Oil paper insulation typical defect residual life estimation method

Abstract

The invention discloses an oil-paper insulation typical defect residual life estimation method, which comprises the following steps of: 1, establishing a partial discharge acquisition system, determining a statistical relationship between an insulation damage rate and a loss life percentage through an oil-paper insulation typical defect discharge experiment, and establishing an oil-paper insulation typical defect residual life estimation model; and 2, carrying out partial discharge detection on target equipment or a target model, obtaining related discharge data, calculating an insulation damage rate, and obtaining an estimation range of the residual life of the target equipment or the target model through the established oil-paper insulation typical defect residual life estimation model. According to the method, the insulation damage rate of the oil-filled equipment in the current time period can be calculated, the insulation loss life percentage range is determined accordingly, and the method has important significance on prediction of the residual life of oil-paper insulation typical defects.

Description

Technical Field

[0001] The invention belongs to the technical field of electrical life estimation of oil-filled equipment, and in particular relates to a method for estimating the remaining life of typical defects of oil-paper insulation. Background Art

[0002] In power systems, oil-paper insulation is widely used in power transmission, substation and distribution systems in high-voltage electrical environments. Due to its superior dielectric properties and long-term operating stability, the core insulation system in electrical equipment usually adopts oil-paper insulation structure. However, with the increase of equipment service time and the influence of external environment, oil-paper insulation may have various defects, such as air gaps, suspension, tip discharge, etc. These defects will directly affect the life and reliability of oil-filled equipment. Therefore, the remaining life prediction of defects in oil-paper insulation system is crucial to ensure the safety and efficient operation of power systems.

[0003] Although the existing life prediction methods can perform life assessment on equipment to a certain extent, they still have some shortcomings, especially in the estimation of remaining life for oil-paper insulation defects.

[0004] First, existing life prediction methods often fail to fully consider the actual insulation defect problem. The usual prediction model assumes that the insulation system is in an ideal state and ignores the existence and development of defects. This model cannot accurately reflect the performance degradation caused by defects during long-term operation of the equipment, which leads to a large deviation between the life prediction results and the actual situation.

[0005] Secondly, most of the current life prediction methods only consider the effect of electric field strength on life, while ignoring the role of partial discharge behavior. Partial discharge is one of the common defect phenomena in oil-paper insulation systems, which can accelerate the aging and degradation of insulation materials, but due to the complexity and uncertainty of the discharge process, many existing methods fail to fully consider this factor.

[0006] In addition, traditional life prediction models are usually based on the deterministic analysis of a single factor and lack statistical analysis of the life dispersion caused by discharge behavior. The life of a device is affected by multiple factors and has significant dispersion. A single model cannot reflect the range of variation in the actual life. Therefore, the prediction methods of existing methods are often too absolute and difficult to apply to devices with large life dispersion, affecting the accuracy and reliability of the prediction.

[0007] In summary, the existing life prediction methods have shortcomings such as not being applicable to defect analysis, ignoring the influence of discharge behavior, and lacking statistical analysis when dealing with typical defects of oil-paper insulation. Therefore, it is of great significance to develop a remaining life prediction method that can comprehensively consider defects, discharge behavior and statistical analysis. Summary of the invention

[0008] In order to solve the problems existing in the above-mentioned prior art, the purpose of the present invention is to provide a method for estimating the remaining life of typical defects of oil-paper insulation, extract effective characteristic quantities to characterize the insulation damage rate of oil-filled equipment, determine the statistical relationship between the insulation damage rate and the percentage of lost life, and thereby determine the percentage range of insulation loss life at the current moment, so as to provide a reference for estimating the remaining life of insulation of oil-filled equipment.

[0009] In order to achieve the above object, the present invention provides the following technical solutions:

[0010] A method for estimating the remaining life of typical defects of oil-paper insulation comprises the following steps:

[0011] S100: Build a partial discharge acquisition system, conduct discharge experiments on typical defects of oil-paper insulation, determine the statistical relationship between insulation damage rate and loss life percentage, and establish a remaining life estimation model for typical defects of oil-paper insulation;

[0012] S200: Perform partial discharge detection on the target device or target model, obtain relevant discharge data, calculate the insulation damage rate, and obtain the estimated range of the remaining life of the target device or target model through the established remaining life estimation model of typical defects of oil-paper insulation.

[0013] Furthermore, the target equipment includes but is not limited to transformers, reactors and other oil-paper insulation equipment, the target equipment includes but is not limited to transformers, reactors and other oil-paper insulation equipment, the target model includes but is not limited to laboratory discharge models such as scaled models and abstract models; the detection methods used by the partial discharge acquisition system include but are not limited to pulse current method, ultra-high frequency detection method, ultrasonic detection method and optical detection method.

[0014] Furthermore, this method is applicable to various detection scenarios, not only for laboratory models but also for online running equipment.

[0015] Furthermore, the method is applicable to various forms of electrical stress, including insulation life estimation under different voltage frequencies and voltage waveforms.

[0016] Furthermore, in step S100, the process of establishing the typical defect remaining life estimation model of oil-paper insulation is as follows:

[0017] S101: Install a partial discharge acquisition system for a target device or a target model;

[0018] S102: Multiple discharge experiments of the same type of defects are carried out on the typical defect model of oil-paper insulation under the same electrical stress form, and the partial discharge acquisition system continuously collects and stores all discharge data;

[0019] S103: Using a% characteristic discharge total amplitude Q C,a％ and a% small amplitude discharge total amplitude Q L,a％ Construct the characteristic coefficient R, and use the consistency of the characteristic coefficient R of discharge data from multiple discharge experiments of the same type of defects under the same electrical stress form to screen out abnormal discharge data;

[0020] S104: Selecting an adapted statistical duration T h , with the statistical duration T h Divide the time period and calculate the total discharge amplitude of each time period in the insulation damage stage Determine the maximum total discharge amplitude in history use and The ratio of constructs the insulation damage rate in each time period The maximum total discharge amplitude in history The time period is taken as the timing starting point, and the insulation damage rate of each experiment is plotted. Relationship curve with discharge time t;

[0021] S105: The insulation damage rate of each test is calculated based on the insulation failure endpoint. The relationship curve with the discharge time t is normalized to obtain the insulation damage rate Percentage of insulation loss life The relationship curve of

[0022] S106: The insulation damage rate of each test Percentage of insulation loss life The relationship curve is plotted on the same coordinate axis, and the upper and lower boundary lines of the curve are plotted to obtain the remaining life estimation model of typical defects of oil-paper insulation.

[0023] Furthermore, in step S102, the defect discharge experiment takes the breakdown of typical defects of oil-paper insulation as the experimental endpoint, and the stress application method includes constant stress, step stress and sequential stress.

[0024] Further, in step S103, the a% characteristic discharge total amplitude Q C,a％ It is defined as the sum of the amplitude data within a% range of the average discharge amplitude of all discharges during a single defect discharge experiment; the total amplitude of the a% small amplitude discharge Q L,a％ It is defined as the sum of amplitude data below the average discharge amplitude of 1+a%; the characteristic coefficient R is defined as the ratio of 15% characteristic discharge to the total amplitude of small amplitude discharge:

[0025]

[0026] The consistency of the characteristic coefficient R is reflected in the fact that the characteristic coefficient R of multiple defect discharge experiments under the same conditions has a small dispersion coefficient and a low fluctuation level.

[0027] Furthermore, in step S104, by setting the adapted statistical time length T h The value of T h The dispersion of small single discharge events leads to instability of the prediction results, and T h Too large a value will lead to hysteresis in the prediction results. Its specific value needs to be set in combination with the model parameters of the target device or target model and the actual operating conditions.

[0028] Furthermore, in step S104, the insulation damage rate of each time period Defined as:

[0029]

[0030] In the formula, is the i-th statistical time T h The total amplitude of the discharge in the divided time interval; For this experiment historical maximum.

[0031] Further, in step S105, the insulation failure endpoint is defined as the maximum total discharge amplitude in history. The time period is the starting point of timing and the electrical aging of the defect discharge experiment ends.

[0032] Furthermore, in step S105, the remaining life estimation model for typical defects of oil-paper insulation obtained based on the normalization of the insulation failure endpoint has an average insulation failure time. For each loss life percentage, the specific value of the remaining insulation life range can be obtained in combination with the average insulation failure time of the model.

[0033] Furthermore, in step S200, the estimation process of obtaining the estimated range of the remaining life of the target device or target model is as follows:

[0034] S201: Use the partial discharge acquisition system to collect data and set the tracing step length T p , calculate the discharge characteristic value F within the tracing interval;

[0035] S202: Setting data storage threshold value F t , when the discharge characteristic value F in the tracer interval exceeds the data storage threshold value F t When the partial discharge acquisition system switches to the continuous acquisition and data storage mode, the process goes to step S203; otherwise, the process returns to step S201;

[0036] S203: The local discharge acquisition system continuously acquires and stores all discharge data, and stores the historical maximum total discharge amplitude The discharge time is taken as the time start, and the statistical time T h Divide the time period and calculate the total discharge amplitude in the i-th time interval

[0037] S204: Set the historical maximum total discharge amplitude when i=1 Compare the total discharge amplitude in the i-th time interval The maximum discharge amplitude in history If the numerical value of Go to step S205, otherwise update the historical maximum discharge total amplitude And return to step S203;

[0038] S205: Total discharge amplitude in each time period according to the insulation damage stage and the historical maximum discharge amplitude The insulation damage rate is calculated by the ratio of

[0039] S206: Determine the corresponding damage rate based on the remaining life estimation model of typical defects of oil-paper insulation Insulation loss life percentage range under This gives an estimated range of remaining life.

[0040] Furthermore, the discharge characteristic value F includes but is not limited to the total number of discharges per unit time, the average discharge amplitude and the acetylene generation, wherein the definition of unit time includes but is not limited to per second, per minute and per power frequency cycle.

[0041] Furthermore, the data storage threshold F t Corresponding to the discharge characteristic value F, by reasonably setting the adapted F t The value of the data storage threshold F is used to judge the development of insulation partial discharge, thereby changing the working mode of the partial discharge instrument and effectively collecting discharge data in the damage stage. t The specific value of needs to be set in combination with the design parameters and actual operating conditions of the target device or target model.

[0042] Furthermore, it is allowed to set the maximum total discharge amplitude in history In the process of discriminating and updating, the maximum overflow percentage α% is set for it. The situation will not be updated, and the insulation damage rate is allowed to be less than 0.

[0043] The beneficial effects achieved by the present invention are as follows: the method of the present invention fully considers the damaging effect of local discharge development under typical defects of oil-paper insulation on insulation, and the life estimation result provided is more suitable for the actual operation of oil-filled equipment; the method of the present invention is based on the physical mechanism of local discharge, and discretizes the cumulative discharge total amplitude of discharge events in continuous time with unit statistical time intervals, replacing the construction method of traditional electrical aging models with continuous action factors such as voltage and electric field strength as characteristic quantities, which is more conducive to equivalent simulation of the cumulative damage of discrete and separate discharge events to insulation on a time scale; the method of the present invention conducts statistical analysis on the local discharge behavior and insulation loss life under the presence of defects, and constructs a remaining life estimation model for typical defects of oil-paper insulation based on the statistical relationship between insulation damage rate and loss life percentage, which can be applicable to life prediction with greater dispersion; the data used by the method of the present invention to construct the insulation damage rate comes from a wide range of sources, and can be obtained through a variety of local measurement technologies that have been maturely developed in engineering applications, with low difficulty in acquisition and high operability. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 It is a flow chart for implementing the method of the present invention.

[0045] Figure 2 It is the numerical relationship between the total amplitude of a% characteristic discharge and the total amplitude of 15% characteristic discharge.

[0046] Figure 3 is the statistical duration T h impact on model results.

[0047] Figure 4a It is the insulation damage rate of the YYG converter transformer valve side winding under electrical stress. Percentage of life lost statistical relationship.

[0048] Figure 4b It is the insulation damage rate of the YYD converter transformer valve side winding under electrical stress. Percentage of life lost statistical relationship. DETAILED DESCRIPTION

[0049] The present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and cannot be used to limit the protection scope of the present invention.

[0050] like Figure 1 As shown, a method for estimating the remaining life of typical defects of oil-paper insulation includes the following steps:

[0051] S100: Build a partial discharge acquisition system, conduct discharge experiments on typical defects of oil-paper insulation, determine the statistical relationship between insulation damage rate and loss life percentage, and establish a remaining life estimation model for typical defects of oil-paper insulation;

[0052] S200: Perform partial discharge detection on the target device or target model, obtain relevant discharge data, calculate the insulation damage rate, and obtain the estimated range of the remaining life of the target device or target model through the established remaining life estimation model of typical defects of oil-paper insulation.

[0053] Furthermore, in step S100, the process of establishing the typical defect remaining life estimation model of oil-paper insulation is as follows:

[0054] S101: Install a partial discharge acquisition system for a target device or a target model;

[0055] S102: Multiple discharge experiments of the same type of defects are carried out on the typical defect model of oil-paper insulation under the same electrical stress form, and the partial discharge acquisition system continuously collects and stores all discharge data;

[0056] S103: Using a% characteristic discharge total amplitude Q C,a％ and a% small amplitude discharge total amplitude Q L,a％ Construct the characteristic coefficient R, and use the consistency of the characteristic coefficient R of discharge data from multiple discharge experiments of the same type of defects under the same electrical stress form to screen out abnormal discharge data;

[0057] S104: Selecting an adapted statistical duration T h , based on the statistical duration T h Divide the time period and calculate the total discharge amplitude of each time period in the insulation damage stage Determine the maximum total discharge amplitude in history use and The ratio of constructs the insulation damage rate in each time period The maximum total discharge amplitude in history The time period is taken as the timing starting point, and the insulation damage rate of each experiment is plotted. Relationship curve with discharge time t;

[0058] S105: The insulation damage rate of each test is calculated based on the insulation failure endpoint. The relationship curve with the discharge time t is normalized to obtain the insulation damage rate Percentage of insulation loss life The relationship curve of

[0059] S106: The insulation damage rate of each test Percentage of insulation loss life The relationship curve is plotted on the same coordinate axis, and the upper and lower boundary lines of the curve are plotted to obtain the remaining life estimation model of typical defects of oil-paper insulation.

[0060] Furthermore, in step S200, the estimation process of obtaining the estimated range of the remaining life of the target device or target model is as follows:

[0061] S201: Use the partial discharge acquisition system to collect data and set the tracing step length T p , calculate the discharge characteristic value F within the tracing interval;

[0062] S202: Setting data storage threshold value F t When the discharge characteristic value F in the tracer interval exceeds the data storage threshold value F during the acquisition process t When the partial discharge acquisition system switches to the continuous acquisition and data storage mode, the process goes to step S203; otherwise, the process returns to step S201;

[0063] S203: The local discharge acquisition system continuously acquires and stores all discharge data, and stores the historical maximum total discharge amplitude The discharge time is taken as the time start, and the statistical time T h Divide the time period and calculate the total discharge amplitude in the i-th time interval

[0064] S204: Set the historical maximum total discharge amplitude when i=1 Compare the total discharge amplitude in the i-th time interval The maximum discharge amplitude in history If the numerical value of Go to step S205, otherwise update the historical maximum discharge total amplitude And return to step S203;

[0065] S205: Total discharge amplitude in each time period according to the insulation damage stage and the historical maximum discharge amplitude The insulation damage rate is calculated by the ratio of

[0066] S206: Determine the corresponding damage rate based on the remaining life estimation model of typical defects of oil-paper insulation Insulation loss life percentage range under This gives an estimated range of remaining life.

[0067] Example

[0068] The feasibility of the method of the present invention is illustrated by taking the electric stress waveform between the windings on the converter transformer valve side and the sine wave voltage waveforms of different frequencies output by the simulation circuit model of the ±800kV DC transmission system as examples. Based on the method of the present invention, multiple groups of partial discharge experiments of oil-paper insulation tip defects were carried out under different electric stress waveforms, such as Figure 2 As shown in the figure, the experimental data of the same electrical stress waveform show common characteristics in statistics. The total amplitude of characteristic discharge of each percentage is Q C,a％ There is a certain linear relationship between them, and the data that does not meet the characteristics is regarded as abnormal data and is screened out by the characteristic coefficient R.

[0069] Figure 3 Nine different statistical durations T are shown h Insulation damage rate under numerical value Percentage of life lost The five broken lines under the same statistical time length represent the results of five repeated discharge experiments on typical defects of oil-paper insulation. Figure 3 As shown, the statistical time length T in the method of the present invention h The choice of T will affect the model results. h If T is too small, the final prediction result will be affected by the dispersion of single discharge events, and the stability will be poor. h If the value is too large, the damage characteristics of the insulation damage stage cannot be carefully described, and the final prediction result has a large hysteresis. Its specific value is related to the transformer model parameters and actual operation conditions. In this example, T h =2s is the best effect.

[0070] According to the method of the present invention, the maximum total discharge amplitude in history occurs The time period is taken as the starting point to characterize the beginning of damage, and the total discharge amplitude data thereafter is calculated to obtain the insulation damage rate of each time interval. On the time scale, the relationship between the damage rate and time in multiple groups of experiments is normalized by the insulation failure endpoint of each experiment and plotted on the same coordinate axis, such as Figure 4a and Figure 4b As shown in the figure, the five broken lines represent the data results of five repeated discharge experiments under the electrical stress between the windings on the valve side of YYG and YYD converter transformers. The boundary line can directly represent the insulation damage rate. and percentage of life lost The relationship between Figure 4a The average insulation failure time of the model under the simulated YYG electrical stress is shown to be 52.0s. Figure 4b The average insulation failure time of the model under simulated YYD electrical stress is shown to be 74.8s.

[0071] In this embodiment, the unit statistical time T h Take 2s, the maximum total discharge amplitude in history The initial value is 0kV, the working cycle T of the partial discharge detector is 1min, the discharge characteristic value F is defined as the discharge repetition rate per second, and the data storage threshold value F is t Take it as 100. According to several experimental results, the prediction results of the method of the present invention are consistent with the facts.

Claims

1. A method for estimating the remaining life of typical defects of oil-paper insulation, characterized in that: The steps include: S100: Build a partial discharge acquisition system, conduct discharge experiments on typical defects of oil-paper insulation, determine the statistical relationship between insulation damage rate and loss life percentage, and establish a remaining life estimation model for typical defects of oil-paper insulation; S200: Perform partial discharge detection on the target device or target model, obtain relevant discharge data, calculate the insulation damage rate, and obtain the estimated range of the remaining life of the target device or target model through the established remaining life estimation model of typical defects of oil-paper insulation.

2. A method for estimating the remaining life of typical defects of oil-paper insulation according to claim 1, characterized in that: In step S100, the process of establishing the typical defect remaining life estimation model of oil-paper insulation is as follows: S101: Install a partial discharge acquisition system for a target device or a target model; S102: Multiple discharge experiments of the same type of defects are carried out on the typical defect model of oil-paper insulation under the same electrical stress form, and the partial discharge acquisition system continuously collects and stores all discharge data; S103: Using a% characteristic discharge total amplitude Q C,a％ and a% small amplitude discharge total amplitude Q L,a％ Construct the characteristic coefficient R, and use the consistency of the characteristic coefficient R of discharge data from multiple discharge experiments of the same type of defects under the same electrical stress form to screen out abnormal discharge data; S104: Selecting an adapted statistical duration T h , based on the statistical duration T h Divide the time period and calculate the total discharge amplitude Q of each time period during the insulation damage stage sum,Th,i , determine the historical maximum discharge total amplitude Q max,Th , using Q sum,Th,i With Q max,Th The ratio of constructs the insulation damage rate D in each time period per,Th,i ; The maximum total discharge amplitude Q max,Th The time period is taken as the timing starting point, and the insulation damage rate D of each experiment is plotted. per,Th Relationship curve with discharge time t; S105: The insulation damage rate D of each test is calculated based on the insulation failure endpoint per,Th The relationship curve with the discharge time t is normalized to obtain the insulation damage rate D per,Th And the percentage of insulation loss life T per,Th The relationship curve of S106: The insulation damage rate D of each test per,Th And the percentage of insulation loss life T per,Th The relationship curve is plotted on the same coordinate axis, and the upper and lower boundary lines of the curve are plotted to obtain the remaining life estimation model of typical defects of oil-paper insulation.

3. The method for estimating the remaining life of typical defects of oil-paper insulation according to claim 1, characterized in that: In step S200, the estimation process of obtaining the estimated range of the remaining life of the target device or target model is as follows: S201: Use the partial discharge acquisition system to collect data and set the tracing step length T p , calculate the discharge characteristic value F within the tracing interval; S202: Setting data storage threshold value F t , when the discharge characteristic value F in the tracer interval during the acquisition process exceeds the data storage threshold value F t When the partial discharge acquisition system switches to the continuous acquisition and data storage mode, the process goes to step S203; otherwise, the process returns to step S201; S203: The local discharge acquisition system continuously acquires and stores all discharge data, and stores the historical maximum total discharge amplitude Q max,Th The discharge time is taken as the time start, and the statistical time T h Divide the time period and calculate the total discharge amplitude Q in the i-th time interval sum,Th,i ; S204: Set the historical maximum total discharge amplitude Q when i=1 max,Th =0, compare the total discharge amplitude Q in the i-th time interval sum,Th,i The maximum discharge amplitude Q max,Th If the value of Q sum,Th,i ≤Q max,Th , go to step S205, otherwise update the historical maximum discharge total amplitude Q max,Th =Q sum,Th,i , and returns to step S203; S205: According to the total discharge amplitude Q of each time period in the insulation damage stage sum,Th,i And the historical maximum discharge total amplitude Q max,Th The insulation damage rate D is calculated by the ratio of per,Th ; S206: According to the remaining life estimation model of typical defects of oil-paper insulation, determine the corresponding damage rate D per,Th Insulation loss life percentage range under T per,Th,min ~T per,Th,max , thus obtaining an estimated range of remaining life.

4. The method for estimating the remaining life of typical defects of oil-paper insulation according to claim 1, characterized in that: The target equipment includes transformers, reactors and oil-paper insulated equipment; the target models include scaled models, abstract models and laboratory discharge models; the detection methods used by the partial discharge acquisition system include pulse current method, ultra-high frequency detection method, ultrasonic detection method or optical detection method.

5. The method for estimating the remaining life of typical defects of oil-paper insulation according to claim 1, characterized in that: This method is applicable to various detection scenarios, not only for laboratory models but also for online running equipment.

6. The method for estimating the remaining life of typical defects of oil-paper insulation according to claim 1, characterized in that: The method is applicable to various forms of electrical stress, including insulation life estimation under different voltage frequencies and voltage waveforms.

7. A method for estimating the remaining life of typical defects of oil-paper insulation according to claim 2, characterized in that: In step S102, the defect discharge experiment takes the breakdown of typical defects of oil-paper insulation as the experimental end point, and the stress application method includes constant stress, step stress and sequential stress.

8. A method for estimating the remaining life of typical defects of oil-paper insulation according to claim 2, characterized in that: In step S103, the a% characteristic discharge total amplitude Q C,a％ It is defined as the sum of the amplitude data within a% range of the average discharge amplitude of all discharges during a single defect discharge experiment; the total amplitude of the a% small amplitude discharge Q L,a％ It is defined as the sum of amplitude data below the average discharge amplitude of 1+a%; the characteristic coefficient R is defined as the ratio of 15% characteristic discharge to the total amplitude of small amplitude discharge: The consistency of the characteristic coefficient R is reflected in the fact that the characteristic coefficient R of multiple defect discharge experiments under the same conditions has a small dispersion coefficient and a low fluctuation level.

9. A method for estimating the remaining life of typical defects of oil-paper insulation according to claim 2, characterized in that: In step S104, by setting the adapted statistical time length T h The value of T h The dispersion of small single discharge events leads to instability of the prediction results, and T h Too large a value will lead to hysteresis in the prediction results. Its specific value needs to be set in combination with the model parameters of the target device or target model and the actual operating conditions.

10. The method for estimating the remaining life of typical defects of oil-paper insulation according to claim 2, characterized in that: In step S104, the insulation damage rate D of each time period per,Th,i Defined as: In the formula, Q sum,Th,i is the i-th statistical time T h The total discharge amplitude of the divided time interval; Q max,Th Q sum,Th,i historical maximum.

11. A method for estimating the remaining life of typical defects of oil-paper insulation according to claim 2, characterized in that: In step S105, the insulation failure endpoint is defined as the maximum total discharge amplitude Q max,Th The time period is the starting point of timing and the electrical aging of the defect discharge experiment ends.

12. A method for estimating the remaining life of typical defects of oil-paper insulation according to claim 2, characterized in that: In step S105, the remaining life estimation model of typical defects of oil-paper insulation obtained based on the normalization of the insulation failure endpoint has an average insulation failure time. For each loss life percentage, the specific value of the insulation remaining life range can be obtained in combination with the average insulation failure time of the model.

13. A method for estimating the remaining life of typical defects of oil-paper insulation according to claim 3, characterized in that: The discharge characteristic value F includes the total number of discharges per unit time, the average discharge amplitude and the acetylene generation amount, wherein the unit time is defined as per second, per minute or per power frequency cycle.

14. A method for estimating the remaining life of typical defects of oil-paper insulation according to claim 3, characterized in that: The data storage threshold value F t Corresponding to the discharge characteristic value F, by setting the adapted F t The value of the data storage threshold F is used to judge the development of insulation partial discharge, thereby changing the working mode of the partial discharge instrument and effectively collecting discharge data in the damage stage. t The specific value of needs to be set in combination with the design parameters and actual operating conditions of the target device or target model.

15. The method for estimating the remaining life of typical defects of oil-paper insulation according to claim 3, characterized in that: The maximum total discharge amplitude Q max,Th In the process of discriminating and updating, the maximum amplitude overflow percentage α% is set for it, and the maximum amplitude overflow percentage α% is set for it, which does not exceed (1+α%)·Q max,Th The situation will not be updated, and the insulation damage rate is allowed to be less than 0.

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