CN114492936A - Wind power plant fan flying wadding intrusion early warning method based on numerical weather forecast - Google Patents
Wind power plant fan flying wadding intrusion early warning method based on numerical weather forecast Download PDFInfo
- Publication number
- CN114492936A CN114492936A CN202111624776.7A CN202111624776A CN114492936A CN 114492936 A CN114492936 A CN 114492936A CN 202111624776 A CN202111624776 A CN 202111624776A CN 114492936 A CN114492936 A CN 114492936A
- Authority
- CN
- China
- Prior art keywords
- early warning
- flying
- fan
- power plant
- intrusion
- 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.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 35
- 241000219000 Populus Species 0.000 claims abstract description 18
- 238000001556 precipitation Methods 0.000 claims description 20
- 230000001186 cumulative effect Effects 0.000 claims description 5
- 238000012545 processing Methods 0.000 claims description 3
- 238000005096 rolling process Methods 0.000 claims description 3
- 238000012423 maintenance Methods 0.000 abstract description 13
- 230000009545 invasion Effects 0.000 abstract description 10
- 230000009286 beneficial effect Effects 0.000 abstract description 4
- 238000004140 cleaning Methods 0.000 abstract description 4
- 238000010276 construction Methods 0.000 abstract description 3
- 230000002093 peripheral effect Effects 0.000 abstract description 2
- 230000005855 radiation Effects 0.000 abstract description 2
- 230000000875 corresponding effect Effects 0.000 description 9
- 229920000742 Cotton Polymers 0.000 description 5
- 230000009471 action Effects 0.000 description 3
- 238000011161 development Methods 0.000 description 3
- 230000008569 process Effects 0.000 description 3
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 241000208340 Araliaceae Species 0.000 description 1
- 206010061217 Infestation Diseases 0.000 description 1
- 235000005035 Panax pseudoginseng ssp. pseudoginseng Nutrition 0.000 description 1
- 235000003140 Panax quinquefolius Nutrition 0.000 description 1
- 241000218982 Populus nigra Species 0.000 description 1
- 239000002775 capsule Substances 0.000 description 1
- 230000034303 cell budding Effects 0.000 description 1
- 238000004590 computer program Methods 0.000 description 1
- 238000001816 cooling Methods 0.000 description 1
- 230000002596 correlated effect Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 235000008434 ginseng Nutrition 0.000 description 1
- 230000036541 health Effects 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 241000894007 species Species 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 238000009423 ventilation Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01W—METEOROLOGY
- G01W1/00—Meteorology
- G01W1/10—Devices for predicting weather conditions
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
- G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/06—Energy or water supply
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
Landscapes
- Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- Human Resources & Organizations (AREA)
- Economics (AREA)
- Strategic Management (AREA)
- General Physics & Mathematics (AREA)
- Entrepreneurship & Innovation (AREA)
- Development Economics (AREA)
- Marketing (AREA)
- Theoretical Computer Science (AREA)
- General Business, Economics & Management (AREA)
- Tourism & Hospitality (AREA)
- Physics & Mathematics (AREA)
- Educational Administration (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Game Theory and Decision Science (AREA)
- Environmental & Geological Engineering (AREA)
- Health & Medical Sciences (AREA)
- General Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Primary Health Care (AREA)
- Water Supply & Treatment (AREA)
- Public Health (AREA)
- Atmospheric Sciences (AREA)
- Biodiversity & Conservation Biology (AREA)
- Ecology (AREA)
- Environmental Sciences (AREA)
- Air Conditioning Control Device (AREA)
- Greenhouses (AREA)
Abstract
The invention discloses a numerical weather forecast-based wind power plant fan flying wadding disturbance early warning method, which is used for analyzing the relation between growth characteristics and meteorological conditions aiming at poplar varieties in an early warning area and a peripheral radiation area thereof, determining the weather conditions of flying wadding, and establishing a meteorological early warning index of flying wadding disturbance by combining with the characteristics of a wind power plant fan. And early warning is given to the flying wadding invasion of the fan in a period of time in the future based on the observation data of the ground meteorological station and the numerical weather forecast result. The wind power plant fan flying wadding intrusion early warning method based on numerical weather forecast can effectively forecast the flying wadding starting time, judge the flying wadding grade and early warn the flying wadding intrusion condition in advance, is beneficial to fan maintenance personnel to formulate a reasonable maintenance plan, can obviously improve the wind power plant fan cleaning construction operation efficiency, provides a powerful basis for formulating a reasonable fan guarantee maintenance operation plan, and simultaneously improves the fan operation efficiency, prolongs the fan key component service life and reduces the wind power plant operation maintenance cost.
Description
Technical Field
The invention relates to the crossing field of wind power plant fan maintenance and weather forecast early warning, in particular to a wind power plant fan flying wadding intrusion early warning method based on numerical weather forecast.
Background
The poplar is distributed in northwest, northeast and Yangtze river basin of China, and the biological characteristics of growth and development of the poplar are closely related to meteorological environment. Relevant researches show that when the temperature, the humidity, the rainfall and the like meet certain conditions, the capsules of the poplar gradually crack to generate the catkin.
The poplar catkins not only reduce the environmental quality, harm the health of people and threaten the public transportation safety, but also influence the safe and stable operation of a wind power plant fan. When the gearbox and the generator in the fan cabin are blocked by the flying flocs, the ventilation volume is reduced, the cooling efficiency is reduced, high-temperature faults occur frequently in the gearbox and the generator, the generated energy is reduced, and the whole service life of the unit is also reduced.
Therefore, based on weather forecast information, the flying wadding intrusion early warning for the wind turbine of the wind power plant is developed, and the method has important guiding effects and economic values on the development of the cleaning operation of the flying wadding of the wind turbine, the improvement of the operation efficiency of the wind turbine, the prolonging of the service life of key parts of the wind turbine and the reduction of the operation and maintenance cost of the wind power plant.
Disclosure of Invention
The invention aims to provide a numerical weather forecast-based early warning method for the flying wadding invasion of a wind power plant fan, which can effectively forecast the starting time of flying wadding, judge the grade of flying wadding and early warn the flying wadding invasion condition in advance, is beneficial to making a reasonable maintenance plan by fan maintenance personnel and improves the working efficiency of fan flying wadding clearing.
In order to achieve the above purpose, the invention provides the following technical scheme:
a wind power plant fan flying wadding intrusion early warning method based on numerical weather forecast comprises the following steps:
s1: judging whether poplar trees exist around the wind power plant, and determining the initial meteorological conditions of the flying time of the poplar trees according to the varieties of the poplar trees;
s2: determining meteorological factors influencing the flying floc concentration after the flying floc is generated;
s3: determining a participating meteorological station of a wind power plant, correcting meteorological factors of the participating meteorological station to obtain historical meteorological factors of the position of the wind power plant;
s4: acquiring and processing meteorological factor data corresponding to the numerical weather forecast;
s5: determining the flying wadding intrusion grade of a fan of a wind power plant and a meteorological factor threshold value corresponding to each grade;
and S6, rolling to calculate the initial flying catkin date according to the historical meteorological factors and the forecast meteorological factors of the wind power plant, and judging the early warning level of the wind power plant fan flying catkin intrusion.
Further, the effective accumulated temperature T is used in step S1eAverage daily temperature
As the meteorological factor for judging the initial flying catkin.
Further, the meteorological factors influencing the concentration of the flying cotton after the flying cotton is generated in step S2 include: niping (Ningping)Mean temperature
Average daily wind speed
Average daily relative humidity
Daily cumulative precipitation P and sunshine hours S.
Further, the weather factors for the weather station are referred in step S3, which includes: historical temperature, wind speed, humidity, precipitation, sunshine hours data.
Further, the method for correcting the weather factors of the participating weather stations in step S3 includes: and correcting the historical temperature sequence of the reference station by adopting an empirical formula according to the height difference between the wind power plant and the reference station to obtain the historical temperature sequence of the position of the wind power plant.
Further, in step S4, the hourly temperature, wind speed, humidity, precipitation and sunshine data of the weather forecast of the future 7-day value at the wind farm location are acquired, and these meteorological factors are processed into the daily data.
Further, step S5 determines that the wind turbine flying catkin intrusion of the wind farm is in three levels, the corresponding pre-warnings include a red pre-warning, a yellow pre-warning and a blue pre-warning, the red pre-warning indicates that the wind turbine is severely flying catkin intrusion, the yellow pre-warning indicates that the wind turbine is moderately flying catkin intrusion, and the blue pre-warning indicates that the wind turbine is slightly flying catkin intrusion.
Further, step S5 determines the temperature, wind speed, humidity, precipitation and sunshine hours threshold corresponding to the early warning level.
Further, step S6 determines the initial date of flying in the catkin according to the corrected historical meteorological factors of the wind farm and the meteorological factors forecasted in the next 7 days by using the daily average temperature and the effective accumulated temperature.
Further, based on the initial date of the flying flocs, the daily average temperature, the daily average wind speed, the daily average relative humidity, the daily accumulated precipitation and the sunshine hours are utilized to judge the early warning level of the wind power plant fan flying flocs intrusion.
Compared with the prior art, the invention has the beneficial effects that:
the invention relates to a wind power plant fan flying wadding intrusion early warning method based on numerical weather forecast, which analyzes the relationship between growth characteristics and meteorological conditions aiming at poplar varieties in an early warning area and a peripheral radiation area thereof, determines the weather conditions of flying wadding occurrence, and establishes a meteorological early warning index of flying wadding intrusion by combining with the characteristics of a wind power plant fan. And early warning is given to the flying wadding invasion of the fan in a period of time in the future based on the observation data of the ground meteorological station and the numerical weather forecast result. The method can effectively forecast the occurrence time of the flying wadding, judge the grade of the flying wadding, early warn the flying wadding invasion condition in advance, help fan maintenance personnel to formulate a reasonable maintenance plan, can obviously improve the fan cleaning construction operation efficiency of the wind power plant, provides a powerful basis for formulating a reasonable fan guarantee maintenance operation plan, and simultaneously improves the fan operation efficiency, prolongs the service life of key components of the fan and reduces the operation and maintenance cost of the wind power plant.
Drawings
In order to more clearly illustrate the embodiments of the present application or technical solutions in the prior art, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments described in the present invention, and other drawings can be obtained by those skilled in the art according to the drawings.
Fig. 1 is a schematic flow chart of an early warning method provided in an embodiment of the present application.
Fig. 2 shows the values of the weather factors obtained by the numerical weather forecast for the next 7 days (4 months, 11 days to 17 days) provided by the embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present invention are clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1, the method for early warning of wind power plant fan catkin invasion based on numerical weather forecast of the invention specifically comprises the following steps:
(1) judging whether poplar exists around the wind farm, comprehensively considering the variety of poplar and the meteorological conditions of the poplar at the stages of blooming and budding to effectively accumulate temperature TeAverage daily temperature
As a meteorological factor for judging the initial flying catkin.
(2) Determining weather factors affecting the concentration of fly-out after fly-out has occurred, the factors including the daily average temperature
Average daily wind speed
Average daily relative humidity
Daily cumulative precipitation P and sunshine hours S.
(3) And determining a participatory meteorological station of the wind power plant, and extracting historical temperature, wind speed, humidity, rainfall and sunshine hours data of the participatory meteorological station. And correcting the historical temperature sequence of the reference station by adopting an empirical formula according to the height difference between the wind power plant and the reference station to obtain the historical temperature sequence of the position of the wind power plant.
(4) Acquiring hourly temperature, wind speed, humidity, precipitation and sunshine data of a weather forecast with a value of 7 days in the future at the position of the wind power plant, and processing the meteorological factors into daily data.
(5) Determining that the wind power plant fan flying wadding intrusion is in three levels, wherein the corresponding early warnings comprise a red early warning, a yellow early warning and a blue early warning, the red early warning indicates that the fan is severely flying wadding intrusion, the yellow early warning indicates that the fan is moderately flying wadding intrusion, and the blue early warning indicates that the fan is slightly flying wadding intrusion. And respectively determining the temperature, wind speed, humidity, precipitation and sunshine hours thresholds corresponding to the early warning levels.
(6) Judging the initial date of flying cotton by using daily average temperature and effective accumulated temperature according to the corrected historical meteorological factors of the wind power plant and meteorological factors forecasted in the future 7 days; on the basis, the daily average temperature, the daily average wind speed, the daily average relative humidity, the daily accumulated precipitation and the sunshine hours are used for judging the flying floc disturbance early warning level of the wind power plant fan.
The present invention will be described in detail below with reference to the embodiments with reference to the attached drawings.
Taking the flying wadding intrusion early warning of a wind farm in Jiangsu province from 4 months to 10 days in 2021 for 7 days in the future (from 11 days in 4 months to 17 days in 4 months) as an example, the implementation steps of the wind farm fan flying wadding intrusion early warning method based on numerical weather forecast are described as follows:
(1) the main distributed tree species around the wind power plant are poplar and black poplar, and the meteorological conditions of growth and development stages such as blooming and bud emergence of the poplar are considered, so that the meteorological conditions for starting flying cotton are judged to be that the effective accumulated temperature is more than or equal to 480 ℃, and the daily average temperature is more than or equal to 14 ℃.
(2) Determining weather factors affecting the concentration of fly-wool after its generation, these factors including the daily average temperature
Mean daily wind speed
Average daily relative humidity
Daily cumulative precipitation P and sunshine hours S. Wherein, the temperature and the sunshine hours are positively correlated with the flying cotton concentration; the humidity and the flying floc concentration are in negative correlation; the precipitation can inhibit the flying of the poplar wadding; the flying cotton-wadding is beneficial to flying in a certain interval of wind speed, and when the wind speed exceeds a certain time, the flying cotton-wadding is spread to a far place, so that the threat of intrusion on a fan is weakened.
(3) Determining a participating meteorological station of the wind power plant, and extracting historical daily average temperature, daily average wind speed, daily average relative humidity, daily accumulated precipitation and sunshine hours data of the participating meteorological station. Correcting the historical temperature sequence of the reference station according to the height difference between the wind power plant and the reference station by adopting the following empirical formula to obtain the historical temperature sequence of the wind power plant position:
wherein,
is the daily average temperature of the position of the wind power plant,
is the daily average temperature of the witness-participating meteorological station, H is the wind power plant altitude, HstaIs the altitude of the meteorological station of the ginseng certificate.
(4) And rolling to obtain temperature (T), wind speed (W), Relative Humidity (RH), precipitation (P) and irradiance (E) data of a weather forecast of a value of 7 days in the future of the position of the wind power plant, wherein the data resolution is 1 hour. Calculating daily average temperature of 7 days and day by day according to the acquired hourly numerical weather forecast data
Average daily wind speed
Average daily relative humidity
Cumulative daily precipitation P and sunshine hours S, wherein the sunshine hours are irradiance E more than or equal to 120W/m2The sum of the number of hours.
(5) Determining that the flying catkin disturbance of a wind power plant fan is divided into three levels, wherein the corresponding early warning comprises red early warning, yellow early warning and blue early warning. The daily average temperature, daily average wind speed, daily average relative humidity, daily cumulative precipitation and sunshine hours threshold values corresponding to each early warning level are respectively determined and shown in table 1. And when the meteorological factors simultaneously meet various early warning conditions, the highest early warning level is preferentially selected.
TABLE 1 early warning ranking
And calculating effective accumulated temperature according to the historical temperature sequence of the wind power plant, determining the flying start date by using the daily average temperature which is continuously higher than 14 ℃ for two days after the effective accumulated temperature is higher than or equal to 480 ℃, wherein the flying start date obtained by the method is 3 months and 25 days, and the early warning period is shown to be after the flying start date. Further judging whether early warning exists in the next seven days (11 days in 4 months to 17 days in 4 months) according to early warning meteorological factor threshold indexes, wherein no early warning exists in one day, namely no flying wadding intrusion occurs; blue early warning is carried out for three days, and a slight flying catkin invasion condition occurs; the early warning is yellow in two days, and moderate flying catkin invasion occurs; the early warning is red in one day, and severe flying catkin invasion occurs. The meteorological factors obtained by the 4-month, 11-day to 17-day numerical weather forecast are shown in fig. 2, and the corresponding early warning results are shown in table 2.
Table 22021 year, 4 month and 10 days of future 7-day flying infestation early warning result of Jiangsu certain wind power plant
| Date | Early warning level |
| 11 mesh at 4 months in 2021 | Is free of |
| 12 days 4 month 2021 | Blue warning |
| 2021 year, 4 months and 13 days | Blue warning |
| 2021 year, 4 months and 14 days | Blue warning |
| 2021 year, 4 months and 15 days | Yellow early warning |
| 2021 year, 4 months and 16 days | Red early warning |
| 2021 year, 4 months and 17 days | Yellow early warning |
In conclusion, the early warning service of the flying-wadding invasion of the wind power plant fans is developed based on the method, the working efficiency of the wind power plant fan cleaning construction can be obviously improved, and a powerful basis is provided for formulating a reasonable fan guarantee maintenance working plan.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. The term "comprising", without further limitation, means that the element so defined is not excluded from the group consisting of additional identical elements in the process, method, article, or apparatus that comprises the element.
All the embodiments in the present specification are described in a related manner, and the same and similar parts among the embodiments may be referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, apparatus embodiments, electronic device embodiments, computer-readable storage medium embodiments, and computer program product embodiments are described with relative simplicity as they are substantially similar to method embodiments, where relevant only as described in portions of the method embodiments.
The above-mentioned embodiments are only specific embodiments of the present application, and are used for illustrating the technical solutions of the present application, but not limiting the same, and the scope of the present application is not limited thereto, and although the present application is described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: those skilled in the art can still make modifications or easily conceive of changes to the technical solutions described in the foregoing embodiments, or make equivalents to some of them, within the technical scope of the present disclosure; such modifications, changes or substitutions do not depart from the spirit and scope of the present disclosure, which should be construed in light of the above teachings. Are intended to be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (10)
1. A wind power plant fan flying wadding intrusion early warning method based on numerical weather forecast is characterized by comprising the following steps:
s1: judging whether poplar trees exist around the wind power plant, and determining the initial meteorological conditions of the flying time of the poplar trees according to the varieties of the poplar trees;
s2: determining meteorological factors influencing the flying floc concentration after the flying floc is generated;
s3: determining a participating meteorological station of a wind power plant, correcting meteorological factors of the participating meteorological station to obtain historical meteorological factors of the position of the wind power plant;
s4: acquiring and processing meteorological factor data corresponding to the numerical weather forecast;
s5: determining the flying wadding intrusion grade of a fan of a wind power plant and a meteorological factor threshold value corresponding to each grade;
and S6, rolling to calculate the initial flying catkin date according to the historical meteorological factors and the forecast meteorological factors of the wind power plant, and judging the early warning level of the wind power plant fan flying catkin intrusion.
2. The numerical weather forecast-based wind farm fan flying catkin intrusion warning method according to claim 1, wherein in step S1, effective accumulated temperature T is usedeAverage daily temperature
As a meteorological factor for judging the initial flying catkin.
3. The wind farm fan flying floc intrusion early warning method based on numerical weather forecast according to claim 1, wherein the meteorological factors influencing the concentration of flying flocs after the flying flocs are generated in step S2 comprise: average daily temperature
Average daily wind speed
Average daily relative humidity
Daily cumulative precipitation P and sunshine hours S.
4. The wind farm fan flying catkin intrusion early warning method based on numerical weather forecast according to claim 1, wherein the step S3 of proving meteorological factors of a meteorological station comprises: historical temperature, wind speed, humidity, precipitation, sunshine hours data.
5. The wind farm fan flying catkin intrusion early warning method based on numerical weather forecast according to claim 4, characterized in that the method for correcting meteorological factors of the witness-participating meteorological station in step S3 is as follows: and correcting the historical temperature sequence of the reference station by adopting an empirical formula according to the height difference between the wind power plant and the reference station to obtain the historical temperature sequence of the position of the wind power plant.
6. The numerical weather forecast-based wind farm fan flying catkin disturbance early warning method according to claim 1, characterized in that time-by-time temperature, wind speed, humidity, precipitation and sunshine data of a weather forecast of a value of 7 days in the future at the position of the wind farm are obtained in step S4, and the meteorological factors are processed into day-by-day data.
7. The wind farm fan flying wadding intrusion early warning method based on the numerical weather forecast according to claim 1, characterized in that step S5 determines that wind farm fan flying wadding intrusion is in three levels, the corresponding early warnings include a red early warning, a yellow early warning and a blue early warning, the red early warning indicates that the fan is severely flying wadding intrusion, the yellow early warning indicates that the fan is moderately flying wadding intrusion, and the blue early warning indicates that the fan is slightly flying wadding intrusion.
8. The wind farm fan flying catkin intrusion early warning method based on numerical weather forecast of claim 7, characterized in that step S5 determines the temperature, wind speed, humidity, precipitation and sunshine hours threshold values corresponding to the early warning level.
9. The wind farm fan flying wadding intrusion early warning method based on the numerical weather forecast as recited in claim 1, wherein step S6 is implemented to determine the initial date of flying wadding by using the daily average temperature and the effective accumulated temperature according to the corrected historical meteorological factors of the wind farm and the meteorological factors forecasted in the next 7 days.
10. The wind farm fan flying wadding intrusion early warning method based on the numerical weather forecast according to claim 9, characterized in that the wind farm fan flying wadding intrusion early warning level is judged by utilizing daily average temperature, daily average wind speed, daily average relative humidity, daily accumulated precipitation and sunshine hours based on the initial flying wadding date.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111624776.7A CN114492936B (en) | 2021-12-28 | 2021-12-28 | Wind farm fan flying wadding invasion early warning method based on numerical weather forecast |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111624776.7A CN114492936B (en) | 2021-12-28 | 2021-12-28 | Wind farm fan flying wadding invasion early warning method based on numerical weather forecast |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114492936A true CN114492936A (en) | 2022-05-13 |
| CN114492936B CN114492936B (en) | 2024-09-06 |
Family
ID=81496683
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111624776.7A Active CN114492936B (en) | 2021-12-28 | 2021-12-28 | Wind farm fan flying wadding invasion early warning method based on numerical weather forecast |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114492936B (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117849908A (en) * | 2024-03-08 | 2024-04-09 | 江苏省气候中心 | Plum-entering and plum-exiting date prediction method and device in plum rainy season based on mode circular flow field |
| CN118396477A (en) * | 2024-06-24 | 2024-07-26 | 江苏省气象服务中心 | Method, device and equipment for forecasting tree flocs |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105678419A (en) * | 2016-01-05 | 2016-06-15 | 天津大学 | Fine grit-based forest fire hazard probability forecasting system |
| CN106157177A (en) * | 2016-07-29 | 2016-11-23 | 国网电力科学研究院武汉南瑞有限责任公司 | A kind of transmission line of electricity snowfall wide area monitoring and pre-alarming method based on miniradar |
| CN106384483A (en) * | 2015-08-05 | 2017-02-08 | 黑龙江真美广播通讯器材有限公司 | Sand-storm early warning broadcast system |
| CN106940359A (en) * | 2017-03-13 | 2017-07-11 | 山东佳星环保科技有限公司 | A kind of warning of air pollution device |
| CN110390343A (en) * | 2018-04-16 | 2019-10-29 | 中国电力科学研究院有限公司 | A kind of correction method and system of space meteorological data |
| US20210166815A1 (en) * | 2018-06-07 | 2021-06-03 | Koninklijke Philips N.V. | A system and method for determining conditions which risk respiratory attacks |
| CN113391582A (en) * | 2021-06-04 | 2021-09-14 | 北京工业大学 | Method for remotely monitoring agricultural and forestry plant diseases and insect pests and microclimate meteorological information |
| CN115220132A (en) * | 2022-07-04 | 2022-10-21 | 山东浪潮智慧医疗科技有限公司 | Method for forecasting pollen concentration in atmosphere |
-
2021
- 2021-12-28 CN CN202111624776.7A patent/CN114492936B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106384483A (en) * | 2015-08-05 | 2017-02-08 | 黑龙江真美广播通讯器材有限公司 | Sand-storm early warning broadcast system |
| CN105678419A (en) * | 2016-01-05 | 2016-06-15 | 天津大学 | Fine grit-based forest fire hazard probability forecasting system |
| CN106157177A (en) * | 2016-07-29 | 2016-11-23 | 国网电力科学研究院武汉南瑞有限责任公司 | A kind of transmission line of electricity snowfall wide area monitoring and pre-alarming method based on miniradar |
| CN106940359A (en) * | 2017-03-13 | 2017-07-11 | 山东佳星环保科技有限公司 | A kind of warning of air pollution device |
| CN110390343A (en) * | 2018-04-16 | 2019-10-29 | 中国电力科学研究院有限公司 | A kind of correction method and system of space meteorological data |
| US20210166815A1 (en) * | 2018-06-07 | 2021-06-03 | Koninklijke Philips N.V. | A system and method for determining conditions which risk respiratory attacks |
| CN113391582A (en) * | 2021-06-04 | 2021-09-14 | 北京工业大学 | Method for remotely monitoring agricultural and forestry plant diseases and insect pests and microclimate meteorological information |
| CN115220132A (en) * | 2022-07-04 | 2022-10-21 | 山东浪潮智慧医疗科技有限公司 | Method for forecasting pollen concentration in atmosphere |
Non-Patent Citations (3)
| Title |
|---|
| 徐景先;李耀宁;张德山;: "空气花粉变化规律和预测预报研究进展", 生态学报, no. 07, 15 July 2009 (2009-07-15), pages 3854 - 3864 * |
| 王成;: "城市花粉、飞絮飞毛等植源性污染特征及其防治", 中国城市林业, no. 01, 2 March 2018 (2018-03-02), pages 1 - 6 * |
| 陈连侠 等: "杨柳飞絮飘飞的气象条件分析", 《第33届中国气象学会年会 S16 气候环境变化与人体健康》, 30 November 2016 (2016-11-30), pages 1 - 3 * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117849908A (en) * | 2024-03-08 | 2024-04-09 | 江苏省气候中心 | Plum-entering and plum-exiting date prediction method and device in plum rainy season based on mode circular flow field |
| CN117849908B (en) * | 2024-03-08 | 2024-05-10 | 江苏省气候中心 | Plum-entering and plum-exiting date prediction method and device in plum rainy season based on mode circular flow field |
| CN118396477A (en) * | 2024-06-24 | 2024-07-26 | 江苏省气象服务中心 | Method, device and equipment for forecasting tree flocs |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114492936B (en) | 2024-09-06 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN114492936A (en) | Wind power plant fan flying wadding intrusion early warning method based on numerical weather forecast | |
| Siegmund et al. | Impact of temperature and precipitation extremes on the flowering dates of four German wildlife shrub species | |
| Bian et al. | Amplification of non-stationary drought to heatwave duration and intensity in eastern China: Spatiotemporal pattern and causes | |
| CN114091980B (en) | Carbon emission calculation method and device based on distributed monitoring and storage medium | |
| Liu et al. | Impact of chilling injury and global warming on rice yield in Heilongjiang Province | |
| CN106960267B (en) | Defoliating agricultural insect pest risk assessment method | |
| CN108921302B (en) | Weed shielding diagnosis and fault elimination judgment method for distributed photovoltaic power station | |
| CN107748933A (en) | Meteorological element message data error correcting method, mist, sunrise, sea of clouds, rime Forecasting Methodology | |
| CN111191936A (en) | Typhoon, wind and rain comprehensive influence index calculation method and storage device | |
| Ragatoa et al. | A change comparison of heat wave aspects in climatic zones of Nigeria | |
| CN109460923A (en) | A kind of ice covering on transmission lines probability forecasting method | |
| CN117312875B (en) | KNN algorithm-based regional high-temperature event similarity discrimination method | |
| CN118071194A (en) | System and method for monitoring meteorological disasters of strawberry tea | |
| CN115438918A (en) | Method for evaluating ecological restoration effect of surface mine | |
| CN116663787B (en) | Wind-solar resource assessment method and device, electronic equipment and storage medium | |
| Archer et al. | Changes in discharge rise and fall rates applied to impact assessment of catchment land use | |
| CN110542936B (en) | Method and system for forecasting power grid rainstorm disaster forecast deviation based on dominant circulation | |
| Forero-Montaña et al. | Population structure, growth rates and spatial distribution of two dioecious tree species in a wet forest in Puerto Rico | |
| CN115166866A (en) | Citrus disease and insect pest occurrence forecasting method and system based on lattice point meteorological data | |
| CN112382977B (en) | Differential lightning protection method and system for power transmission line | |
| CN115660314A (en) | Shadow shielding diagnosis method and device, electronic equipment and storage medium | |
| CN106897814A (en) | Operation states of electric power system reliability evaluation system and application based on multiple factors | |
| LUCENA et al. | Long term correlations and lacunarity of wind direction in Fernando de Noronha | |
| Łabędzki et al. | Indicator-based monitoring and forecasting water deficit and surplus in agriculture in Poland | |
| Toda et al. | Revealing the spatial characteristics of rice heat exposure in Japan through panicle temperature analysis |
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 |