CN116519858B - Transformer oil nursing device with real-time monitoring function - Google Patents
Transformer oil nursing device with real-time monitoring function Download PDFInfo
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- CN116519858B CN116519858B CN202310801122.XA CN202310801122A CN116519858B CN 116519858 B CN116519858 B CN 116519858B CN 202310801122 A CN202310801122 A CN 202310801122A CN 116519858 B CN116519858 B CN 116519858B
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- 230000000474 nursing effect Effects 0.000 title claims abstract description 38
- 238000012544 monitoring process Methods 0.000 title claims abstract description 24
- 238000007872 degassing Methods 0.000 claims abstract description 28
- 230000018044 dehydration Effects 0.000 claims abstract description 28
- 238000006297 dehydration reaction Methods 0.000 claims abstract description 28
- 238000005070 sampling Methods 0.000 claims abstract description 21
- 239000002245 particle Substances 0.000 claims abstract description 4
- 239000007789 gas Substances 0.000 claims description 128
- 238000001514 detection method Methods 0.000 claims description 76
- 238000000034 method Methods 0.000 claims description 21
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 5
- 230000008859 change Effects 0.000 claims description 4
- VNWKTOKETHGBQD-UHFFFAOYSA-N methane Chemical compound C VNWKTOKETHGBQD-UHFFFAOYSA-N 0.000 description 16
- 239000000306 component Substances 0.000 description 11
- CURLTUGMZLYLDI-UHFFFAOYSA-N Carbon dioxide Chemical compound O=C=O CURLTUGMZLYLDI-UHFFFAOYSA-N 0.000 description 10
- VGGSQFUCUMXWEO-UHFFFAOYSA-N Ethene Chemical compound C=C VGGSQFUCUMXWEO-UHFFFAOYSA-N 0.000 description 8
- 239000005977 Ethylene Substances 0.000 description 8
- HSFWRNGVRCDJHI-UHFFFAOYSA-N alpha-acetylene Natural products C#C HSFWRNGVRCDJHI-UHFFFAOYSA-N 0.000 description 8
- 125000002534 ethynyl group Chemical group [H]C#C* 0.000 description 8
- 229910052739 hydrogen Inorganic materials 0.000 description 8
- 239000001257 hydrogen Substances 0.000 description 8
- 150000002431 hydrogen Chemical class 0.000 description 6
- UGFAIRIUMAVXCW-UHFFFAOYSA-N Carbon monoxide Chemical compound [O+]#[C-] UGFAIRIUMAVXCW-UHFFFAOYSA-N 0.000 description 5
- 229910002092 carbon dioxide Inorganic materials 0.000 description 5
- 239000001569 carbon dioxide Substances 0.000 description 5
- 229910002091 carbon monoxide Inorganic materials 0.000 description 5
- OTMSDBZUPAUEDD-UHFFFAOYSA-N Ethane Chemical compound CC OTMSDBZUPAUEDD-UHFFFAOYSA-N 0.000 description 4
- UFHFLCQGNIYNRP-UHFFFAOYSA-N Hydrogen Chemical compound [H][H] UFHFLCQGNIYNRP-UHFFFAOYSA-N 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 238000001914 filtration Methods 0.000 description 2
- 238000002347 injection Methods 0.000 description 2
- 239000007924 injection Substances 0.000 description 2
- 238000012423 maintenance Methods 0.000 description 2
- 238000001834 photoacoustic spectrum Methods 0.000 description 2
- 230000008569 process Effects 0.000 description 2
- 239000008358 core component Substances 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000036541 health Effects 0.000 description 1
- 238000009413 insulation Methods 0.000 description 1
- 229910052751 metal Inorganic materials 0.000 description 1
- 239000002184 metal Substances 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000000737 periodic effect Effects 0.000 description 1
- 238000010926 purge Methods 0.000 description 1
- 239000007787 solid Substances 0.000 description 1
- 239000000243 solution Substances 0.000 description 1
- 239000000126 substance Substances 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 238000003828 vacuum filtration Methods 0.000 description 1
Classifications
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N30/00—Investigating or analysing materials by separation into components using adsorption, absorption or similar phenomena or using ion-exchange, e.g. chromatography or field flow fractionation
- G01N30/02—Column chromatography
- G01N30/04—Preparation or injection of sample to be analysed
- G01N30/06—Preparation
- G01N30/14—Preparation by elimination of some components
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N30/00—Investigating or analysing materials by separation into components using adsorption, absorption or similar phenomena or using ion-exchange, e.g. chromatography or field flow fractionation
- G01N30/02—Column chromatography
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16C—COMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
- G16C20/00—Chemoinformatics, i.e. ICT specially adapted for the handling of physicochemical or structural data of chemical particles, elements, compounds or mixtures
- G16C20/20—Identification of molecular entities, parts thereof or of chemical compositions
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16C—COMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
- G16C20/00—Chemoinformatics, i.e. ICT specially adapted for the handling of physicochemical or structural data of chemical particles, elements, compounds or mixtures
- G16C20/70—Machine learning, data mining or chemometrics
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- 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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- Bioinformatics & Computational Biology (AREA)
- Analytical Chemistry (AREA)
- Biochemistry (AREA)
- Crystallography & Structural Chemistry (AREA)
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- Housings And Mounting Of Transformers (AREA)
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- Computer Vision & Pattern Recognition (AREA)
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Abstract
The invention provides a transformer oil nursing device with real-time monitoring, which belongs to the technical field of transformers and comprises an oil inlet pipeline connected with a bottom end interface of a transformer and an oil return pipeline connected with a top end interface of the transformer, wherein the oil inlet pipeline and the oil return pipeline form a closed oil way in the nursing device; the oil way is sequentially provided with an overflow valve, a dehydration and degassing device, a filter pressure switch, a particle filter and a stop valve assembly along the flowing direction, and two ends of the dehydration and degassing device are connected with a motor pump; the dehydration degassing device is connected with a gas sampling device, a control device is further arranged in the nursing device, the gas sampling device enables gas in oil to enter a chromatographic column arranged in the nursing device, and the control device sends data of the content of each component of the gas in the oil calculated through the chromatographic column to a control terminal.
Description
Technical Field
The invention relates to the technical field of power transformers, in particular to a transformer oil nursing device with real-time monitoring function.
Background
The purpose of transformer oil quality supervision is to ensure that the transformer oil quality meets the safety operation requirement of oil-filled electrical equipment by monitoring various physical, chemical and electrical properties of the transformer oil, and whether the transformer is normal or not in the operation process can be analyzed and diagnosed by using the analysis technology of dissolved gas in the transformer oil, so that the latent faults in the transformer can be found in time, and the health condition of the oil-filled electrical equipment can be mastered. The transformer oil quality and photoacoustic spectrum analysis supervision has the advantages that the transformer oil quality and photoacoustic spectrum analysis supervision can be detected without power failure, technical support can be provided for state maintenance, and the importance is more outstanding after the state maintenance is implemented at present.
Therefore, the analysis of the gas content in the transformer oil is an important standard for judging the faults of the transformer, and the current common oil inspector gets to the side of the transformer to collect an oil sample through a special oil sampling hole and brings the oil sample back to a laboratory for analysis and judgment.
Such drawbacks are:
1. the detection period is fixed, and the oil inspector samples at regular intervals, so that the transformer is likely to fail when the transformer does not reach the next detection period due to lack of trend analysis and judgment.
2. The detection accuracy is not high, and the expansion tank is connected to the transformer, and some gases, such as carbon monoxide, carbon dioxide, hydrogen, methane, ethane, ethylene, acetylene etc. can diffuse to the outside, lead to the gas content that detects in the oil sample inconsistent with the gas content that really produces in the transformer oil.
3. The detection and the degassing and dehydration are separated, and the degassing and the dehydration are carried out by a special equipment transformer oil filter.
Disclosure of Invention
The invention aims to solve the technical problem and provides a transformer oil nursing device with real-time monitoring.
The technical scheme of the invention is that the transformer oil nursing device with the real-time monitoring function comprises an oil inlet pipeline connected with a bottom end interface of a transformer and an oil return pipeline connected with a top end interface of the transformer, wherein the oil inlet pipeline and the oil return pipeline form a closed oil way in the nursing device;
the oil way is sequentially provided with an overflow valve, a dehydration and degassing device, a filter pressure switch, a particle filter and a stop valve assembly along the flowing direction, and two ends of the dehydration and degassing device are connected with a motor pump;
the dehydration degassing device is connected with a gas sampling device, a control device is further arranged in the nursing device, the gas sampling device enables gas in oil to enter a chromatographic column arranged in the nursing device, and the control device sends data of the content of each component of the gas in the oil calculated through the chromatographic column to a control terminal.
As an implementation manner, the oil inlet pipeline is connected with the bottom end connector of the transformer through a one-way valve.
As one embodiment, the shut-off valve assembly includes an automatic shut-off valve and a manual shut-off valve.
As an implementation manner, a vacuum pressure gauge is connected to the automatic stop valve.
As an implementation manner, the oil path is provided with a water sensor at the front end of the dehydration degassing device and at the rear end of the automatic stop valve.
As an implementation manner, the gas sampling device is provided with a gas pressure sensor.
As an implementation manner, the dehydration degassing device is provided with a bleed valve.
As an implementation manner, the control device controls the oil path to be periodically opened so that the gas sampling device detects the content of each component of the gas in the oil;
the control device is connected with a target association library, wherein a target model corresponding to various transformer faults is configured in the target association library, and the target model is a pre-established functional change relation set of the characteristic gas content corresponding to the transformer faults along with time;
the control device is configured with a generation method of a next detection period, the generation method comprising:
acquiring current detection data of the content of each component of the gas in the oil, which is detected by the gas sampling device;
determining the existing characteristic gas and the existing characteristic gas content increment in the current detection according to the current detection data and the previously stored previous detection data obtained in the previous detection;
according to the components of the existing characteristic gas and the content increment of the existing characteristic gas, a target model with the highest correlation degree is called;
substituting the content of the characteristic gas existing in the previous detection data and the content of the characteristic gas existing in the current detection data into functions corresponding to various characteristic gases in the extracted target model to obtain a first time length corresponding to the increment of the content of various characteristic gases;
selecting the characteristic gas corresponding to a first time length closest to a second time length, and predicting a third time length required by the content of the characteristic gas reaching an upper limit early warning value by adopting a weighted moving average method according to the content of the characteristic gas in two times of detection, wherein the second time length is the time length of the current detection and the last detection;
generating a next detection period having a time length less than the third time length.
As an embodiment, the method for predicting the third time length includes:
setting a time coordinate corresponding to the content of the characteristic gas obtained by the last detection as a zero coordinate;
predicting the content of the characteristic gas to be detected next time according to the content of the characteristic gas obtained by the last detection and the current detection by a weighted moving average method;
repeatedly predicting the characteristic gas content detected last time and the predicted characteristic gas content detected next time as the basis until the predicted value reaches the maximum value which does not exceed the upper limit early warning value;
and selecting the time coordinate corresponding to the final predicted value as the time coordinate of the predicted third time.
Compared with the prior art, the transformer oil nursing device has the advantages that the transformer oil nursing device and the transformer are connected into a closed loop, so that the transformer oil nursing device is used for detecting gas in oil and filtering the transformer oil, the detection accuracy is higher, and part of gas such as carbon monoxide, carbon dioxide, hydrogen, methane, ethane, ethylene, acetylene and the like is not diffused outside any more. In addition, detection and degassing and dehydration are integrated, and degassing and dehydration are carried out without a special equipment transformer oil filter. Thus, the transformer oil can be better treated.
Drawings
FIG. 1 is a schematic diagram of a transformer oil nursing device with real-time monitoring according to an embodiment of the present invention;
fig. 2 is a flowchart of a method for generating a next detection cycle of the transformer oil nursing device with real-time monitoring according to an embodiment of the present invention.
In the figure: 1. a transformer; 2. an oil inlet pipe; 3. an oil return pipeline; 4. a care device; 5. an overflow valve; 6. a dehydration and degassing device; 7. a filter pressure switch; 8. a particulate filter; 9. a shut-off valve assembly; 10. a motor pump; 11. a gas sampling device; 12. a control device; 13. a one-way valve; 14. an automatic shut-off valve; 15. a manual shut-off valve; 16. a vacuum pressure gauge; 17. a moisture sensor; 18. an air pressure sensor; 19. and a bleed valve.
Detailed Description
The foregoing and other embodiments and advantages of the invention will be apparent from the following, more complete, description of the invention, taken in conjunction with the accompanying drawings. It will be apparent that the described embodiments are merely some, but not all, embodiments of the invention.
In one embodiment, as shown in FIG. 1.
The transformer oil nursing device with the real-time monitoring function comprises an oil inlet pipeline 2 connected with a bottom end interface of a transformer 1 and an oil return pipeline 3 connected with a top end interface of the transformer 1, wherein the oil inlet pipeline 2 and the oil return pipeline 3 form a closed oil way in a nursing device 4; an overflow valve 5, a dehydration and degassing device 6, a filter pressure switch 7, a particle filter 8 and a stop valve assembly 9 are sequentially arranged on the oil path along the flowing direction, and two ends of the dehydration and degassing device 6 are connected with a motor pump 10; the dehydration degassing device 6 is connected with a gas sampling device 11, a control device 12 is further arranged in the nursing device 4, the gas sampling device 11 enables gas in oil to enter a chromatographic column arranged in the nursing device, and the control device 12 sends data of the content of each component of the gas in the oil calculated through the chromatographic column to a control terminal.
In this embodiment, by connecting the transformer oil nursing device 4 and the transformer 1 into a closed loop, the device is used for detecting gas in oil and filtering the transformer oil, so that the detection accuracy is higher, and a part of gas such as carbon monoxide, carbon dioxide, hydrogen, methane, ethane, ethylene, acetylene and the like is not diffused to the outside any more. In addition, detection and degassing and dehydration are integrated, and degassing and dehydration are carried out without a special equipment transformer oil filter. Thus, the transformer oil can be better treated. In addition, the dehydration and degassing device 6 is a core component of the existing transformer oil vacuum filtration device, and can be independently used to achieve the purposes of vacuum dehydration and degassing.
In the present embodiment, since the control device 12 is connected to each component in the care device 4, the detection data can be transmitted to the control terminal by the control device 12 to perform remote real-time monitoring.
In one embodiment, as shown in FIG. 1.
The transformer oil nursing device with the real-time monitoring function is provided in the embodiment, and the oil inlet pipeline 2 is connected with a bottom end interface of the transformer 1 through the one-way valve 13.
In the present embodiment, the check valve 13 is provided to prevent the transformer oil from flowing backward.
In one embodiment, as shown in FIG. 1.
The transformer oil nursing device with the real-time monitoring function is provided in the embodiment, the stop valve assembly 9 comprises an automatic stop valve 14 and a manual stop valve 15, and a vacuum pressure gauge 16 is connected to the position of the automatic stop valve 14.
In the present embodiment, by providing the automatic shutoff valve 14 in combination with the vacuum pressure gauge 16, automatic shutoff is achieved when the oil passage is evacuated and negative pressure occurs. The transformer oil nursing device has two working modes of injection and evacuation, in the injection mode, the oil way is injected with transformer oil, and the vacuum pressure gauge 16 detects positive pressure. In the drain mode, the oil circuit drains the transformer oil and the vacuum gauge 16 detects a negative pressure. A manual shut-off valve 15 is furthermore provided, which requires manual shut-off.
In one embodiment, as shown in FIG. 1.
The transformer oil nursing device with real-time monitoring provided by the embodiment is characterized in that the water sensor 17 is respectively arranged at the front end of the dehydration degassing device 6 and the rear end of the automatic stop valve 14 on the oil path.
In the present embodiment, the moisture sensors 17 are provided at two positions, and the function of the front end of the dehydration degassing device 6 is that the moisture content in the transformer oil can be detected, and the transformer oil is timely sent to the control terminal through the control device 12, so that the remote real-time monitoring is realized by detecting the moisture in addition to the fault gas. The water sensor 17 provided at the rear end of the automatic shutoff valve 14 is used in combination with the water sensor 17, so that the dewatering effect of the dewatering and deaerating device 6 can be detected.
The gas sampling device 11 is provided with a gas pressure sensor 18, and the dehydration degassing device 6 is provided with a purge valve 19.
In one embodiment, as shown in FIGS. 1-2.
According to the transformer oil nursing device with the real-time monitoring provided by the embodiment, the control device 12 controls the oil path to be periodically opened so that the gas sampling device 11 detects the content of each component of the gas in the oil; the control device 12 is connected with a target association library, and a target model corresponding to various faults of the transformer 1 is configured in the target association library, wherein the target model is a preset functional change relation set of the characteristic gas content corresponding to the faults of the transformer 1 along with time; the control device 12 is configured with a generation method of the next detection period, the generation method including: acquiring current detection data of the content of each component of the gas in the oil, which is detected by the gas sampling device 11; determining the existing characteristic gas and the existing characteristic gas content increment in the current detection according to the current detection data and the previously stored previous detection data obtained in the previous detection; according to the components of the existing characteristic gas and the content increment of the existing characteristic gas, a target model with the highest correlation degree is called; substituting the content of the characteristic gas existing in the previous detection data and the content of the characteristic gas existing in the current detection data into functions corresponding to various characteristic gases in the extracted target model to obtain a first time length corresponding to the increment of the content of various characteristic gases; selecting the characteristic gas corresponding to a first time length closest to a second time length, and predicting a third time length required by the content of the characteristic gas reaching an upper limit early warning value by adopting a weighted moving average method according to the content of the characteristic gas in two times of detection, wherein the second time length is the time length of the current detection and the last detection; generating a next detection period having a time length less than the third time length.
In this embodiment, the transformer oil nursing device can pre-determine a latent fault for detecting the gas in the transformer oil, and in order to achieve the aim of improving the probability of making an effective determination on the latent fault of the transformer, that is, avoiding that the fault gas is not detected before the transformer triggers a fault alarm, the transformer oil nursing device in this embodiment sets the period of detecting the content of each component of the gas in the oil by the gas sampling device 11 to be elastic, non-fixed, and different from the traditional periodic detection.
The transformer faults are classified into overheat faults and discharge faults, and the faults mainly correspond to seven characteristic gases of carbon monoxide, carbon dioxide, hydrogen, methane, ethane, ethylene and acetylene. The super-heat fault comprises bare metal super-heat, corresponding characteristic gases of methane and ethylene, solid insulation super-heat, corresponding characteristic gases of carbon monoxide and carbon dioxide, low-temperature super-heat and corresponding characteristic gases of methane, ethylene, hydrogen and acetylene. The discharge faults comprise high-energy discharge faults, the corresponding characteristic gases are acetylene, hydrogen, ethylene and methane, and the discharge faults also comprise low-energy discharge faults, and the corresponding characteristic gases are acetylene, hydrogen, ethylene and methane. However, although the same characteristic gas is generated by each fault, the increase of the content of each characteristic gas varies with time due to the difference of the fault type, which is also the basis for judging the fault. Therefore, a target model corresponding to various transformer faults is pre-configured in the target association library, and the target model is a pre-established functional change relation set of the characteristic gas content corresponding to the transformer faults along with time. The type of potential faults of the transformer can be more accurately analyzed through the trend judgment of the content of the characteristic gas. In order to complete the detection before the transformer triggers the fault alarm, it is necessary to calculate the detection period using these target models corresponding to various faults.
The generation method of the next detection period comprises the following steps: acquiring current detection data of the content of each component of the gas in the oil, which is detected by the gas sampling device 11; determining the existing characteristic gas and the existing characteristic gas content increment in the current detection according to the current detection data and the previously stored previous detection data obtained in the previous detection; because a small amount of characteristic gas is allowed to exist in the transformer oil, in order to prevent misjudgment, the embodiment is based on the acquisition of the existing characteristic gas content increment. Since the length of time of the current detection and the last detection is known, i.e. the last detection period is known. And according to the relation between the existing characteristic gas content increment and time, the target model with the highest correlation degree is called. The correlation degree means that, taking a high-energy discharge fault as an example, the fault produces gas rapidly, the gas quantity is large, and the gas is mainly acetylene and hydrogen. And judging whether the target model corresponding to the high-energy discharge fault is close or not according to the existing relation between the characteristic gas content increment and the time.
Considering that multiple latent faults possibly exist at the same time, substituting the content of the characteristic gas existing in the previous detection data and the content of the characteristic gas existing in the current detection data into functions corresponding to various characteristic gases in the extracted target model so as to obtain a first time length corresponding to various characteristic gas content generation increment; the plurality of characteristic gases correspond to a plurality of first time lengths, and the characteristic gas corresponding to one of the first time lengths closest to the second time length is selected, so that the influence of the characteristic gas generated by other latent faults which are not related to the called target model is eliminated. The primary and secondary are distinguished, the latent fault corresponding to the extracted target model is taken as the primary, then a third time length required by the content of the characteristic gas reaching an upper limit early warning value is predicted by adopting a weighted moving average method according to the content of the characteristic gas in the two detection processes, and the next detection period with the generation time length smaller than the third time length is generated.
Therefore, the next detection period is calculated through the existing data, the occurrence of transformer faults in the detection period is avoided as much as possible, and the probability of effectively judging the latent faults of the transformer is improved.
In one embodiment, the method for predicting the third length of time for a transformer oil care device with real-time monitoring includes: setting a time coordinate corresponding to the content of the characteristic gas obtained by the last detection as a zero coordinate; predicting the content of the characteristic gas to be detected next time according to the content of the characteristic gas obtained by the last detection and the current detection by a weighted moving average method; repeatedly predicting the characteristic gas content detected last time and the predicted characteristic gas content detected next time as the basis until the predicted value reaches the maximum value which does not exceed the upper limit early warning value; and selecting the time coordinate corresponding to the final predicted value as the time coordinate of the predicted third time.
In the present embodiment, the weighted moving average method is to give different weights to the feature gas contents obtained by the previous detection and the current detection as observation values, and then determine a moving average value, and determine a predicted value based on the obtained moving average value. And then taking the three values as observation values, and continuing to determine the next predicted value. And finally, reversely solving the third time length when the predicted value reaches the maximum value which does not exceed the upper limit early warning value. Thereby avoiding the occurrence of an excessive detection period, causing the transformer to trigger a fault alarm and missing a prospective judgment.
The above-described embodiments are provided to further explain the objects, technical solutions, and advantageous effects of the present invention in detail. It should be understood that the foregoing is only illustrative of the present invention and is not intended to limit the scope of the present invention. It should be noted that any modifications, equivalent substitutions, improvements, etc. made by those skilled in the art without departing from the spirit and principles of the present invention are intended to be included in the scope of the present invention.
Claims (7)
1. The transformer oil nursing device with the real-time monitoring function is characterized by comprising an oil inlet pipeline connected with a bottom end interface of a transformer and an oil return pipeline connected with a top end interface of the transformer, wherein the oil inlet pipeline and the oil return pipeline form a closed oil way in the nursing device;
the oil way is sequentially provided with an overflow valve, a dehydration and degassing device, a filter pressure switch, a particle filter and a stop valve assembly along the flowing direction, and two ends of the dehydration and degassing device are connected with a motor pump;
the dehydration degassing device is connected with a gas sampling device, a control device is further arranged in the nursing device, the gas sampling device enables gas in oil to enter a chromatographic column arranged in the nursing device, and the control device sends data of the content of each component of the gas in the oil calculated by the chromatographic column to a control terminal;
the control device controls the oil way to be periodically opened so that the gas sampling device detects the content of each component of the gas in the oil;
the control device is connected with a target association library, wherein a target model corresponding to various transformer faults is configured in the target association library, and the target model is a pre-established functional change relation set of the characteristic gas content corresponding to the transformer faults along with time;
the control device is configured with a generation method of a next detection period, the generation method comprising:
acquiring current detection data of the content of each component of the gas in the oil, which is detected by the gas sampling device;
determining the existing characteristic gas and the existing characteristic gas content increment in the current detection according to the current detection data and the previously stored previous detection data obtained in the previous detection;
according to the components of the existing characteristic gas and the content increment of the existing characteristic gas, a target model with the highest correlation degree is called;
substituting the content of the characteristic gas existing in the previous detection data and the content of the characteristic gas existing in the current detection data into functions corresponding to various characteristic gases in the extracted target model to obtain a first time length corresponding to the increment of the content of various characteristic gases;
selecting the characteristic gas corresponding to a first time length closest to a second time length, and predicting a third time length required by the content of the characteristic gas reaching an upper limit early warning value by adopting a weighted moving average method according to the content of the characteristic gas in two times of detection, wherein the second time length is the time length of the current detection and the last detection;
generating a next detection period with a time length smaller than the third time length;
the method for predicting the third time length comprises the following steps:
setting a time coordinate corresponding to the content of the characteristic gas obtained by the last detection as a zero coordinate;
predicting the content of the characteristic gas to be detected next time according to the content of the characteristic gas obtained by the last detection and the current detection by a weighted moving average method;
repeatedly predicting the characteristic gas content detected last time and the predicted characteristic gas content detected next time as the basis until the predicted value reaches the maximum value which does not exceed the upper limit early warning value;
and selecting the time coordinate corresponding to the final predicted value as the time coordinate of the predicted third time.
2. The transformer oil nursing device with real-time monitoring function according to claim 1, wherein the oil inlet pipeline is connected with a bottom end interface of the transformer through a one-way valve.
3. The transformer oil care device with real-time monitoring of claim 1, wherein the shut-off valve assembly comprises an automatic shut-off valve and a manual shut-off valve.
4. The transformer oil nursing device with real-time monitoring function according to claim 3, wherein a vacuum pressure gauge is connected to the automatic stop valve.
5. The transformer oil nursing device with real-time monitoring function according to claim 3, wherein the oil way is provided with a water sensor at the front end of the dehydration degassing device and at the rear end of the automatic stop valve.
6. The transformer oil nursing device with real-time monitoring function according to claim 1, wherein a gas pressure sensor is arranged on the gas sampling device.
7. The transformer oil nursing device with real-time monitoring function according to claim 1, wherein a gas release valve is arranged on the dehydration degassing device.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310801122.XA CN116519858B (en) | 2023-07-03 | 2023-07-03 | Transformer oil nursing device with real-time monitoring function |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310801122.XA CN116519858B (en) | 2023-07-03 | 2023-07-03 | Transformer oil nursing device with real-time monitoring function |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN116519858A CN116519858A (en) | 2023-08-01 |
| CN116519858B true CN116519858B (en) | 2023-09-05 |
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