A method and apparatus for dissolved gas in oil prediction considering physical information

Abstract

The application provides a dissolved gas in oil prediction method and device considering physical information, and the method comprises the following steps: performing an oil-paper insulation discharge test; collecting operating state parameters during the discharge process; collecting oil samples during the discharge process and obtaining concentration data of dissolved gas in the oil; constructing a neural network prediction model by using the operating state parameters and the concentration data and performing training; receiving real-time monitoring data of the oil-paper insulation discharge, and realizing dissolved gas production prediction in the oil by using the neural network prediction model. The application adds physical information constraints to the neural network model, uses energy and current as constraint conditions, avoids the shortcomings that an artificial intelligence data-driven model excessively depends on the number and quality of data samples, and can accurately predict the dissolved gas production in the oil, which has certain guiding significance for improving the fault diagnosis capability of deeply mining the dissolved gas in the oil and ensuring the safe operation of power transformers.

Description

TECHNICAL FIELD

[0001] The present application relates to oil dissolved gas prediction diagnosis technology, and in particular to an oil dissolved gas prediction method and device considering physical information. BACKGROUND

[0002] With the continuous expansion of the scale of the power system and the continuous improvement of the voltage level, the safety and reliability of the operation of power transformers as the core equipment in the power grid are directly related to the stability of the entire power system. The oil-paper insulation system is one of the most commonly used insulation forms in power transformers, and ester insulating oil has been widely used in power transformers in recent years due to its excellent biodegradability, fireproof performance and electrical characteristics. However, during long-term operation, the ester insulating oil oil-paper insulation system may be subject to insulation aging and failure due to factors such as partial discharge, thereby affecting the normal operation of the transformer. Therefore, accurately predicting the generation of dissolved gases in oil (DGA) is crucial for early detection of potential faults and prevention of accidents.

[0003] In addition, although the traditional DGA analysis method based on empirical formula can reflect the fault state inside the transformer to some extent, it has slow diagnosis speed and is difficult to cope with actual working conditions. In recent years, machine learning and deep learning technologies have made significant progress in the field of power equipment fault diagnosis, and neural network models have attracted attention due to their strong non-linear fitting capabilities. However, most existing neural network models mainly rely on data-driven training and lack understanding of physical mechanisms, resulting in poor model generalization ability and insufficient interpretability. Moreover, traditional neural networks have high requirements for data quality and data size, and often perform poorly in small data size scenarios.

[0004] Therefore, it is urgent to propose an oil dissolved gas prediction method and device considering physical information to solve the contradiction between the poor timeliness of current mainstream DGA detection methods and the high requirements for timeliness and reliability of modern smart grid fault diagnosis.

[0005] The above information disclosed in this BACKGROUND section is only for the purpose of understanding the background of the present application concept, and therefore, it can contain information that does not constitute prior art. SUMMARY

[0006] The present application aims to solve the problems in the prior art in the background art, taking synthetic esters as the research object, and proposes an oil dissolved gas prediction method and device considering physical information, constructs a neural network model based on physical information to predict the content of oil dissolved gas, and solves the contradiction between the poor timeliness of the current mainstream DGA detection method and the poor interpretability, strong dependence on data quality and scale, poor generalization and easy overfitting of common neural network algorithm models, and the high requirements of modern smart grid fault diagnosis on timeliness and reliability.

[0007] The present application provides an oil dissolved gas prediction method and device considering physical information, comprising the following steps:

[0008] Step 1: Perform oil-paper insulation discharge test;

[0009] Step 2: Collect operating state parameters during the discharge process;

[0010] Step 3: Collect oil samples during the discharge process and obtain the concentration data of oil dissolved gas;

[0011] Step 4: Use the operating state parameters and the concentration data to construct a neural network prediction model and train it;

[0012] Step 5: Receive oil-paper insulation discharge real-time monitoring data, and use the neural network prediction model to realize oil dissolved gas production prediction.

[0013] Preferably, the step 1 further comprises:

[0014] A transformer oil-paper insulation discharge experiment platform is established, and oil-paper insulation discharge test is carried out, and the experiment platform comprises a discharge test loop, a test oil tank, a defect model and a signal measurement system.

[0015] Preferably, the discharge test loop, the power supply is a no-corona power frequency test transformer with a rated voltage of 100kV and a capacity of 20kVA, and the output voltage thereof can be adjusted through a voltage regulator; the protective resistor uses a water resistor with a resistance value of about 250MΩ; and the voltage divider is a resistance-capacitance type voltage divider with a division ratio of 2000:1, used for measuring high-voltage signals.

[0016] Preferably, the test oil tank, the tank body is made of organic glass, and is provided with an oil valve, a pressure gauge and a pressure release valve, the oil valve is used for oil injection and oil sampling, and the oil tank is provided with electrode grooves at the top and bottom, and the electrodes can be freely replaced.

[0017] Preferably, the defect model adopts a surface discharge defect model.

[0018] Preferably, the signal measurement system adopts a high-frequency current sensor to collect high-frequency current during the discharge process, the sensitivity of which is 10 V / A, the effective detection bandwidth is 45 kHz-125 MHz, the effective use temperature is 0-65℃, and the BNC interface is used for signal transmission.

[0019] Preferably, the oil paper is a synthetic ester oil paper.

[0020] Preferably, the synthetic ester oil paper selects Midel 7131 synthetic ester, and the Midel 7131 synthetic ester is pretreated by filtering, drying and vacuum oil immersion before the experiment.

[0021] Preferably, the operating state parameters in step 2 include current data and discharge energy.

[0022] Preferably, the concentration data of the dissolved gas in the oil obtained in step 3 specifically include:

[0023] The oil valve is used to take oil samples every 15 minutes, and a gas chromatograph is used to detect and analyze the composition and content of the gas in the insulating oil sample.

[0024] The contents of hydrogen (H2), carbon monoxide (CO), methane (CH4), ethane (C2H6), ethylene (C2H4) and acetylene (C2H2) are collected.

[0025] The step 4 further includes analyzing the correlation between the current, gas production energy and the dissolved gas production in the oil, training a neural network considering physical information constraints by using the collected series of data during the discharge process, and constructing a neural network prediction model fusing physical information rules.

[0026] Preferably, constructing a neural network prediction model fusing physical information rules specifically includes:

[0027] The monotonicity relationship between the current, gas production energy and the dissolved gas production in the oil is explored, which is the physical constraint condition and is put into the loss function as a regularization term.

[0028] Specifically, the structure framework of the neural network considering physical information constraints is:

[0029] The number of input layer nodes is 8, the input quantities are current data, discharge energy and the contents of hydrogen (H2), carbon monoxide (CO), methane (CH4), ethane (C2H6), ethylene (C2H4) and acetylene (C2H2), the number of hidden layer nodes is 12, the number of output layer nodes is 6, and the output quantities are the predicted production quantities of hydrogen (H2), carbon monoxide (CO), methane (CH4), ethane (C2H6), ethylene (C2H4) and acetylene (C2H2).

[0030] The application also discloses an oil dissolved gas prediction device considering physical information, comprising a parameter acquisition function area, a gas analysis function area, a model construction function area, a data prediction function area and a visual interactive function area.

[0031] Specifically, the parameter acquisition function area is used for collecting multi-dimensional operation state parameters such as voltage and current in the discharge process and synthetic ester oil samples in real time.

[0032] Specifically, the gas analysis function area is used for measuring the concentration of dissolved gas in the synthetic ester oil sample.

[0033] Specifically, the model construction function area is used for analyzing the complex correlation between current, gas production energy and dissolved gas production in oil, and constructing a neural network prediction model fusing physical rules.

[0034] Specifically, the data prediction function area is used for receiving and processing real-time monitoring data of synthetic ester oil paper insulation discharge, and realizing dynamic prediction of dissolved gas production in oil.

[0035] Specifically, the visual interactive function area is used for providing a visual interface to display prediction results, analysis reports and other key data, and supporting user interactive operation.

[0036] Compared with the related art, the oil dissolved gas prediction method and device considering physical information have the beneficial effects that the neural network model is added with physical information constraints, the gas production energy and current are used as constraint conditions, the shortcomings that an artificial intelligence data-driven model excessively depends on the number and quality of data samples are avoided, the dissolved gas production in oil can be accurately predicted, the fault diagnosis capability of deeply mining the dissolved gas in oil is improved, and the safe operation of power transformers is ensured, which has certain guiding significance.

[0037] The description is only a summary of the technical solutions of the application, in order to make the technical means of the application more clear and understandable, to the extent that the content of the description can be implemented by those skilled in the art, and in order to make the said and other purposes, features and advantages of the application more obvious and easy to understand, the following will be illustrated by the accompanying drawings and the specific embodiments of the application. BRIEF DESCRIPTION OF DRAWINGS

[0038] In order to make the specific embodiments of the application more clear, the following will briefly introduce the drawings needed in the prior art description. The drawings are only used for the purpose of illustrating the preferred embodiments, and are not considered as limiting the application. Obviously, the drawings described below are only some embodiments of the application, and other drawings can be obtained from these drawings without creative labor for those skilled in the art.

[0039] Figure 1 A flowchart of a method for predicting dissolved gas in oil considering physical information according to the present application;

[0040] Figure 2 A neural network schematic diagram considering physical information constraints according to the present application;

[0041] Figure 3 A functional partition diagram of a device for predicting dissolved gas in oil considering physical information according to the present application;

[0042] Figure 4 A transformer oil-paper insulation discharge experiment platform according to the present application. DETAILED DESCRIPTION

[0043] The specific embodiments of the present application will be described below with reference to the accompanying drawings. Figures 1 to 4 The specific embodiments of the present application will be described below with reference to the accompanying drawings.

[0044] It should also be understood that the application can be implemented or applied in other different specific embodiments, and various details of the specification can be modified or changed based on different views and applications without departing from the spirit of the application. Based on the embodiments of the present application, all other embodiments obtained by those of ordinary skill in the art without creative labor fall within the scope of the present application.

[0045] For better understanding of the embodiments of the present application, the following will be further explained and described with reference to the accompanying drawings based on one specific embodiment, and each drawing does not constitute a limitation on the embodiments of the present application.

[0046] In one embodiment, as shown in Figure 1 A method for predicting dissolved gas in oil considering physical information, specifically comprising:

[0047] Step 1: Perform synthetic ester oil-paper insulation discharge test;

[0048] Step 2: Collect operating state parameters such as voltage and current during discharge;

[0049] Step 3: Collect synthetic ester oil samples during discharge and obtain concentration data of dissolved gas in oil;

[0050] Step 4: Analyze the correlation between the current, energy production, and dissolved gas production in oil, and use the series of data collected during the discharge process to build and train a neural network model that considers physical information constraints;

[0051] Step 5: Receive real-time monitoring data of synthetic ester oil paper insulation discharge, realize dissolved gas production prediction in oil, and compare with other models to highlight the advantages of the neural network model considering physical information constraints.

[0052] Further, in step 1, synthetic ester oil paper insulation discharge test is performed.

[0053] Specifically, a transformer oil paper insulation discharge test platform is established to carry out synthetic ester oil paper insulation discharge test. The experimental platform is composed of a discharge test circuit, a test oil tank and a defect model, and a signal measurement system.

[0054] In another embodiment, the discharge test circuit is as shown in Figure 4 The power supply is a no-corona power frequency test transformer with a rated voltage of 100kV and a capacity of 20kVA, and the output voltage can be adjusted by a voltage regulator. The protective resistor uses a water resistor with a resistance of about 250MΩ. The voltage divider is a capacitance voltage divider with a division ratio of 2000:1, which is used to measure high-voltage signals.

[0055] In another embodiment, the test oil tank is made of organic glass as a whole, and is provided with an oil valve, a pressure gauge, and a pressure relief valve. The oil valve is used for oil injection and oil extraction, and the oil tank is provided with electrode grooves at the top and bottom, which can freely replace the electrodes.

[0056] In another embodiment, the defect model uses a surface discharge defect model.

[0057] In another embodiment, the signal measurement system uses a high-frequency current sensor to collect high-frequency current during the discharge process. The sensitivity is 10V / A, the effective detection bandwidth is 45kHz~125MHz, the effective use temperature is 0~65℃, and the BNC interface is used for signal transmission.

[0058] In another embodiment, the synthetic ester oil paper selects Midel 7131 synthetic ester, and the Midel 7131 synthetic ester is pretreated by filtering, drying, vacuum oil immersion, etc. before the experiment.

[0059] Further, in step 2, the voltage, current, and other operating state parameters during the discharge process are collected.

[0060] In another embodiment, a capacitance voltage divider with a division ratio of 2000:1 is used to collect voltage signals during the discharge process.

[0061] In another embodiment, a high-frequency current sensor is used to collect the current signal during the discharge process.

[0062] Further, in step 3, the synthetic ester oil sample during the discharge process is collected, and the concentration data of the dissolved gas in the oil is obtained.

[0063] Specifically, the oil valve is used to take an oil sample every 15 minutes, and a gas chromatograph is used to detect and analyze the composition and content of the gas in the insulating oil sample.

[0064] In another embodiment, the contents of hydrogen (H2), carbon monoxide (CO), methane (CH4), ethane (C2H6), ethylene (C2H4), and acetylene (C2H2) are collected.

[0065] Further, in step 4, the correlation between the current, gas production energy, and the dissolved gas production in the oil is analyzed, and a neural network model considering physical information constraints is constructed and trained using the series of data collected during the discharge process.

[0066] Specifically, the standard enthalpy of formation of H2 is 128.5 kJ / mol, the standard enthalpy of formation of CO is 110.5 kJ / mol, the standard enthalpy of formation of CH4 is 77.7 kJ / mol, the standard enthalpy of formation of C2H6 is 93.5 kJ / mol, the standard enthalpy of formation of C2H4 is 104.1 kJ / mol, and the standard enthalpy of formation of C2H2 is 278.3 kJ / mol.

[0067] The calculation formula of the gas production energy E is:

[0068]

[0069] In the formula, represents the gas content concentration of hydrogen, and the rest of the gases are the same.

[0070] The current values (mA), gas production energy (kJ / L), and solvent gas content concentration in the oil (μL / L) at three times during the discharge process collected in the embodiment are shown in the following table:

[0071]

[0072] Specifically, the monotonic relationship between the current, gas production energy, and the dissolved gas production in the oil is explored, and this monotonic relationship is the physical constraint condition, which is put into the loss function as a regularization term.

[0073] The monotonic relationship is that during the discharge process, as the current value and the gas production energy increase, the dissolved gas production in the oil also increases, showing a positive correlation.

[0074] In another embodiment, as shown in Figure 2 the structure framework of the neural network considering physical information constraints is:

[0075] The number of input layer nodes is 8, the input quantities are current data, discharge energy, and the contents of hydrogen (H2), carbon monoxide (CO), methane (CH4), ethane (C2H6), ethylene (C2H4), and acetylene (C2H2), the number of hidden layer nodes is 12, the number of output layer nodes is 6, and the output quantities are the predicted generation amounts of hydrogen (H2), carbon monoxide (CO), methane (CH4), ethane (C2H6), ethylene (C2H4), and acetylene (C2H2).

[0076] In another embodiment, the training process of the neural network considering physical information constraints is: the input layer inputs data, the hidden layer receives the data delivered by the input layer, uses the activation function tanh to perform nonlinear transformation on the data, and delivers the data to the output layer, and the output layer outputs the prediction result. In this network structure, the input layer is a fully connected layer, receives input data and directly delivers the data to the hidden layer; the hidden layer is also composed of a fully connected layer, performs calculation through the weight connection between nodes, completes nonlinear transformation and extracts features by combining the tanh activation function; the output layer is also a fully connected layer, maps the features of the hidden layer to the prediction result. The entire network does not involve pooling or recursive structure, but is based on layer-by-layer transmission of the fully connected layer, calculates the output through forward propagation, optimizes through the loss function combined with the prediction error and the physical constraint residual error, and iteratively updates the weights by using the gradient descent method to achieve minimization of the objective function.

[0077] Further, tanh activation function is used between layers for nonlinear conversion; when calculating the error, the prediction result is compared with the true value to calculate the data error, and the physical quantity predicted by the network is substituted into the corresponding physical constraint to calculate the residual error, and the sum of the two is the error obtained by the loss function; the error is used for back propagation for multiple iterations, and the weights between each node on the network are updated, and the optimization algorithm uses the gradient descent method, and when the value of the loss function decreases to a threshold 10e -4The iteration is stopped. It should be noted that the training data used in the model training process are obtained by experimental real collection to form an expert database. Before training the model, data collection and recording are completed through multiple experiments. Specifically, the experiment includes steps 1 to 3. In each experiment, the concentration of 6 kinds of gas, current and discharge energy data are obtained by oil sample collection, which are used as model input to predict the future 6 kinds of gas concentration value, i.e. predicted value; and the actual concentration value of 6 kinds of gas collected in the experiment is the true value. Through repeated experiments, an expert database containing rich experimental data is constructed to train the neural network model. After training, the model can accurately predict the input data of newly collected oil samples.

[0078] Data error between predicted value and true value The calculation formula is:

[0079]

[0080] In the formula, represents the true value; represents the model prediction value; represents the time step index, and the value range is [1, N-1]; represents the output prediction value index, and the value range is [1, 6]; N represents the total number of time steps, i.e. the total number of steps contained in the time series data. In this embodiment, N refers to the number of times of taking oil samples, i.e. N oil samples are taken at N different time points.

[0081] Residual error calculated by physical constraint The calculation formula is:

[0082]

[0083] In the formula, represents the time step index, and the value range is [1, N-1]; and represent two consecutive time points; represents the output prediction value index, and the value range is [1, 6]; is a positive number approximately equal to 0 to prevent the denominator from being zero; represents the current; represents the gas production energy; represents the predicted amount of dissolved gas in the oil.

[0084] If the monotonic relationship between the current, the gas production energy and the dissolved gas production in the oil is satisfied, the loss function L only includes the data error; if the monotonic relationship is not satisfied, the loss function L is the sum of the data error and the residual error calculated by the physical constraint to punish the violation of the monotonicity. That is:

[0085]

[0086] In the formula, is a weight parameter for adjusting the influence of the physical constraint residual and the data error, and the embodiment of the present application takes .

[0087] Further, in step 5, the synthetic ester oil paper insulation discharge real-time monitoring data is received, the gas production of the dissolved gas in oil is predicted, and compared with other models, the advantages of the neural network model considering the physical information constraint are highlighted.

[0088] In another embodiment, a common neural network model not considering the physical information constraint is trained by using the same data, and the advantages of the neural network model considering the physical information constraint are highlighted by determining the coefficient index.

[0089] The coefficient of determination The calculation formula is:

[0090]

[0091] In the formula, represents the true value, represents the average value of the true value, represents the model prediction value.

[0092] In another embodiment, 450 groups of data are used to train the neural network model considering the physical information constraint and the common neural network model not considering the physical information constraint, 150 groups of data are used to test the two models, and the coefficient of determination is calculated.

[0093] In this embodiment, the coefficient of determination of the neural network model considering the physical information constraint is , and the coefficient of determination of the common neural network model not considering the physical information constraint is . It can be seen that the neural network model considering the physical information constraint designed in the present application not only can accurately predict the data of the dissolved gas in oil, but also has stronger robustness and generalization ability when processing small-scale data due to the constraint of the existing physical prior knowledge, and the interpretability of the model reasoning process is also improved.

[0094] In addition, the present application also provides a device for predicting the dissolved gas in oil considering the physical information, as shown in Figure 3 The device comprises a parameter acquisition functional area Div1, a gas analysis functional area Div2, a model construction functional area Div3, a data prediction functional area Div4, and a visual interactive functional area Div5.

[0095] Specifically, the parameter acquisition function area Div1 is used for real-time acquisition of multi-dimensional operating state parameters such as voltage and current in the discharge process and synthetic ester oil samples.

[0096] Specifically, the gas analysis function area Div2 is used for determining the concentration of dissolved gas in the synthetic ester oil sample.

[0097] Specifically, the model construction function area Div3 is used for analyzing the complex correlation between current, gas production energy and dissolved gas production in oil, and constructing and training a neural network model considering physical information constraints.

[0098] Specifically, the data prediction function area Div4 is used for receiving and processing real-time monitoring data of synthetic ester oil paper insulation discharge, and realizing dynamic prediction of dissolved gas production in oil.

[0099] Specifically, the visual interaction function area Div5 is used for providing a visual interface to display prediction results, analysis reports and other key data, and supporting user interaction operations.

[0100] In summary, the present application discloses a dissolved gas prediction method and device considering physical information, including the following steps: 1. performing synthetic ester oil paper insulation discharge test; 2. acquiring operating state parameters such as voltage and current during discharge; 3. acquiring synthetic ester oil samples during discharge and obtaining concentration data of dissolved gas in oil; 4. analyzing the correlation between current, gas production energy and dissolved gas production in oil, and constructing and training a neural network model considering physical information constraints by using series of data collected during discharge; 5. receiving real-time monitoring data of synthetic ester oil paper insulation discharge, realizing dissolved gas production prediction in oil, and comparing with other models to highlight the advantages of the neural network model considering physical information constraints. The present application adds physical information constraints to the neural network model, uses gas production energy and current as constraint conditions, avoids the shortcomings of artificial intelligence data-driven models that rely too much on data sample quantity and quality, and can accurately predict dissolved gas production in oil, which has certain guiding significance for improving fault diagnosis ability of dissolved gas in oil and ensuring safe operation of power transformers.

[0101] The above embodiments are only for illustrating the technical concept and characteristics of the present application, and the purpose is to enable those skilled in the art to understand the content of the present application and implement it, and cannot limit the protection scope of the present application. Any equivalent changes or modifications made according to the spirit and essence of the present application should be covered within the protection scope of the present application.

Claims

1. A method for predicting dissolved gases in oil that considers physical information, characterized in that, Includes the following steps: Step 1: Conduct an oil-paper insulation discharge test; Step 2: Collect operating status parameters during the discharge process; Step 3: Collect oil samples during the discharge process and obtain the concentration data of dissolved gases in the oil; Step 4: Construct a neural network prediction model using the operating status parameters and the concentration data, and train it. Step 5: Receive real-time monitoring data of oil-paper insulation discharge, and use the neural network prediction model to predict the generation of dissolved gas in the oil; in, The operating status parameters in step 2 include: current data and discharge energy; Step 4 further includes: analyzing the correlation between current, gas production energy and dissolved gas production in oil, training a neural network that considers physical information constraints using a series of data collected during the discharge process, and constructing a neural network prediction model that integrates physical information rules. Constructing a neural network prediction model that integrates physical information rules specifically includes: The monotonicity relationship between current, gas production energy and dissolved gas production in oil was investigated. This monotonicity relationship is the physical constraint condition, which is then included as a regularization term in the loss function. The structural framework of the neural network considering physical information constraints is as follows: The input layer has 8 nodes, and the input quantities are current data, discharge energy, and the content of six gases: hydrogen (H2), carbon monoxide (CO), methane (CH4), ethane (C2H6), ethylene (C2H4), and acetylene (C2H2). The hidden layer has 12 nodes, and the output layer has 6 nodes, and the output quantities are the predicted generation quantities of the six gases: hydrogen (H2), carbon monoxide (CO), methane (CH4), ethane (C2H6), ethylene (C2H4), and acetylene (C2H2). The data error L between the predicted value and the actual value data The calculation formula is: ， In the formula, Represents the actual value; The value represents the model's predicted value; t represents the time step index, with a value range of [1, N-1]; j represents the output predicted value index, with a value range of [1, 6]; N represents the total number of time steps, i.e., the total number of steps contained in the time series data, i.e., N oil samples were taken at N different times. The residual L obtained from physical constraint calculation phy The calculation formula is: ， In the formula, t represents the time step index, which ranges from [1, N-1]; t and t+1 represent two consecutive time points; j represents the output prediction index, which ranges from [1, 6]. It is a positive number approximately equal to 0 to prevent the denominator from being zero; I represents the current; E represents the energy of gas production; G represents the predicted amount of dissolved gas in the oil; If the current, gas production energy, and dissolved gas production in the oil satisfy the aforementioned monotonicity relationship, the loss function L only includes data errors; if the monotonicity relationship is not satisfied, the loss function L is the sum of the data errors and the residuals calculated from the physical constraints, to penalize violations of monotonicity, i.e.: ， In the formula, α is a weighting parameter used to adjust the influence of physical constraint residuals and data errors, and α is taken as 0.

8.

2. The method according to claim 1, characterized in that, Step 1 further includes: A transformer oil-paper insulation discharge test platform was established to conduct oil-paper insulation discharge tests. The test platform includes a discharge test circuit, a test oil tank and defect model, and a signal measurement system.

3. The method according to claim 2, characterized in that, The discharge test circuit is powered by a corona-free power frequency test transformer, and its output voltage is adjusted by a voltage regulator.

4. The method according to claim 2, characterized in that, The test oil tank is made entirely of plexiglass and is equipped with an oil valve, a pressure gauge, and a pressure relief valve.

5. The method according to claim 2, characterized in that, The defect model described above adopts the surface discharge defect model.

6. The method according to claim 2, characterized in that, The signal measurement system uses a high-frequency current sensor to collect the high-frequency current during the discharge process.

7. The method according to claim 1, characterized in that, The oil paper is a synthetic ester oil paper.

8. The method according to claim 7, characterized in that, The synthetic ester paper is made from Midel 7131 synthetic ester.

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