0122] This embodiment details the process described in the above embodiment of performing aging characterization processing based on the test database of each resistor sample to obtain the aging judgment result of each resistor sample. The specific implementation of this process includes the following steps:

[0123] Step d1: Perform linear fitting based on the test database of each resistor sample to obtain the average change curve of multiple data change rates corresponding to each resistor sample, and determine a set of fitting linear relationships based on the average change curve of each data change rate.

[0124] Specifically, a linear fit is performed on the DC reference voltage of each resistor sample to obtain the average change curve of the DC reference voltage change rate of each resistor sample, and the first fitting linear relationship is determined based on the average change curve of the DC reference voltage change rate.

[0125] Linear fitting is performed on the AC reference voltage of each resistor sample to obtain the average change curve of the AC reference voltage change rate for each sample. Based on this curve, a second linear fitting equation is determined. Linear fitting is performed on the leakage current of each resistor sample to obtain the average change curve of the leakage current change rate for each sample. Based on this curve, a third linear fitting equation is determined. Linear fitting is performed on the resistive component of the leakage current of each resistor sample to obtain the average change curve of the resistive component of the leakage current change rate for each sample. Based on this curve, a fourth linear fitting equation is determined. Linear fitting is performed on the harmonic variation rate of each resistor sample to obtain the average change curve of the harmonic variation rate for each sample. Based on this curve, a fifth linear fitting equation is determined.

[0126] In this embodiment, the AC nonlinear coefficient and DC nonlinear coefficient of each AC resistor sample are further fitted until the fitting relationship between the AC nonlinear coefficient and DC nonlinear coefficient of each resistor sample is obtained. The specific fitting process is the same as the linear fitting process described above, and will not be repeated here.

[0127] The following example illustrates the concept of an 8 / 20μs waveform impulse current test:

[0128] The average change rate of the DC reference voltage for each valve plate during the impact aging process was calculated to obtain the trend of the average change rate of the DC reference voltage for the valve plates during the impact process. Linear fitting was then used to obtain the linear relationship between the average change rate of the DC reference voltage U0.1mA and U1mA on the front side of each resistor plate and the number of impact groups, as shown below. Figure 4 The corresponding linear relationship is fitted as shown in the following equation:

[0129]

[0130]

[0131] The average value of the AC reference voltage change rate of each valve plate during the impact aging process is obtained to obtain the trend of the average value of the AC reference voltage change rate of the resistor plate during the impact process. The linear relationship between the average value of the AC reference voltage change rate U0.1mA and U1mA on the front of each resistor plate and the number of impact groups is obtained by linear fitting, and the corresponding linear relationship is fitted. The trend graph and the structure of the linear relationship are consistent with the linear relationship between the average value of the change rate and the number of impact groups.

[0132] The average values ​​of the AC nonlinear coefficient, DC nonlinear coefficient, leakage current, resistive component of leakage current and its harmonic variation rate for each valve plate during the impact aging process are obtained, and the corresponding fitting linear formula is obtained.

[0133] Step d2 involves classifying and analyzing the average change curves of various data change rates for each resistor sample to obtain the characteristic parameter change rate thresholds for each type of data.

[0134] Specifically, since different characteristic parameters correspond to different aging endpoint change rates, and different impact currents correspond to different characteristic parameter change rates, as in the example above, we find the change rate thresholds corresponding to other characteristic parameters when the change rate of the DC reference voltage U1mA is ≤5% or the leakage current I0.75U1mA is ≤50μA.

[0135] Step d3: Based on the different impact currents experienced by each resistive sample, match the average change curve of a set of data change rate corresponding to each data to obtain the estimated value of the change rate of characteristic parameters, and determine the current estimated value of the change rate corresponding to each data based on the estimated value of the change rate of characteristic parameters.

[0136] Specifically, based on the different waveforms of the impact current experienced by the resistor, the corresponding linear fitting formula in step d1 is selected according to the actual applied impact current amplitude, and the corresponding linear fitting curve is matched for each characteristic parameter to estimate the estimated value of the rate of change of the characteristic parameter corresponding to the impact.

[0137] Step d4: When the estimated current rate of change for each type of data reaches the threshold of the characteristic parameter rate of change, the aging judgment result of the resistor sample is obtained.

[0138] Specifically, the current estimated rate of change for each parameter is compared with its corresponding characteristic parameter rate of change threshold. When one of the characteristic parameters reaches the threshold, it is determined that the parameter has aged. For example, if the DC voltage rate of change exceeds the threshold, it is determined to be aged.

[0139] The embodiments of the present invention transform experimental data into quantifiable aging indicators through linear fitting, comprehensive analysis and dynamic matching, and combine the change rates of parameters such as DC, AC and residual voltage to effectively improve the accuracy of aging determination.

[0140] This embodiment details the process of training a target intelligent prediction model based on impact aging test data from multiple resistive sheet samples and an initial intelligent prediction model, as described in the above embodiment. The specific implementation of this process includes the following steps:

[0141] Step e1: Perform data preprocessing based on the impact aging test data of multiple resistor sample specimens to obtain the test data to be extracted.

[0142] Specifically, data cleaning removes outliers (such as voltage / current measurements that deviate significantly from the normal range), duplicates, or missing values ​​from the experimental data to ensure data quality; standardization maps parameters of different dimensions (such as voltage, current, and time) to the same scale (such as the 0-1 interval or a standard normal distribution) to eliminate the impact of dimensional differences on model training; and for samples with uneven aging (such as a high proportion of early-aged samples), oversampling (SMOTE) or undersampling methods are used to balance the dataset to avoid the model being biased towards the majority class.

[0143] Step e2: Perform feature extraction processing on the experimental data to be extracted to obtain the input dataset.

[0144] Specifically, parameters directly related to the aging of the resistor are extracted, such as the rate of change of DC / AC voltage, the rate of change of residual voltage, and the energy density of the impulse current. Redundant features are eliminated, and features strongly related to aging are retained to improve the accuracy of model prediction.

[0145] Step e3: Divide the input dataset into a training set and a test set to train the initial intelligent prediction model and obtain the target intelligent prediction model.

[0146] Specifically, the model's weights or tree structure can be adjusted through the training set to improve predictive ability; the model's generalization ability can be tested using the test set to avoid overfitting.

[0147] For example, a dataset can be extracted from the test of waveform impacts such as 15 / 35μs, 8 / 20μs, and 2 / 20μs, which includes waveform characteristics (wavehead and wave tail), impact amplitude, number of impacts, and rate of change of characteristic parameters. The numerical data can be normalized by taking the minimum and maximum values ​​that do not depend on the data, which is suitable for Z-score normalization with unknown or complex data distribution.

[0148] The surge current's wavefront time, wavetail time, and energy (E=∫i(t)u(t)dt) are considered. Next, a time series feature is constructed: the number of surges is used as the time step, the reference voltage change is used as the time series output, and the mean and variance of the characteristic parameters from the past N surges are calculated as additional features.

[0149] LSTM (Long Short-Term Memory) networks are employed, suitable for time series forecasting, and capable of capturing the cumulative effect of the number of shocks on the rate of change of feature parameters. The data is divided into a training set (80%) and a test set (20%). Input waveform parameters (encoded as numerical or categorical) and the number of shocks; the output target is the rate of change of feature parameters, with optimal parameters selected through Bayesian optimization.

[0150] Below is an analysis of the target intelligent prediction model's input parameters, output results, and actual prediction success rate, showing high accuracy.

[0151]

[0152] The embodiments of the present invention reduce the interference of outliers and noise on the model through data preprocessing, improve the model's generalization ability, and combine electrical aging mechanism with data-driven approach to ensure prediction accuracy.

[0153] This embodiment supplements the above embodiment and also includes: responding to the user's input operation to obtain the input waveform data to be predicted; inputting the input waveform data to be predicted into the target intelligent prediction model to obtain the rate of change of the feature parameters.

[0154] Specifically, by using user-input waveform data (such as real-time monitored current / voltage waveforms), the system can quickly predict the dynamic changing trends of resistor characteristic parameters (such as DC / AC voltage change rate, residual voltage, etc.), providing a basis for equipment status analysis. Based on the change rate, it can determine whether the resistor is approaching its aging threshold or has potential defects, assisting maintenance personnel in developing repair or replacement plans. It adapts to input waveforms under different operating conditions (such as different inrush current amplitudes and frequencies), enabling flexible prediction.

[0155] Transforming abstract aging conditions into measurable rate-of-change indicators facilitates horizontal comparisons and historical analysis, providing a scientific basis for condition-based maintenance of power equipment, while improving operation and maintenance efficiency and reliability.

[0156] The above embodiments can be implemented in various specific ways, but the present invention is not limited to these embodiments. The following will verify and illustrate the above in conjunction with specific embodiments:

[0157] The table below shows the threshold values ​​and corresponding linear fits for the characteristic parameters of the resistance element impact aging, where y is the rate of change and x is the number of impacts.

[0158] Table 2 Thresholds and Corresponding Linear Fits for Characteristic Parameters of Resistance Element Impact Aging

[0159]

[0160] The linear relationship between the average rate of change of the resistive component of the leakage current of each valve plate, the fundamental amplitude, and the third harmonic amplitude, and the number of impact groups was obtained by linear fitting, where y is the rate of change and x is the number of impacts:

[0161] U1mA: y=-0.00179*x-0.00381;

[0162] U1mA: y=-0.00331*x-0.02786;

[0163] like Figure 5 As shown, the fitting effect is better in the middle and late stages of aging, and it can well reflect the aging of the valve plate. The average change rates of U1mA and U*1mA have linear fitting correlation coefficients of -0.97434 and -0.94264 with the number of impact groups, respectively, indicating a relatively good fit. (See Table 2 and...) Figure 5 It can be seen that when the DC reference voltage U*1mA on the front of the valve plate drops by 5%, the number of impact groups corresponding to this is 26, and the drop rate of U*0.1mA at this time is 11%.

[0164] The linear relationship between the average rate of change of the resistive component of the leakage current of each valve plate, the fundamental amplitude, and the third harmonic amplitude, and the number of impact groups was obtained by linear fitting, where y is the rate of change and x is the number of impacts:

[0165] Resistive component of leakage current: y = 0.02157*x + 0.09563;

[0166] Fundamental amplitude: y = 0.02191*x + 0.13466;

[0167] Third harmonic amplitude: y = 0.02555 * x + 0.11474; (e.g., ...) Figure 6 The diagram shows a linear fitting of the average rate of change of the amplitude of the resistive component of the leakage current and its fundamental and third harmonic frequencies. The correlation coefficients between the average rate of change of the amplitude of the resistive component of the leakage current and its fundamental and third harmonic frequencies and the number of impact groups are 0.97253, 0.97671, and 0.97438, respectively, indicating a relatively good fit. Taking a 5% drop in the DC reference voltage on the valve plate as the standard for complete valve plate degradation, the number of impact groups x=26 when the DC reference voltage U*1mA on the valve plate drops by 5%. This is consistent with Table 2 and... Figure 6 It can be seen that the amplitudes of the corresponding leakage current resistive component and its fundamental and third harmonic frequencies increase by approximately 65%, 65%, and 75%, respectively.

[0168] During the impact aging process, the average rate of change of the AC reference voltages U*1mA and U*0.1mA of each valve plate showed a linear decreasing trend to some extent. Figure 7 This is a schematic diagram of linear fitting of the average value of the AC reference voltage change rate in the embodiments provided in this application. The linear relationship between the average AC reference voltages U*1mA and U*0.1mA of each valve plate and the number of impact groups is obtained through linear fitting:

[0169] U*1mA: y=-0.00311*x-0.03101;

[0170] U*0.1mA: y=-0.00148*x+0.00611;

[0171] Furthermore, the fitting results show that the correlation coefficient between the average rate of change of AC reference voltage U*0.1mA and the number of impact groups is -0.97832, and the correlation coefficient between the average rate of change of AC reference voltage U*1mA and the number of impact groups is -0.95869, indicating relatively good fitting. The linear relationship between AC reference voltage U*0.1mA and the number of impact groups is slightly better than that between AC reference voltage U*1mA. Taking a 5% decrease in the DC reference voltage on the valve plate's front side as the standard for complete valve plate degradation, combined with Table 2 and... Figure 7 It can be seen that when the DC reference voltage U*1mA on the valve plate decreases by 5%, the number of impact groups corresponding to this decrease is 26, which corresponds to a decrease of approximately 11% in the AC reference voltage U*0.1mA; and a decrease of approximately 3% in the AC reference voltage U*1mA. The aging threshold can be defined as the change rate of the AC reference voltage U*1mA exceeding 3% or the change rate of U*0.1mA exceeding 11%.

[0172] Average rate of change of DC nonlinear coefficient: y = -0.00898 * x - 0.24938.

[0173] Figure 8 The following is a schematic diagram of the linear fitting of the average value of the change rate of the DC nonlinear coefficient in the embodiments provided in this application; when the front DC reference voltage U1mA drops by about 5%, the number of impact groups is 26, and the front DC nonlinear coefficient drops by about 45%~50%. As a characteristic value for judging the aging degree of the valve plate, as shown in Table 2, the threshold is 45%.

[0174] Average rate of change of AC nonlinear coefficient: y = -0.00491 * x - 0.13466

[0175] Figure 9 The diagram below illustrates the linear fitting of the average rate of change of the AC nonlinear coefficient in the embodiments provided in this application. The correlation coefficient between the average rate of change of the AC nonlinear coefficient and the number of impact groups is -0.97623, indicating a relatively good fit. A 5% decrease in the DC reference voltage on the front of the valve plate is taken as the standard for complete valve plate degradation, corresponding to a decrease of approximately 25% in the AC nonlinear coefficient. It can be proposed that a decrease of approximately 25% in the rate of change of the AC nonlinear coefficient can be used as a threshold for assessing the impact aging state of the valve plate.

[0176] The linear relationship between the average rate of change of residual pressure ratio of each valve plate and the number of impact groups was obtained by linear fitting:

[0177] y = 0.00236 * x - 0.00367

[0178] Figure 10 The diagram below illustrates the linear fitting of the average residual pressure ratio change rate in the embodiments provided in this application. It can be seen that the correlation coefficient between the average residual pressure ratio change rate and the number of impact groups is 0.96138, indicating a relatively good fit. A 5% decrease in the DC reference voltage on the front of the valve plate is taken as the standard for complete valve plate degradation, corresponding to an increase in the residual pressure ratio of approximately 6%, as shown in Table 2. The threshold is set at 6%.

[0179] Figure 11 The linear fitting plots of the rate of change of each feature parameter in the embodiments provided in this application have been verified to accurately capture the changing patterns of parameters during the aging process, providing a reliable tool for quantitative evaluation, thereby enabling accurate judgment of the aging state of the resistor sheet; the multi-parameter synergistic change characteristics verify the necessity of database construction and multi-dimensional analysis.

[0180] Figure 12 This is a schematic diagram of the data processing device for the impact aging test of the resistor element provided in this application. Figure 12 As shown, the data processing device 120 for the impact aging test of the resistor includes:

[0181] The acquisition module 1201 is used to acquire various surge arrester simulation impulse current waveform data based on a pre-built simulation model;

[0182] The determination module 1202 is used to determine the test circuit based on the simulated impulse current waveform data of various surge arresters;

[0183] The acquisition module 1201 is also used to acquire impact aging test data of multiple resistor sample specimens based on the test circuit;

[0184] Module 1203 is used to construct a test database for each resistor sample under multiple different impulse current waveforms under different surge arrester simulated impulse currents, based on the impulse aging test data of each resistor sample.

[0185] The processing module 1204 is used to perform aging characterization processing based on the test database of each resistor sample to obtain the aging judgment result of each resistor sample.

[0186] Training module 1205 is used to train a target intelligent prediction model based on the impact aging test data of multiple resistor samples and the initial intelligent prediction model. The input of the target intelligent prediction model is a variety of impact current waveform parameters and the number of impact groups corresponding to each impact current waveform parameter. The output is the rate of change of electrical parameters of the resistor under various impact current waveforms to determine the degree of aging.

[0187] In one possible implementation, the acquisition module 1201 is specifically used for: building an electromagnetic transient simulation analysis model of internal overvoltage; performing simulation analysis operations based on the electromagnetic transient simulation analysis model to obtain the surge arrester impulse current waveform parameters and range under various preset lightning overvoltage conditions, and to obtain multiple surge arrester simulated impulse current waveform data, wherein each surge arrester simulated impulse current waveform data corresponds to a preset lightning overvoltage condition.

[0188] In one possible implementation, the determining module 1202 is specifically used to: determine the impulse current generation principle based on the simulated impulse current waveform data of various surge arresters; and determine the electrical components, component connection relationships, and resistance-capacitance-inductance parameters according to the impulse current generation principle, so as to determine the test circuit.

[0189] In one possible implementation, the acquisition module 1201 is specifically used for: conducting impact tests on multiple resistor sample specimens based on a test circuit, so as to acquire the forward and reverse DC reference voltage, AC reference voltage, and residual voltage change data of each resistor sample under different impact currents through measuring instruments; performing coefficient calculation processing on the impact current and DC reference voltage in the impact aging test data of each resistor sample to determine the nonlinear coefficient of each resistor sample; and determining the forward and reverse DC reference voltage, AC reference voltage, residual voltage change data, and nonlinear coefficient of each resistor sample under different impact currents as the impact aging test data of multiple resistor sample specimens.

[0190] In one possible implementation, the determining module 1202 is further configured to: perform coefficient calculation processing based on the impact current and DC reference voltage in the impact aging test data of each resistor sample, and determine the calculation formula for the nonlinear coefficient of each resistor sample as follows:

[0191]

[0192] In the formula, α is the nonlinear coefficient of the resistance sample, lg is the logarithmic function, U1 is the DC reference voltage under the impulse current I1, and U2 is the DC reference voltage under the impulse current I2.

[0193] In one possible implementation, the processing module 1204 is specifically configured to: perform linear fitting based on the test database of each resistor sample to obtain the average change curve of multiple data change rates corresponding to each resistor sample, and determine a set of fitting linear relationships based on the average change curve of each data change rate; perform classification analysis processing based on the average change curve of multiple data change rates corresponding to each resistor sample to obtain the characteristic parameter change rate threshold corresponding to each data; match a set of average change curves of data change rates corresponding to each data according to the different impact currents suffered by each resistor sample to obtain the characteristic parameter change rate estimate, and determine the current change rate estimate corresponding to each data based on the characteristic parameter change rate estimate; when it is detected that the current change rate estimate corresponding to each data reaches the characteristic parameter change rate threshold, the aging judgment result of the resistor sample is obtained.

[0194] In one possible implementation, the training module 1205 is specifically used for: performing data preprocessing based on the impact aging test data of multiple resistor sheet samples to obtain the test data to be extracted; performing feature extraction processing based on the test data to be extracted to obtain the input dataset; dividing the input dataset into a training set and a test set to train the initial intelligent prediction model to obtain the target intelligent prediction model.

[0195] In one possible implementation, a prediction module 1206 is further included, specifically used for: responding to a user's input operation to obtain input waveform data to be predicted; inputting the input waveform data to be predicted into a target intelligent prediction model to obtain the rate of change of feature parameters.

[0196] The data processing device for the impact aging test of the resistor sheet provided in this embodiment can be used to execute the above-mentioned data processing method for the impact aging test of the resistor sheet. Its implementation principle and technical effect are similar, and will not be described again in this embodiment.

[0197] Figure 13 A schematic diagram of the hardware structure of the electronic device provided in this application, such as... Figure 13 As shown, the electronic device 130 includes at least one processor 1301 and a memory 1302. Optionally, the electronic device 130 also includes a communication component 1303. The processor 1301, memory 1302, and communication component 1303 are connected via a bus 1304.

[0198] In the specific implementation process, at least one processor 1301 executes computer execution instructions stored in memory 1302, causing at least one processor 1301 to perform the above method.

[0199] The specific implementation process of processor 1301 can be found in the above method embodiments, and its implementation principle and technical effect are similar. It will not be repeated here.

[0200] In the above embodiments, it should be understood that the processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor.

[0201] The memory may include random access memory (RAM) and may also include non-volatile memory (NVM), such as at least one disk storage device.

[0202] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of illustration, the buses shown in the accompanying drawings are not limited to a single bus or a single type of bus.

[0203] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0204] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the above-described method.

[0205] The aforementioned readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The readable storage medium can be any available medium accessible to a general-purpose or special-purpose computer.

[0206] An exemplary readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can reside in an Application Specific Integrated Circuit (ASIC). Alternatively, the processor and the readable storage medium can exist as discrete components in the device.

[0207] The division of units is merely a logical functional division; in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.

[0208] The units described as separate components may or may not be physically separate. 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 network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0209] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0210] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a 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 to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0211] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0212] Finally, it should be noted that other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This invention is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein, and is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of the invention is limited only by the appended claims.

Claims

1. A method for processing impact aging test data of a resistor element, characterized in that, include: An electromagnetic transient simulation analysis model for internal overvoltage was constructed. The electromagnetic transient simulation analysis model is used to perform simulation analysis to obtain the surge current waveform parameters and range of the surge arrester under various preset lightning overvoltage conditions, and to obtain various surge arrester simulated surge current waveform data, wherein each surge arrester simulated surge current waveform data corresponds to a preset lightning overvoltage condition. Based on the simulated impulse current waveform data of the various surge arresters, the test circuit was determined; Impact aging test data of multiple resistor samples were obtained based on the test circuit; the impact aging test data included the forward and reverse DC reference voltage, AC reference voltage, residual voltage change data and nonlinear coefficient of each resistor sample under different impact currents; Based on the impact aging test data of each resistor sample, a test database for each resistor sample under multiple different impact current waveforms under different surge arrester simulated impact currents was constructed. Aging characterization was performed on each resistor sample based on the test database to obtain the aging judgment results of each resistor sample. Based on the impact aging test data of the multiple resistor sample specimens and the initial intelligent prediction model, the model is trained to obtain the target intelligent prediction model. The input of the target intelligent prediction model is a variety of impact current waveform parameters and the number of impact groups corresponding to each impact current waveform parameter. The output is the rate of change of electrical parameters of the resistor under various impact current waveforms to determine the degree of aging.

2. The method according to claim 1, characterized in that, The step of determining the test circuit based on the simulated impulse current waveform data of the various surge arresters includes: Based on the simulated impulse current waveform data of various surge arresters, the principle of impulse current generation is determined. Based on the principle of inrush current generation, determine the electrical components, component connections, and resistance-capacitance-inductance parameters to determine the test circuit.

3. The method according to claim 1, characterized in that, Impact aging test data of multiple resistor sample specimens were obtained based on the aforementioned test circuit, including: Impact tests were conducted on multiple resistor sample specimens based on the aforementioned test circuit, so as to obtain the forward and reverse DC reference voltage, AC reference voltage and residual voltage change data of each resistor sample under different impact currents through measuring instruments; The nonlinear coefficients of each resistor sample are determined by calculating the impact current and DC reference voltage from the impact aging test data of each resistor sample. The forward and reverse DC reference voltage, AC reference voltage, residual voltage variation data, and nonlinear coefficient of each resistor sample under different impact currents were determined as the impact aging test data for multiple resistor samples.

4. The method according to claim 3, characterized in that, The formula for calculating the nonlinear coefficient of each resistor sample is as follows: The calculation is performed based on the impact current and DC reference voltage from the impact aging test data of each resistor sample. In the formula, α is the nonlinear coefficient of the resistance sample, lg is the logarithmic function, U1 is the DC reference voltage under the impulse current I1, and U2 is the DC reference voltage under the impulse current I2.

5. The method according to claim 1, characterized in that, Aging characterization was performed on each resistor sample based on the test database to obtain the aging judgment results for each resistor sample, including: Linear fitting is performed based on the test database of each resistor sample to obtain the average change curve of multiple data change rates for each resistor sample, and a set of fitting linear relationships is determined based on the average change curve of each data change rate. The average change curves of multiple data change rates for each resistor sample are classified and analyzed to obtain the characteristic parameter change rate thresholds for each type of data. Based on the different impact currents experienced by each resistive sample, a set of average change curves of the rate of change of a set of data corresponding to each data is matched to obtain the estimated value of the rate of change of characteristic parameters, and based on the estimated value of the rate of change of characteristic parameters, the current estimated value of the rate of change corresponding to each data is determined. When the estimated current rate of change for each type of data reaches the threshold of the characteristic parameter rate of change, the aging judgment result of the resistor sample is obtained.

6. The method according to claim 1, characterized in that, The step of training the target intelligent prediction model based on the impact aging test data of the multiple resistive sheet samples and the initial intelligent prediction model includes: Data preprocessing is performed based on the impact aging test data of the multiple resistor sample specimens to obtain the test data to be extracted; The experimental data to be extracted is used to perform feature extraction processing to obtain the input dataset. The input dataset is divided into a training set and a test set to train the initial intelligent prediction model and obtain the target intelligent prediction model.

7. The method according to any one of claims 1 to 6, characterized in that, Also includes: In response to user input, the input waveform data to be predicted is obtained; The input waveform data to be predicted is input into the target intelligent prediction model to obtain the rate of change of the feature parameters.

8. An electronic device, characterized in that, include: Memory, processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory, causing the processor to perform the method as described in any one of claims 1 to 7.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the method as described in any one of claims 1-7.

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