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 PDF

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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
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wind
maximum
power
maximum wind
prediction
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NL2033883A
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NL2033883B1 (en
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Shen Weicheng
He Chang'an
Wang Lu
Fu Jiayu
Ma Liancai
Zhang Yuanfeng
Sun Jianhua
Zhang Xu
Sun Jian
Han Zifen
Zhang Jianmei
Guo Kai
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State Grid Gansu Electric Power Co
Gansu Tongxing Intelligent Tech Development Co Ltd
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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
Technical Field
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.
Description of the Invention
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.
Brief Description of the Drawings
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)

ConclusiesConclusions 1. Voorspellingsmethode voor de windkracht bij extreem stormachtig weer, met het kenmerk dat de windsnelheid en het vermogen van een windmolen worden gebruikt om te passen in het model van een vermogenscurve voor capaciteitsvermindering bij maximale wind, waarbij met behulp van de meteorologische bron van de voorspelling van de maximale wind het machine leermodel van de voorspelling van de maximale wind wordt uitgevoerd om een nauwkeurige voorspelling van de maximale windsnelheid te verkrijgen, waarbij de voorspelde maximale windsnelheid wordt ingevoerd in het model van de vermogenscurve voor capaciteitsvermindering bij maximale wind om het voorspelde maximale windvermogen te verkrijgen.1. Wind force forecasting method in extremely stormy weather, characterized in that the wind speed and power of a wind turbine are used to fit the model of a capacity reduction power curve at maximum wind, using the meteorological source of the forecast of the maximum wind the machine learning model of the maximum wind forecast is performed to obtain an accurate prediction of the maximum wind speed, inputting the predicted maximum wind speed into the model of the capacity reduction power curve at maximum wind to obtain the predicted maximum wind power to obtain. 2. Voorspellingsmethode voor de windkracht bij extreem stormachtig weer volgens conclusie 1, met het kenmerk dat de methode de volgende stappen omvat: Stap 1: Toepassing van de waarnemingsgegevens van het windveld om in te voegen in de curve van de capaciteitsvermindering; Stap 2: Selectie van meteorologische bronnen en het bouwen van modellen. Stap 3: Toepassing van het model om de maximale windkracht van het weer te voor- spellen volgens de meteorologische voorspelling van de windsnelheid. Stap 4: Tenslotte het uitvoeren van de volgorde van de capaciteitsvermindering.A wind force forecasting method in extreme stormy weather according to claim 1, characterized in that the method comprises the following steps: Step 1: Application of the wind field observation data to insert into the capacity reduction curve; Step 2: Selection of meteorological sources and model building. Step 3: Application of the model to predict the maximum wind force of the weather according to the meteorological forecast of the wind speed. Step 4: Finally, perform the capacity reduction sequence. 3. Voorspellingsmethode voor de windkracht bij extreem stormachtig weer volgens conclusie 2, met het kenmerk, dat de voorziening van de windsnelheid standaard wordt uitgeschakeld door de capaciteitsverminderingscurve in stap 1.The extreme stormy weather wind force forecasting method according to claim 2, characterized in that the wind speed provision is disabled by default by the capacity reduction curve in step 1. 4. Voorspellingsmethode voor de windkracht bij extreem stormachtig weer volgens conclusie 2, met het kenmerk, dat de meteorologische bron in stap 2 standaard door IBM wordt geleverd, maar dat de optimale meteorologische bron voor de voorspelling wordt geselecteerd volgens de actuele situatie van het station.The extreme stormy weather forecasting method according to claim 2, characterized in that the meteorological source in step 2 is provided by IBM by default, but the optimal meteorological source for the forecast is selected according to the actual situation of the station. 5. Voorspellingssysteem voor de windkracht bij extreem stormachtig weer, onder toepassing van de voorspellingsmethode voor de windkracht bij extreem stormachtig weer zoals beschreven in conclusie 1, met het kenmerk, dat het systeem omvat: - Vermogenscurve-aanpassingsmodule voor capaciteitsvermindering, om wind- molens te gebruiken om de windsnelheid en het vermogen waar te nemen om te passen in het vermogenscurvemodel voor capaciteitsvermindering bij maximale wind; -Machine leermodelmodule voor de voorspelling van de maximale wind, om de meteorologische bron voor de voorspelling van de maximale wind te gebruiken om het machine leermodel voor de voorspelling van de maximale wind uit te voeren en een nauwkeurige voorspelling van de windsnelheid van de maximale wind te verkrijgen; - Windkrachtvoorspellingsmodule voor maximaal stormweer, om een voorspelde windsnelheid bij maximale wind in het vermogenscurvemodel voor capaciteitsvermindering bij maximale wind te brengen om het voorspelde vermogen bij maximale wind te verkrijgen.An extreme stormy weather wind force forecasting system, applying the extreme stormy weather wind force forecasting method as set forth in claim 1, characterized in that the system comprises: - Power curve adjustment module for capacity reduction, for wind turbines to be use to observe the wind speed and power to fit the power curve model for capacity reduction at maximum wind; Maximum wind forecast machine learning model module, to use the maximum wind forecast meteorological source to run the maximum wind forecast machine learning model, and make an accurate maximum wind forecast wind speed prediction to acquire; - Wind force prediction module for maximum storm weather, to insert a predicted wind speed at maximum wind into the power curve model for capacity reduction at maximum wind to obtain the predicted power at maximum wind. 6. Door een computer leesbaar opslagmedium, waarin een computerprogramma wordt opgeslagen, met het kenmerk, dat wanneer het computerprogramma door de processor wordt uitgevoerd, de processor de stappen uitvoert van de windkrachtvoorspellingsmethode voor extreem stormachtig weer, zoals beschreven in één van de conclusies 1-5.A computer-readable storage medium in which a computer program is stored, characterized in that when the computer program is executed by the processor, the processor performs the steps of the wind force prediction method for extreme stormy weather as described in any one of claims 1- 5.
NL2033883A 2022-12-30 2022-12-30 A Wind Power Forecasting Method, System and Medium for Extremely Windy Weather NL2033883B1 (en)

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Citations (1)

* Cited by examiner, † Cited by third party
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

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* Cited by examiner, † Cited by third party
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

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* Cited by examiner, † Cited by third party
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] *

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