CN114483479B - Fan Gao Wenjiang capacity state assessment method based on random forest - Google Patents
Fan Gao Wenjiang capacity state assessment method based on random forest Download PDFInfo
- Publication number
- CN114483479B CN114483479B CN202111590033.2A CN202111590033A CN114483479B CN 114483479 B CN114483479 B CN 114483479B CN 202111590033 A CN202111590033 A CN 202111590033A CN 114483479 B CN114483479 B CN 114483479B
- Authority
- CN
- China
- Prior art keywords
- module
- environment temperature
- fan
- box body
- data
- 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
Classifications
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F03—MACHINES OR ENGINES FOR LIQUIDS; WIND, SPRING, OR WEIGHT MOTORS; PRODUCING MECHANICAL POWER OR A REACTIVE PROPULSIVE THRUST, NOT OTHERWISE PROVIDED FOR
- F03D—WIND MOTORS
- F03D17/00—Monitoring or testing of wind motors, e.g. diagnostics
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F03—MACHINES OR ENGINES FOR LIQUIDS; WIND, SPRING, OR WEIGHT MOTORS; PRODUCING MECHANICAL POWER OR A REACTIVE PROPULSIVE THRUST, NOT OTHERWISE PROVIDED FOR
- F03D—WIND MOTORS
- F03D80/00—Details, components or accessories not provided for in groups F03D1/00 - F03D17/00
- F03D80/60—Cooling or heating of wind motors
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/243—Classification techniques relating to the number of classes
- G06F18/24323—Tree-organised classifiers
-
- 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
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E10/00—Energy generation through renewable energy sources
- Y02E10/70—Wind energy
- Y02E10/72—Wind turbines with rotation axis in wind direction
Landscapes
- Engineering & Computer Science (AREA)
- General Engineering & Computer Science (AREA)
- Life Sciences & Earth Sciences (AREA)
- Sustainable Energy (AREA)
- Chemical & Material Sciences (AREA)
- Mechanical Engineering (AREA)
- Combustion & Propulsion (AREA)
- Data Mining & Analysis (AREA)
- Sustainable Development (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Artificial Intelligence (AREA)
- Thermal Sciences (AREA)
- Evolutionary Computation (AREA)
- Evolutionary Biology (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Bioinformatics & Computational Biology (AREA)
- Testing Resistance To Weather, Investigating Materials By Mechanical Methods (AREA)
- Wind Motors (AREA)
Abstract
The invention discloses a fan Gao Wenjiang capacity state assessment method based on a random forest, which comprises the following steps of: step one: the data acquisition module acquires data of the working environment of the wind turbine generator; step two: the data transmission module transmits the current running environment temperature data acquired in the data acquisition module to the data comparison module through the control module, and the current running environment temperature data is compared with the preset environment temperature range in the data comparison module; step three: the control module performs random forest generation training on the obtained training set and test set to generate a random forest intelligent body in a high-temperature lifting capacity state of the fan; step four: the analysis module utilizes random forest agents meeting accuracy to conduct real-time operation risk judgment analysis, and therefore a fan high-temperature capacity reduction state assessment result is obtained in real time. The invention is convenient for cooling the working environment temperature before evaluating the capacity state of the fan Gao Wenjiang, and avoids various faults caused by further deteriorating the state of the unit.
Description
Technical Field
The invention relates to the technical field of wind power generation, in particular to a fan Gao Wenjiang capacity state assessment method based on random forests.
Background
Wind power generation refers to converting kinetic energy of wind into electrical energy. The wind turbine generator is equipment for wind power generation, the working environment of the wind turbine generator is complex, the wind turbine generator is used as a mechanical transmission system and is easily influenced by environmental factors, such as randomly-changed wind speed and temperature with large fluctuation range, so that various system parts cannot operate under stable working conditions, the wind turbine generator can operate in a sub-health state in certain time periods, the wind turbine generator can not stop the wind turbine generator, the output and the generated energy of the wind turbine generator can be reduced, and the economic benefit of a wind turbine operation enterprise is influenced.
In order to reduce the power generation loss of the wind turbine under the sub-health state, the state of the wind turbine is required to be judged and evaluated, when the state of the capacity of the fan Gao Wenjiang is judged and evaluated at present, when the evaluation judges that the working of the wind turbine is influenced by the excessively high ambient temperature, the cooling preparation of the excessively high ambient temperature cannot be performed before the operation of the wind turbine, and the rise of the ambient temperature cannot be slowed down, so that even when the result is evaluated in the high-temperature capacity-reducing state of the fan, the cooling operation of the working ambient temperature is not performed, and the state of the wind turbine is further deteriorated to cause various faults. Therefore, we propose a fan Gao Wenjiang capacity state evaluation method based on random forest.
Disclosure of Invention
The invention aims to overcome the problems in the prior art and provide a fan Gao Wenjiang capacity state evaluation method based on a random forest, which is convenient for cooling the working environment temperature before evaluating the fan Gao Wenjiang capacity state, so that even if the fan high-temperature capacity reduction state evaluates the result, the working environment temperature can be cooled, and various faults caused by further deteriorating the unit state are avoided.
In order to achieve the technical purpose and the technical effect, the invention is realized by the following technical scheme:
a fan Gao Wenjiang capacity state assessment method based on random forests comprises a machine box body, a heat dissipation assembly, a data acquisition module, a data transmission module, a control module, a data comparison module, a high-temperature capacity reduction control table, a low Wen Shengrong control table, a calculation module and an analysis module;
the method also comprises the following steps:
step one: the data acquisition module acquires data of the working environment of the wind turbine generator, particularly acquires the current operating environment temperature, and preprocesses the data by the data center module to respectively obtain a training set, a verification set and a test set;
step two: the method comprises the steps that a data transmission module transmits current running environment temperature data acquired in a data acquisition module to a data comparison module through a control module, the current running environment temperature data are compared with a preset environment temperature range in the data comparison module, when the current running environment temperature is larger than a rated maximum value in the preset environment temperature range and smaller than an actual demand maximum value in the preset environment temperature range and a wind turbine generator set is in a normal working state, dry cold air is firstly conveyed into a machine box through a heat radiation assembly, the working environment of the wind turbine generator set is cooled, and capacity reduction control is carried out on the wind turbine generator set according to output power corresponding to the current running environment temperature in a preset high-temperature capacity reduction control table; when the current running environment temperature is smaller than the rated maximum value with capacity increasing requirement in the preset environment temperature range and the wind generating set is in a normal working state, carrying out capacity increasing control on the wind generating set according to the output power corresponding to the current running environment temperature in the preset low-temperature capacity increasing control table;
step three: the control module performs random forest generation training on the obtained training set and test set to generate a random forest intelligent body in a high-temperature capacity-lifting state of the fan, and the control module is combined with the calculation module to judge whether the accuracy of the verification set meets the result output requirement;
step four: the analysis module utilizes random forest agents meeting accuracy to conduct real-time operation risk judgment analysis, and therefore a fan high-temperature capacity reduction state assessment result is obtained in real time.
Preferably, the heat dissipation assembly comprises an S-shaped air pipe, a cooling and drying mechanism and an air pump, wherein the air pump is electrically connected with the control module in an input mode, the S-shaped air pipe is arranged in the machine box, the input end of the S-shaped air pipe extends out of the machine box, and air holes are uniformly formed in the bottom of the S-shaped air pipe.
Based on the technical characteristics, the cold air conveyed into the S-shaped air pipe is conveniently and uniformly conveyed into the machine box body through the air holes.
Preferably, the cooling and drying mechanism comprises a box body, an air inlet pipe is arranged at the lower end of the right side wall of the box body, and the output end of the air pump is communicated with the air inlet pipe through a guide pipe.
Based on the technical characteristics, the air pumped by the air pump from the external environment is conveniently conveyed into the box body through the air inlet pipe.
Preferably, a condensing coil is arranged at the bottom end of the inner cavity of the box body, the upper water inlet end of the condensing coil penetrates through the front side wall of the box body, and the lower water outlet pipe of the condensing coil penetrates through the rear side wall of the box body.
Based on the technical characteristics, through the cooling water input to the water inlet end of the condensing coil from the outside, the cooling water flows in the condensing coil and is finally discharged from the water outlet end of the condensing coil, so that the flowing cooling water is introduced into the condensing coil to reduce the temperature in the box body, and after the air extracted from the outside is conveyed into the box body, the air input into the box body is further cooled from the bottom end of the inner cavity of the box body.
Preferably, the upper end of the box body is provided with a drying filter, the upper end of the left side wall of the box body is provided with an air outlet pipe, and the air outlet pipe is communicated with an input end of the S-shaped air pipe extending out of the box body through a guide pipe.
Based on the technical characteristics, air is cooled and then is dried and filtered by the drying and filtering piece, and then is conveyed into the S-shaped air pipe from the box body through the air outlet pipe.
Preferably, the drying filter piece comprises a sealing cover, the sealing cover is matched with the top of the box body in a clamping way, the front side and the rear side of the top of the inner cavity of the sealing cover are fixedly connected with supporting plates, a drying agent filling box is arranged between the two groups of supporting plates, the drying agent filling box is of an upper-lower through type structure, and a steel wire blocking net is arranged at the bottom of the inner cavity of the drying agent filling box.
Based on the technical characteristics, before the air pumped into the box body is discharged in the box body, the cooled air is firstly conveyed from bottom to top in the box body and is blown on the drying agent filled in the drying agent filling box, so that the drying agent is convenient for drying the cold air.
Preferably, a ventilation filter cotton is arranged between the two groups of support plates, and the ventilation filter cotton is positioned at the top of the desiccant filling box.
Based on the technical characteristics, the dried cold air is subjected to dust removal and filtration through the ventilation and filtration cotton, so that dust in the air is reduced and finally blown into the machine box along with the air.
Compared with the prior art, the invention has the following beneficial effects:
the data acquisition module is used for acquiring the data of the working environment of the wind turbine generator, the acquired data are compared with the preset environment temperature range in the data comparison module, when the current operation environment temperature is larger than the rated maximum value in the preset environment temperature range and smaller than the actual demand maximum value in the preset environment temperature range and the wind turbine generator is in a normal working state, dry cold air is conveyed into the machine box body through the heat radiation assembly to cool the working environment of the wind turbine generator, and then the capacity reduction control is carried out on the wind turbine generator according to the output power corresponding to the current operation environment temperature in the preset high-temperature capacity reduction control table, so that the working environment temperature is cooled before the capacity reduction state of the fan Gao Wenjiang is evaluated, transition buffer is formed for temperature rise, and therefore even when the high-temperature capacity reduction state of the fan evaluates the result, the cooling work is carried out on the working environment temperature, and various faults caused by further deterioration of the state of the wind turbine generator are avoided.
Of course, it is not necessary for any one product to practice the invention to achieve all of the advantages set forth above at the same time.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed for the description of the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic block diagram of an evaluation method according to the present invention;
FIG. 2 is a schematic view of a case and a heat dissipating assembly according to the present invention;
FIG. 3 is a schematic view of the structure of the S-shaped air pipe of the present invention;
FIG. 4 is a schematic diagram of a cooling and drying mechanism according to the present invention;
FIG. 5 is a schematic view of the structure of the dry filter element of the present invention;
FIG. 6 is a left cross-sectional view of FIG. 5 in accordance with the present invention;
in the drawings, the list of components represented by the various numbers is as follows:
the device comprises a 1-machine box body, a 2-heat radiating component, a 201-S-shaped air pipe, a 202-cooling and drying mechanism, a 2021-drying filter piece, a 20211-sealing cover, 20212-ventilation filter cotton, a 20213-desiccant filling box, a 20214-supporting plate, a 2022-air inlet pipe, a 2023-air outlet pipe, a 2024-box body, a 2025-condensing coil, a 203-air extracting pump, 204-air holes, a 3-data acquisition module, a 4-data transmission module, a 5-control module, a 6-data comparison module, a 7-Gao Wenjiang capacity control table, an 8-low temperature rise capacity control table, a 9-calculation module and a 10-analysis module.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The invention provides a technical scheme that: the fan Gao Wenjiang capacity state assessment method based on random forests comprises a machine box body 1, a heat radiation assembly 2, a data acquisition module 3, a data transmission module 4, a control module 5, a data comparison module 6, a high-temperature capacity reduction control table 7, a low Wen Shengrong control table 8, a calculation module 9 and an analysis module 10, wherein the data acquisition module 3, the data transmission module 4, the data comparison module 6, the high-temperature capacity reduction control table 7, the low Wen Shengrong control table 8, the calculation module 9 and the analysis module 10 are respectively and electrically connected with the control module 5;
the method also comprises the following steps:
step one: the data acquisition module 3 acquires data of the working environment of the wind turbine generator, particularly acquires the current running environment temperature, and carries out preprocessing by the data center module to respectively obtain a training set, a verification set and a test set;
step two: the data transmission module 4 transmits the current operation environment temperature data acquired in the data acquisition module 3 to the data comparison module 6 through the control module 5, and the current operation environment temperature is compared with the preset environment temperature range in the data comparison module 6, when the current operation environment temperature is larger than the rated maximum value in the preset environment temperature range and smaller than the actual demand maximum value in the preset environment temperature range and the wind turbine generator set is in a normal working state, dry cold air is firstly conveyed into the machine box 1 through the heat radiation assembly 2 to cool the working environment of the wind turbine generator set, and then the capacity reduction control is carried out on the wind turbine generator set according to the output power corresponding to the current operation environment temperature in the preset high-temperature capacity reduction control table 7, so that the working environment temperature is cooled before the capacity state evaluation of the fan Gao Wenjiang, and the temperature is increased to form transition buffer, so that even when the high-temperature capacity reduction state evaluation result of the fan is obtained, the cooling operation is carried out on the working environment temperature, the maintenance of the wind turbine generator set can be realized in enough time, and various faults caused by further deterioration of the state of the wind turbine generator set can be avoided;
the heat dissipation assembly 2 comprises an S-shaped air pipe 201, a cooling and drying mechanism 202 and an air pump 203, the air pump 203 is electrically connected with a control module 5 in an input mode, the S-shaped air pipe 201 is installed in the machine box 1, the input end of the S-shaped air pipe 201 extends out of the machine box 1 (refer to fig. 2 and 3 in the attached drawing), the cooling and drying mechanism 202 comprises a box 2024, the installation positions of the air pump 203 and the box 2024 are selected according to actual needs, an air inlet pipe 2022 is arranged at the lower end of the right side wall of the box 2024, the output end of the air pump 203 is communicated with the air inlet pipe 2022 through a conduit, the air pump 203 is controlled to work through the control module 5, air pumped by the air pump 203 from the external environment is conveniently conveyed into the box 2024 through the air inlet pipe 2022, a condensing coil 2025 is arranged at the bottom end of the inner cavity of the box 2024, the upper end of the condensing coil 2025 penetrates through the front side wall of the box 2024, the lower end of the condensing coil 2025 penetrates through the rear side wall of the box 2024, cooling water is input from the outside to the water inlet end of the condensing coil 2025, the cooling water flows in the condensing coil 2025 and is finally discharged from the water outlet end of the condensing coil 2025, so that the flowing cooling water is introduced into the condensing coil 2025 to lower the temperature in the box 2024, after air extracted from the outside is conveyed into the box 2024, the air input into the box 2024 is further cooled from the bottom end of the inner cavity of the box 2024, the upper end of the box 2024 is provided with a drying filter 2021, the drying filter 2021 comprises a sealing cover 20211, the sealing cover 20211 is in clamping fit with the top of the box 2024, the front side and the rear side of the inner cavity top of the sealing cover 20211 are fixedly connected with supporting plates 20214, a drying agent filling box 20213 is arranged between the two groups of supporting plates 20214, the drying agent filling box 20213 is in an up-down penetrating structure, the inner cavity bottom of the drying agent filling box 20213 is provided with a steel wire blocking net, before the air pumped into the box body 2024 is discharged, the cooled air is firstly conveyed from bottom to top in the box body 2024 and is blown onto the drying agent filled in the drying agent filling box, so that the cold air is conveniently dried by the drying agent, ventilation filtering cotton 20212 is arranged between the two groups of support plates 20214, the ventilation filtering cotton 20212 is positioned at the top of the drying agent filling box 20213, the dried cold air is subjected to dust removal and filtration through the ventilation filtering cotton 20212, and dust in the air is reduced and finally blown into the box body 1 along with the air (refer to fig. 5 and 6 in the attached drawing of the specification); the upper end of the left side wall of the box body 2024 is provided with an air outlet pipe 2023, the air outlet pipe 2023 is communicated with an input end of the S-shaped air pipe 201 extending out of the box body 1 through a conduit, air is dried and filtered through a drying and filtering piece 2021 after being cooled, and is conveyed into the S-shaped air pipe 201 from the box body 2024 through the air outlet pipe 2023 (refer to fig. 4 in the attached drawing of the specification); air holes 204 are uniformly formed in the bottom of the S-shaped air pipe 201, so that cold air conveyed into the S-shaped air pipe 201 can be conveniently and uniformly conveyed into the machine box 1 through the air holes 204, and the working environment of the wind turbine generator is cooled (see figure 3 in the specification);
when the current running environment temperature is smaller than the rated maximum value with capacity increasing requirement in the preset environment temperature range and the wind generating set is in a normal working state, carrying out capacity increasing control on the wind generating set according to the output power corresponding to the current running environment temperature in the preset low-temperature capacity increasing control table 8;
step three: the control module 5 carries out random forest generation training on the obtained training set and test set to generate a random forest intelligent body in a high-temperature capacity-lifting state of the fan, and the calculation module 9 is combined to judge whether the accuracy of the verification set meets the result output requirement;
step four: the analysis module 10 performs real-time operation risk judgment analysis by using random forest agents meeting accuracy, so as to obtain a fan high-temperature capacity reduction state evaluation result in real time.
In the description of the present specification, the descriptions of the terms "one embodiment," "example," "specific example," and the like, mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The preferred embodiments of the invention disclosed above are intended only to assist in the explanation of the invention. The preferred embodiments are not exhaustive or to limit the invention to the precise form disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best understand and utilize the invention. The invention is limited only by the claims and the full scope and equivalents thereof.
Claims (3)
1. A fan Gao Wenjiang capacity state assessment method based on random forests is characterized in that: the device comprises a machine box body (1), a heat dissipation assembly (2), a data acquisition module (3), a data transmission module (4), a control module (5), a data comparison module (6), a high-temperature capacity reduction control table (7), a low Wen Shengrong control table (8), a calculation module (9) and an analysis module (10);
the method also comprises the following steps:
step one: the data acquisition module (3) acquires data of the working environment of the wind turbine generator, acquires the current running environment temperature, and performs preprocessing by the data center module to respectively obtain a training set, a verification set and a test set;
step two: the data transmission module (4) transmits the current running environment temperature data acquired in the data acquisition module (3) to the data comparison module (6) through the control module (5), the current running environment temperature is compared with the preset environment temperature range in the data comparison module (6), when the current running environment temperature is larger than the rated maximum value in the preset environment temperature range and smaller than the actual demand maximum value in the preset environment temperature range and the wind turbine generator is in a normal working state, dry cold air is conveyed into the machine box (1) through the heat dissipation assembly (2), the working environment of the wind turbine generator is cooled, and capacity reduction control is carried out on the wind turbine generator according to the output power corresponding to the current running environment temperature in the preset high-temperature capacity reduction control table (7); when the current running environment temperature is smaller than the rated maximum value with capacity increasing requirement in the preset environment temperature range and the wind generating set is in a normal working state, carrying out capacity increasing control on the wind generating set according to the output power corresponding to the current running environment temperature in a preset low-temperature capacity increasing control table (8);
step three: the control module (5) performs random forest generation training on the obtained training set and test set to generate a random forest intelligent body in a high-temperature capacity-lifting state of the fan, and the calculation module (9) is combined to judge whether the accuracy of the verification set meets the result output requirement;
step four: the analysis module (10) utilizes random forest intelligent agents meeting accuracy to conduct real-time operation risk judgment analysis, so that a fan high-temperature capacity reduction state evaluation result is obtained in real time;
the heat dissipation assembly (2) comprises an S-shaped air pipe (201), a cooling and drying mechanism (202) and an air pump (203), wherein the air pump (203) is electrically connected with the control module (5) in an input mode, the S-shaped air pipe (201) is installed in the case body (1), the input end of the S-shaped air pipe (201) extends out of the case body (1), and air holes (204) are uniformly formed in the bottom of the S-shaped air pipe (201);
the cooling and drying mechanism (202) comprises a box body (2024), an air inlet pipe (2022) is arranged at the lower end of the right side wall of the box body (2024), and the output end of the air pump (203) is communicated with the air inlet pipe (2022) through a guide pipe;
the upper end of the box body (2024) is provided with a drying filter (2021), the upper end of the left side wall of the box body (2024) is provided with an air outlet pipe (2023), and the air outlet pipe (2023) is communicated with an input end of the S-shaped air pipe (201) extending out of the box body (1) through a guide pipe;
the drying filter (2021) comprises a sealing cover (20211), the sealing cover (20211) is matched with the top of the box body (2024) in a clamping mode, supporting plates (20214) are fixedly connected to the front side and the rear side of the top of the inner cavity of the sealing cover (20211), a drying agent filling box (20213) is arranged between the two groups of supporting plates (20214), the drying agent filling box (20213) is of an upper-lower through type structure, and a steel wire blocking net is arranged at the bottom of the inner cavity of the drying agent filling box (20213).
2. The method for evaluating the capacity state of the fan Gao Wenjiang based on the random forest according to claim 1, wherein the method comprises the following steps of: the inner cavity bottom of box body (2024) is provided with condensing coil (2025), the upper end of condensing coil (2025) advances the preceding lateral wall that the water end runs through box body (2024), the rear side wall of box body (2024) is run through to the lower extreme outlet pipe of condensing coil (2025).
3. The method for evaluating the capacity state of the fan Gao Wenjiang based on the random forest according to claim 1, wherein the method comprises the following steps of: an air-permeable filter cotton (20212) is arranged between the two groups of support plates (20214), and the air-permeable filter cotton (20212) is positioned at the top of the desiccant filling box (20213).
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111590033.2A CN114483479B (en) | 2021-12-23 | 2021-12-23 | Fan Gao Wenjiang capacity state assessment method based on random forest |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111590033.2A CN114483479B (en) | 2021-12-23 | 2021-12-23 | Fan Gao Wenjiang capacity state assessment method based on random forest |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114483479A CN114483479A (en) | 2022-05-13 |
| CN114483479B true CN114483479B (en) | 2023-09-12 |
Family
ID=81493890
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111590033.2A Active CN114483479B (en) | 2021-12-23 | 2021-12-23 | Fan Gao Wenjiang capacity state assessment method based on random forest |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114483479B (en) |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106704100A (en) * | 2016-12-30 | 2017-05-24 | 北京金风科创风电设备有限公司 | Wind generating set and power control method and device thereof |
| CN107179503A (en) * | 2017-04-21 | 2017-09-19 | 美林数据技术股份有限公司 | The method of Wind turbines intelligent fault diagnosis early warning based on random forest |
| WO2019153388A1 (en) * | 2018-02-12 | 2019-08-15 | 大连理工大学 | Power spectral entropy random forest-based aeroengine rolling bearing fault diagnosis method |
| AU2020101854A4 (en) * | 2020-08-17 | 2020-09-24 | China Communications Construction Co., Ltd. | A method for predicting concrete durability based on data mining and artificial intelligence algorithm |
| CN112363061A (en) * | 2020-11-11 | 2021-02-12 | 上海电享信息科技有限公司 | Thermal runaway risk assessment method based on big data |
| WO2021232675A1 (en) * | 2020-05-19 | 2021-11-25 | 西安热工研究院有限公司 | High-synchronization-precision distributed measurement system and method for wind turbine generator set |
| CN113775484A (en) * | 2021-10-14 | 2021-12-10 | 中国船舶重工集团海装风电股份有限公司 | Combined control method and device based on temperature change of torsion slip ring and unit power |
-
2021
- 2021-12-23 CN CN202111590033.2A patent/CN114483479B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106704100A (en) * | 2016-12-30 | 2017-05-24 | 北京金风科创风电设备有限公司 | Wind generating set and power control method and device thereof |
| CN107179503A (en) * | 2017-04-21 | 2017-09-19 | 美林数据技术股份有限公司 | The method of Wind turbines intelligent fault diagnosis early warning based on random forest |
| WO2019153388A1 (en) * | 2018-02-12 | 2019-08-15 | 大连理工大学 | Power spectral entropy random forest-based aeroengine rolling bearing fault diagnosis method |
| WO2021232675A1 (en) * | 2020-05-19 | 2021-11-25 | 西安热工研究院有限公司 | High-synchronization-precision distributed measurement system and method for wind turbine generator set |
| AU2020101854A4 (en) * | 2020-08-17 | 2020-09-24 | China Communications Construction Co., Ltd. | A method for predicting concrete durability based on data mining and artificial intelligence algorithm |
| CN112363061A (en) * | 2020-11-11 | 2021-02-12 | 上海电享信息科技有限公司 | Thermal runaway risk assessment method based on big data |
| CN113775484A (en) * | 2021-10-14 | 2021-12-10 | 中国船舶重工集团海装风电股份有限公司 | Combined control method and device based on temperature change of torsion slip ring and unit power |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114483479A (en) | 2022-05-13 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN106523282B (en) | The environmental control system and its control method of wind power generating set | |
| CN104795968B (en) | Frequency converter | |
| CN104806743B (en) | The control method of wind-driven generator and its gearbox lubrication cooling system | |
| CN114483479B (en) | Fan Gao Wenjiang capacity state assessment method based on random forest | |
| CN113847824B (en) | Method for adjusting cooling water quantity of independent condenser of small turbine suitable for feed pump | |
| CN205210274U (en) | Motor test water cooling system | |
| CN213807869U (en) | Gas turbine air inlet double-loop cooling system utilizing refrigerating capacity allowance of refrigerating station | |
| CN209072070U (en) | A kind of novel active distribution network energy management apparatus | |
| CN201230256Y (en) | Cooling machine for wind power electricity generation current transformer | |
| WO2020125543A1 (en) | Temperature control system of high-power converter of wind turbine generator set | |
| CN204117767U (en) | A kind of Oversea wind power generation dry-type transformer | |
| CN213807870U (en) | Gas turbine air inlet single-loop cooling system utilizing refrigerating capacity allowance of refrigerating station | |
| CN210948999U (en) | Be applied to wind generating set's wheel hub water cooling circulation system | |
| US20230160372A1 (en) | Cooling system and wind power generator set | |
| CN109727689B (en) | Loop system for simulating working environment of helium fan driving motor | |
| CN210274997U (en) | Integral high-voltage inverter cooling device | |
| CN207183893U (en) | A kind of power distribution cabinet with automatic dissipating heat function | |
| CN214998047U (en) | Hub heat dissipation system of wind power electric unit | |
| CN213175941U (en) | Centralized water cooling system of wind generating set | |
| CN208633996U (en) | A kind of liquid cooling system and wind power generating set | |
| CN219244058U (en) | Open circulating water cooling mechanism of steam turbine and energy-saving device | |
| CN112302806B (en) | Gas turbine air inlet single-loop cooling system and method utilizing refrigeration station cold energy allowance | |
| CN219302553U (en) | Ferromagnetic resonance overvoltage monitoring device for stator winding of hydraulic generator | |
| CN219572426U (en) | Series connection energy-saving circulation system of refrigerant for photovoltaic module test | |
| CN214673849U (en) | Intelligent low-voltage switch cabinet |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| TA01 | Transfer of patent application right | ||
| TA01 | Transfer of patent application right |
Effective date of registration: 20221012 Address after: No. 2, xizhuan Hutong, Caishikou street, Xicheng District, Beijing 100052 Applicant after: Datang renewable energy test and Research Institute Co.,Ltd. Address before: 100041 room a-1725, 2nd floor, building 3, yard 30, Shixing street, Shijingshan District, Beijing Applicant before: CHINA DATANG CORPORATION NEW ENERGY SCIENCE AND TECHNOLOGY RESEARCH INSTITUTE Co.,Ltd. |
|
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |