CN112632836A - Method and system for quickly acquiring size and position of hot spot temperature of transformer - Google Patents
Method and system for quickly acquiring size and position of hot spot temperature of transformer Download PDFInfo
- Publication number
- CN112632836A CN112632836A CN202011627361.0A CN202011627361A CN112632836A CN 112632836 A CN112632836 A CN 112632836A CN 202011627361 A CN202011627361 A CN 202011627361A CN 112632836 A CN112632836 A CN 112632836A
- Authority
- CN
- China
- Prior art keywords
- transformer
- temperature
- hot spot
- point
- acquiring
- 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.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 26
- 238000004088 simulation Methods 0.000 claims abstract description 35
- 238000003062 neural network model Methods 0.000 claims abstract description 30
- 238000012549 training Methods 0.000 claims abstract description 12
- 239000000463 material Substances 0.000 claims description 20
- 238000004804 winding Methods 0.000 claims description 19
- 238000013528 artificial neural network Methods 0.000 claims description 6
- 238000004364 calculation method Methods 0.000 claims description 3
- 238000012795 verification Methods 0.000 claims description 3
- XEEYBQQBJWHFJM-UHFFFAOYSA-N Iron Chemical group [Fe] XEEYBQQBJWHFJM-UHFFFAOYSA-N 0.000 description 2
- 230000005540 biological transmission Effects 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 239000011810 insulating material Substances 0.000 description 2
- 238000009413 insulation Methods 0.000 description 2
- 230000032683 aging Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000009529 body temperature measurement Methods 0.000 description 1
- 238000004422 calculation algorithm Methods 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000005855 radiation Effects 0.000 description 1
- 239000007787 solid Substances 0.000 description 1
- 238000012546 transfer Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
- G06F30/23—Design optimisation, verification or simulation using finite element methods [FEM] or finite difference methods [FDM]
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01D—MEASURING NOT SPECIALLY ADAPTED FOR A SPECIFIC VARIABLE; ARRANGEMENTS FOR MEASURING TWO OR MORE VARIABLES NOT COVERED IN A SINGLE OTHER SUBCLASS; TARIFF METERING APPARATUS; MEASURING OR TESTING NOT OTHERWISE PROVIDED FOR
- G01D21/00—Measuring or testing not otherwise provided for
- G01D21/02—Measuring two or more variables by means not covered by a single other subclass
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
- G06F30/27—Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
- G06F2119/08—Thermal analysis or thermal optimisation
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Evolutionary Computation (AREA)
- General Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- Software Systems (AREA)
- Artificial Intelligence (AREA)
- General Health & Medical Sciences (AREA)
- Mathematical Physics (AREA)
- Computational Linguistics (AREA)
- Molecular Biology (AREA)
- Computing Systems (AREA)
- Biophysics (AREA)
- Biomedical Technology (AREA)
- Data Mining & Analysis (AREA)
- Life Sciences & Earth Sciences (AREA)
- Health & Medical Sciences (AREA)
- Geometry (AREA)
- Computer Hardware Design (AREA)
- Medical Informatics (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Investigating Or Analyzing Materials Using Thermal Means (AREA)
Abstract
The invention discloses a method and a system for quickly acquiring the temperature and the position of a hot spot of a transformer, wherein the method comprises the following steps: constructing a neural network model; training a neural network model by using the temperature points and the positions of the temperature points on the outer surface of the transformer and the magnitude and the position of the hot spot temperature of the transformer in different states obtained by simulation; the trained neural network model is used for obtaining the temperature of the highest temperature point of the transformer to be tested and the position of the highest temperature point, and the method and the system can accurately obtain the temperature and the position of the highest temperature point when the transformer runs.
Description
Technical Field
The invention belongs to the field of power system transformers, and relates to a method and a system for quickly acquiring the temperature and the position of a hot spot of a transformer.
Background
The main components of the transformer of the power system are an iron core and a coil, the coil is provided with two or more than two windings, when the power system normally operates, the transformer undertakes the tasks of power conversion and electric energy transmission, at present, the voltage grade of the power system is higher and higher, the transmission power is higher and higher, the loss of the operating transformer is increased gradually, the temperature of the transformer is increased, and how to quickly obtain the hot spot temperature and the position of the transformer is very important. The temperature measurement method commonly used at present is to collect the temperature of a fixed point on the surface or inside of a transformer by using an infrared sensor or a temperature sensor, and whether the temperature of a measurement point contains the highest temperature is still to be discussed, but the highest temperature has an important influence on the insulation state of the transformer, and how to obtain the hot spot temperature of the transformer is very important. For the transformer temperature of the A-level insulating material, the aging speed of the transformer is doubled every time the temperature is increased by 8 ℃, and when the temperature of a certain point of a transformer winding exceeds the heat-resisting limit of the insulating material, the transformer can be unstable or even damaged, so that huge economic loss is caused. It is important to obtain the temperature and position of the highest temperature point when the transformer is operated.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides a method and a system for quickly acquiring the temperature and the position of a hot spot of a transformer, and the method and the system can accurately acquire the temperature and the position of the highest temperature point of the transformer during operation.
In order to achieve the above purpose, the method for rapidly acquiring the magnitude and position of the hot spot temperature of the transformer comprises the following steps:
constructing a neural network model;
training a neural network model by using the temperature points and the positions of the temperature points on the outer surface of the transformer and the magnitude and the position of the hot spot temperature of the transformer in different states obtained by simulation;
and acquiring the temperature of the highest temperature point of the transformer to be tested and the position of the highest temperature point by using the trained neural network model.
The specific process of obtaining the temperature point and the position of the temperature point on the outer surface of the transformer and the size and the position of the transformer hot spot temperature in different states through simulation is as follows:
acquiring geometric parameters and material parameters of the transformer;
performing finite element modeling on the transformer according to the geometric parameters and the material parameters of the transformer, and calculating the magnetic field distribution and the loss distribution of the transformer;
simulating a thermal field of the transformer according to the magnetic field distribution and the loss distribution of the transformer to obtain thermal field distribution data of the transformer;
measuring temperature data of different positions of a winding in the transformer, comparing the measured temperature data of the different positions of the winding in the transformer with the obtained thermal field distribution data of the transformer, judging whether thermal field simulation is correct or not according to a comparison result, and obtaining temperature points on the outer surface of the transformer in different states, positions of the temperature points and the size and the position of hot point temperature of the transformer by changing boundary conditions of the transformer simulation when the thermal field simulation is correct; otherwise, the geometric parameters and the material parameters of the transformer are obtained again.
The specific process of acquiring the temperature of the highest temperature point of the transformer to be tested and the position of the highest temperature point by using the trained neural network model is as follows:
the method comprises the steps of obtaining the temperature of the outer surface temperature point of the transformer to be tested and the position of the temperature point, and then inputting the temperature of the outer surface temperature point of the transformer to be tested and the position of the temperature point into a trained neural network to obtain the temperature of the highest temperature point of the transformer to be tested and the position of the highest temperature point.
And acquiring the temperature of the outer surface temperature point of the transformer to be tested and the position of the temperature point by using the temperature sensor.
A transformer hot spot temperature size and position fast acquisition system comprises:
the model building module is used for building a neural network model;
the model training module is used for training the neural network model by utilizing the temperature points and the positions of the temperature points on the outer surface of the transformer and the magnitude and the position of the hot spot temperature of the transformer in different states obtained by simulation;
and the prediction module is used for acquiring the temperature of the highest temperature point of the transformer to be tested and the position of the highest temperature point by using the trained neural network model.
Further comprising:
the first acquisition module is used for acquiring geometric parameters and material parameters of the transformer;
the calculation module is used for carrying out finite element modeling on the transformer according to the geometric parameters and the material parameters of the transformer and calculating the magnetic field distribution and the loss distribution of the transformer;
the second acquisition module is used for simulating the thermal field of the transformer according to the magnetic field distribution and the loss distribution of the transformer and acquiring the thermal field distribution data of the transformer;
the verification module is used for measuring temperature data of different positions of a winding in the transformer, comparing the measured temperature data of the different positions of the winding in the transformer with the obtained thermal field distribution data of the transformer, judging whether thermal field simulation is correct according to a comparison result, and obtaining temperature points of the outer surface of the transformer, the positions of the temperature points and the size and the position of the hot spot temperature of the transformer in different states by changing boundary conditions of the transformer simulation when the thermal field simulation is correct; otherwise, the geometric parameters and the material parameters of the transformer are obtained again.
The invention has the following beneficial effects:
according to the method and the system for rapidly acquiring the temperature and the position of the hot spot of the transformer, during specific operation, the neural network model is trained by using the temperature point and the position of the temperature point on the outer surface of the transformer and the temperature and the position of the hot spot of the transformer under different states acquired by simulation, the data source is convenient, and the trained neural network model is used for acquiring the temperature of the highest temperature point of the transformer to be tested and the position of the highest temperature point so as to accurately acquire the temperature and the position of the highest temperature point when the transformer operates.
Furthermore, the temperature of the outer surface temperature point of the transformer to be tested and the position of the temperature point are input into the trained neural network so as to obtain the temperature of the highest temperature point of the transformer to be tested and the position of the highest temperature point, and the data source is convenient.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, are included to provide a further understanding of the invention, and are incorporated in and constitute a part of this specification, illustrate exemplary embodiments of the invention and together with the description serve to explain the invention and not to limit the invention. In the drawings:
FIG. 1 is a distribution diagram of temperature points on one side of a transformer;
FIG. 2 is a distribution diagram of temperature points on the other side of the transformer;
FIG. 3 is a flow chart of the present invention.
Detailed Description
The present invention will be described in detail below with reference to the embodiments with reference to the attached drawings. It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict.
The following detailed description is exemplary in nature and is intended to provide further details of the invention. Unless otherwise defined, all technical terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the invention.
Referring to fig. 1, fig. 2 and fig. 3, the method for rapidly acquiring the magnitude and the position of the hot spot temperature of the transformer according to the present invention includes:
1) constructing a neural network model;
2) training a neural network model by using the temperature points and the positions of the temperature points on the outer surface of the transformer and the magnitude and the position of the hot spot temperature of the transformer in different states obtained by simulation;
3) and acquiring the temperature of the highest temperature point of the transformer to be tested and the position of the highest temperature point by using the trained neural network model.
In order to verify the correctness of the hot spot data, the temperature of a certain fixed point of the transformer during simulation and the hot spot temperature can be selected as the output of the neural network model, the neural network training is carried out, the temperature of the fixed point is measured by using a temperature sensor in the transformer, the comparison with the output result of the neural network model is carried out, and the correctness of the hot spot temperature is indirectly proved.
The specific process of obtaining the temperature point and the position of the temperature point on the outer surface of the transformer and the size and the position of the transformer hot spot temperature in different states through simulation is as follows:
21) acquiring geometric parameters and material parameters of the transformer;
22) performing finite element modeling on the transformer according to the geometric parameters and the material parameters of the transformer, and calculating the magnetic field distribution and the loss distribution of the transformer;
23) simulating the thermal field of the transformer according to the magnetic field distribution and the loss distribution of the transformer, and realizing the thermal field simulation of the transformer through solid heat transfer, laminar flow and surface-to-surface radiation multi-physical field simulation to obtain the thermal field distribution data of the transformer;
24) measuring temperature data of different positions of a winding in the transformer, comparing the measured temperature data of the different positions of the winding in the transformer with the obtained thermal field distribution data of the transformer, judging whether thermal field simulation is correct or not according to a comparison result, and obtaining temperature points on the outer surface of the transformer in different states, positions of the temperature points and the size and the position of hot point temperature of the transformer by changing boundary conditions of the transformer simulation when the thermal field simulation is correct; otherwise, the geometric parameters and the material parameters of the transformer are obtained again.
The three-phase transformer comprises a winding, wherein 36 groups of outer surface temperature points and corresponding positions of the three-phase transformer are respectively distributed on the upper, middle, lower, front, back, left and right sides of the winding, 12 temperature points and positions of one winding are provided, 36 groups of temperature data and positions of 3 windings are provided, the 36 groups of data are input data, and the hot spot temperature and position of the transformer are output data.
The specific process of acquiring the temperature of the highest temperature point of the transformer to be tested and the position of the highest temperature point by using the trained neural network model is as follows:
the method comprises the steps of acquiring the temperature of the outer surface temperature point of the transformer to be tested and the position of the temperature point by using a temperature sensor, and then inputting the temperature of the outer surface temperature point of the transformer to be tested and the position of the temperature point into a trained neural network to acquire the temperature of the highest temperature point of the transformer to be tested and the position of the highest temperature point.
Example one
The method takes a dry type transformer in actual operation as an embodiment, a dry type transformer winding is exposed in the air after insulation treatment, and the temperature data of the outer surface of the transformer can be acquired by using an infrared sensor, but the size and the position of the highest internal temperature cannot be acquired, and the size and the position of the highest temperature play a key role in operation and maintenance of the transformer. According to the invention, the geometric parameters and the material parameters of the transformer can be obtained firstly, a geometric model of the transformer is established in finite element software, the material parameters are added, and the hysteresis loss caused by the iron core can be obtained through a loss curve or an empirical formula. Performing electromagnetic thermal field simulation on the transformer in finite element software; comparing the data with the transformer temperature data collected by experiments, changing different environments to obtain the position of the outer surface temperature data of the transformer and the hot spot temperature data and position of the transformer during simulation after ensuring that a transformer simulation model is correct and reliable, and training a neural network model by taking the outer surface temperature data and position of the transformer as the input of a neural network algorithm and taking the hot spot temperature data and position of the transformer as the output; the temperature data of the fixed point on the outer surface of the transformer is measured by the infrared thermometer, and the position and the temperature data are correspondingly input into the neural network model, so that the temperature and the position of the hot spot can be obtained. The invention solves the problem that the temperature sensor can only collect fixed point temperature data but can not determine the state of hot spot temperature data and position when the transformer normally operates.
A transformer hot spot temperature size and position fast acquisition system comprises:
the model building module is used for building a neural network model;
the model training module is used for training the neural network model by utilizing the temperature points and the positions of the temperature points on the outer surface of the transformer and the magnitude and the position of the hot spot temperature of the transformer in different states obtained by simulation;
and the prediction module is used for acquiring the temperature of the highest temperature point of the transformer to be tested and the position of the highest temperature point by using the trained neural network model.
Further comprising:
the first acquisition module is used for acquiring geometric parameters and material parameters of the transformer;
the calculation module is used for carrying out finite element modeling on the transformer according to the geometric parameters and the material parameters of the transformer and calculating the magnetic field distribution and the loss distribution of the transformer;
the second acquisition module is used for simulating the thermal field of the transformer according to the magnetic field distribution and the loss distribution of the transformer and acquiring the thermal field distribution data of the transformer;
the verification module is used for measuring temperature data of different positions of a winding in the transformer, comparing the measured temperature data of the different positions of the winding in the transformer with the obtained thermal field distribution data of the transformer, judging whether thermal field simulation is correct according to a comparison result, and obtaining temperature points of the outer surface of the transformer, the positions of the temperature points and the size and the position of the hot spot temperature of the transformer in different states by changing boundary conditions of the transformer simulation when the thermal field simulation is correct; otherwise, the geometric parameters and the material parameters of the transformer are obtained again.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting the same, and although the present invention is described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications and equivalents may be made to the embodiments of the invention without departing from the spirit and scope of the invention, which is to be covered by the claims.
Claims (6)
1. A method for rapidly acquiring the temperature and the position of a hot spot of a transformer is characterized by comprising the following steps:
constructing a neural network model;
training a neural network model by using the temperature points and the positions of the temperature points on the outer surface of the transformer and the magnitude and the position of the hot spot temperature of the transformer in different states obtained by simulation;
and acquiring the temperature of the highest temperature point of the transformer to be tested and the position of the highest temperature point by using the trained neural network model.
2. The method for rapidly acquiring the magnitude and the position of the transformer hot spot temperature according to claim 1, wherein the specific process of acquiring the magnitude and the position of the temperature point and the magnitude and the position of the transformer hot spot temperature on the outer surface of the transformer in different states through simulation comprises the following steps:
acquiring geometric parameters and material parameters of the transformer;
performing finite element modeling on the transformer according to the geometric parameters and the material parameters of the transformer, and calculating the magnetic field distribution and the loss distribution of the transformer;
simulating a thermal field of the transformer according to the magnetic field distribution and the loss distribution of the transformer to obtain thermal field distribution data of the transformer;
measuring temperature data of different positions of a winding in the transformer, comparing the measured temperature data of the different positions of the winding in the transformer with the obtained thermal field distribution data of the transformer, judging whether thermal field simulation is correct or not according to a comparison result, and obtaining temperature points on the outer surface of the transformer in different states, positions of the temperature points and the size and the position of hot point temperature of the transformer by changing boundary conditions of the transformer simulation when the thermal field simulation is correct; otherwise, the geometric parameters and the material parameters of the transformer are obtained again.
3. The method for rapidly acquiring the temperature size and the position of the hot spot of the transformer according to claim 1, wherein the specific process of acquiring the temperature of the highest temperature point and the position of the highest temperature point of the transformer to be tested by using the trained neural network model comprises the following steps:
the method comprises the steps of obtaining the temperature of the outer surface temperature point of the transformer to be tested and the position of the temperature point, and then inputting the temperature of the outer surface temperature point of the transformer to be tested and the position of the temperature point into a trained neural network to obtain the temperature of the highest temperature point of the transformer to be tested and the position of the highest temperature point.
4. The method for rapidly acquiring the magnitude and the position of the temperature of the hot spot of the transformer according to claim 3, wherein the temperature sensor is used for acquiring the temperature of the temperature point on the outer surface of the transformer to be tested and the position of the temperature point.
5. A transformer hot spot temperature size and position fast acquisition system is characterized by comprising:
the model building module is used for building a neural network model;
the model training module is used for training the neural network model by utilizing the temperature points and the positions of the temperature points on the outer surface of the transformer and the magnitude and the position of the hot spot temperature of the transformer in different states obtained by simulation;
and the prediction module is used for acquiring the temperature of the highest temperature point of the transformer to be tested and the position of the highest temperature point by using the trained neural network model.
6. The system for rapidly acquiring the magnitude and the position of the transformer hot spot temperature according to claim 5, further comprising:
the first acquisition module is used for acquiring geometric parameters and material parameters of the transformer;
the calculation module is used for carrying out finite element modeling on the transformer according to the geometric parameters and the material parameters of the transformer and calculating the magnetic field distribution and the loss distribution of the transformer;
the second acquisition module is used for simulating the thermal field of the transformer according to the magnetic field distribution and the loss distribution of the transformer and acquiring the thermal field distribution data of the transformer;
the verification module is used for measuring temperature data of different positions of a winding in the transformer, comparing the measured temperature data of the different positions of the winding in the transformer with the obtained thermal field distribution data of the transformer, judging whether thermal field simulation is correct according to a comparison result, and obtaining temperature points of the outer surface of the transformer, the positions of the temperature points and the size and the position of the hot spot temperature of the transformer in different states by changing boundary conditions of the transformer simulation when the thermal field simulation is correct; otherwise, the geometric parameters and the material parameters of the transformer are obtained again.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011627361.0A CN112632836A (en) | 2020-12-30 | 2020-12-30 | Method and system for quickly acquiring size and position of hot spot temperature of transformer |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011627361.0A CN112632836A (en) | 2020-12-30 | 2020-12-30 | Method and system for quickly acquiring size and position of hot spot temperature of transformer |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN112632836A true CN112632836A (en) | 2021-04-09 |
Family
ID=75289864
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011627361.0A Pending CN112632836A (en) | 2020-12-30 | 2020-12-30 | Method and system for quickly acquiring size and position of hot spot temperature of transformer |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112632836A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114485540A (en) * | 2022-01-20 | 2022-05-13 | 西安交通大学 | Method and system for rapidly acquiring deformation degree and position of transformer winding |
-
2020
- 2020-12-30 CN CN202011627361.0A patent/CN112632836A/en active Pending
Non-Patent Citations (1)
| Title |
|---|
| EDGAR ALFREDO JUAREZ-BALDERAS 等: ""Hot-Spot Temperature Forecasting of the Instrument Transformer Using an Artificial Neural Network"", 《IEEE ACCESS》 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114485540A (en) * | 2022-01-20 | 2022-05-13 | 西安交通大学 | Method and system for rapidly acquiring deformation degree and position of transformer winding |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN103399241B (en) | Based on substation transformer fault diagnosis system and the method for temperature rise and load relation | |
| CN112115628B (en) | Hot spot temperature detection method based on distribution calculation of temperature field of oil-immersed transformer | |
| CN106126944B (en) | A kind of power transformer top-oil temperature interval prediction method and system | |
| CN108038795B (en) | Transformer hot spot temperature inversion method and system based on streamline and support vector machine | |
| CN103808426B (en) | The indirect measurement method of cable core temperature | |
| CN108090556A (en) | A kind of hot appraisal procedure of distribution transformer | |
| CN109698521B (en) | Low-penetration characteristic identification method of photovoltaic inverter based on measured data | |
| CN112632836A (en) | Method and system for quickly acquiring size and position of hot spot temperature of transformer | |
| CN109142991A (en) | A kind of infrared survey zero-temperature coefficient threshold determination method of porcelain insulator based on Burr distribution | |
| CN115719019A (en) | Transformer winding temperature field simulation method suitable for digital twinning background | |
| CN105243232A (en) | Electromagnetic transient simulation method and system for integrating field analysis into electric network to determine | |
| CN111371088B (en) | Method and system for correcting SVG control strategy based on BP neural network | |
| CN108254712A (en) | A kind of electric energy meter electric source loop protection Simulation Analysis method and device | |
| CN106482848B (en) | Three-core cable conductor temperature dynamic acquisition method based on M-P generalized inverse | |
| Jing et al. | Research and analysis of power transformer remaining life prediction based on digital twin technology | |
| Cheng et al. | Real-time dynamic line rating of transmission lines using live simulation model and tabu search | |
| CN106228033A (en) | Three-core cable conductor temperature real-time computing technique based on RBF neural | |
| CN108233356B (en) | Consistency evaluation method and evaluation platform for photovoltaic inverter controller | |
| Ben-gang et al. | The improved thermal-circuit model for hot-spot temperature calculation of oil-immersed power transformers | |
| CN115422808A (en) | Transformer temperature field model order reduction method based on Krylov subspace | |
| CN107977339A (en) | A kind of GIS load capacity appraisal procedures based on support vector regression | |
| CN108388744A (en) | A kind of emulation mode and system of stable state transformer fuel factor | |
| CN112462180B (en) | Converter transformer loss measuring method | |
| Yong-Qiang et al. | Research of temperature sensor arrangement based on transformer hot-spot temperature estimates | |
| Luo et al. | A method for hot spot temperature monitoring of oil-immersed transformers combining physical simulation and intelligent neural network |
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 | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20210409 |