CN113030666A - Large-scale transformer discharge fault diagnosis method and device - Google Patents
Large-scale transformer discharge fault diagnosis method and device Download PDFInfo
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Abstract
The embodiment of the invention provides a large transformer discharge fault diagnosis method and a large transformer discharge fault diagnosis device, wherein the method comprises the following steps: establishing a 3D model of the transformer, and carrying out ultrasonic partial discharge detection on the transformer; when the discharge in the transformer is detected, sending a sampling trigger signal to a gas monitoring device; acquiring gas data sampled by a gas monitoring device, and determining whether a discharge fault exists in a transformer by a preset detection method; when the transformer has a discharge fault, determining the discharge position of the transformer according to the position information of the discharge fault and the 3D model; and acquiring working information of the discharge position, performing data analysis on the gas data, inputting the analysis results of the working information and the gas data into a fault case library, matching corresponding fault reasons and outputting the matching results. By adopting the method, the fault position can be accurately judged, the defective part and the corresponding fault reason can be searched, and the fault property and the discharge severity can be reflected.
Description
Technical Field
The invention relates to the technical field of transformer online monitoring and diagnosis, in particular to a large transformer discharge fault diagnosis method and device.
Background
The large power transformer bears the important responsibilities of electric energy conversion and transmission, and the stable operation of the large power transformer has important influence on the safety and stability of a power system. In recent years, attention has been paid to online monitoring of large power transformers, and monitoring means have become abundant. The core of transformer monitoring is insulation monitoring as electrical equipment, and the effectiveness and practicability of dissolved gas monitoring and partial discharge monitoring in oil are widely accepted.
At present, dissolved gas in oil becomes one of the most effective means for analyzing transformer faults. However, since the transformer oil almost contacts with all the components in the transformer body, the method is difficult to perform effective fault location and search for defective components, and after abnormal alarm occurs, various off-line tests are often required to be supplemented, even disassembly inspection is performed, so that the fault reason can be determined, and great inconvenience is brought to fault analysis.
Compared with an ultrahigh frequency method and a pulse current method, the ultrasonic method has the advantages that the non-discharge state/non-discharge state monitoring is eliminated, the system can also realize the 3D positioning of the space according to the time difference of the same partial discharge signal (reflected as a primary ultrasonic impact event) received by different sensors, but the method cannot effectively reflect the fault property and the discharge severity degree, and brings certain inconvenience to fault judgment.
In view of the above shortcomings of the prior art, a technical solution to the above problems is needed.
Disclosure of Invention
Aiming at the problems in the prior art, the embodiment of the invention provides a large transformer discharge fault diagnosis method and device.
The embodiment of the invention provides a large transformer discharge fault diagnosis method, which comprises the following steps:
establishing a 3D model of a transformer, and carrying out ultrasonic partial discharge detection on the transformer;
when the discharge in the transformer is detected, sending a sampling trigger signal to a gas monitoring device;
acquiring gas data sampled by the gas monitoring device, and determining whether the transformer has a discharge fault or not by a preset detection method;
when the transformer has a discharge fault, determining the discharge position of the transformer according to the position information of the discharge fault and the 3D model;
and acquiring the working information of the discharge position, performing data analysis on the gas data, inputting the working information and the analysis result of the gas data into a fault case library, matching corresponding fault reasons and outputting.
In one embodiment, the method further comprises:
and acquiring a chromatographic analysis result of the gas sampled by the gas monitoring device, and judging whether the transformer has a discharge fault or not according to the chromatographic analysis result.
In one embodiment, the method further comprises:
analyzing gas components corresponding to the gas data;
analyzing the gas data through a preset three-ratio method, and judging the fault type of the transformer;
acquiring the content corresponding to the gas component, and acquiring the corresponding fault severity according to the content;
and acquiring the growth rates of carbon monoxide and carbon dioxide in the gas components, and judging whether the transformer contains solid insulation or not according to the growth rates.
In one embodiment, the fault type includes:
low temperature overheating (< 150 ℃), low temperature overheating (150-300 ℃), medium temperature overheating (300-700 ℃), high temperature overheating (> 700 ℃), partial discharge, low energy discharge, arc discharge, low energy discharge with overheating, high energy discharge with overheating.
In one embodiment, the working information includes:
the number of power frequency cycles per second for which there is an impact event, and the number of impact events within each power frequency cycle.
In one embodiment, the method further comprises:
and matching the corresponding checking and processing method according to the fault reason, and outputting the checking and processing method.
The embodiment of the invention provides a large-scale transformer discharge fault diagnosis device, which comprises:
the model establishing module is used for establishing a 3D model of the transformer and carrying out ultrasonic partial discharge detection on the transformer;
the detection module is used for sending a sampling trigger signal to the gas monitoring device when the discharge in the transformer is detected;
the acquisition module is used for acquiring gas data sampled by the gas monitoring device and determining whether the transformer has a discharge fault or not by a preset detection method;
the position determining module is used for determining the discharging position of the transformer according to the position information of the discharging fault and the 3D model when the discharging fault exists in the transformer;
and the output module is used for acquiring the working information of the discharge position, performing data analysis on the gas data, inputting the working information and the analysis result of the gas data into a fault case library, matching the corresponding fault reason and outputting the fault reason.
In one embodiment, the apparatus further comprises:
and the second acquisition module is used for acquiring a chromatographic analysis result of the gas sampled by the gas monitoring device and judging whether the transformer has a discharge fault or not according to the chromatographic analysis result.
The embodiment of the invention provides electronic equipment which comprises a memory, a processor and a computer program which is stored on the memory and can run on the processor, wherein the processor executes the program to realize the steps of the large transformer discharge fault diagnosis method.
An embodiment of the present invention provides a non-transitory computer readable storage medium, on which a computer program is stored, and the computer program, when executed by a processor, implements the steps of the above-mentioned large transformer discharge fault diagnosis method.
According to the discharge fault diagnosis method and device for the large transformer, provided by the embodiment of the invention, a 3D model of the transformer is established, and ultrasonic partial discharge detection is carried out on the transformer; when the discharge in the transformer is detected, sending a sampling trigger signal to a gas monitoring device; acquiring gas data sampled by a gas monitoring device, and determining whether a discharge fault exists in a transformer by a preset detection method; when the transformer has a discharge fault, determining the discharge position of the transformer according to the position information of the discharge fault and the 3D model; and acquiring working information of the discharge position, performing data analysis on the gas data, inputting the analysis results of the working information and the gas data into a fault case library, matching corresponding fault reasons and outputting the matching results. Therefore, the fault position can be accurately judged, the defective component and the corresponding fault reason can be searched, and the fault property and the discharge severity can be reflected.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a flow chart of a discharge fault diagnosis method for a large transformer according to an embodiment of the present invention;
FIG. 2 is a flow chart of a discharge fault diagnosis method for a large transformer according to another embodiment of the present invention;
fig. 3 is a structural diagram of a discharge fault diagnosis device for a large transformer in an embodiment of the present invention;
fig. 4 is a schematic structural diagram of an electronic device in an embodiment of the invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Fig. 1 is a schematic flow chart of a discharge fault diagnosis method for a large transformer according to an embodiment of the present invention, and as shown in fig. 1, the embodiment of the present invention provides a discharge fault diagnosis method for a large transformer, including:
step S101, establishing a 3D model of the transformer, and carrying out ultrasonic partial discharge detection on the transformer.
Specifically, 3D modeling can be performed according to the design drawing of the transformer, the size and the position of key components inside the 3D modeling can be marked, and then ultrasonic partial discharge detection is performed on the transformer.
And S102, when the discharge in the transformer is detected, sending a sampling trigger signal to a gas monitoring device.
Specifically, when the phenomenon of discharging in the transformer is detected, a sampling trigger signal is sent to the oil dissolved gas monitoring device, the sampling trigger signal can enable the oil dissolved gas monitoring device to monitor the oil dissolved gas of the transformer, and in addition, in the monitoring process, the trigger signal from the partial discharge equipment is also received except for a timing sampling strategy considering that the sampling interval of the monitoring device is long.
Step S103, gas data sampled by the gas monitoring device is obtained, and whether the transformer has a discharge fault is determined through a preset detection method.
Specifically, when the discharge phenomenon in the transformer is detected, gas data sampled by the gas monitoring device is obtained, a chromatographic analysis result corresponding to the gas is analyzed through a preset chromatographic analysis method, if partial discharge exists in the partial discharge device, the chromatographic analysis result belongs to a discharge fault, if partial discharge exists in the partial discharge device, and if the chromatographic analysis result is normal, the partial discharge or the false alarm of the partial discharge device may exist, and the situation is to be further observed.
And step S104, when the transformer has a discharge fault, determining the discharge position of the transformer according to the position information of the discharge fault and the 3D model.
Specifically, when the transformer has a discharge fault, the partial discharge device can be used for carrying out space comparison with the 3D model of the transformer according to the 3D positioning coordinates output by the discharge fault position, so as to determine a specific component corresponding to the vicinity of the discharge position, and conveniently analyze the discharge condition after the discharge position is determined.
And step S105, acquiring the working information of the discharge position, performing data analysis on the gas data, inputting the working information and the analysis result of the gas data into a fault case library, matching corresponding fault reasons and outputting.
Specifically, after the discharge position is determined, the working information of the discharge position may also be obtained, where the working information of the discharge position may include the number of power frequency cycles for which an impact event exists per second and the number of impact events in each power frequency cycle, and the method for calculating the number of power frequency cycles for which an impact event (corresponding to one discharge) exists per second includes: and (3) within a range of 2 hours ahead of the current time, counting whether discharge (not counting the discharge times) exists in each power frequency period by seconds, if so, counting the discharge periods by +1, otherwise, keeping unchanged. Finally, averaging the statistical results per second; the method for calculating the number of the impact events in each power frequency period comprises the following steps: and (4) within a range of 2 hours before the current time, counting the discharge times in each power frequency period, and finally averaging. And then analyzing the gas data, wherein the analysis process can comprise the following steps:
(a) the major gas component of the fault. The analysis method comprises the following steps: h2、CH4、C2H6、C2H4、C2H2In (b), the ratio is more than 20%, and the gas component with the content of the top 3 is defined as the main component.
(b) Nature of the failure. Determined by a three-ratio method, comprising: low-temperature overheating (< 150 ℃), low-temperature overheating (150-300 ℃), medium-temperature overheating (300-700 ℃), high-temperature overheating (> 700 ℃), partial discharge, low-energy discharge, arc discharge, low-energy discharge and overheating, and high-energy discharge and overheating 9 types.
(c) Severity of the failure. The severity of the fault is divided into 3 grades, the gas growth in the last day is considered, and the judgment mode is as shown in table 1:
TABLE 1
(d) Whether solid insulation is included. Investigating CO and CO in the last day2Increase of two gases by Δ CQ(Q represents CO or CO 2) and for a gas content of C10Q of approximately 10 balances, for either gas, if Δ CQ>max (10% xc 10Q, 200 μ L/L), then the fault comprises solid insulation; otherwise it is not included.
(4) And the correlation of working conditions can be analyzed by combining chromatography and partial discharge monitoring. And measuring the correlation between the features by using a Pearson correlation coefficient, wherein the correlation coefficient is larger than 0.4, and considering that the fault has correlation with the relevant investigation index, otherwise, the fault is not correlated. The key research indexes comprise:
(a) discharge-voltage dependence. Monitoring partial discharge, and calculating the discharge frequency of 1 minute, namely the Pearson coefficient of voltage; for the chromatogram, the gas production rate, the Pearson coefficient of the voltage peak over the sampling period, was calculated.
(b) Discharge-current dependence. Monitoring partial discharge, and calculating the discharge frequency of 1 minute, namely the Pearson coefficient of current; for the chromatogram, the gas production rate, the Pearson coefficient of the current peak in the sampling period, was calculated.
(c) Discharge-oil pump start-up correlation. Monitoring partial discharge, and calculating the 1-minute discharge frequency, namely the Pearson coefficient of the starting state (1 is starting and 0 is stopping) of the oil pump; for the chromatogram, the gas production rate, the Pearson coefficient of the oil pump start time in the sampling period, was calculated.
After the analysis result is obtained, the analysis result of the working information and the gas data is input into a fault case library, one case in the matched fault cause fault case library is implemented as a mapping relation of 'symptom set-fault cause', and corresponding maintenance treatment measures are additionally recorded, as shown in the following table 2. The 'symptom' and the 'failure reason' are both structured storage and are represented by fixed codes. When the similarity between a case to be diagnosed and a case (reference case) in the case base is calculated, the symptom set of the reference case is used as an analysis basis, and for any symptom, if the symptom can be found to be matched with the symptom in the case to be diagnosed, the similarity is 1, otherwise, the similarity is 0, all the symptom similarities are averaged, and the overall similarity between the two cases is obtained. And sorting the cases in the case base according to the similarity, and performing duplicate removal processing on the same fault reason, namely outputting a final diagnosis result.
TABLE 2
According to the discharge fault diagnosis method for the large transformer, provided by the embodiment of the invention, a 3D model of the transformer is established, and ultrasonic partial discharge detection is carried out on the transformer; when the discharge in the transformer is detected, sending a sampling trigger signal to a gas monitoring device; acquiring gas data sampled by a gas monitoring device, and determining whether a discharge fault exists in a transformer by a preset detection method; when the transformer has a discharge fault, determining the discharge position of the transformer according to the position information of the discharge fault and the 3D model; and acquiring working information of the discharge position, performing data analysis on the gas data, inputting the analysis results of the working information and the gas data into a fault case library, matching corresponding fault reasons and outputting the matching results. Therefore, the fault position can be accurately judged, the defective component and the corresponding fault reason can be searched, and the fault property and the discharge severity can be reflected.
On the basis of the above embodiment, the method for diagnosing discharge fault of large transformer further includes:
and matching the corresponding checking and processing method according to the fault reason, and outputting the checking and processing method.
In the embodiment of the invention, after the corresponding checking and processing method is matched according to the fault reason, the corresponding checking and processing method can be output to the corresponding binding terminal, so that a relevant engineer can know the corresponding solving method in time and solve the problem of the discharge fault of the transformer in time.
The embodiment of the invention can enable related engineers to know the corresponding solution in time and solve the problem of the discharge fault of the transformer in time.
Fig. 2 is a schematic flow chart of a discharge fault diagnosis method for a large transformer according to another embodiment of the present invention, in this embodiment, specific steps of the fault diagnosis method may include:
1) 3D modeling is performed according to a transformer design drawing, especially the size and position of internal key components.
2) And carrying out real-time ultrasonic partial discharge monitoring on the transformer, if discharge occurs, sending a sampling trigger signal to a device for monitoring gas dissolved in oil, and simultaneously adding a discharge state and original data into subsequent logic judgment.
3) The method is used for monitoring dissolved gas in oil for the transformer, and in consideration of the fact that a monitoring device is long in sampling interval, the method also receives a trigger signal from partial discharge equipment besides a timing sampling strategy.
4) After data of each component of dissolved gas in oil are obtained, fault determination is carried out by adopting a three-ratio method, and the types of the fault determination method comprise: low-temperature overheating (< 150 ℃), low-temperature overheating (150-300 ℃), medium-temperature overheating (300-700 ℃), high-temperature overheating (> 700 ℃), partial discharge, low-energy discharge, arc discharge, low-energy discharge and overheating, and high-energy discharge and overheating 9 types.
5) If the partial discharge equipment judges that partial discharge exists and the chromatographic analysis result belongs to a discharge fault, continuing to perform the step 6); if the partial discharge equipment judges that partial discharge exists and the chromatographic equipment analysis result is normal, slight discharge or false alarm of the partial discharge equipment may exist and needs to be further observed.
6) For the transformer judged to be in discharge fault, the system automatically extracts a key fault symptom set:
(1) the 3D positioning information output by the partial discharge equipment is spatially compared with the 3D model of the transformer, and a specific component corresponding to a discharge position is identified;
(2) extracting the following key features from a result file obtained by analyzing the partial discharge equipment:
(a) number of power frequency cycles per second with impact event (corresponding to one discharge)
(b) Number of impact events per power frequency cycle
(3) From the gas composition data, the following key features were extracted:
(a) the major gas component of the fault;
(b) nature of the failure. Determining by a three-ratio method;
(c) severity of the failure. And judging according to the short-term delivery date rate.
(d) Whether solid insulation is included. The determination is carried out according to whether the two gases of CO and CO2 have abnormal growth.
(4) And (5) analyzing the correlation of the working conditions by combining chromatography and partial discharge monitoring. Correlation between features is measured using Pearson correlation coefficient, where correlation coefficient is greater than 0.4, considered correlated, otherwise not correlated. The key research indexes comprise:
(a) discharge-voltage dependence.
(b) Discharge-current dependence.
(c) Discharge-oil pump start-up correlation.
7) Inputting the symptom set extracted in the step 6) into a fault case library to match fault reasons. One case in the fault case library is realized as a mapping relation of a symptom set and a fault reason, and corresponding maintenance treatment measures are additionally recorded. The 'symptom' and the 'failure reason' are both structured storage and are represented by fixed codes. When the similarity between a case to be diagnosed and a case (reference case) in the case base is calculated, the symptom set of the reference case is used as an analysis basis, and for any symptom, if the symptom can be found to be matched with the symptom in the case to be diagnosed, the similarity is 1, otherwise, the similarity is 0, all the symptom similarities are averaged, and the overall similarity between the two cases is obtained. And sorting the cases in the case base according to the similarity, and performing duplicate removal processing on the same fault reason, namely outputting a final diagnosis result.
Fig. 3 is a discharge fault diagnosis device for a large transformer according to an embodiment of the present invention, including: a model establishing module S201, a detecting module S202, an obtaining module S203, a position determining module S204 and an output module S205, wherein:
the model establishing module S201 is used for establishing a 3D model of the transformer and carrying out ultrasonic partial discharge detection on the transformer.
The detection module S202 is configured to send a sampling trigger signal to the gas monitoring apparatus when detecting that discharge occurs in the transformer.
An obtaining module S203, configured to obtain gas data sampled by the gas monitoring device, and determine whether a discharge fault exists in the transformer by using a preset detection method.
And the position determining module S204 is used for determining the discharging position of the transformer according to the position information of the discharging fault and the S3D model when the discharging fault exists in the transformer.
And the output module 205 is configured to obtain the working information of the discharge position, perform data analysis on the gas data, input the working information and the analysis result of the gas data into a fault case library, match a corresponding fault reason, and output the result.
In one embodiment, the apparatus may further comprise:
and the second acquisition module is used for acquiring a chromatographic analysis result of the gas sampled by the gas monitoring device and judging whether the transformer has a discharge fault or not according to the chromatographic analysis result.
In one embodiment, the apparatus may further comprise:
and the first analysis module is used for analyzing the gas components corresponding to the gas data.
And the second analysis module is used for analyzing the gas data through a preset three-ratio method and judging the fault type of the transformer.
And the third acquisition module is used for acquiring the content corresponding to the gas component and acquiring the corresponding fault severity according to the content.
And the fourth acquisition module is used for acquiring the growth rates of carbon monoxide and carbon dioxide in the gas components and judging whether the transformer contains solid insulation or not according to the growth rates.
In one embodiment, the apparatus may further comprise:
and the second output module is used for matching the corresponding checking and processing method according to the fault reason and outputting the checking and processing method.
For specific limitations of the large transformer discharge fault diagnosis device, reference may be made to the above limitations of the large transformer discharge fault diagnosis method, and details are not described here. All or part of each module in the large transformer discharge fault diagnosis device can be realized by software, hardware and a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
Fig. 4 illustrates a physical structure diagram of an electronic device, which may include, as shown in fig. 4: a processor (processor)301, a memory (memory)302, a communication Interface (Communications Interface)303 and a communication bus 304, wherein the processor 301, the memory 302 and the communication Interface 303 complete communication with each other through the communication bus 304. The processor 301 may call logic instructions in the memory 302 to perform the following method: establishing a 3D model of the transformer, and carrying out ultrasonic partial discharge detection on the transformer; when the discharge in the transformer is detected, sending a sampling trigger signal to a gas monitoring device; acquiring gas data sampled by a gas monitoring device, and determining whether a discharge fault exists in a transformer by a preset detection method; when the transformer has a discharge fault, determining the discharge position of the transformer according to the position information of the discharge fault and the 3D model; and acquiring working information of the discharge position, performing data analysis on the gas data, inputting the analysis results of the working information and the gas data into a fault case library, matching corresponding fault reasons and outputting the matching results.
Furthermore, the logic instructions in the memory 302 may be implemented in software functional units and stored in a computer readable storage medium when sold or used as a stand-alone product. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
In another aspect, an embodiment of the present invention further provides a non-transitory computer-readable storage medium, on which a computer program is stored, where the computer program is implemented to perform the transmission method provided in the foregoing embodiments when executed by a processor, and for example, the method includes: establishing a 3D model of the transformer, and carrying out ultrasonic partial discharge detection on the transformer; when the discharge in the transformer is detected, sending a sampling trigger signal to a gas monitoring device; acquiring gas data sampled by a gas monitoring device, and determining whether a discharge fault exists in a transformer by a preset detection method; when the transformer has a discharge fault, determining the discharge position of the transformer according to the position information of the discharge fault and the 3D model; and acquiring working information of the discharge position, performing data analysis on the gas data, inputting the analysis results of the working information and the gas data into a fault case library, matching corresponding fault reasons and outputting the matching results.
The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. With this understanding in mind, the above-described technical solutions may be embodied in the form of a software product, which can be stored in a computer-readable storage medium such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in the embodiments or some parts of the embodiments.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. A discharge fault diagnosis method for a large transformer is characterized by comprising the following steps:
establishing a 3D model of a transformer, and carrying out ultrasonic partial discharge detection on the transformer;
when the discharge in the transformer is detected, sending a sampling trigger signal to a gas monitoring device;
acquiring gas data sampled by the gas monitoring device, and determining whether the transformer has a discharge fault or not by a preset detection method;
when the transformer has a discharge fault, determining the discharge position of the transformer according to the position information of the discharge fault and the 3D model;
and acquiring the working information of the discharge position, performing data analysis on the gas data, inputting the working information and the analysis result of the gas data into a fault case library, matching corresponding fault reasons and outputting.
2. The method for diagnosing the discharge fault of the large transformer according to claim 1, wherein the determining whether the discharge fault exists in the transformer through a preset detection method comprises the following steps:
and acquiring a chromatographic analysis result of the gas sampled by the gas monitoring device, and judging whether the transformer has a discharge fault or not according to the chromatographic analysis result.
3. The large transformer discharge fault diagnosis method according to claim 1, wherein the performing data analysis on the gas data comprises:
analyzing gas components corresponding to the gas data;
analyzing the gas data through a preset three-ratio method, and judging the fault type of the transformer;
acquiring the content corresponding to the gas component, and acquiring the corresponding fault severity according to the content;
and acquiring the growth rates of carbon monoxide and carbon dioxide in the gas components, and judging whether the transformer contains solid insulation or not according to the growth rates.
4. The large transformer discharging fault diagnosis method according to claim 3, wherein the fault types include:
low temperature overheating (< 150 ℃), low temperature overheating (150-300 ℃), medium temperature overheating (300-700 ℃), high temperature overheating (> 700 ℃), partial discharge, low energy discharge, arc discharge, low energy discharge with overheating, high energy discharge with overheating.
5. The large transformer discharging fault diagnosis method according to claim 1, wherein the operation information includes:
the number of power frequency cycles per second for which there is an impact event, and the number of impact events within each power frequency cycle.
6. The large transformer discharge fault diagnosis method according to claim 1, further comprising:
and matching the corresponding checking and processing method according to the fault reason, and outputting the checking and processing method.
7. A large transformer discharge fault diagnosis apparatus, characterized in that the apparatus comprises:
the model establishing module is used for establishing a 3D model of the transformer and carrying out ultrasonic partial discharge detection on the transformer;
the detection module is used for sending a sampling trigger signal to the gas monitoring device when the discharge in the transformer is detected;
the acquisition module is used for acquiring gas data sampled by the gas monitoring device and determining whether the transformer has a discharge fault or not by a preset detection method;
the position determining module is used for determining the discharging position of the transformer according to the position information of the discharging fault and the 3D model when the discharging fault exists in the transformer;
and the output module is used for acquiring the working information of the discharge position, performing data analysis on the gas data, inputting the working information and the analysis result of the gas data into a fault case library, matching the corresponding fault reason and outputting the fault reason.
8. The large transformer discharge fault diagnosis device according to claim 7, characterized in that the device further comprises:
and the second acquisition module is used for acquiring a chromatographic analysis result of the gas sampled by the gas monitoring device and judging whether the transformer has a discharge fault or not according to the chromatographic analysis result.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor when executing the program implements the steps of the method for diagnosing discharge faults of a large transformer according to any one of claims 1 to 6.
10. A non-transitory computer readable storage medium, on which a computer program is stored, wherein the computer program, when executed by a processor, implements the steps of the method for diagnosing discharge faults of a large transformer according to any one of claims 1 to 6.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114324710A (en) * | 2021-11-15 | 2022-04-12 | 杭州柯林电气股份有限公司 | Transformer oil chromatographic detection system and detection method |
| CN114487283A (en) * | 2021-12-31 | 2022-05-13 | 武汉怡特环保科技有限公司 | Remote intelligent diagnosis and operation and maintenance method and system for air quality monitoring system |
| CN115436767A (en) * | 2022-11-07 | 2022-12-06 | 江苏黑马高科股份有限公司 | Transformer partial discharge monitoring and analyzing method and system |
Citations (18)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20010077602A (en) * | 2000-02-03 | 2001-08-20 | 이종훈 | The Preventive Diagnostic System for the Electric Power Substation Equipments |
| JP2002118912A (en) * | 2000-10-11 | 2002-04-19 | Mitsubishi Electric Corp | Gas-insulated electric apparatus and anomaly detecting method therein |
| KR20110046226A (en) * | 2009-10-27 | 2011-05-04 | 주식회사 태광이엔시 | Combined partial discharge monitoring system and method of transformer |
| CN102981084A (en) * | 2012-12-07 | 2013-03-20 | 深圳市双合电气股份有限公司 | Comprehensive monitoring system for electric power system transformers |
| CN104076230A (en) * | 2014-07-16 | 2014-10-01 | 胡小青 | Electrical fault detecting system for power transmission transformer |
| CN104569724A (en) * | 2015-01-06 | 2015-04-29 | 国家电网公司 | Transformer short-circuit fault comprehensive diagnosis method |
| CN104749506A (en) * | 2015-04-03 | 2015-07-01 | 国家电网公司 | Method for calculating running electrical equipment partial discharge quantity through SF6 decomposition product content |
| CN105676085A (en) * | 2016-01-31 | 2016-06-15 | 国家电网公司 | Extra-high voltage GIS partial discharge detection method based on multi-sensor information fusion |
| KR101686133B1 (en) * | 2016-09-14 | 2017-01-02 | 도우디지텍 주식회사 | Apparatus of partial diacharge position multiple detecting with 3-D for oil filled power transformer |
| KR20170019545A (en) * | 2015-08-11 | 2017-02-22 | 한전케이디엔주식회사 | Apparatus for partial dishcarge detection to power equipment |
| CN107703215A (en) * | 2017-08-16 | 2018-02-16 | 国网浙江省电力公司电力科学研究院 | A kind of GIS device discharge fault chemical diagnosis method |
| CN110261741A (en) * | 2019-05-24 | 2019-09-20 | 国网河北省电力有限公司电力科学研究院 | Discharge position localization method, device and the terminal device of high-tension switch gear |
| CN209432952U (en) * | 2018-11-21 | 2019-09-24 | 贵州电网有限责任公司 | Inside transformer insulation defect Discharge Simulation device |
| CN110297841A (en) * | 2019-06-25 | 2019-10-01 | 国网浙江省电力有限公司电力科学研究院 | A kind of transformer fault diagnosis and fast indexing method and system |
| CN110726676A (en) * | 2019-09-24 | 2020-01-24 | 厦门恳灏电气有限公司 | Online operation and maintenance system of smart substation based on photoacoustic spectroscopy technology |
| CN111008462A (en) * | 2019-11-20 | 2020-04-14 | 武汉大学 | Switch cabinet partial discharge detection method and device based on gas component analysis method |
| CN111273196A (en) * | 2020-03-11 | 2020-06-12 | 杭州安脉盛智能技术有限公司 | Health management system and method applied to nuclear power large-scale power transformer |
| CN111983394A (en) * | 2020-07-03 | 2020-11-24 | 国网浙江省电力有限公司电力科学研究院 | Based on SF6GIS discharge fault diagnosis method for analysis of decomposition products |
-
2021
- 2021-03-22 CN CN202110303377.4A patent/CN113030666B/en active Active
Patent Citations (18)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20010077602A (en) * | 2000-02-03 | 2001-08-20 | 이종훈 | The Preventive Diagnostic System for the Electric Power Substation Equipments |
| JP2002118912A (en) * | 2000-10-11 | 2002-04-19 | Mitsubishi Electric Corp | Gas-insulated electric apparatus and anomaly detecting method therein |
| KR20110046226A (en) * | 2009-10-27 | 2011-05-04 | 주식회사 태광이엔시 | Combined partial discharge monitoring system and method of transformer |
| CN102981084A (en) * | 2012-12-07 | 2013-03-20 | 深圳市双合电气股份有限公司 | Comprehensive monitoring system for electric power system transformers |
| CN104076230A (en) * | 2014-07-16 | 2014-10-01 | 胡小青 | Electrical fault detecting system for power transmission transformer |
| CN104569724A (en) * | 2015-01-06 | 2015-04-29 | 国家电网公司 | Transformer short-circuit fault comprehensive diagnosis method |
| CN104749506A (en) * | 2015-04-03 | 2015-07-01 | 国家电网公司 | Method for calculating running electrical equipment partial discharge quantity through SF6 decomposition product content |
| KR20170019545A (en) * | 2015-08-11 | 2017-02-22 | 한전케이디엔주식회사 | Apparatus for partial dishcarge detection to power equipment |
| CN105676085A (en) * | 2016-01-31 | 2016-06-15 | 国家电网公司 | Extra-high voltage GIS partial discharge detection method based on multi-sensor information fusion |
| KR101686133B1 (en) * | 2016-09-14 | 2017-01-02 | 도우디지텍 주식회사 | Apparatus of partial diacharge position multiple detecting with 3-D for oil filled power transformer |
| CN107703215A (en) * | 2017-08-16 | 2018-02-16 | 国网浙江省电力公司电力科学研究院 | A kind of GIS device discharge fault chemical diagnosis method |
| CN209432952U (en) * | 2018-11-21 | 2019-09-24 | 贵州电网有限责任公司 | Inside transformer insulation defect Discharge Simulation device |
| CN110261741A (en) * | 2019-05-24 | 2019-09-20 | 国网河北省电力有限公司电力科学研究院 | Discharge position localization method, device and the terminal device of high-tension switch gear |
| CN110297841A (en) * | 2019-06-25 | 2019-10-01 | 国网浙江省电力有限公司电力科学研究院 | A kind of transformer fault diagnosis and fast indexing method and system |
| CN110726676A (en) * | 2019-09-24 | 2020-01-24 | 厦门恳灏电气有限公司 | Online operation and maintenance system of smart substation based on photoacoustic spectroscopy technology |
| CN111008462A (en) * | 2019-11-20 | 2020-04-14 | 武汉大学 | Switch cabinet partial discharge detection method and device based on gas component analysis method |
| CN111273196A (en) * | 2020-03-11 | 2020-06-12 | 杭州安脉盛智能技术有限公司 | Health management system and method applied to nuclear power large-scale power transformer |
| CN111983394A (en) * | 2020-07-03 | 2020-11-24 | 国网浙江省电力有限公司电力科学研究院 | Based on SF6GIS discharge fault diagnosis method for analysis of decomposition products |
Non-Patent Citations (4)
| Title |
|---|
| 刘兴华 等: "变压器局部放电带电检测技术与故障案例分析", 变压器, vol. 56, no. 05, 25 May 2019 (2019-05-25), pages 72 - 76 * |
| 刘新苗: "基于油中溶解气体在线监测的变压器故障动态预警方法分析", 广东科技, no. 22, pages 96 - 97 * |
| 朱余启: "基于案例推理的大型电力变压器故障诊断系统的研究", 中国优秀硕士学位论文全文数据库 工程科技Ⅱ辑, no. 02, pages 042 - 110 * |
| 陈浩 等: "变压器油中溶解气体分析和故障诊断", 东北电力技术, vol. 38, no. 10, 20 October 2017 (2017-10-20), pages 29 - 32 * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114324710A (en) * | 2021-11-15 | 2022-04-12 | 杭州柯林电气股份有限公司 | Transformer oil chromatographic detection system and detection method |
| CN114487283A (en) * | 2021-12-31 | 2022-05-13 | 武汉怡特环保科技有限公司 | Remote intelligent diagnosis and operation and maintenance method and system for air quality monitoring system |
| CN114487283B (en) * | 2021-12-31 | 2024-01-30 | 武汉怡特环保科技有限公司 | Remote intelligent diagnosis and operation and maintenance method and system for air quality monitoring system |
| CN115436767A (en) * | 2022-11-07 | 2022-12-06 | 江苏黑马高科股份有限公司 | Transformer partial discharge monitoring and analyzing method and system |
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