CN111562036B - Online calibration method for transformer oil temperature gauge - Google Patents
Online calibration method for transformer oil temperature gauge Download PDFInfo
- Publication number
- CN111562036B CN111562036B CN202010405820.4A CN202010405820A CN111562036B CN 111562036 B CN111562036 B CN 111562036B CN 202010405820 A CN202010405820 A CN 202010405820A CN 111562036 B CN111562036 B CN 111562036B
- Authority
- CN
- China
- Prior art keywords
- evaluation index
- oil temperature
- transformer oil
- temperature
- load
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01K—MEASURING TEMPERATURE; MEASURING QUANTITY OF HEAT; THERMALLY-SENSITIVE ELEMENTS NOT OTHERWISE PROVIDED FOR
- G01K15/00—Testing or calibrating of thermometers
- G01K15/007—Testing
Abstract
The invention discloses an online checking method for a transformer oil temperature gauge, which comprises the following steps: s1: respectively constructing an environment temperature, a transformer oil temperature and a load time sequence; s2: calculating the temperature rise rate and taking the temperature rise rate as a first evaluation index; s3: judging whether the first evaluation index abnormal standard is met, executing the step S4 when the first evaluation index abnormal standard is met, and executing the step S5 when the first evaluation index abnormal standard is not met; s4: starting an abnormal alarm, and entering step S6; s5: calculating the gray correlation degree as a second evaluation index, and entering step S6; s6: and judging whether the second evaluation index abnormal standard is met, starting an abnormal alarm when the second evaluation index abnormal standard is met, and constructing a new environment temperature, transformer oil temperature and load time sequence and then returning to the step S2 when the second evaluation index abnormal standard is not met. The invention can detect the transformer oil temperature meter in real time without power failure.
Description
Technical Field
The invention relates to the field of electric power system instrument calibration, in particular to an online calibration method for a transformer oil temperature meter.
Background
The oil-immersed transformer is the most core device in a power system, the oil temperature is an important monitoring parameter in the operation of the transformer, and the change of the oil temperature has important influence on the safe operation of the transformer. When the transformer normally operates, the temperature of other parts is raised due to the loss of the iron core and the winding, the heat transfer radiating fins generated by the loss of the iron core and the winding are transmitted to the external environment by utilizing the circulation and convection of oil, the oil temperature tends to be stable when the radiating temperature and the heating temperature tend to be balanced, and the top oil temperature is generally regulated to be not more than 85 ℃ for ensuring the service life of the transformer. The oil temperature of each transformer is uploaded to the dispatching automation system through the oil temperature meter, and it is particularly important whether the oil temperature meter is normal, that is, whether the oil temperature meter can correctly reflect the actual oil temperature of the transformer.
At present, whether the oil temperature meter is normal or not needs to be checked and judged through periodic power failure, three years or six years are generally adopted according to different voltage grades, and the mode has two defects: firstly, equipment power failure is needed, and power supply reliability is affected; and secondly, the time between two verification periods belongs to the control vacuum. Therefore, there is a need for a non-stop verification method that can be performed in real-time or near real-time.
Disclosure of Invention
The invention provides an online calibration method for a transformer oil temperature gauge, which can calibrate the transformer oil temperature gauge in real time without power outage.
In order to solve the technical problems, the technical scheme of the invention is as follows:
an online calibration method for a transformer oil temperature gauge comprises the following steps:
s1: respectively constructing an environment temperature, a transformer oil temperature and a load time sequence;
s2: calculating the temperature rise rate and taking the temperature rise rate as a first evaluation index;
s3: judging whether the first evaluation index abnormal standard is met, executing the step S4 when the first evaluation index abnormal standard is met, and executing the step S5 when the first evaluation index abnormal standard is not met;
s4: starting an abnormal alarm, and entering step S6;
s5: calculating the gray correlation degree as a second evaluation index, and entering step S6;
s6: and judging whether the second evaluation index abnormal standard is met, starting an abnormal alarm when the second evaluation index abnormal standard is met, and constructing a new environment temperature, transformer oil temperature and load time sequence and then returning to the step S2 when the second evaluation index abnormal standard is not met.
The oil temperature is mainly caused by load, the oil temperature change is highly consistent with the load change trend, the fluctuation rules of the oil temperature change and the load change have strong similarity, and meanwhile, the oil temperature cannot be lower than the ambient temperature, so that the abnormal online detection of the oil temperature meter can be realized.
Preferably, each transformer is provided with an oil temperature gauge, a current transformer and a microclimate sensor, the oil temperature gauge has an oil temperature collecting and uploading function, the current transformer has a load collecting and uploading function, the microclimate sensor has an ambient temperature collecting and uploading function, and data are uploaded to the dispatching automation system every 15 minutes, and the data of the ambient temperature, the oil temperature of the transformer and the load in the step S1 are all derived by the dispatching automation system.
Preferably, the construction environment temperature, the transformer oil temperature and the load time sequence in step S1 are respectively expressed as:
ambient temperature time series T:
T＝{T1，T2，…，Tm}；
time series θ of transformer oil temperature:
θ＝{θ1，θ2，…，θm}；
load time series I:
I＝{I1，I2，…，Im}；
wherein, TmIs a remote measurement of the ambient temperature at time m, thetamIs a remote measurement value of the oil temperature at a time point m, ImThe load remote measurement value at the time point m is shown.
Preferably, the temperature increase rate K in step S2 is a weighted value obtained by numerically calculating the transformer oil temperature and the ambient temperature, and specifically includes:
wherein, Delta TkTemperature rise for binarization, θkIs the oil temperature remote measurement value T of the transformer at the time point kkIs a remote measurement of the ambient temperature at time k.
Preferably, the first evaluation index abnormality criterion in step S3 is a preset first evaluation index judgment threshold, and the first evaluation index abnormality criterion is not satisfied when the temperature increase rate is greater than or equal to the first evaluation index judgment threshold, and the first evaluation index abnormality criterion is satisfied when the temperature increase rate is less than the first evaluation index judgment threshold.
Preferably, the first evaluation index is a threshold value of 90%, the first evaluation index is based on the fact that the oil temperature is unlikely to be lower than the ambient temperature, and when the oil temperature is lower than the ambient temperature, it is certain that the oil temperature gauge is abnormal, and the threshold value is set to 90% as an accidental case where the oil temperature is slightly lower than the ambient temperature due to a measurement error of the oil temperature gauge itself when the avoidance load is zero.
Preferably, the gray correlation degree r in step S5 is a calculated value obtained by performing gray correlation analysis on the transformer oil temperature and the load, and specifically includes:
where ξ (k) is the kth grey-related coefficient, θ'kIs the invaluity of time point kCompendized oil temperature telemetering value of transformer, I'kIs a dimensionless remote load value at time k.
Preferably, the second evaluation index abnormality criterion in step S6 is a preset second evaluation index judgment threshold, and the second evaluation index abnormality criterion is not satisfied when the gray relevance is greater than or equal to the second evaluation index judgment threshold, and the second evaluation index abnormality criterion is satisfied when the gray relevance is less than the second evaluation index judgment threshold.
Preferably, the second evaluation index judgment threshold is 0.95, when the degree of correlation is less than 0.95, the oil temperature indicating number is less affected by the load, namely the oil temperature gauge is judged to be abnormal, and an alarm is started; when the correlation degree is greater than or equal to 0.95, the oil temperature indicating number is influenced by the load brute force, and the oil temperature table is not found to be abnormal by the existing sample data.
Preferably, the newly-created new environmental temperature, transformer oil temperature, and load time sequence in step S6 specifically include:
in order to reduce the calculation amount, the new environment temperature, the transformer oil temperature and the load time sequence discard a group of historical data while adding a group of new data, and the number m of elements of the time sequence is kept unchanged.
Compared with the prior art, the technical scheme of the invention has the beneficial effects that:
according to the invention, the scheduling automation system data is fully utilized to perform correlation analysis on the environmental temperature telemetering data, the oil temperature telemetering data and the load telemetering data, and the online uninterrupted power verification of the oil temperature meter is realized according to the principle that the oil temperature is mainly caused by the load, the oil temperature change and the load change trend are highly consistent, the fluctuation laws of the oil temperature change and the load change trend have strong similarity, and the oil temperature cannot be lower than the environmental temperature.
Drawings
FIG. 1 is a schematic flow chart of the method of the present invention.
Detailed Description
The drawings are for illustrative purposes only and are not to be construed as limiting the patent;
for the purpose of better illustrating the embodiments, certain features of the drawings may be omitted, enlarged or reduced, and do not represent the size of an actual product;
it will be understood by those skilled in the art that certain well-known structures in the drawings and descriptions thereof may be omitted.
The technical solution of the present invention is further described below with reference to the accompanying drawings and examples.
Example 1
The embodiment provides an online calibration method for a transformer oil temperature gauge, as shown in fig. 1, including the following steps:
s1: respectively constructing an environment temperature, a transformer oil temperature and a load time sequence;
s2: calculating the temperature rise rate and taking the temperature rise rate as a first evaluation index;
s3: judging whether the first evaluation index abnormal standard is met, executing the step S4 when the first evaluation index abnormal standard is met, and executing the step S5 when the first evaluation index abnormal standard is not met;
s4: starting an abnormal alarm, and entering step S6;
s5: calculating the gray correlation degree as a second evaluation index, and entering step S6;
s6: and judging whether the second evaluation index abnormal standard is met, starting an abnormal alarm when the second evaluation index abnormal standard is met, and constructing a new environment temperature, transformer oil temperature and load time sequence and then returning to the step S2 when the second evaluation index abnormal standard is not met.
The oil temperature is mainly caused by load, the oil temperature change is highly consistent with the load change trend, the fluctuation rules of the oil temperature change and the load change have strong similarity, and meanwhile, the oil temperature cannot be lower than the ambient temperature, so that the abnormal online detection of the oil temperature meter can be realized.
Because each transformer is provided with an oil temperature meter, a current transformer and a microclimate sensor, the oil temperature meter has the function of oil temperature acquisition and uploading, the current transformer has the function of load acquisition and uploading, the microclimate sensor has the function of environmental temperature acquisition and uploading, and data is uploaded to the dispatching automation system every 15 minutes, and the environmental temperature, the oil temperature of the transformer and the data of the load in the step S1 are all derived by the dispatching automation system.
The construction environment temperature, the transformer oil temperature, and the load time series in step S1 are respectively expressed as:
ambient temperature time series T:
T＝{T1，T2，…，Tm}；
time series θ of transformer oil temperature:
θ＝{θ1，θ2，…，θm}；
load time series I:
I＝{I1，I2，…，Im}；
wherein, TmIs a remote measurement of the ambient temperature at time m, thetamIs a remote measurement value of the oil temperature at a time point m, ImThe load remote measurement value at the time point m is shown.
In step S2, the temperature rise K is a weighted value obtained by numerically calculating the transformer oil temperature and the ambient temperature, and specifically includes:
wherein, Delta TkTemperature rise for binarization, θkIs the oil temperature remote measurement value T of the transformer at the time point kkIs a remote measurement of the ambient temperature at time k.
The first evaluation index abnormality criterion in step S3 is a preset first evaluation index judgment threshold, and is satisfied when the temperature increase rate is equal to or greater than the first evaluation index judgment threshold, and is satisfied when the temperature increase rate is less than the first evaluation index judgment threshold.
The first evaluation index is set to 90%, the first evaluation index is based on the fact that the oil temperature cannot be lower than the ambient temperature, when the oil temperature is lower than the ambient temperature, the oil temperature meter is determined to be abnormal, and the threshold is set to 90% to be an accidental situation that the oil temperature is slightly lower than the ambient temperature due to the measurement error of the oil temperature meter when the avoidance load is zero.
In step S5, the gray correlation r is a calculated value obtained by performing gray correlation analysis on the transformer oil temperature and the load, and specifically includes:
where ξ (k) is the kth grey-related coefficient, θ'kIs a dimensionless transformer oil temperature remote measurement value I 'at a time point k'kIs a non-dimensionalized load remote measurement value of time point k;
oil temperature and load are in different dimensions and in order to be comparable, the dimensions need to be normalized out.
Normalization adopts averaging method, and sample data thetaiAnd Ii(i ═ 1, 2, …, m) three expression methods can be used, the maximum-minimum method, the mean method and the median method. Wherein the Max-Min method is used to sample data to [0, 1 ]]Within the range; the averaging method is used for normalizing the sample data to be in any range, but the signs of the maximum value and the minimum value cannot be changed simultaneously; the median method is used to normalize the sample data to [ -1, 1 [)]。
The embodiment of the invention adopts an averaging method, wherein the averaging method is to select a sample average value as a reference scalar alpha, divide the oil temperature time sequence elements and the load time sequence elements by the reference scalar alpha, and perform normalization calculation as follows:
the second evaluation index abnormality criterion in step S6 is a preset second evaluation index judgment threshold, and is satisfied when the gray correlation degree is greater than or equal to the second evaluation index judgment threshold, and is satisfied when the gray correlation degree is less than the second evaluation index judgment threshold.
The second evaluation index judgment threshold value is 0.95, when the degree of correlation is less than 0.95, the oil temperature indicating number is less affected by the load, namely the oil temperature meter is judged to be abnormal, and an alarm is started; when the correlation degree is greater than or equal to 0.95, the oil temperature indicating number is influenced by the load brute force, and the oil temperature table is not found to be abnormal by the existing sample data.
Step S6, creating a new environment temperature, transformer oil temperature, and load time sequence, specifically:
in order to reduce the calculation amount, the new environment temperature, the transformer oil temperature and the load time sequence discard a group of historical data while adding a group of new data, and the number m of elements of the time sequence is kept unchanged.
In the specific implementation process, historical data of the environment temperature, the oil temperature and the load of a certain day of the selected 110kV oil-immersed transformer are derived from the dispatching automation system, a time sequence T of the environment temperature, a time sequence theta of the oil temperature and a time sequence I of the load are constructed, and for convenience and comprehensibility, the method is represented by the table 1
TABLE 1
Step S2 is executed, and the temperature increase rate is calculated as follows (see table 1 for intermediate calculation amount):
step S3 is executed, where the first evaluation index determination threshold value of the present invention is 90%, the temperature increase rate calculated from the data that do not satisfy the criterion is 90% or more, and the temperature increase rate calculated from the data that satisfy the criterion is 90%, so that it can be determined that the first evaluation index determination threshold value is not satisfied.
Step S5 is executed to calculate the gray correlation as follows (see table 1 for intermediate calculation amount):
step S6 is executed, the second evaluation index judgment threshold of the present invention is 0.95, the gray correlation degree calculated by the data not meeting the standard is greater than or equal to 0.95, the gray correlation degree calculated by the data meeting the standard is less than 0.95, the second evaluation index judgment threshold can be judged to be met, and an alarm is started.
After offline verification after power failure, the lifting variation of the oil temperature meter does not meet the regulation requirement, and the alarm is correct.
The same or similar reference numerals correspond to the same or similar parts;
the terms describing positional relationships in the drawings are for illustrative purposes only and are not to be construed as limiting the patent;
it should be understood that the above-described embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. And are neither required nor exhaustive of all embodiments. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the claims of the present invention.
Claims (8)
1. An online calibration method for a transformer oil temperature gauge is characterized by comprising the following steps:
s1: respectively constructing an environment temperature, a transformer oil temperature and a load time sequence;
s2: calculating the temperature rise rate and taking the temperature rise rate as a first evaluation index;
s3: judging whether the first evaluation index abnormal standard is met, executing the step S4 when the first evaluation index abnormal standard is met, and executing the step S5 when the first evaluation index abnormal standard is not met;
s4: starting an abnormal alarm, and entering step S6;
s5: calculating the gray correlation degree as a second evaluation index, and entering step S6;
s6: judging whether the second evaluation index abnormal standard is met or not, starting an abnormal alarm when the second evaluation index abnormal standard is met, and returning to the step S2 after a new environment temperature, transformer oil temperature and load time sequence is constructed when the second evaluation index abnormal standard is not met;
in step S2, the temperature rise K is a weighted value obtained by numerically calculating the transformer oil temperature and the ambient temperature, and specifically includes:
wherein, Delta TkTemperature rise for binarization, θkIs the oil temperature remote measurement value T of the transformer at the time point kkIs the environment temperature remote measurement value of the time point k;
in step S5, the gray correlation r is a calculated value obtained by performing gray correlation analysis on the transformer oil temperature and the load, and specifically includes:
where ξ (k) is the kth grey-related coefficient, θ'kIs a dimensionless transformer oil temperature remote measurement value I 'at a time point k'kIs a dimensionless remote load value at time k.
2. The online verification method for the transformer oil temperature gauge according to claim 1, wherein the data of the environmental temperature, the transformer oil temperature and the load in step S1 are derived by a dispatching automation system.
3. The online verification method for the transformer oil temperature gauge according to claim 2, wherein the constructed ambient temperature, the transformer oil temperature and the load time sequence in step S1 are respectively represented as:
ambient temperature time series T:
T＝{T1，T2，…，Tm}；
time series θ of transformer oil temperature:
θ＝{θ1，θ2，…，θm}；
load time series I:
I＝{I1，I2，…，Im}；
wherein, TmIs a remote measurement of the ambient temperature at time m, thetamIs a remote measurement value of the oil temperature at a time point m, ImThe load remote measurement value at the time point m is shown.
4. The online calibration method for the transformer oil temperature gauge according to claim 3, wherein the first evaluation index abnormality criterion in step S3 is a preset first evaluation index judgment threshold, and the first evaluation index abnormality criterion is not satisfied when the temperature rise rate is greater than or equal to the first evaluation index judgment threshold, and the first evaluation index abnormality criterion is satisfied when the temperature rise rate is less than the first evaluation index judgment threshold.
5. The online verification method for the transformer oil temperature gauge according to claim 4, wherein the first evaluation index judgment threshold is 90%.
6. The online calibration method for the transformer oil temperature gauge according to claim 5, wherein the second evaluation index abnormality criterion in step S6 is a preset second evaluation index judgment threshold, and the second evaluation index abnormality criterion is not satisfied when the gray correlation degree is greater than or equal to the second evaluation index judgment threshold, and the second evaluation index abnormality criterion is satisfied when the gray correlation degree is less than the second evaluation index judgment threshold.
7. The online verification method for the transformer oil temperature gauge according to claim 6, wherein the second evaluation index judgment threshold is 0.95.
8. The online calibration method for the transformer oil temperature gauge according to claim 7, wherein the new environment temperature, transformer oil temperature and load time sequence in step S6 is specifically:
and discarding a group of historical data while adding a group of new data in the new environment temperature, transformer oil temperature and load time sequence, and keeping the number m of elements in the time sequence unchanged.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010405820.4A CN111562036B (en) | 2020-05-14 | 2020-05-14 | Online calibration method for transformer oil temperature gauge |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010405820.4A CN111562036B (en) | 2020-05-14 | 2020-05-14 | Online calibration method for transformer oil temperature gauge |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111562036A CN111562036A (en) | 2020-08-21 |
| CN111562036B true CN111562036B (en) | 2022-01-21 |
Family
ID=72074760
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010405820.4A Active CN111562036B (en) | 2020-05-14 | 2020-05-14 | Online calibration method for transformer oil temperature gauge |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111562036B (en) |
Families Citing this family (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112527788A (en) * | 2020-12-17 | 2021-03-19 | 北京中恒博瑞数字电力科技有限公司 | Method and device for detecting and cleaning abnormal value of transformer monitoring data |
| CN115358355B (en) * | 2022-10-24 | 2023-01-24 | 广东电网有限责任公司佛山供电局 | Method and device for judging main transformer oil temperature gauge and top layer oil temperature abnormity |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101387562A (en) * | 2008-10-31 | 2009-03-18 | 上海市电力公司 | On-site calibration apparatus and method of optical fiber temperature measurement system |
| CN103592545A (en) * | 2013-11-22 | 2014-02-19 | 国家电网公司 | Transformer temperature rise abnormity monitoring and diagnosis method based on probability statistics |
Family Cites Families (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1632996A (en) * | 2004-12-22 | 2005-06-29 | 刘建南 | An intelligent transformer operating and controlling device and its use |
| CN103678765B (en) * | 2013-10-31 | 2016-04-27 | 上海交通大学 | Based on the running state of transformer comprehensive estimation method of on-line monitoring |
| US20180284758A1 (en) * | 2016-05-09 | 2018-10-04 | StrongForce IoT Portfolio 2016, LLC | Methods and systems for industrial internet of things data collection for equipment analysis in an upstream oil and gas environment |
| CN106226621B (en) * | 2016-07-18 | 2019-01-18 | 南京国电南自电网自动化有限公司 | A kind of secondary device fault diagnosis and method for early warning based on grey correlation analysis |
| CN106251042A (en) * | 2016-07-19 | 2016-12-21 | 广西电网有限责任公司电力科学研究院 | A kind of method that distribution transformer is carried out health state evaluation |
| CN107330286A (en) * | 2017-07-10 | 2017-11-07 | 华南理工大学 | A kind of large oil immersed power transformer reliability assessment dynamic correcting method |
| CN108663501A (en) * | 2017-11-29 | 2018-10-16 | 全球能源互联网研究院有限公司 | A kind of predicting model for dissolved gas in transformer oil method and system |
| CN109190314A (en) * | 2018-10-30 | 2019-01-11 | 河海大学 | The prediction technique of the power transformer top-oil temperature of neural network based on Adam optimization |
-
2020
- 2020-05-14 CN CN202010405820.4A patent/CN111562036B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101387562A (en) * | 2008-10-31 | 2009-03-18 | 上海市电力公司 | On-site calibration apparatus and method of optical fiber temperature measurement system |
| CN103592545A (en) * | 2013-11-22 | 2014-02-19 | 国家电网公司 | Transformer temperature rise abnormity monitoring and diagnosis method based on probability statistics |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111562036A (en) | 2020-08-21 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111562036B (en) | Online calibration method for transformer oil temperature gauge | |
| CN108592352B (en) | Method, device and system for predicting running state health degree of water-cooled air-conditioning system | |
| CN110095744B (en) | Error prediction method for electronic transformer | |
| CN116304778B (en) | Maintenance data processing method for miniature circuit breaker | |
| CN110763929A (en) | Intelligent monitoring and early warning system and method for convertor station equipment | |
| CN111461922A (en) | Transformer hot spot temperature real-time prediction method based on extreme learning machine | |
| EP1085635A2 (en) | Fluid-filled electrical equipment intelligent analysis system and method | |
| US20160282400A1 (en) | Equipment failure prediction system, equipment failure prediction device and equipment failure prediction method | |
| CN104992377A (en) | Method for analyzing reliability of transformer based on service year and load level | |
| CN115218963B (en) | Multivariable built-in panoramic sensing transformer state comprehensive fuzzy evaluation method | |
| CN104834305B (en) | Distribution automation terminal remote measurement exception analysis system and method based on DMS systems | |
| CN111931354A (en) | Transformer top layer oil temperature prediction method based on gray-autoregressive differential moving average model | |
| CN117318310B (en) | Remote unmanned intelligent monitoring system for explosion-proof power distribution cabinet | |
| CN114638268A (en) | High tension switchgear superheat control system based on wireless temperature measurement technology | |
| CN115730256A (en) | Transformer load capacity increasing strategy based on oil temperature self-adaptive control | |
| CN106325258B (en) | Relay protection device state evaluation method based on online monitoring information | |
| CN108416386A (en) | A kind of method and system judged extremely for Hydropower Unit bearing temperature | |
| CN113128707A (en) | Situation risk assessment method for distribution automation terminal | |
| CN116862478A (en) | Transformer health assessment method based on layered architecture and dynamic weighting | |
| CN111580575B (en) | Transformer temperature control method and transformer | |
| CN114692787B (en) | Multi-source data-based three-winding main transformer event analysis processing method and system | |
| CN116071036A (en) | Transformer temperature state evaluation method, equipment and medium | |
| CN112504509B (en) | Power equipment temperature monitoring system and method | |
| CN114169683A (en) | Generator excitation system state evaluation method based on fuzzy comprehensive evaluation theory | |
| CN112461390A (en) | Distributed comparison type temperature measurement module and method suitable for oil-immersed transformer |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |