CN116148113A - Diagnosis method and device for metal abrasion state of transformer power component - Google Patents
Diagnosis method and device for metal abrasion state of transformer power component Download PDFInfo
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- 239000002184 metal Substances 0.000 title claims abstract description 60
- 229910052751 metal Inorganic materials 0.000 title claims abstract description 60
- 238000000034 method Methods 0.000 title claims abstract description 39
- 238000005299 abrasion Methods 0.000 title claims abstract description 22
- 238000003745 diagnosis Methods 0.000 title description 4
- 239000002245 particle Substances 0.000 claims abstract description 374
- 230000005294 ferromagnetic effect Effects 0.000 claims abstract description 211
- 239000002923 metal particle Substances 0.000 claims abstract description 97
- 238000005315 distribution function Methods 0.000 claims abstract description 45
- 238000004458 analytical method Methods 0.000 claims description 21
- 238000010586 diagram Methods 0.000 claims description 8
- 238000004062 sedimentation Methods 0.000 claims description 7
- 238000004140 cleaning Methods 0.000 claims description 5
- 238000003921 particle size analysis Methods 0.000 claims description 4
- 238000007873 sieving Methods 0.000 claims description 4
- 238000004364 calculation method Methods 0.000 claims description 2
- 238000010276 construction Methods 0.000 claims description 2
- 238000012544 monitoring process Methods 0.000 abstract description 10
- XEEYBQQBJWHFJM-UHFFFAOYSA-N Iron Chemical compound [Fe] XEEYBQQBJWHFJM-UHFFFAOYSA-N 0.000 description 6
- 239000010419 fine particle Substances 0.000 description 4
- RYGMFSIKBFXOCR-UHFFFAOYSA-N Copper Chemical compound [Cu] RYGMFSIKBFXOCR-UHFFFAOYSA-N 0.000 description 3
- 229910052802 copper Inorganic materials 0.000 description 3
- 239000010949 copper Substances 0.000 description 3
- 229910052742 iron Inorganic materials 0.000 description 3
- 238000011179 visual inspection Methods 0.000 description 3
- 230000001186 cumulative effect Effects 0.000 description 2
- 230000005674 electromagnetic induction Effects 0.000 description 2
- 238000012360 testing method Methods 0.000 description 2
- 229910052782 aluminium Inorganic materials 0.000 description 1
- XAGFODPZIPBFFR-UHFFFAOYSA-N aluminium Chemical compound [Al] XAGFODPZIPBFFR-UHFFFAOYSA-N 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 239000002826 coolant Substances 0.000 description 1
- 238000001816 cooling Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000005672 electromagnetic field Effects 0.000 description 1
- 230000005284 excitation Effects 0.000 description 1
- 239000012530 fluid Substances 0.000 description 1
- 230000005484 gravity Effects 0.000 description 1
- 230000017525 heat dissipation Effects 0.000 description 1
- 238000009413 insulation Methods 0.000 description 1
- 230000007774 longterm Effects 0.000 description 1
- 239000007769 metal material Substances 0.000 description 1
- 238000005070 sampling Methods 0.000 description 1
- 230000004584 weight gain Effects 0.000 description 1
- 235000019786 weight gain Nutrition 0.000 description 1
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- G01N3/00—Investigating strength properties of solid materials by application of mechanical stress
- G01N3/56—Investigating resistance to wear or abrasion
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- G—PHYSICS
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- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
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- G01N15/02—Investigating particle size or size distribution
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- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N2203/00—Investigating strength properties of solid materials by application of mechanical stress
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- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
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Abstract
The application provides a method and a device for diagnosing a metal abrasion state of a transformer power component, wherein the method comprises the following steps: constructing a distribution function of ferromagnetic particles and non-ferromagnetic particles in the transformer; acquiring acquired data in a first period; inputting the acquired data into a distribution function of ferromagnetic particles and non-ferromagnetic particles, and calculating to obtain ferromagnetic particles and non-ferromagnetic particles; adding the ferromagnetic particle data and the non-ferromagnetic particle data to obtain metal particle data of a first period; acquiring metal particle data of a second period; calculating the median particle diameter and the weight increase rate of the metal particles according to the metal particle data of two periods; and judging the metal abrasion state of the transformer power component according to the median particle size and the weight increase rate. The statistical distribution function of the metal particles is obtained by collecting and counting the metal particles in the fault transformer oil, and the prediction of the data of the small-particle-diameter metal particles which cannot be directly measured is realized by combining actual monitoring data.
Description
Technical Field
The application relates to the technical field of transformers, in particular to a method and a device for diagnosing a metal abrasion state of a power component of a transformer.
Background
Power transformers are expensive core node devices in power systems. Once an accident occurs in the power transformer, regional power failure can be possibly caused, and serious social and economic losses are caused. Oil-immersed transformers are a major form of power transformers, which employ insulating oil as insulating and cooling medium. For large power transformers with voltage levels of 500kV and above, in order to enhance heat dissipation capacity, a cooling mode of forced oil circulation is often performed by a transformer oil pump. The oil pump and the oil flow indicator attached to the oil pump are main power components, and metal particles are generated by abrasion of the power components in long-term operation and enter the transformer body along with the flowing of transformer oil. Wear of the power components requires visual inspection after disassembly or indirect diagnosis by metal particle conditions in the transformer oil.
The main metal materials of the transformer power component are copper and iron, so the types of metal particles mainly comprise copper particles and iron particles, and the particle size of the metal particles is distributed in the range from micrometers to millimeters along with different wear degrees, wherein the metal particles with the particle size of more than 5 micrometers have great influence on the electrical performance of insulation, and are important points of attention. The existing monitoring sensor for metal particles in fluid mainly comprises a metal particle sensor utilizing an electromagnetic induction principle, and can be used for on-line monitoring of ferromagnetic particles above 40 microns and nonferromagnetic particles above 135 microns.
However, metal abrasive particle sensors do not meet the monitoring of small sized ferromagnetic and non-ferromagnetic particles. Therefore, at present, no method for diagnosing the abrasion state of the power component of the transformer by on-line monitoring data exists, but the method for visual inspection after the power component is disassembled has the defects of large workload and poor timeliness, and the damage of the metal abrasion of the power component to the transformer cannot be prevented in advance.
Disclosure of Invention
The application provides a method and a device for diagnosing a metal abrasion state of a transformer power component, which are used for solving the problem that a metal abrasive particle sensor cannot meet the monitoring of small-size ferromagnetic and nonferromagnetic particles.
In a first aspect, the present application provides a method of diagnosing a metal wear state of a transformer power assembly, comprising: constructing a distribution function and a particle size range of ferromagnetic particles in a transformer, and a distribution function and a particle size range of non-ferromagnetic particles; acquiring a first particle size distribution of the collected ferromagnetic particles and a second particle size distribution of the collected non-ferromagnetic particles in a first period; the first particle size distribution and the second particle size distribution are acquired by an abrasive particle sensor; inputting the first particle size distribution into a distribution function of the ferromagnetic particles, and calculating to obtain ferromagnetic particle data; the ferromagnetic particulate data includes: the particle size and mass fraction of the collected ferromagnetic particles, and the predicted particle size and mass fraction of the ferromagnetic particles, are within the particle size range of the ferromagnetic particles; inputting the second particle size distribution into a distribution function of the nonferromagnetic particles, and calculating to obtain nonferromagnetic particle data; the non-ferromagnetic particulate data includes: the particle size and mass fraction of the collected non-ferromagnetic particles, and the predicted particle size and mass fraction of the non-ferromagnetic particles, are within the particle size range of the non-ferromagnetic particles; adding the ferromagnetic particle data and the non-ferromagnetic particle data to obtain metal particle data of a first period; acquiring metal particle data of a second period, wherein the metal particle data of the second period is the same as the metal particle data of the first period in calculation mode; calculating the median particle diameter of the metal particles according to the metal particle data of the second period; the median particle diameter is 50% of the mass fraction; calculating the weight increase rate of the metal particles according to the metal particle data of the first period and the metal particle data of the second period; the weight increase rate is the weight increase percentage of the metal particles in the second period compared with the metal particles in the first period; judging the metal abrasion state of the transformer power component according to the median particle size and the weight increase rate; the construction of the distribution function and the grain size range of ferromagnetic grains and the distribution function and the grain size range of non-ferromagnetic grains in the transformer comprises the following steps: obtaining an analysis oil sample, and offline detecting and analyzing ferromagnetic particles and non-ferromagnetic particles in the oil sample to obtain the particle size distribution of the ferromagnetic particles and the non-ferromagnetic particles of the oil sample; the analysis oil sample is obtained through accident fault disintegration caused by metal particles in the power transformer oil; fitting according to the particle size distribution of the oil-like ferromagnetic particles and the non-ferromagnetic particles to obtain the distribution function and the particle size range of the ferromagnetic particles and the distribution function and the particle size range of the non-ferromagnetic particles.
Optionally, fitting is performed according to the particle size distribution of the oil-like ferromagnetic particles and the non-ferromagnetic particles to obtain a distribution function and a particle size range of the ferromagnetic particles and a distribution function and a particle size range of the non-ferromagnetic particles, including: according to the particle size distribution of the oil-like ferromagnetic particles and the non-ferromagnetic particles, respectively according to the actually measured particle sizes of the ferromagnetic particles and the non-ferromagnetic particles from small to large, drawing a scatter diagram of the particle size d and the accumulated mass fraction G, and obtaining the particle size range of the ferromagnetic particles and the particle size range of the non-ferromagnetic particles; fitting the scatter diagram data of the ferromagnetic particles and the non-ferromagnetic particles according to a formula to obtain a distribution function of the ferromagnetic particles and a distribution function of the non-ferromagnetic particles; the formula is:
G＝1-exp(-ad n )
wherein G is the accumulated mass fraction of the metal particles; d is the particle size of the metal particles; n is a size distribution index; a is a dimensionless coefficient.
Optionally, obtaining an analysis oil sample, offline detecting ferromagnetic particles and non-ferromagnetic particles in the oil sample, and obtaining a particle size distribution of the ferromagnetic particles and the non-ferromagnetic particles of the oil sample, including: through accident fault disintegration caused by metal particles in the power transformer oil, taking a transformer oil sample and an insulating paper enclosure; cleaning insulating oil on an insulating paper enclosure screen, collecting metal particles, and mixing the cleaned insulating oil with a transformer oil sample to obtain an analysis oil sample; adopting a sedimentation method to enrich and analyze metal particles in the oil sample; and (3) carrying out particle size analysis on the ferromagnetic particles and the nonferromagnetic particles of the enriched analysis oil sample by adopting a sieving method to obtain the particle size distribution of the ferromagnetic particles and the nonferromagnetic particles of the oil sample.
Optionally, the determining the metal wear state of the transformer power component according to the median particle size and the weight increase rate includes: if the median particle size is less than or equal to 5 microns and the weight increase rate is less than or equal to 10%, it is determined that the metal wear of the transformer power component is not affected.
Optionally, the determining the metal wear state of the transformer power component according to the median particle size and the weight increase rate includes: and if the median particle diameter is more than 5 microns and less than or equal to 200 microns, or the weight increase rate is more than 10% and less than or equal to 20%, judging that the metal abrasion of the power component of the transformer reaches a warning state.
Optionally, the determining the metal wear state of the transformer power component according to the median particle size and the weight increase rate includes: if the median particle diameter is greater than 200 microns, or the weight increase rate is greater than 20%, then it is determined that the transformer power component metal wear is in a severe condition.
Optionally, the collected ferromagnetic particles are ferromagnetic particles with a particle size greater than 40 microns, and the predicted ferromagnetic particles are ferromagnetic particles with a particle size less than or equal to 40 microns.
Optionally, the collected non-ferromagnetic particles are non-ferromagnetic particles with a particle size greater than 135 microns, and the predicted non-ferromagnetic particles are non-ferromagnetic particles with a particle size less than or equal to 135 microns.
In a second aspect, the present application also provides a diagnostic device for a metal wear state of a transformer power assembly, comprising: a metal abrasive particle sensor configured to collect a particle size distribution of the ferromagnetic particles and the non-ferromagnetic particles; a controller configured to perform a method of diagnosing a metal wear condition of a transformer power assembly according to the first aspect.
Compared with the prior art, the application has the following beneficial effects:
(1) The statistical distribution function of the metal particles is obtained by collecting and counting the metal particles in the fault transformer oil, and the prediction of the data of the small-particle-diameter metal particles which cannot be directly measured is realized by combining actual monitoring data.
(2) The method adopts an on-line monitoring means, and simultaneously obtains the particle size distribution of the metal particles and the weight of the metal particles in the transformer oil in a time period, so that the metal abrasion state can be diagnosed in real time.
(3) The metal abrasion state of the transformer power assembly is diagnosed by adopting the criteria of median particle size and weight increase rate of the metal particles, disassembly visual inspection of the transformer power assembly is not needed, and convenience is higher.
Drawings
In order to more clearly illustrate the technical solutions of the present application, the drawings that are needed in the embodiments will be briefly described below, and it will be obvious to those skilled in the art that other drawings can be obtained from these drawings without inventive effort.
FIG. 1 is a schematic flow chart of a method for diagnosing a metal wear state of a transformer power assembly according to the present disclosure;
FIG. 2 is a schematic illustration of fitting scatter plot data according to a formula as described herein;
fig. 3 is a schematic structural diagram of a diagnostic device for a metal wear state of a transformer power assembly according to the present application.
Detailed Description
Reference will now be made in detail to the embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, the same numbers in different drawings refer to the same or similar elements, unless otherwise indicated. The embodiments described in the examples below do not represent all embodiments consistent with the present application. Merely as examples of systems and methods consistent with some aspects of the present application as detailed in the claims.
The application provides a diagnosis method for a metal abrasion state of a transformer power component, which is shown in fig. 1 and comprises the following steps:
s100: constructing a distribution function and a particle size range of ferromagnetic particles and a distribution function and a particle size range of non-ferromagnetic particles in a transformer, as shown in fig. 2, comprising:
s110: obtaining an analysis oil sample, and offline detecting and analyzing ferromagnetic particles and non-ferromagnetic particles in the oil sample to obtain the particle size distribution of the ferromagnetic particles and the non-ferromagnetic particles of the oil sample; the analysis oil sample is obtained through accident fault disintegration caused by metal particles in the power transformer oil.
In an exemplary embodiment, obtaining an analysis oil sample, offline detecting ferromagnetic and non-ferromagnetic particles in the oil sample, obtaining a particle size distribution of the oil sample ferromagnetic and non-ferromagnetic particles, comprising:
s111: and (3) taking the transformer oil and the insulating paper enclosure screen through accident fault disintegration caused by metal particles in the power transformer oil. When the power transformer is damaged due to accidents caused by metal particles, fault disintegration analysis can be performed on the damaged power transformer, and the transformer oil sampling position comprises the bottom of an oil tank and a lifting seat.
For example, for accident disassembly analysis caused by metal particles of a power transformer together in a certain place, 500mL of oil sample at the bottom of the transformer is taken, and 1 square meter of insulating enclosure with the attached metal particles is taken.
S112: and (3) cleaning the insulating paper enclosure screen with insulating oil, collecting metal particles, and mixing the cleaned insulating oil with a transformer oil sample to obtain an analysis oil sample. The metal particles remain in the insulating paper enclosure, so that the insulating oil is collected, and the cleaned insulating oil is mixed with the transformer oil sample, so that the complete metal particle analysis oil sample can be obtained.
For example, 500mL of insulating oil of a 25# transformer is used for cleaning an insulating enclosure, and the insulating oil after cleaning is collected and mixed with 500mL of an oil sample at the bottom of the transformer to obtain 1000mL of analysis oil sample.
S113: and (5) adopting a sedimentation method to enrich and analyze the metal particles in the oil sample. The sedimentation method can be gravity sedimentation or centrifugal sedimentation.
For example, 1000mL of the analysis oil sample was subjected to centrifugal sedimentation to obtain 30mL of a settled bottom oil sample containing metal particles.
S114: and (3) carrying out particle size analysis on the ferromagnetic particles and the nonferromagnetic particles of the enriched analysis oil sample by adopting a sieving method to obtain the particle size distribution of the ferromagnetic particles and the nonferromagnetic particles of the oil sample. The sieving method can separate the metal particles in the analysis oil sample into ferromagnetic particles and nonferromagnetic particles, wherein the main components of the ferromagnetic particles are iron skiving, and the main components of the nonferromagnetic particles are copper and aluminum. The particle size distribution of the oil-like ferromagnetic particles and the non-ferromagnetic particles can be obtained by respectively subjecting the ferromagnetic particles and the non-ferromagnetic particles to particle size analysis.
S120: fitting according to the particle size distribution of the oil-like ferromagnetic particles and the non-ferromagnetic particles to obtain the distribution function and the particle size range of the ferromagnetic particles and the distribution function and the particle size range of the non-ferromagnetic particles.
In an exemplary embodiment, fitting is performed based on the particle size distribution of the oil-like ferromagnetic particles and the non-ferromagnetic particles to obtain a distribution function and a particle size range of the ferromagnetic particles and a distribution function and a particle size range of the non-ferromagnetic particles, comprising:
s121: according to the particle size distribution of the oil-like ferromagnetic particles and the non-ferromagnetic particles, the scatter diagram of the particle size d and the cumulative mass fraction G is drawn according to the actually measured particle sizes of the ferromagnetic particles and the non-ferromagnetic particles from small to large, respectively, and the particle size range of the ferromagnetic particles and the particle size range of the non-ferromagnetic particles [ dmin, dmax ] are obtained.
The fitting process of the ferromagnetic fine particles is the same as the fitting process of the non-ferromagnetic fine particles, taking the ferromagnetic fine particles as an example, as shown in fig. 2, a scatter diagram of the particle diameter d and the cumulative mass fraction G is plotted, and the particle diameter range of the ferromagnetic fine particles is actually measured to be [1 micron, 1100 microns ].
S122: fitting the scatter diagram data of the ferromagnetic particles and the non-ferromagnetic particles according to the formula (1) to obtain a distribution function of the ferromagnetic particles and a distribution function GY of the non-ferromagnetic particles;
the formula (1) is:
G＝1-exp(-ad n ) (1)
wherein G is the accumulated mass fraction of the metal particles; d is the particle size of the metal particles; n is a size distribution index; a is a dimensionless coefficient.
Taking ferromagnetic particles as an example, as shown in fig. 2, a fitting curve is obtained according to formula (1), that is, a distribution function GY of the ferromagnetic particles.
S200: acquiring a first particle size distribution of the collected ferromagnetic particles and a second particle size distribution of the collected non-ferromagnetic particles in a first period; the first particle size distribution and the second particle size distribution are acquired by an abrasive particle sensor.
The metal abrasive particle sensor is arranged on an oil duct of the transformer, and can monitor ferromagnetic particles with the particle size of more than 40 microns and non-ferromagnetic particles with the particle size of more than 135 microns. The data collected by the metal-abrasive particle sensor is transmitted to the controller, and a first particle size distribution of the collected ferromagnetic particles and a second particle size distribution of the collected non-ferromagnetic particles can be obtained.
S300: inputting the first particle size distribution into a distribution function of the ferromagnetic particles, and calculating to obtain ferromagnetic particle data; the ferromagnetic particulate data includes: the particle size and mass fraction of the collected ferromagnetic particles, and the predicted particle size and mass fraction of the ferromagnetic particles, are within the particle size range of the ferromagnetic particles; inputting the second particle size distribution into a distribution function of the nonferromagnetic particles, and calculating to obtain nonferromagnetic particle data; the non-ferromagnetic particulate data includes: the particle size and mass fraction of the collected non-ferromagnetic particles, as well as the predicted particle size and mass fraction of the non-ferromagnetic particles, are within the particle size range of the non-ferromagnetic particles.
In an exemplary embodiment, the collected ferromagnetic particles are ferromagnetic particles having a particle size greater than 40 microns, and the predicted ferromagnetic particles are ferromagnetic particles having a particle size less than or equal to 40 microns. The collected non-ferromagnetic particles are non-ferromagnetic particles with the particle size of more than 135 microns, and the predicted non-ferromagnetic particles are non-ferromagnetic particles with the particle size of less than or equal to 135 microns. Fitting the data of the ferromagnetic particles with the particle size smaller than or equal to 40 microns according to the distribution function of the ferromagnetic particles; from the distribution function of the non-ferromagnetic particles, data for non-ferromagnetic particles having a particle size less than or equal to 135 microns can be fitted. Thus, prediction of data of small-particle-diameter metal particles which cannot be directly measured can be realized.
Taking ferromagnetic particles as an example, the first period is one month, the actually measured particle size range of the ferromagnetic particles is [1 micrometer, 1100 micrometer ], and the ferromagnetic particle data of the particles with the particle size range of 1-40 are obtained by fitting according to the distribution function GY of the ferromagnetic particles.
S400: the ferromagnetic particulate data and the non-ferromagnetic particulate data are summed to obtain a first period of metal particulate data.
S500: and acquiring metal particle data of a second period, wherein the metal particle data of the second period is calculated in the same way as the metal particle data of the first period. Two cycles of metal particle data are acquired to compare the weight gain of the metal particles.
S600: calculating the median particle diameter of the metal particles according to the metal particle data of the second period; the median particle diameter is 50% of the mass fraction; calculating the weight increase rate of the metal particles according to the metal particle data of the first period and the metal particle data of the second period; the weight increase rate is the weight increase percentage of the metal particles of the second period over the metal particles of the first period.
S700: and judging the metal abrasion state of the transformer power component according to the median particle size and the weight increase rate.
If the median particle size is less than or equal to 5 microns and the weight increase rate is less than or equal to 10%, it is determined that the metal wear of the transformer power component is not affected.
And if the median particle diameter is more than 5 microns and less than or equal to 200 microns, or the weight increase rate is more than 10% and less than or equal to 20%, judging that the metal abrasion of the power component of the transformer reaches a warning state.
If the median particle diameter is greater than 200 microns, or the weight increase rate is greater than 20%, then it is determined that the transformer power component metal wear is in a severe condition.
For example, taking 1 month as a statistical period, and continuously taking 2 months as a first period and a second period respectively, obtaining that the median particle diameter of all metal particles in the transformer oil is 25 microns and the weight increase rate of the metal particles is 7%, and judging that the metal abrasion of the power component of the transformer is in an alert state.
Based on the method for diagnosing the metal wear state of the power assembly of the transformer described in the foregoing embodiments, some embodiments of the present application further provide a device for diagnosing the metal wear state of the power assembly of the transformer, as shown in fig. 3, including:
a metal abrasive particle sensor configured to collect a particle size distribution of the ferromagnetic particles and the non-ferromagnetic particles. The metal abrasive particle sensor can adopt a sensor based on an electromagnetic induction principle. Preferably, a tri-wire type metal abrasive particle sensor is adopted, wherein the tri-wire type metal abrasive particle sensor is provided with 2 excitation coils and 1 test coil, and the anti-interference capability is stronger. The metal abrasive particle sensor is mechanically connected with the transformer oil channel, and oil in the transformer oil channel passes through a pipeline in the middle of the metal abrasive particle sensor so as to monitor ferromagnetic particles and nonferromagnetic particles.
The working process of the metal abrasive particle sensor is as follows: wear of the transformer power components results from metal particles, such as those of fig. 3, entering the transformer oil gallery with the oil flow and then entering the metal particle sensor. After the metal particles are in a motion state and enter the middle pipeline of the metal particle sensor, under the action of an electromagnetic field generated by the exciting coil, a voltage pulse signal is output in the testing coil, the voltage pulse signal is calculated to obtain information of the metal particles, and the information of the metal particles is transmitted to the controller.
A controller configured to perform a method of diagnosing a metal wear state of a transformer power assembly as described in the above embodiments.
The controller may be a device having a storage and operation function, such as a mobile phone, a tablet computer, a notebook computer, a desktop computer, a server, an industrial personal computer, a single chip microcomputer, a PLC (Programmable Logic Control ler ), a DSP (digital signal processor, digital signal processor), an FPGA (Field Programmable Gate Array ), an ASIC (application-specific integrated circuit), or the like, which is not limited in this embodiment of the present application.
The application provides a method and a device for diagnosing a metal abrasion state of a transformer power component, wherein the method comprises the following steps: constructing a distribution function of ferromagnetic particles and non-ferromagnetic particles in the transformer; acquiring acquired data in a first period; inputting the acquired data into a distribution function of ferromagnetic particles and non-ferromagnetic particles, and calculating to obtain ferromagnetic particles and non-ferromagnetic particles; adding the ferromagnetic particle data and the non-ferromagnetic particle data to obtain metal particle data of a first period; acquiring metal particle data of a second period; calculating the median particle diameter and the weight increase rate of the metal particles according to the metal particle data of two periods; and judging the metal abrasion state of the transformer power component according to the median particle size and the weight increase rate. The statistical distribution function of the metal particles is obtained by collecting and counting the metal particles in the fault transformer oil, and the prediction of the data of the small-particle-diameter metal particles which cannot be directly measured is realized by combining actual monitoring data. The method adopts an on-line monitoring means, and simultaneously obtains the particle size distribution of the metal particles and the weight of the metal particles in the transformer oil in a time period, so that the metal abrasion state can be diagnosed in real time.
The foregoing detailed description of the embodiments is merely illustrative of the general principles of the present application and should not be taken in any way as limiting the scope of the invention. Any other embodiments developed in accordance with the present application without inventive effort are within the scope of the present application for those skilled in the art.
Claims (9)
1. A method for diagnosing a metal wear state of a power assembly of a transformer, comprising:
constructing a distribution function and a particle size range of ferromagnetic particles in a transformer, and a distribution function and a particle size range of non-ferromagnetic particles;
acquiring a first particle size distribution of the collected ferromagnetic particles and a second particle size distribution of the collected non-ferromagnetic particles in a first period; the first particle size distribution and the second particle size distribution are acquired by an abrasive particle sensor;
inputting the first particle size distribution into a distribution function of the ferromagnetic particles, and calculating to obtain ferromagnetic particle data; the ferromagnetic particulate data includes: the particle size and mass fraction of the collected ferromagnetic particles, and the predicted particle size and mass fraction of the ferromagnetic particles, are within the particle size range of the ferromagnetic particles;
inputting the second particle size distribution into a distribution function of the nonferromagnetic particles, and calculating to obtain nonferromagnetic particle data; the non-ferromagnetic particulate data includes: the particle size and mass fraction of the collected non-ferromagnetic particles, and the predicted particle size and mass fraction of the non-ferromagnetic particles, are within the particle size range of the non-ferromagnetic particles;
adding the ferromagnetic particle data and the non-ferromagnetic particle data to obtain metal particle data of a first period;
acquiring metal particle data of a second period, wherein the metal particle data of the second period is the same as the metal particle data of the first period in calculation mode;
calculating the median particle diameter of the metal particles according to the metal particle data of the second period; the median particle diameter is 50% of the mass fraction;
calculating the weight increase rate of the metal particles according to the metal particle data of the first period and the metal particle data of the second period; the weight increase rate is the weight increase percentage of the metal particles in the second period compared with the metal particles in the first period;
judging the metal abrasion state of the transformer power component according to the median particle size and the weight increase rate;
the construction of the distribution function and the grain size range of ferromagnetic grains and the distribution function and the grain size range of non-ferromagnetic grains in the transformer comprises the following steps:
obtaining an analysis oil sample, and offline detecting and analyzing ferromagnetic particles and non-ferromagnetic particles in the oil sample to obtain the particle size distribution of the ferromagnetic particles and the non-ferromagnetic particles of the oil sample; the analysis oil sample is obtained through accident fault disintegration caused by metal particles in the power transformer oil;
fitting according to the particle size distribution of the oil-like ferromagnetic particles and the non-ferromagnetic particles to obtain the distribution function and the particle size range of the ferromagnetic particles and the distribution function and the particle size range of the non-ferromagnetic particles.
2. The method of claim 1, wherein the fitting is performed based on the particle size distribution of the oil-like ferromagnetic particles and the non-ferromagnetic particles to obtain the distribution function and the particle size range of the ferromagnetic particles and the distribution function and the particle size range of the non-ferromagnetic particles, comprising:
according to the particle size distribution of the oil-like ferromagnetic particles and the non-ferromagnetic particles, respectively according to the actually measured particle sizes of the ferromagnetic particles and the non-ferromagnetic particles from small to large, drawing a scatter diagram of the particle size d and the accumulated mass fraction G, and obtaining the particle size range of the ferromagnetic particles and the particle size range of the non-ferromagnetic particles;
fitting the scatter diagram data of the ferromagnetic particles and the non-ferromagnetic particles according to a formula to obtain a distribution function of the ferromagnetic particles and a distribution function of the non-ferromagnetic particles;
the formula is:
G＝1-exp(-ad n )
wherein G is the accumulated mass fraction of the metal particles; d is the particle size of the metal particles; n is a size distribution index; a is a dimensionless coefficient.
3. The method for diagnosing a metal wear state of a transformer power assembly according to claim 1, wherein obtaining an analysis oil sample, detecting ferromagnetic particles and non-ferromagnetic particles in the oil sample off-line, obtaining a particle size distribution of the ferromagnetic particles and the non-ferromagnetic particles of the oil sample, comprises:
through accident fault disintegration caused by metal particles in the power transformer oil, taking a transformer oil sample and an insulating paper enclosure;
cleaning insulating oil on an insulating paper enclosure screen, collecting metal particles, and mixing the cleaned insulating oil with a transformer oil sample to obtain an analysis oil sample;
adopting a sedimentation method to enrich and analyze metal particles in the oil sample;
and (3) carrying out particle size analysis on the ferromagnetic particles and the nonferromagnetic particles of the enriched analysis oil sample by adopting a sieving method to obtain the particle size distribution of the ferromagnetic particles and the nonferromagnetic particles of the oil sample.
4. The method for diagnosing a metal wear state of a transformer power assembly as recited in claim 1, wherein said determining a metal wear state of a transformer power assembly based on said median particle size and said weight increase rate comprises:
if the median particle size is less than or equal to 5 microns and the weight increase rate is less than or equal to 10%, it is determined that the metal wear of the transformer power component is not affected.
5. The method for diagnosing a metal wear state of a transformer power assembly as recited in claim 1, wherein said determining a metal wear state of a transformer power assembly based on said median particle size and said weight increase rate comprises:
and if the median particle diameter is more than 5 microns and less than or equal to 200 microns, or the weight increase rate is more than 10% and less than or equal to 20%, judging that the metal abrasion of the power component of the transformer reaches a warning state.
6. The method for diagnosing a metal wear state of a transformer power assembly as recited in claim 1, wherein said determining a metal wear state of a transformer power assembly based on said median particle size and said weight increase rate comprises:
if the median particle diameter is greater than 200 microns, or the weight increase rate is greater than 20%, then it is determined that the transformer power component metal wear is in a severe condition.
7. The method of claim 1, wherein the collected ferromagnetic particles are ferromagnetic particles having a particle size of greater than 40 microns, and the predicted ferromagnetic particles are ferromagnetic particles having a particle size of less than or equal to 40 microns.
8. The method of claim 1, wherein the collected non-ferromagnetic particles are non-ferromagnetic particles having a particle size greater than 135 microns, and the predicted non-ferromagnetic particles are non-ferromagnetic particles having a particle size less than or equal to 135 microns.
9. A diagnostic device for a metal wear condition of a transformer power assembly, comprising:
a metal abrasive particle sensor configured to collect a particle size distribution of the ferromagnetic particles and the non-ferromagnetic particles;
a controller configured to perform a method of diagnosing a metal wear condition of a transformer power assembly of any one of claims 1-8.
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