Dry-type transformer insulation aging evaluation method and device

Abstract

The invention discloses a dry-type transformer insulation aging evaluation method and device, and the method comprises the steps: obtaining the operation data of a dry-type transformer, and obtaining the target breakdown time data of an insulating material based on the two-parameter Weibull distribution and the operation data of the dry-type transformer; the operation data of the dry-type transformer comprises temperature data, electric field intensity data and breakdown time data; and constructing an electric heating aging life model based on the target breakdown time data, inputting real-time temperature data and electric field intensity data into the electric heating aging life model, and obtaining a part aging distribution condition of the dry-type transformer. An insulation life aging model is constructed based on temperature and electric field real-time data of online monitoring of the dry-type transformer, so that the insulation life of the dry-type transformer is predicted according to the insulation life aging model. The service life distribution conditions of different parts of the dry-type transformer are obtained, and visual reference is provided for maintenance personnel to carry out equipment maintenance and make a maintenance plan.

Description

Technical Field

[0001] The present invention relates to the field of aging assessment of high-voltage power equipment, and in particular to a method and device for evaluating insulation aging of a dry-type transformer. Background Art

[0002] A dry-type transformer is a transformer that uses air as the insulating medium instead of oil or other liquids. Dry-type transformers are an important power transmission and distribution equipment that plays a key role in the power system. They are safe, environmentally friendly, and easy to maintain, so they are widely used in residential, commercial, and industrial fields. As the core component of the transformer, the performance of the insulating material directly affects the operating life and safety of the transformer. The insulating material will gradually age under long-term electric fields and high temperature environments, which will affect the reliability and service life of the entire transformer. In actual operation, the environment in which the insulating material of the dry-type transformer is located is more complex and changeable, so it is more difficult to accurately evaluate the life of the entire machine. In addition, the operating conditions of different parts of the transformer are different, and the degree of aging of the insulating material is also different. Some parts age faster, becoming a weak link that affects the overall life of the transformer. In actual operation, although maintenance personnel can monitor the electric field and temperature changes of the transformer, they cannot accurately determine which parts may face the risk of failure, and cannot quantitatively evaluate the aging state of the corresponding parts, so they cannot take targeted preventive measures.

[0003] Existing dry-type transformer life assessment methods usually rely on destructive experiments on insulating materials under laboratory conditions, which is not only time-consuming and costly, but also cannot be directly applied to the status assessment of transformer equipment in actual operation. In addition, existing life assessment methods are mostly based on single-factor aging life assessment, and rarely consider the aging effect under the action of electrothermal coupling. There is a lack of accurate mathematical models to deal with the combined effects of multiple aging factors, resulting in insufficient accuracy and reliability of the assessment results. Summary of the invention

[0004] The invention provides a dry-type transformer insulation aging assessment method and device to improve the accuracy and reliability of the dry-type transformer insulation aging assessment.

[0005] In order to solve the above technical problems, an embodiment of the present invention provides a dry-type transformer insulation aging assessment method, comprising:

[0006] Acquire dry-type transformer operation data, and acquire target breakdown time data of the insulating material based on the dual-parameter Weibull distribution and the dry-type transformer operation data; the dry-type transformer operation data includes temperature data, electric field strength data and breakdown time data;

[0007] An electrothermal aging life model is constructed based on the target breakdown time data, and real-time temperature data and electric field strength data are input into the electrothermal aging life model to obtain the aging distribution of the dry-type transformer.

[0008] The present invention constructs an insulation life aging model based on the real-time data of temperature and electric field monitored online by the dry-type transformer, thereby predicting the insulation life of the dry-type transformer according to the insulation life aging model, and obtaining the life distribution of different parts of the dry-type transformer, so as to determine the insulation weak points of the dry-type transformer and provide a visual reference for maintenance personnel to perform equipment maintenance and formulate maintenance plans.

[0009] Furthermore, the obtaining of dry-type transformer operation data includes:

[0010] Based on preset prediction condition parameters, several aging tests are performed on the insulation material of the dry-type transformer, and breakdown time data of the insulation of the dry-type transformer under different condition parameters are obtained; the prediction condition parameters include temperature and electric field strength;

[0011] Dry-type transformer operation data is generated based on the prediction condition parameters and the corresponding breakdown time data set.

[0012] Furthermore, the method of obtaining target breakdown time data of the insulating material based on the dual-parameter Weibull distribution and the dry-type transformer operation data includes:

[0013] Calculating a two-parameter Weibull distribution based on the temperature data, the electric field strength data, and the breakdown time data, and determining a target breakdown time corresponding to a prediction condition parameter based on the two-parameter Weibull distribution;

[0014] The two-parameter Weibull distribution is specifically:

[0015]

[0016] Among them, P(t i ) is the breakdown time probability; t i is the breakdown time of the i-th aging test, 1≤i≤n, n is the number of aging tests; α is the scale parameter, and β is the shape parameter.

[0017] Furthermore, the electrothermal aging life model includes:

[0018]

[0019] Among them, A1, A2, B1, B2 are constants related to the insulation materials of dry-type transformers, E is the electric field strength, T is the temperature, and L is the insulation life.

[0020] Furthermore, the real-time temperature data and electric field strength data are input into the electrothermal aging life model to obtain the aging distribution of the dry-type transformer, including:

[0021] Real-time temperature data and electric field strength data of various parts of the dry-type transformer obtained based on the condition monitoring sensor;

[0022] Inputting the real-time temperature data and electric field strength data of each part into the electrothermal aging life model respectively to obtain the aging distribution of each part;

[0023] The insulation weak points are obtained based on the aging distribution of each part, and the maintenance plan is matched based on the insulation weak points.

[0024] In a second aspect, the present invention provides a dry-type transformer insulation aging assessment device, comprising: a test module and a prediction module;

[0025] The test module is used to obtain the dry-type transformer operation data, and obtain the target breakdown time data of the insulating material based on the dual-parameter Weibull distribution and the dry-type transformer operation data; the dry-type transformer operation data includes temperature data, electric field strength data and breakdown time data;

[0026] The prediction module is used to construct an electrothermal aging life model based on the target breakdown time data, input real-time temperature data and electric field strength data into the electrothermal aging life model, and obtain the aging distribution of the dry-type transformer.

[0027] Furthermore, the test module includes:

[0028] Based on preset prediction condition parameters, several aging tests are performed on the dry-type transformer, and breakdown time data of the dry-type transformer under different condition parameters are obtained; the prediction condition parameters include temperature and electric field strength;

[0029] Dry-type transformer operation data is generated based on the prediction condition parameters and the corresponding breakdown time data set.

[0030] Furthermore, the test module includes:

[0031] Calculating a two-parameter Weibull distribution based on the temperature data, the electric field strength data, and the breakdown time data, and determining a target breakdown time corresponding to a prediction condition parameter based on the two-parameter Weibull distribution;

[0032] The two-parameter Weibull distribution is specifically:

[0033]

[0034] Among them, P(t i) is the breakdown time probability; t i is the breakdown time of the i-th aging test, 1≤i≤n, n is the number of aging tests; α is the scale parameter, and β is the shape parameter.

[0035] Furthermore, the prediction module includes:

[0036]

[0037] Among them, A1, A2, B1, B2 are constants related to the insulation materials of dry-type transformers, E is the electric field strength, T is the temperature, and L is the insulation life.

[0038] Furthermore, the prediction module includes:

[0039] Real-time temperature data and electric field strength data of various parts of the dry-type transformer obtained based on the condition monitoring sensor;

[0040] Inputting the real-time temperature data and electric field strength data of each part into the electrothermal aging life model respectively to obtain the aging distribution of each part;

[0041] The insulation weak points are obtained based on the aging distribution of each part, and the maintenance plan is matched based on the insulation weak points. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 A schematic flow chart of a method for evaluating insulation aging of a dry-type transformer provided in an embodiment of the present invention;

[0043] Figure 2 A schematic structural diagram of a dry-type transformer insulation aging assessment device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0044] The specific implementation of the present invention is further described in detail below in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0045] The terms "first" and "second" and the like in the specification, claims and drawings of this application are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device comprising a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products or devices.

[0046] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0047] First, some terms in this application are explained to facilitate understanding by those skilled in the art.

[0048] (1) The two-parameter Weibull distribution is an important continuous probability distribution that is often used in fields such as survival analysis, reliability engineering, and failure analysis. It is often used to model the fracture strength of materials, the failure time of products, etc.

[0049] Example 1

[0050] See also Figure 1 , Figure 1 The present invention provides a dry-type transformer insulation aging assessment method, which includes steps 101 to 102, as follows:

[0051] Step 101: obtaining dry-type transformer operating data, and obtaining target breakdown time data of an insulating material based on a dual-parameter Weibull distribution and the dry-type transformer operating data; the dry-type transformer operating data includes temperature data, electric field strength data, and breakdown time data;

[0052] In this embodiment, the dry-type transformer operation data includes breakdown time data of the main insulation material of the dry-type transformer under different influencing factors, which can be obtained based on historical operation data collected when the dry-type transformer is in operation, or can be obtained based on experiments.

[0053] In this embodiment, the step of obtaining the dry-type transformer operation data includes:

[0054] Based on preset prediction condition parameters, several aging tests are performed on the dry-type transformer, and breakdown time data of the dry-type transformer under different condition parameters are obtained; the prediction condition parameters include temperature and electric field strength;

[0055] Dry-type transformer operation data is generated based on the prediction condition parameters and the corresponding breakdown time data set.

[0056] In this embodiment, the breakdown time data of the main insulating material of the dry-type transformer is obtained through experiments, which includes: presetting prediction condition parameters, the prediction condition parameters including temperature and electric field strength. By adjusting the data of the prediction condition parameters, the breakdown time data of the main insulating material of the dry-type transformer under different temperatures and electric field strengths are obtained.

[0057] As a specific example of an embodiment of the present invention, a long-term breakdown test is performed on the insulating material silicone rubber of a dry-type transformer under temperature and electric field, and the aging electric field strength is 15kV / mm, 18kV / mm and 21kV / mm, and the aging temperature is 120°C, 150°C and 180°C. A group of experiments is formed by combining the electric field strength and temperature in pairs, and each group of experiments is tested for 10 breakdown times.

[0058] Please refer to Table 1, which is a dry-type transformer operation data table provided in an embodiment of the present invention.

[0059]

[0060] Table 1

[0061] In this embodiment, each group of data in Table 1 is obtained through experiments.

[0062] In this embodiment, the method of obtaining target breakdown time data of the insulating material based on the dual-parameter Weibull distribution and the dry-type transformer operation data includes:

[0063] Calculating a two-parameter Weibull distribution based on the temperature data, the electric field strength data, and the breakdown time data, and determining a target breakdown time corresponding to a prediction condition parameter based on the two-parameter Weibull distribution;

[0064] The two-parameter Weibull distribution is specifically:

[0065]

[0066] Among them, P(t i ) is the breakdown time probability; t i is the breakdown time of the i-th aging test, 1≤i≤n, n is the number of aging tests; α is the scale parameter, and β is the shape parameter.

[0067] In this embodiment, a two-parameter Weibull distribution is constructed based on temperature data, electric field strength data and breakdown time data, and the target breakdown time is obtained according to the two-parameter Weibull distribution. The two-parameter Weibull distribution includes an α scale parameter and a β shape parameter, wherein α is a scale parameter, representing the breakdown time corresponding to a breakdown time probability of 63.2%, i.e., the target breakdown time; β is a shape parameter, representing the dispersion of the data.

[0068] In this embodiment, by utilizing the characteristics of the two-parameter Weibull distribution, the breakdown time equal to the scale parameter is taken as the target breakdown time, so as to evaluate the aging life of the dry-type transformer.

[0069] Please refer to Table 2, which is a target breakdown time data table provided by an embodiment of the present invention.

[0070] In this embodiment, based on the temperature data, electric field strength data and breakdown time data in Table 1, a two-parameter Weibull distribution is constructed, and the target breakdown time is obtained.

[0071]

[0072] Table 2

[0073] Step 102: constructing an electrothermal aging life model based on the target breakdown time data, inputting real-time temperature data and electric field strength data into the electrothermal aging life model, and obtaining the aging distribution of the dry-type transformer.

[0074] In this embodiment, an electrothermal aging life model is constructed, and constant parameters in the electrothermal aging life model are calculated based on target breakdown time data.

[0075] In this embodiment, the electrothermal aging life model includes:

[0076]

[0077] Among them, A1, A2, B1, B2 are constants related to the insulation materials of dry-type transformers, E is the electric field strength, T is the temperature, and L is the insulation life.

[0078] In this embodiment, the target breakdown time data is input into the electrothermal aging life model to obtain constants A1, A2, B1, and B2 related to the insulation material of the dry-type transformer.

[0079] In this embodiment, the target breakdown time data obtained based on the conditional parameters and the two-parameter Weibull distribution is input into the electrothermal aging life model to obtain A1, A2, B1, and B2 as 306.42, -297.35, 1306.23, and -43.46, respectively, and the electrothermal aging life model is constructed as follows:

[0080]

[0081] In this embodiment, the step of inputting the real-time temperature data and the electric field strength data into the electrothermal aging life model to obtain the aging distribution of the dry-type transformer includes:

[0082] Real-time temperature data and electric field strength data of various parts of the dry-type transformer obtained based on the condition monitoring sensor;

[0083] Inputting the real-time temperature data and electric field strength data of each part into the electrothermal aging life model respectively to obtain the aging distribution of each part;

[0084] The insulation weak points are obtained based on the aging distribution of each part, and the maintenance plan is matched based on the insulation weak points.

[0085] In this embodiment, real-time temperature data and electric field strength data of various parts of the dry-type transformer are obtained, and the insulation life of each part is calculated based on the real-time temperature data and electric field strength data, and the aging distribution of each part is obtained based on the insulation life.

[0086] In this embodiment, an insulation life aging model is constructed based on the real-time data of temperature and electric field monitored online by the dry-type transformer, so that the insulation life of the dry-type transformer is predicted according to the insulation life aging model, and the life distribution of different parts of the dry-type transformer is obtained, so as to determine the insulation weak points of the dry-type transformer, and provide a visual reference for maintenance personnel to perform equipment maintenance and formulate maintenance plans.

[0087] In this embodiment, based on the preset life threshold and aging distribution, insulation weak points in various parts of the dry-type transformer are screened out, and a maintenance plan is matched based on the insulation weak points and the corresponding insulation life.

[0088] In this embodiment, based on the aging distribution and the three-dimensional model of the insulation weak point set dry-type transformer, the aging distribution is visualized.

[0089] In this embodiment, when the insulation life of a certain part is lower than a preset life threshold, the part is an insulation weak point of the dry-type transformer, and when the difference between the insulation life of the part and the life threshold is less than the preset threshold, early warning information is generated based on the part and its insulation life.

[0090] In this embodiment, a chart is drawn based on the aging distribution to visualize the aging of the insulation material of the dry-type transformer.

[0091] In this embodiment, the number of insulation weak points in the dry-type transformer is counted, and a score is given based on the number of insulation weak points and the total number of locations to obtain an aging score of the dry-type transformer.

[0092]

[0093] Wherein, n is the number of insulation weak points, N is the total number of weak points, d is the interpolation between the insulation weak points and the preset life threshold, and a1 and a2 are preset weight coefficients.

[0094] In this embodiment, an evaluation report is generated based on the aging score, the location and number of insulation weak points, and the warning information.

[0095] In this embodiment, an insulation life aging model is constructed based on the real-time data of temperature and electric field monitored online by the dry-type transformer, so that the insulation life of the dry-type transformer is predicted according to the insulation life aging model, and the life distribution of different parts of the dry-type transformer is obtained, so as to determine the insulation weak points of the dry-type transformer, and provide a visual reference for maintenance personnel to perform equipment maintenance and formulate maintenance plans.

[0096] Please refer to Figure 2 , Figure 2 A schematic structural diagram of a dry-type transformer insulation aging assessment device provided by an embodiment of the present invention includes: a test module 201 and a prediction module 202;

[0097] The test module 201 is used to obtain dry-type transformer operation data, and obtain target breakdown time data of the insulating material based on the dual-parameter Weibull distribution and the dry-type transformer operation data; the dry-type transformer operation data includes temperature data, electric field strength data and breakdown time data;

[0098] The prediction module 202 is used to construct an electrothermal aging life model based on the target breakdown time data, input real-time temperature data and electric field strength data into the electrothermal aging life model, and obtain the aging distribution of the dry-type transformer.

[0099] In this embodiment, the test module includes:

[0100] Based on preset prediction condition parameters, several aging tests are performed on the dry-type transformer, and breakdown time data of the dry-type transformer under different condition parameters are obtained; the prediction condition parameters include temperature and electric field strength;

[0101] Dry-type transformer operation data is generated based on the prediction condition parameters and the corresponding breakdown time data set.

[0102] In this embodiment, the test module includes:

[0103] Calculating a two-parameter Weibull distribution based on the temperature data, the electric field strength data, and the breakdown time data, and determining a target breakdown time corresponding to a prediction condition parameter based on the two-parameter Weibull distribution;

[0104] The two-parameter Weibull distribution is specifically:

[0105]

[0106] Among them, P(t i ) is the breakdown time probability; ti is the breakdown time of the i-th aging test, 1≤i≤n, n is the number of aging tests; α is the scale parameter, and β is the shape parameter.

[0107] In this embodiment, the prediction module includes:

[0108]

[0109] Among them, A1, A2, B1, B2 are constants related to the insulation materials of dry-type transformers, E is the electric field strength, T is the temperature, and L is the insulation life.

[0110] In this embodiment, the prediction module includes:

[0111] Real-time temperature data and electric field strength data of various parts of the dry-type transformer obtained based on the condition monitoring sensor;

[0112] Inputting the real-time temperature data and electric field strength data of each part into the electrothermal aging life model respectively to obtain the aging distribution of each part;

[0113] The insulation weak points are obtained based on the aging distribution of each part, and the maintenance plan is matched based on the insulation weak points.

[0114] In this embodiment, an insulation life aging model is constructed based on the real-time data of temperature and electric field monitored online by the dry-type transformer, so that the insulation life of the dry-type transformer is predicted according to the insulation life aging model, and the life distribution of different parts of the dry-type transformer is obtained, so as to determine the insulation weak points of the dry-type transformer, and provide a visual reference for maintenance personnel to perform equipment maintenance and formulate maintenance plans.

[0115] The embodiment of the present invention provides a dry-type transformer insulation aging life assessment system, the system comprising a dry-type transformer monitoring data acquisition module, a target breakdown time determination module, an electrothermal aging life construction module, an electrothermal aging life calculation module and a life distribution display module;

[0116] The dry-type transformer monitoring data acquisition module includes a real-time temperature acquisition unit, a temperature sensor, a real-time electric field acquisition unit, and an electric field measurement sensor, which are used to obtain real-time temperature and electric field strength data of various parts of the dry-type transformer;

[0117] The target breakdown time determination module is used to determine the target breakdown time under the corresponding temperature and electric field through a two-parameter Weibull distribution according to the breakdown time data under each set of aging temperature and electric field conditions;

[0118] The electrothermal aging life construction module is used to construct an electrothermal aging life model according to the target breakdown time and the corresponding temperature and electric field;

[0119] The electrothermal aging life calculation module is used to calculate the aging life of different parts of the dry-type transformer according to the obtained real-time temperature and electric field strength data of each part and the constructed electrothermal aging life model;

[0120] The life distribution display module is used to display the calculated aging life distribution of each part in the dry-type transformer model.

[0121] In this embodiment, the electrothermal aging life model is combined with the electric thermal field distribution law of the dry-type transformer to obtain the life distribution of different parts. The method adopted is to set the life characteristics of the material as a function of temperature T and electric field E, and the life is bidirectionally coupled through the electric thermal field distribution law of the dry-type transformer obtained by real-time acquisition.

[0122] In this embodiment, an insulation life aging model is constructed based on the real-time data of temperature and electric field monitored online by the dry-type transformer, so that the insulation life of the dry-type transformer is predicted according to the insulation life aging model, and the life distribution of different parts of the dry-type transformer is obtained, so as to determine the insulation weak points of the dry-type transformer, and provide a visual reference for maintenance personnel to perform equipment maintenance and formulate maintenance plans.

[0123] In an embodiment of the present invention, a computer device is also provided, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, the above-mentioned dry-type transformer insulation aging assessment method is implemented.

[0124] In an embodiment of the present invention, a computer-readable storage medium is also provided, the computer-readable storage medium including a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute the above-mentioned dry-type transformer insulation aging assessment method.

[0125] Exemplarily, the computer program may be divided into one or more modules, one or more modules are stored in a memory and executed by a processor to implement the present invention. One or more modules may be a series of computer program instruction segments capable of implementing specific functions, and the instruction segments are used to describe the execution process of the computer program in a computer device.

[0126] The computer device may be a computing device such as a desktop computer, a notebook, a PDA, and a cloud server. The computer device may include, but is not limited to, a processor, a memory, and a display. Those skilled in the art will appreciate that the above components are merely examples of computer devices and do not constitute a limitation on computer devices. The computer device may include more or fewer components than the components, or may combine certain components, or different components. For example, the computer device may also include input and output devices, network access devices, buses, and the like.

[0127] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of a computer device, and uses various interfaces and lines to connect various parts of the entire computer device.

[0128] The memory can be used to store computer programs and / or modules. The processor realizes various functions of the computer device by running or executing the computer programs and / or modules stored in the memory, and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, an application required for at least one function (such as a sound playback function, a text conversion function, etc.), etc.; the data storage area can store data created according to the use of the mobile phone (such as audio data, text message data, etc.), etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Med ia Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0129] Among them, if the module based on the processing of the multi-device access platform is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, RandomAccess Memory), electric carrier signal, telecommunication signal and software distribution medium, etc. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0130] The specific embodiments described above further illustrate the purpose, technical solutions and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. It is particularly pointed out that for those skilled in the art, any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for evaluating insulation aging of a dry-type transformer, characterized in that: include: Acquire dry-type transformer operation data, and acquire target breakdown time data of the insulating material based on the dual-parameter Weibull distribution and the dry-type transformer operation data; The dry-type transformer operation data includes temperature data, electric field strength data and breakdown time data; An electrothermal aging life model is constructed based on the target breakdown time data, and real-time temperature data and electric field strength data are input into the electrothermal aging life model to obtain the aging distribution of the dry-type transformer.

2. A dry-type transformer insulation aging assessment method as claimed in claim 1, characterized in that: The step of obtaining the dry-type transformer operation data includes: Based on preset prediction condition parameters, several aging tests are performed on the insulation of the dry-type transformer, and breakdown time data of the insulation material of the dry-type transformer under different condition parameters are obtained; the prediction condition parameters include temperature and electric field strength; Dry-type transformer operation data is generated based on the prediction condition parameters and the corresponding breakdown time data set.

3. A dry-type transformer insulation aging assessment method as claimed in claim 2, characterized in that: The method of obtaining target breakdown time data of the insulating material based on the dual-parameter Weibull distribution and the dry-type transformer operation data includes: Calculating a two-parameter Weibull distribution based on the temperature data, the electric field strength data, and the breakdown time data, and determining a target breakdown time corresponding to a prediction condition parameter based on the two-parameter Weibull distribution; The two-parameter Weibull distribution is specifically: Among them, P(t i ) is the breakdown time probability; t i is the breakdown time of the i-th aging test, 1≤i≤n, n is the number of aging tests; α is the scale parameter, and β is the shape parameter.

4. A dry-type transformer insulation aging assessment method as claimed in claim 3, characterized in that: The electrothermal aging life model includes: Among them, A1, A2, B1, B2 are constants related to the insulation materials of dry-type transformers, E is the electric field strength, T is the temperature, and L is the insulation life.

5. A dry-type transformer insulation aging assessment method as claimed in claim 4, characterized in that: The step of inputting the real-time temperature data and the electric field strength data into the electrothermal aging life model to obtain the aging distribution of the dry-type transformer includes: Real-time temperature data and electric field strength data of various parts of the dry-type transformer obtained based on the condition monitoring sensor; Inputting the real-time temperature data and electric field strength data of each part into the electrothermal aging life model respectively to obtain the aging distribution of each part; The insulation weak points are obtained based on the aging distribution of each part, and the maintenance plan is matched based on the insulation weak points.

6. A dry-type transformer insulation aging assessment device, characterized in that: include: Testing module and prediction module; The test module is used to obtain the dry-type transformer operation data, and obtain the target breakdown time data of the insulating material based on the dual-parameter Weibull distribution and the dry-type transformer operation data; the dry-type transformer operation data includes temperature data, electric field strength data and breakdown time data; The prediction module is used to construct an electrothermal aging life model based on the target breakdown time data, input real-time temperature data and electric field strength data into the electrothermal aging life model, and obtain the aging distribution of the dry-type transformer.

7. The dry-type transformer insulation aging evaluation device according to claim 6, characterized in that: The test module comprises: Based on preset prediction condition parameters, several aging tests are performed on the dry-type transformer, and breakdown time data of the dry-type transformer under different condition parameters are obtained; the prediction condition parameters include temperature and electric field strength; Dry-type transformer operation data is generated based on the prediction condition parameters and the corresponding breakdown time data set.

8. The dry-type transformer insulation aging evaluation device according to claim 7, characterized in that: The test module comprises: Calculating a two-parameter Weibull distribution based on the temperature data, the electric field strength data, and the breakdown time data, and determining a target breakdown time corresponding to a prediction condition parameter based on the two-parameter Weibull distribution; The two-parameter Weibull distribution is specifically: Among them, P(t i ) is the breakdown time probability; t i is the breakdown time of the i-th aging test, 1≤i≤n, n is the number of aging tests; α is the scale parameter, and β is the shape parameter.

9. The dry-type transformer insulation aging evaluation device according to claim 8, characterized in that: The prediction module comprises: Among them, A1, A2, B1, B2 are constants related to the insulation materials of dry-type transformers, E is the electric field strength, T is the temperature, and L is the insulation life.

10. The dry-type transformer insulation aging evaluation device according to claim 9, characterized in that: The prediction module comprises: Real-time temperature data and electric field strength data of various parts of the dry-type transformer obtained based on the condition monitoring sensor; Inputting the real-time temperature data and electric field strength data of each part into the electrothermal aging life model respectively to obtain the aging distribution of each part; The insulation weak points are obtained based on the aging distribution of each part, and the maintenance plan is matched based on the insulation weak points.

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