A method and device for diagnosing the metal wear state of a transformer power assembly

Abstract

This application provides a method and apparatus for diagnosing the metal wear condition of transformer power components. The diagnostic method includes: constructing a distribution function of ferromagnetic and non-ferromagnetic particles in the transformer; acquiring data collected in a first period; inputting the collected data into the distribution function of ferromagnetic and non-ferromagnetic particles to calculate ferromagnetic and non-ferromagnetic particle data; adding the ferromagnetic and non-ferromagnetic particle data to obtain the metal particle data for the first period; acquiring the metal particle data for the second period; calculating the median particle size and weight growth rate of the metal particles based on the metal particle data from the two periods; and determining the metal wear condition of the transformer power components based on the median particle size and weight growth rate. By collecting and statistically analyzing metal particles in the oil of a faulty transformer, a statistical distribution function of the metal particles is obtained, and combined with actual monitoring data, prediction of small-diameter metal particle data that cannot be directly measured is achieved.

Description

Technical Field

[0001] This application relates to the field of transformer technology, and in particular to a method and apparatus for diagnosing the metal wear condition of transformer power components. Background Technology

[0002] Power transformers are expensive core equipment in power systems. A power transformer failure can trigger regional power outages, causing severe socio-economic losses. Oil-immersed transformers are the main type of power transformer, using insulating oil as both insulation and cooling medium. For large power transformers with voltage levels of 500kV and above, a forced oil circulation cooling method using transformer oil pumps is often employed to enhance heat dissipation. The oil pump and its associated oil flow indicator are the main power components. During long-term operation, wear of these power components generates metal particles, which enter the transformer body with the transformer oil. Wear of the power components needs to be diagnosed indirectly through visual inspection after disassembly or by observing the presence of metal particles in the transformer oil.

[0003] The main metallic materials of transformer power components are copper and iron. Therefore, the types of metal particles generated are mainly copper particles and iron particles. Depending on the degree of wear, the particle size distribution ranges from micrometers to millimeters. Among these, metal particles with a diameter of 5 micrometers or larger have a significant impact on the electrical performance of insulation and are a key focus. Existing sensors for monitoring metal particles in fluids mainly include metal abrasive sensors that utilize the principle of electromagnetic induction, capable of online monitoring of ferromagnetic particles larger than 40 micrometers and non-ferromagnetic particles larger than 135 micrometers.

[0004] However, metal abrasive sensors cannot monitor small-sized ferromagnetic and non-ferromagnetic particles. Therefore, there is currently no method to diagnose the wear condition of transformer power components through online monitoring data. Visual inspection after disassembling the power components is labor-intensive, time-consuming, and cannot prevent damage to the transformer caused by metal wear of the power components. Summary of the Invention

[0005] This application provides a diagnostic method and apparatus for the metal wear condition of transformer power components, in order to solve the problem that metal abrasive sensors cannot meet the monitoring requirements for small-sized ferromagnetic and non-ferromagnetic particles.

[0006] In a first aspect, this application provides a method for diagnosing the wear condition of metal components in a transformer power assembly, comprising: constructing a distribution function and particle size range of ferromagnetic particles in the transformer, and a distribution function and particle size range of non-ferromagnetic particles; acquiring a first particle size distribution of ferromagnetic particles and a second particle size distribution of non-ferromagnetic particles collected within a first period; the first and second particle size distributions are acquired by a wear sensor; inputting the first particle size distribution into the distribution function of the ferromagnetic particles to calculate ferromagnetic particle data; the ferromagnetic particle data includes: the particle size and mass fraction of ferromagnetic particles collected within the particle size range of ferromagnetic particles, and the predicted particle size and mass fraction of ferromagnetic particles; inputting the second particle size distribution into the distribution function of the non-ferromagnetic particles to calculate non-ferromagnetic particle data; the non-ferromagnetic particle data includes: the particle size and mass fraction of non-ferromagnetic particles collected within the particle size range of non-ferromagnetic particles, and the predicted particle size and mass fraction of non-ferromagnetic particles; adding the ferromagnetic particle data and the non-ferromagnetic particle data to obtain the metal particle data for the first period; and acquiring the metal particle data for the second period. The data pertains to particulate matter. The calculation method for the metal particulate matter data in the second period is the same as that in the first period. Based on the metal particulate matter data in the second period, the median particle size of the metal particles is calculated. The median particle size is the particle size with a mass fraction of 50%. Based on the metal particulate matter data in the first and second periods, the weight growth rate of the metal particles is calculated. The weight growth rate is the percentage increase in weight of the metal particles in the second period compared to the first period. Based on the median particle size and weight growth rate, the metal wear state of the transformer power components is determined. The construction of the distribution function and particle size range of ferromagnetic particles and non-ferromagnetic particles in the transformer includes: obtaining an analytical oil sample, offline detection and analysis of ferromagnetic and non-ferromagnetic particles in the oil sample, and obtaining the particle size distribution of ferromagnetic and non-ferromagnetic particles in the oil sample. The analytical oil sample is obtained through the disassembly of an accident fault caused by metal particles in power transformer oil. Based on the particle size distribution of ferromagnetic and non-ferromagnetic particles in the oil sample, a fitting process is performed to obtain the distribution function and particle size range of ferromagnetic particles and the distribution function and particle size range of non-ferromagnetic particles.

[0007] Optionally, based on the particle size distributions of ferromagnetic and non-ferromagnetic particles in the oil sample, fitting is performed to obtain the distribution function and particle size range of ferromagnetic particles and the distribution function and particle size range of non-ferromagnetic particles. This includes: based on the particle size distributions of ferromagnetic and non-ferromagnetic particles in the oil sample, plotting scatter plots of particle size d versus cumulative mass fraction G according to the measured particle sizes of ferromagnetic and non-ferromagnetic particles from smallest to largest, and obtaining the particle size ranges of ferromagnetic and non-ferromagnetic particles; fitting the scatter plot data of ferromagnetic and non-ferromagnetic particles according to formulas to obtain the distribution functions of ferromagnetic and non-ferromagnetic particles respectively; the formulas are:

[0008] G = 1 - exp(-ad) n )

[0009] Where G is the cumulative mass fraction of metal particles; d is the particle size of metal particles; n is the size distribution index; and a is the dimensionless coefficient.

[0010] Optionally, an analytical oil sample is obtained, and ferromagnetic and non-ferromagnetic particles in the oil sample are detected offline to obtain the particle size distribution of ferromagnetic and non-ferromagnetic particles in the oil sample. This includes: disassembling the transformer oil sample and the insulating paper screen through an accident fault caused by metal particles in the power transformer oil; cleaning the insulating paper screen with insulating oil to collect metal particles; mixing the cleaned insulating oil with the transformer oil sample to obtain an analytical oil sample; enriching the metal particles in the analytical oil sample using a sedimentation method; and performing particle size analysis of the enriched analytical oil sample using a sieving method to obtain the particle size distribution of ferromagnetic and non-ferromagnetic particles in the oil sample.

[0011] Optionally, determining the metal wear state of the transformer power component based on the median particle size and weight growth rate includes: if the median particle size is less than or equal to 5 micrometers and the weight growth rate is less than or equal to 10%, then it is determined that the metal wear of the transformer power component has no impact.

[0012] Optionally, determining the metal wear state of the transformer power component based on the median particle size and weight growth rate includes: if the median particle size is greater than 5 micrometers and less than or equal to 200 micrometers, or the weight growth rate is greater than 10% and less than or equal to 20%, then the metal wear of the transformer power component is determined to have reached a warning state.

[0013] Optionally, determining the metal wear state of the transformer power component based on the median particle size and weight growth rate includes: if the median particle size is greater than 200 micrometers, or the weight growth rate is greater than 20%, then the metal wear of the transformer power component is determined to be severe.

[0014] Optionally, the collected ferromagnetic particles are ferromagnetic particles with a diameter greater than 40 micrometers, and the predicted ferromagnetic particles are ferromagnetic particles with a diameter less than or equal to 40 micrometers.

[0015] Optionally, the collected nonferromagnetic particles are nonferromagnetic particles with a particle size greater than 135 micrometers, and the predicted nonferromagnetic particles are nonferromagnetic particles with a particle size less than or equal to 135 micrometers.

[0016] Secondly, this application also provides a diagnostic device for the metal wear condition of a transformer power component, comprising: a metal abrasive sensor configured to collect the particle size distribution of ferromagnetic particles and non-ferromagnetic particles; and a controller configured to execute the diagnostic method for the metal wear condition of a transformer power component as described in the first aspect.

[0017] Compared with the prior art, this application has the following beneficial effects:

[0018] (1) By collecting and statistically analyzing the metal particles in the faulty transformer oil, the statistical distribution function of the metal particles is obtained, and the prediction of small-diameter metal particle data that cannot be directly measured is realized by combining actual monitoring data.

[0019] (2) By using online monitoring, the particle size distribution of metal particles and the weight of metal particles in transformer oil within a time period can be obtained simultaneously, and the metal wear condition can be diagnosed in real time.

[0020] (3) The wear condition of the transformer power components is diagnosed by using the criteria of median particle size and weight growth rate of metal particles. There is no need to disassemble the transformer power components for visual inspection, which is more convenient. Attached Figure Description

[0021] To more clearly illustrate the technical solution of this application, the drawings used in the embodiments will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 This is a flowchart illustrating a method for diagnosing the metal wear condition of a transformer power component as described in this application.

[0023] Figure 2 This is a schematic diagram illustrating the fitting of scatter plot data according to the formula described in this application;

[0024] Figure 3 This is a schematic diagram of the structure of a diagnostic device for the metal wear condition of a transformer power component as described in this application. Detailed Implementation

[0025] The embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described below do not represent all embodiments consistent with this application. They are merely examples of systems and methods consistent with some aspects of this application as detailed in the claims.

[0026] This application provides a method for diagnosing the metal wear condition of transformer power components, such as... Figure 1 As shown, it includes:

[0027] S100: Construct the distribution function and particle size range of ferromagnetic particles in the transformer, as well as the distribution function and particle size range of non-ferromagnetic particles, such as... Figure 2 As shown, it includes:

[0028] S110: Obtain an analytical oil sample, conduct offline detection and analysis of ferromagnetic and non-ferromagnetic particles in the oil sample, and obtain the particle size distribution of ferromagnetic and non-ferromagnetic particles in the oil sample; the analytical oil sample is obtained through the disassembly of an accident fault caused by metal particles in power transformer oil.

[0029] In one illustrative embodiment, an analytical oil sample is obtained, and ferromagnetic and non-ferromagnetic particles in the oil sample are detected offline to obtain the particle size distribution of the ferromagnetic and non-ferromagnetic particles in the oil sample, including:

[0030] S111: Disassembly of transformer oil and insulating paper shielding in response to accidents caused by metal particles in the transformer oil. When a power transformer is damaged due to an accident caused by metal particles, a fault disassembly analysis can be performed on the damaged power transformer. Transformer oil sampling locations include the bottom of the oil tank and the riser.

[0031] For example, in the disassembly analysis of an accident caused by metal particles from a power transformer in a certain area, 500 mL of oil sample was taken from the bottom of the transformer, and 1 square meter of insulating screen with attached metal particles was taken.

[0032] S112: Clean the insulating paper screen with insulating oil, collect metal particles, and mix the cleaned insulating oil with the transformer oil sample to obtain an analytical oil sample. Metal particles may remain in the insulating paper screen, so they need to be collected with insulating oil. The cleaned insulating oil is then mixed with the transformer oil sample to obtain a complete analytical oil sample containing metal particles.

[0033] For example, 500 mL of No. 25 transformer insulating oil was used to clean the insulating screen. The cleaned insulating oil was collected and mixed with 500 mL of oil sample from the bottom of the transformer to obtain 1000 mL of analytical oil sample.

[0034] S113: Use sedimentation method to enrich and analyze metal particles in oil samples. Sedimentation method can be gravity sedimentation or centrifugal sedimentation.

[0035] For example, a 1000 mL analytical oil sample is centrifuged to obtain a 30 mL bottom oil sample containing metal particles after sedimentation.

[0036] S114: Using a sieving method, particle size analysis of ferromagnetic and non-ferromagnetic particles is performed on the enriched analytical oil sample to obtain the particle size distribution of ferromagnetic and non-ferromagnetic particles in the oil sample. The sieving method can separate metallic particles in the analytical oil sample into ferromagnetic and non-ferromagnetic particles. Generally, the main component of ferromagnetic particles is iron filings, while the main components of non-ferromagnetic particles are copper and aluminum. Particle size analysis of ferromagnetic and non-ferromagnetic particles separately yields their particle size distributions in the oil sample.

[0037] S120: Based on the particle size distribution of ferromagnetic and non-ferromagnetic particles in the oil sample, fit the data to obtain the distribution function and particle size range of the ferromagnetic particles and the distribution function and particle size range of the non-ferromagnetic particles.

[0038] In one illustrative embodiment, fitting is performed based on the particle size distributions of ferromagnetic and non-ferromagnetic particles in the oil sample to obtain the distribution function and particle size range of the ferromagnetic particles and the distribution function and particle size range of the non-ferromagnetic particles, including:

[0039] S121: Based on the particle size distribution of ferromagnetic and non-ferromagnetic particles in the oil sample, plot the particle size d versus cumulative mass fraction G according to the actual measured particle size of ferromagnetic and non-ferromagnetic particles from smallest to largest, and obtain the particle size range of ferromagnetic particles [dmin, dmax].

[0040] The fitting process for ferromagnetic microparticles is the same as that for non-ferromagnetic microparticles. Taking ferromagnetic microparticles as an example, such as... Figure 2 As shown, a scatter plot of particle size d versus cumulative mass fraction G is plotted. The actual measured particle size range of the ferromagnetic microparticles is [1 μm, 1100 μm].

[0041] S122: According to formula (1), the scatter plot data of ferromagnetic particles and non-ferromagnetic particles are fitted respectively to obtain the distribution function of ferromagnetic particles and the distribution function of non-ferromagnetic particles GY.

[0042] The formula (1) is:

[0043] G = 1 - exp(-ad) n (1)

[0044] Where G is the cumulative mass fraction of metal particles; d is the particle size of metal particles; n is the size distribution index; and a is the dimensionless coefficient.

[0045] For example, taking ferromagnetic microparticles as an example, such as Figure 2 As shown, the fitted curve obtained according to formula (1) is the distribution function GY of the ferromagnetic particles.

[0046] S200: Acquire the first particle size distribution of ferromagnetic particles and the second particle size distribution of non-ferromagnetic particles collected during the first cycle; the first and second particle size distributions are acquired by abrasive particle sensors.

[0047] A metal abrasive sensor is installed on the oil passage of a transformer. This sensor can monitor ferromagnetic particles larger than 40 micrometers and non-ferromagnetic particles larger than 135 micrometers. Data collected by the metal abrasive sensor is transmitted to a controller, allowing the acquisition of the first particle size distribution of the collected ferromagnetic particles and the second particle size distribution of the collected non-ferromagnetic particles.

[0048] S300: Input the first particle size distribution into the distribution function of the ferromagnetic particles to calculate the ferromagnetic particle data; the ferromagnetic particle data includes: the particle size and mass fraction of the collected ferromagnetic particles within the particle size range of the ferromagnetic particles, and the predicted particle size and mass fraction of the ferromagnetic particles; input the second particle size distribution into the distribution function of the non-ferromagnetic particles to calculate the non-ferromagnetic particle data; the non-ferromagnetic particle data includes: the particle size and mass fraction of the collected non-ferromagnetic particles within the particle size range of the non-ferromagnetic particles, and the predicted particle size and mass fraction of the non-ferromagnetic particles.

[0049] In one illustrative embodiment, the collected ferromagnetic particles are those with a diameter greater than 40 micrometers, and the predicted ferromagnetic particles are those with a diameter less than or equal to 40 micrometers. The collected non-ferromagnetic particles are those with a diameter greater than 135 micrometers, and the predicted non-ferromagnetic particles are those with a diameter less than or equal to 135 micrometers. Based on the distribution function of the ferromagnetic particles, data for ferromagnetic particles with a diameter less than or equal to 40 micrometers can be fitted; similarly, based on the distribution function of the non-ferromagnetic particles, data for non-ferromagnetic particles with a diameter less than or equal to 135 micrometers can be fitted. This allows for the prediction of data for small-diameter metal particles that cannot be directly measured.

[0050] For example, taking ferromagnetic microparticles as an example, the first cycle is one month, and the actual measured particle size range of ferromagnetic microparticles is [1 micrometer, 1100 micrometers]. Based on the distribution function GY of ferromagnetic microparticles, the data of ferromagnetic microparticles with a particle size range of 1 to 40 micrometers are obtained by fitting.

[0051] S400: Add the ferromagnetic particle data and the non-ferromagnetic particle data to obtain the metal particle data for the first period.

[0052] S500: Obtain the metal particle data for the second period. The calculation method for the metal particle data in the second period is the same as that in the first period. Metal particle data from both periods needs to be obtained to compare the weight growth rate of the metal particles.

[0053] S600: Calculate the median particle size of the metal particles based on the metal particle data of the second period; the median particle size is the particle size with a mass fraction of 50%; calculate the weight growth rate of the metal particles based on the metal particle data of the first period and the metal particle data of the second period; the weight growth rate is the percentage increase in weight of the metal particles in the second period compared to the metal particles in the first period.

[0054] S700: Determine the metal wear condition of the transformer power components based on the median particle size and weight growth rate.

[0055] If the median particle size is less than or equal to 5 micrometers and the weight growth rate is less than or equal to 10%, then it is determined that the wear of the transformer power components has no effect.

[0056] If the median particle size is greater than 5 micrometers and less than or equal to 200 micrometers, or if the weight growth rate is greater than 10% and less than or equal to 20%, then the metal wear of the transformer power component is determined to have reached a warning state.

[0057] If the median particle size is greater than 200 micrometers, or the weight growth rate is greater than 20%, then the metal wear of the transformer power component is determined to be severe.

[0058] For example, using one month as the statistical period, with two consecutive months as the first and second periods respectively, if the median particle size of all metal particles in the transformer oil is 25 micrometers and the weight growth rate of the metal particles is 7%, then the metal wear of the transformer power components is determined to be in a state of alert.

[0059] Based on the diagnostic method for the metal wear condition of a transformer power component described in the above embodiments, some embodiments of this application also provide a diagnostic device for the metal wear condition of a transformer power component, such as... Figure 3 As shown, it includes:

[0060] A metal abrasive sensor is configured to collect the particle size distribution of ferromagnetic and non-ferromagnetic particles. The metal abrasive sensor can employ the principle of electromagnetic induction. A three-wire metal abrasive sensor is preferred, as it has two excitation coils and one test coil, providing stronger anti-interference capabilities. The metal abrasive sensor is mechanically connected to the transformer oil passage, with oil flowing through the central conduit of the sensor to monitor both ferromagnetic and non-ferromagnetic particles.

[0061] The working process of a metal abrasive sensor is as follows: wear of transformer power components generates abrasive particles, such as... Figure 3Metal particles in the oil flow enter the transformer oil passages and then the metal abrasive sensor. These particles are in motion. After entering the central pipe of the metal abrasive sensor, under the influence of the electromagnetic field generated by the excitation coil, a voltage pulse signal is output to the test coil. Information about the metal particles can be obtained by calculating this voltage pulse signal and then transmitted to the controller.

[0062] The controller is configured to perform a diagnostic method for the metal wear condition of a transformer power component as described in the above embodiments.

[0063] The controller can be a mobile phone, tablet computer, laptop computer, desktop computer, server, industrial control computer, microcontroller, PLC (Programmable Logic Controller), DSP (digital signal processor), FPGA (Field Programmable Gate Array), ASIC (Application-specific integrated circuit), or other devices with storage and computing functions. This application does not limit the types of devices that can be used.

[0064] This application provides a method and apparatus for diagnosing the metal wear condition of transformer power components. The diagnostic method includes: constructing a distribution function of ferromagnetic and non-ferromagnetic particles in the transformer; acquiring data collected within a first period; inputting the collected data into the distribution function of ferromagnetic and non-ferromagnetic particles to calculate ferromagnetic and non-ferromagnetic particle data; adding the ferromagnetic and non-ferromagnetic particle data to obtain the metal particle data for the first period; acquiring the metal particle data for the second period; calculating the median particle size and weight growth rate of the metal particles based on the metal particle data from the two periods; and determining the metal wear condition of the transformer power components based on the median particle size and weight growth rate. By collecting and statistically analyzing metal particles in the transformer oil of a faulty transformer, a statistical distribution function of the metal particles is obtained, and combined with actual monitoring data, prediction of small-diameter metal particle data that cannot be directly measured is achieved. By employing online monitoring, the particle size distribution of metal particles and the weight of metal particles in the transformer oil within a time period are obtained simultaneously, enabling real-time diagnosis of the metal wear condition.

[0065] Similar parts between the embodiments provided in this application can be referred to mutually. The specific implementation methods provided above are only a few examples under the overall concept of this application and do not constitute a limitation on the scope of protection of this application. For those skilled in the art, any other implementation methods extended from the solution of this application without creative effort shall fall within the scope of protection of this application.

Claims

1. A method of diagnosing the metal wear state of a transformer power assembly, characterized by, include: The distribution function and particle size range of ferromagnetic particles in transformers, as well as the distribution function and particle size range of non-ferromagnetic particles, are constructed. The first particle size distribution of ferromagnetic particles and the second particle size distribution of non-ferromagnetic particles are acquired during the first period; the first and second particle size distributions are acquired by a wear particle sensor. The first particle size distribution is input into the distribution function of the ferromagnetic particles to calculate the ferromagnetic particle data; The ferromagnetic particle data includes: the particle size and mass fraction of the collected ferromagnetic particles within the particle size range, and the predicted particle size and mass fraction of the ferromagnetic particles. The second particle size distribution is input into the distribution function of the non-ferromagnetic particles to calculate the non-ferromagnetic particle data; the non-ferromagnetic particle data includes: the particle size and mass fraction of the non-ferromagnetic particles collected within the particle size range of the non-ferromagnetic particles, and the predicted particle size and mass fraction of the non-ferromagnetic particles. The ferromagnetic particle data and the non-ferromagnetic particle data are added together to obtain the metal particle data for the first period; Obtain the metal particle data for the second period, which is calculated in the same way as the metal particle data for the first period; Based on the metal particle data from the second period, the median particle size is calculated; the median particle size is the particle size at a mass fraction of 50%. The weight growth rate of metal particles is calculated based on the metal particle data from the first period and the metal particle data from the second period; the weight growth rate is the percentage increase in weight of metal particles in the second period compared to the first period. The wear condition of the transformer power components is determined based on the median particle size and weight growth rate. The distribution function and particle size range of ferromagnetic particles in the transformer are compared with those of non-ferromagnetic particles, including: An analytical oil sample was obtained, and ferromagnetic and non-ferromagnetic particles in the oil sample were detected and analyzed offline to obtain the particle size distribution of ferromagnetic and non-ferromagnetic particles in the oil sample; the analytical oil sample was obtained through the disassembly of an accident fault caused by metal particles in power transformer oil; Based on the particle size distribution of ferromagnetic and non-ferromagnetic particles in the oil sample, scatter plots of particle size d versus cumulative mass fraction G were drawn according to the actual measured particle size of ferromagnetic and non-ferromagnetic particles from small to large, respectively, and the particle size range of ferromagnetic particles and non-ferromagnetic particles were obtained. The scatter plot data of ferromagnetic particles and non-ferromagnetic particles were fitted according to the formula to obtain the distribution function of ferromagnetic particles and the distribution function of non-ferromagnetic particles respectively. The formula is: Where G is the cumulative mass fraction of metal particles; d is the particle size of metal particles; n is the size distribution index; and a is the dimensionless coefficient.

2. The method of claim 1, wherein the metal wear state of the transformer power assembly is diagnosed by: An oil sample was obtained for analysis, and ferromagnetic and non-ferromagnetic particles in the sample were detected offline to obtain the particle size distribution of the ferromagnetic and non-ferromagnetic particles in the oil sample, including: Disassembly of the transformer oil to remove samples of the transformer oil and insulating paper screens, based on the accident fault caused by metal particles in the transformer oil. The insulating paper screen was cleaned with insulating oil, and metal particles were collected. The cleaned insulating oil was mixed with transformer oil sample to obtain an analytical oil sample. The sedimentation method was used to enrich and analyze metal particles in the oil sample; The particle size distribution of ferromagnetic and non-ferromagnetic particles in the enriched oil sample was obtained by sieving.

3. The method of claim 1, wherein the method further comprises: The step of determining the metal wear state of the transformer power components based on the median particle size and weight growth rate includes: If the median particle size is less than or equal to 5 micrometers and the weight growth rate is less than or equal to 10%, then it is determined that the wear of the transformer power components has no effect.

4. The method of claim 1, wherein the method further comprises: The step of determining the metal wear state of the transformer power components based on the median particle size and weight growth rate includes: If the median particle size is greater than 5 micrometers and less than or equal to 200 micrometers, or if the weight growth rate is greater than 10% and less than or equal to 20%, then the metal wear of the transformer power component is determined to have reached a warning state.

5. The method of claim 1, wherein the method further comprises: The step of determining the metal wear state of the transformer power components based on the median particle size and weight growth rate includes: If the median particle size is greater than 200 micrometers, or the weight growth rate is greater than 20%, then the metal wear of the transformer power component is determined to be severe.

6. The method of claim 1, wherein the method further comprises: The collected ferromagnetic particles are ferromagnetic particles with a diameter greater than 40 micrometers, and the predicted ferromagnetic particles are ferromagnetic particles with a diameter less than or equal to 40 micrometers.

7. The method of claim 1, wherein the method further comprises: The collected nonferromagnetic particles are nonferromagnetic particles with a diameter greater than 135 micrometers, and the predicted nonferromagnetic particles are nonferromagnetic particles with a diameter less than or equal to 135 micrometers.

8. A diagnostic device for the metal wear condition of a transformer power component, characterized in that, include: A metal abrasive sensor is configured to acquire the particle size distribution of ferromagnetic and non-ferromagnetic particles; The controller is configured to perform a diagnostic method for the metal wear condition of a transformer power component as described in any one of claims 1-7.

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