NL2033883A - A Wind Power Forecasting Method, System and Medium for Extremely Windy Weather - Google Patents
A Wind Power Forecasting Method, System and Medium for Extremely Windy Weather Download PDFInfo
- Publication number
- NL2033883A NL2033883A NL2033883A NL2033883A NL2033883A NL 2033883 A NL2033883 A NL 2033883A NL 2033883 A NL2033883 A NL 2033883A NL 2033883 A NL2033883 A NL 2033883A NL 2033883 A NL2033883 A NL 2033883A
- Authority
- NL
- Netherlands
- Prior art keywords
- wind
- maximum
- power
- maximum wind
- prediction
- Prior art date
Links
- 238000013277 forecasting method Methods 0.000 title claims abstract description 18
- 238000000034 method Methods 0.000 claims abstract description 31
- 238000010801 machine learning Methods 0.000 claims abstract description 16
- 238000004590 computer program Methods 0.000 claims description 4
- 238000010248 power generation Methods 0.000 abstract description 11
- 238000005516 engineering process Methods 0.000 abstract description 6
- 238000003491 array Methods 0.000 description 2
- 239000000969 carrier Substances 0.000 description 2
- 238000005457 optimization Methods 0.000 description 2
- 238000004458 analytical method Methods 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000029087 digestion Effects 0.000 description 1
- 238000011038 discontinuous diafiltration by volume reduction Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000007500 overflow downdraw method Methods 0.000 description 1
- 230000008092 positive effect Effects 0.000 description 1
- 239000004065 semiconductor Substances 0.000 description 1
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
- 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
Landscapes
- Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- Economics (AREA)
- Human Resources & Organizations (AREA)
- Strategic Management (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Health & Medical Sciences (AREA)
- Marketing (AREA)
- General Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- Tourism & Hospitality (AREA)
- Quality & Reliability (AREA)
- Game Theory and Decision Science (AREA)
- Operations Research (AREA)
- Development Economics (AREA)
- Entrepreneurship & Innovation (AREA)
- Public Health (AREA)
- Water Supply & Treatment (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Wind Motors (AREA)
Abstract
The invention belongs to the field of new energy power generation technology, discloses a wind power forecasting method, system and medium for maximum wind weather, and uses wind field fans to observe wind speed and power to fit the maximum wind capacity reduction power curve model; Using the maximum wind prediction meteorological source, the machine learning modeling of maximum wind prediction is carried out to obtain the accurate maximum wind prediction wind speed. The maximum wind forecasted wind speed is put into the maximum wind capacity reduction power curve model to obtain the maximum wind forecasted power. The invention belongs to a method for wind power prediction in new energy power generation, and in particular relates to a method for wind power prediction under extreme weather (extreme wind). The wind power prediction of extreme wind scenarios is accurately predicted by using the forecast results of various meteorological sources. The invention uses the maximum wind prediction meteorological sources of several meteorological institutions to carry out machine learning modeling and accurately predict the maximum wind speed, so as to fill the technical gap in the field of maximum wind prediction at home and abroad.
Description
State Grid Gansu Electric Power Company, and
Gansu Tongxing Intelligent Technology Development CO., LTD. 22/111 PDNL
A Wind Power Forecasting Method, System and Medium for Extremely Windy Weather
The invention belongs to the technical field of new energy power generation and in particular relates to a wind power forecasting method, system and medium for extremely windy weather.
Background Technology
At present, wind power generation has the characteristics of intermittency and uncertainty, especially the output power is closely related to meteorological wind speed conditions, which makes its power generation characteristics very different from conventional power. Wind power grid connection is an important form to achieve large-scale and efficient utilization of wind power. Due to the intermittency, uncertainty and uncontrollability of wind power generation, when large-scale and large-capacity wind power generation system is connected to the power grid, it brings great challenges to the safe operation of public power grid. Therefore, if the power generation of wind power system can be predicted more accurately, it is of great significance to the security and stability of wind power system connected to grid and operation, as well as the economic dispatch of power grid, it can promote the acceptance and digestion of such unstable energy by power grid, and reduce the impact of the uncertainty of wind power system output power on the public power grid.
At present, the day-ahead wind rate forecast is mainly based on the wind speed forecast of meteorological model. The anemometer data of each fan in the wind field or the power of the field station are used as the optimization target to carry out single machine learning model modeling, or simple multi-model fusion methods such as arithmetic average and weighted average are used. In the existing technology, the prediction model cannot accurately predict the maximum wind, so it cannot predict the situation of automatic capacity reduction operation or shutdown of wind turbines under the maximum wind scenario, resulting in the decrease of power generation.
Through the above analysis, the problems and defects of the existing technology are as follows: the prediction model in the existing technology cannot accurately predict the maximum wind, so it cannot predict the situation of automatic capacity reduction operation or shutdown of the wind generator under the maximum wind scenario, resulting in the decrease of power generation.
In view of the problems existing in the prior art, the invention provides a wind power forecasting method, system and medium for extremely windy weather, in particular a wind power forecasting method, system and medium for extremely windy weather based on multiple meteorological sources.
The invention is realized as follows: a wind power forecasting method for maximum wind weather includes: using wind field fan to observe wind speed and power to fit the maximum wind capacity reduction power curve model; Using the maximum wind prediction meteorological source, the machine learning modeling of maximum wind prediction is carried out to obtain the accurate maximum wind prediction wind speed. The maximum wind forecasted wind speed is put into the maximum wind capacity reduction power curve model to obtain the maximum wind forecasted power.
Further, the maximum wind forecast meteorological sources are purchased from a number of internationally renowned meteorological forecasting agencies.
Further, internationally renowned Weather forecasting institutions include The
European Center for Numerical Mesoscale Forecasting (ECMWF), the US Federal
Administration of Oceanic and Atmospheric Administration (NOAA), China Meteorological
Administration (CMA), IBM's Weather Company, the Met Office of the United Kingdom, the
Bureau of Meteorology of Australia, The Meteorological Agency of Japan and Meteo France.
Furthermore, the wind power prediction method of maximum wind weather includes the following steps:
Step 1: The observed wind field data are used to fit the capacity reduction curve, and the observed wind speed data and the real capacity reduction power data of the fan are used for linear fitting under the maximum wind scenario, so as to capture the real operation mode of the fan under the maximum wind condition.
Step 2: Optimize meteorological sources and construct models. By analyzing and optimizing the prediction performance of a large number of different types of meteorological sources under extreme wind conditions, the best performing meteorological sources are used as the input features of the machine learning model for each station.
Step 3: Use the model to predict the wind power of maximum wind weather according to the meteorological wind speed forecast, build a machine learning model to deeply mine the wind speed, wind direction, air pressure, temperature, humidity and other variables provided by meteorological sources, and then predict the future wind speed.
Step 4: Finally, the capacity reduction sequence is output, and the future wind speed predicted by the model is put into the fitted capacity reduction curve, so as to obtain the predicted capacity reduction power of the fan.
Furthermore, the volume reduction curve in step 1 cuts out the wind speed by default.
Furthermore, the meteorological source in step 2 is preferred as IBM.
Another purpose of the present invention is to provide a wind power forecasting system for maximum wind weather applying the wind power forecasting method for maximum wind weather. The wind power forecasting system for maximum wind weather comprises:
Capacity reduction power curve fitting module, which is used to use wind field fans to observe wind speed and power to fit the maximum wind capacity reduction power curve model;
The maximum wind prediction machine learning modeling module is used to use the maximum wind prediction meteorological source to carry out maximum wind prediction machine learning modeling, and obtain accurate maximum wind prediction wind speed;
The wind power prediction module of maximum wind weather is used to bring the maximum wind prediction wind speed into the maximum wind capacity reduction power curve model to obtain the maximum wind prediction power.
Another purpose of the invention is to provide a computer device comprising a memory and a processor, the memory storing a computer program which, when executed by the processor, causes the processor to perform the steps of the wind power forecasting method for extremely windy weather.
Another purpose of the present invention is to provide a computer readable storage medium containing a computer program which, when executed by the processor, causes the processor to perform the steps of the wind power forecasting method for extremely windy weather.
Another purpose of the invention is to provide an information data processing terminal, which is used to realize the wind power forecasting system for extremely windy weather.
Combined with the above technical scheme and the technical problem solved, the advantages and positive effects of the technical scheme to be protected by the invention are as follows:
The invention belongs to a method for wind power prediction in new energy power generation, and in particular relates to a method for wind power prediction under extreme weather (extreme wind). The wind power prediction of extreme wind scenarios is accurately predicted by using the forecast results of various meteorological sources. The invention uses a wind field fan to observe the wind speed and power, and fits the maximum wind reduction capacity power curve model. The invention uses extreme wind forecasting meteorological sources purchased from several internationally renowned meteorological forecasting institutions, including European Center for Numerical Mesoscale Forecasting (ECMWF),
NOAA, China Meteorological Administration, The Weather Company under IBM, the Met
Office of the United Kingdom, The Australian Bureau of Meteorology, Japan Meteorological
Agency and France Meteorological Agency conducted machine learning modeling for maximum wind prediction to obtain accurate maximum wind prediction. Finally, the maximum wind prediction power is obtained by bringing the maximum wind prediction wind speed into the maximum wind capacity reduction power curve model.
The wind power prediction method of maximum wind weather based on multiple meteorological sources provided by the invention uses the maximum wind prediction meteorological sources of multiple meteorological institutions to carry out machine learning modeling and accurately predict the maximum wind speed, which fills the technical gap in the field of maximum wind prediction at home and abroad.
In order to more clearly explain the technical scheme of the implementation methods of the invention, a brief introduction will be made to the attached drawings required in the implementation methods of the invention in the following. Obviously, the attached drawings described below are only some implementation methods of the invention. For ordinary technicians in the art, other attached drawings can be obtained according to these attached drawings without paying creative labor.
Figure 1 is the flow chart of the wind power forecasting method provided by the implementation method of the invention in extremely windy weather;
Figure 2 is the schematic diagram of the wind power forecasting method for extremely windy weather provided by the implementation method of the invention;
Figure 3 is the predicted wind speed and the actual wind speed of the maximum wind case provided by the implementation method of the invention;
Figure 4 is the original power prediction power of the maximum wind case provided by the implementation method of the invention and the result of the maximum wind algorithm.
Specific Implementation Methods
In order to make the purpose, technical scheme and advantages of the invention more clearly, the invention is further explained in the following implementation methods. It should be understood that the specific implementation methods described herein are intended only to explain the invention and are not intended to qualify it.
In view of the problems existing in the prior art, the invention provides a wind power forecasting method, system and medium for extremely windy weather. The following is a detailed description of the invention combined with the attached drawings.
As shown in Figure 1, the wind power prediction method provided by the implementation method of the invention comprises the following steps:
S101. Using wind field observation data to fit the capacity reduction curve;
S102. Meteorological source optimization and model construction; 5 S103. Using the model to predict the wind power of maximum wind weather according to the meteorological wind speed forecast;
S104. The final output capacity reduction sequence.
The drawdown curve in step S101 provided in the implementation method of the invention cuts out the wind speed by default.
The meteorological source in step S102 provided in the implementation method of the invention is preferred to be IBM.
As a preferred implementation method, as shown in Figure 2, the wind power prediction method of maximum wind weather provided in the implementation method of the invention specifically includes: using wind field fans to observe wind speed and power to fit the maximum wind reduction capacity power curve model; Using the maximum wind prediction meteorological source, the machine learning modeling of maximum wind prediction is carried out to obtain the accurate maximum wind prediction wind speed. The maximum wind forecasted wind speed is put into the maximum wind capacity reduction power curve model to obtain the maximum wind forecasted power.
The maximum wind forecasting meteorological source provided in the implementation method of the invention is purchased from several internationally renowned meteorological forecasting institutions; Among them, The international well-known Weather forecasting institutions include the European Center for Numerical Mesoscale Forecasting ECMWF, the
US federal government's NOAA, the China Meteorological Administration, IBM’s Weather
Company, the UK Met Office, the Australian Bureau of Meteorology, the Japan
Meteorological Agency and the French Meteorological Agency.
The wind power forecasting system provided in the implementation method of the invention comprises:
Capacity reduction power curve fitting module, which is used to use wind field fans to observe wind speed and power to fit the maximum wind capacity reduction power curve model;
The maximum wind prediction machine learning modeling module is used to use the maximum wind prediction meteorological source to carry out maximum wind prediction machine learning modeling, and obtain accurate maximum wind prediction wind speed;
The wind power prediction module of maximum wind weather is used to bring the maximum wind prediction wind speed into the maximum wind capacity reduction power curve model to obtain the maximum wind prediction power.
The wind power prediction method of maximum wind weather based on multiple meteorological sources provided in the implementation method of the invention has been used in the application of power prediction of wind farm stations.
It should be noted that the implementation methods of the present invention can be realized by hardware, software or a combination of software and hardware. The hardware part can be implemented by special logic; The software component can be stored in memory and executed by an appropriate instruction execution system, such as a microprocessor or specially designed hardware. A person of ordinary skill in the art can understand that the above devices and methods can be implemented using computer executable instructions and/or included in the processor control code, For example, such code is provided on carrier media such as disks, CDS, or DVD-ROMs, programmable memory such as read-only memory (firmware), or data carriers such as optical or electronic signal carriers. The devices of the present invention and their modules may be implemented by hardware circuits such as very large scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, etc., or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc., or by software executed by various types of processors. It can also be implemented by a combination of the above hardware circuits and software such as firmware.
The above is only the specific implementation of the invention, but the protection scope of the invention is not limited to this, any modification, equivalent replacement and improvement made by any skilled person familiar with the technical field within the technical scope disclosed by the invention and within the spirit and principle of the invention shall be covered within the protection scope of the invention.
Claims (6)
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
NL2033883A NL2033883B1 (en) | 2022-12-30 | 2022-12-30 | A Wind Power Forecasting Method, System and Medium for Extremely Windy Weather |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
NL2033883A NL2033883B1 (en) | 2022-12-30 | 2022-12-30 | A Wind Power Forecasting Method, System and Medium for Extremely Windy Weather |
Publications (2)
Publication Number | Publication Date |
---|---|
NL2033883A true NL2033883A (en) | 2023-02-10 |
NL2033883B1 NL2033883B1 (en) | 2023-09-07 |
Family
ID=85202271
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
NL2033883A NL2033883B1 (en) | 2022-12-30 | 2022-12-30 | A Wind Power Forecasting Method, System and Medium for Extremely Windy Weather |
Country Status (1)
Country | Link |
---|---|
NL (1) | NL2033883B1 (en) |
Citations (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN111950764A (en) * | 2020-07-03 | 2020-11-17 | 国网冀北电力有限公司 | Extreme weather condition power grid wind power prediction correction method |
-
2022
- 2022-12-30 NL NL2033883A patent/NL2033883B1/en active
Patent Citations (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN111950764A (en) * | 2020-07-03 | 2020-11-17 | 国网冀北电力有限公司 | Extreme weather condition power grid wind power prediction correction method |
Non-Patent Citations (1)
Title |
---|
"IEC TR 63043 ED1: Renewable Energy Power Forecasting Technology", 5 April 2019 (2019-04-05), pages 1 - 126, XP082015319, Retrieved from the Internet <URL:https://www.iec.ch/cgi-bin/restricted/getfile.pl/8A_54e_CD.pdf?dir=8A&format=pdf&type=_CD&file=54e.pdf> [retrieved on 20190405] * |
Also Published As
Publication number | Publication date |
---|---|
NL2033883B1 (en) | 2023-09-07 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
CN110259646B (en) | Wind generating set component state early warning method based on historical data | |
TWI476430B (en) | A wind energy forecasting method with extreme wind speed prediction function | |
Zhao et al. | Resilient unit commitment for day-ahead market considering probabilistic impacts of hurricanes | |
CN113496311A (en) | Photovoltaic power station generated power prediction method and system | |
Cutler et al. | Using nacelle‐based wind speed observations to improve power curve modeling for wind power forecasting | |
US10331089B2 (en) | Forecasting solar power generation using weather forecasts | |
WO2024046137A1 (en) | Power prediction model construction method for multi-energy combined power generation system and power prediction method for multi-energy combined power generation system | |
CN104598715B (en) | A kind of region wind-powered electricity generation power predicating method based on Climatological forecasting wind speed | |
CN110460038A (en) | It is a kind of to be related to more scene method for expansion planning of power transmission network of new-energy grid-connected | |
JP2022115061A (en) | Weather-related overhead distribution line failures online prediction | |
NL2033883B1 (en) | A Wind Power Forecasting Method, System and Medium for Extremely Windy Weather | |
Cabezon et al. | Comparison of methods for power curve modelling | |
CN117878958A (en) | Night reactive compensation analysis method and system for water-light complementary photovoltaic power station | |
Genton et al. | Blowing in the wind | |
CN115586591B (en) | Automatic forecasting method and device for storm surge | |
CN111509705A (en) | Power distribution network prediction system | |
Zhang et al. | Data-driven feature description of heat wave effect on distribution system | |
CN113153657A (en) | Fan power generation rate loss prediction method, system, device and medium | |
CN107276073A (en) | A kind of bus load Forecasting Methodology based on the honourable equivalent load of load | |
CN118035875B (en) | Wind power generation power prediction method and system based on extreme weather influence factors | |
Rostami et al. | Weather data analysis and building performance assessment during extreme climate events: A Canadian AMY weather file data set | |
Hu et al. | A Study on Short‐Term Wind Power Forecasting Method Based on Wind Speed Spatio‐Temporal Calibration and Power Self‐adaptive Correction | |
TWM555983U (en) | Generated power assessment device for solar panel | |
CN116227707A (en) | Method, device, equipment and storage medium for predicting power generation power of distributed power supply | |
CN116247651A (en) | Intelligent prediction method and device for distributed photovoltaic power |