CN115360704A - Offshore wind power output prediction method, device, equipment and storage medium - Google Patents
Offshore wind power output prediction method, device, equipment and storage medium Download PDFInfo
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Abstract
The application discloses an output prediction method, device, equipment and storage medium of offshore wind power. The method can comprehensively and accurately predict the wind speed and the output condition of the offshore wind power under the typhoon weather condition, so that the safety regulation and control can be performed in time according to the prediction result, and the offshore wind power plant can run more safely. And then the output prediction level of the offshore wind power is improved, the safe operation control of an offshore wind power plant is realized, the typhoon prevention capability of the wind power plant and a power grid under a future large-scale offshore wind power access scene is improved, and the power generation benefit of the wind power plant is improved by using the typhoon.
Description
Technical Field
The application relates to the technical field of power grid power, in particular to an offshore wind power output prediction method, device, equipment and storage medium.
Background
The offshore wind turbine is mainly installed in coastal zones, which are often attacked by typhoon weather, so that the generated power of the offshore wind turbine is inevitably affected by the typhoon weather. At present, for the wind turbine wind speed and the power generation output of an offshore wind farm, the prior art mainly predicts the meteorological environment under the stable condition, but is difficult to adapt to the prediction of the wind turbine wind speed and the power generation output under the typhoon weather condition, so that the operation safety and the stability of the offshore wind farm cannot be effectively guaranteed.
Disclosure of Invention
The application provides an offshore wind power output prediction method, device, equipment and storage medium, and aims to solve the technical problem that the prior art is difficult to adapt to prediction of wind speed and power generation output of a fan under typhoon weather conditions.
In order to solve the above technical problem, in a first aspect, the present application provides a method for predicting an output of offshore wind power, including:
acquiring operation monitoring data and weather forecast data of an offshore wind farm, wherein the weather forecast data comprises forecast data of typhoon weather;
predicting fan wind speed data of the offshore wind power plant under the typhoon weather condition according to the operation monitoring data and the weather forecast data by using a preset offshore wind power wind speed prediction model;
and predicting the generated output data of the offshore wind power plant based on the wind speed data of the fan and the operation monitoring data.
Preferably, the method for predicting the wind speed data of the fan of the offshore wind farm under the typhoon weather condition by using a preset offshore wind power wind speed prediction model according to the operation monitoring data and the weather forecast data comprises the following steps:
predicting wind resource space-time distribution data under typhoon weather conditions based on weather forecast data;
and predicting fan wind speed data of the offshore wind power plant under the typhoon weather condition according to wind resource space-time distribution data and operation monitoring data by using a preset offshore wind power wind speed prediction model.
Preferably, before predicting the wind resource space-time distribution data under the typhoon weather conditions based on the weather forecast data, the method further comprises:
and correcting the weather forecast data by using a preset weather error correction model to obtain corrected weather forecast data, and analyzing and constructing the preset weather error correction model based on historical weather forecast data of a weather monitoring station and local weather monitoring data of an offshore wind farm.
Preferably, the step of predicting the generated output data of the offshore wind farm based on the wind speed data of the wind turbine and the operation monitoring data comprises the following steps:
and determining the power generation output data of the offshore wind farm by utilizing the preset correlation characteristic relationship between the wind speed data of the wind turbine and the operation monitoring data and the power generation output data.
Preferably, the method for predicting the wind speed data of the wind turbine of the offshore wind farm under the typhoon weather condition by using the preset offshore wind power wind speed prediction model according to the operation monitoring data and the weather forecast data further comprises the following steps:
acquiring historical operation monitoring data and historical typhoon meteorological data of an offshore wind farm;
extracting wind resource space-time distribution characteristic data of an offshore wind farm under the typhoon weather condition according to historical typhoon meteorological data;
extracting wind speed influence characteristic data of the fan under the typhoon weather condition according to the historical operation monitoring data and the historical typhoon meteorological data;
and performing model training on the wind resource space-time distribution characteristic data and the wind speed influence characteristic data by using a preset deep learning algorithm to generate a preset offshore wind power wind speed prediction model.
Preferably, the method for extracting the wind resource space-time distribution characteristic data of the offshore wind farm under the typhoon weather condition according to the historical typhoon meteorological data comprises the following steps:
and learning the wind resource space-time distribution characteristics under various typhoon weather conditions by using an associated learning algorithm according to the historical typhoon meteorological data to obtain wind resource space-time distribution characteristic data, wherein the typhoon weather conditions comprise typhoon weather conditions corresponding to various typhoon grades.
Preferably, the method for extracting the wind speed influence characteristic data of the fan under the typhoon weather condition according to the historical operation monitoring data and the historical typhoon meteorological data comprises the following steps:
and mining the correlation influence characteristics among meteorological factors according to the historical operation monitoring data and the historical typhoon meteorological data by utilizing a time sequence correlation mining algorithm to obtain wind speed influence characteristic data of the fan under the typhoon weather condition, wherein the meteorological factors comprise at least one of wind speed, temperature and air pressure.
In a second aspect, the present application provides a method for predicting offshore wind power output, including:
the acquisition module is used for acquiring operation monitoring data and weather forecast data of the offshore wind farm, wherein the weather forecast data comprises forecast data of typhoon weather;
the first prediction module is used for predicting wind speed data of a fan of an offshore wind farm under a typhoon weather condition according to operation monitoring data and weather forecast data by using a preset offshore wind power wind speed prediction model;
and the second prediction module is used for predicting the generated output data of the offshore wind farm based on the wind speed data of the wind turbine and the operation monitoring data.
In a third aspect, the present application provides a computer device comprising a processor and a memory, the memory being used for storing a computer program, which when executed by the processor, implements the method for predicting offshore wind power output according to the first aspect.
In a fourth aspect, the present application provides a computer readable storage medium storing a computer program, which when executed by a processor, implements the method for predicting offshore wind power output according to the first aspect.
Compared with the prior art, the application at least has the following beneficial effects:
the method comprises the steps that operation monitoring data and weather forecast data of the offshore wind farm are obtained, wherein the weather forecast data comprise forecast data of typhoon weather, so that the influence of the typhoon weather and other multidimensional data on offshore wind power output is considered; predicting wind speed data of a fan of an offshore wind farm under a typhoon weather condition by utilizing a preset offshore wind power wind speed prediction model according to operation monitoring data and weather forecast data so as to predict wind speed influence factors of the environment where an offshore wind power unit is located; the method is used for predicting the power generation output data of the offshore wind power plant based on the wind speed data of the wind turbine and the operation monitoring data so as to comprehensively and accurately predict the wind speed and the output condition of the offshore wind power plant under the typhoon weather condition, and therefore safety regulation and control are performed in time according to the prediction result, and the offshore wind power plant can operate more safely. And then the output prediction level of the offshore wind power is improved, the safe operation control of an offshore wind power plant is realized, the typhoon prevention capability of the wind power plant and a power grid under a future large-scale offshore wind power access scene is improved, and the power generation benefit of the wind power plant is improved by using the typhoon.
Drawings
Fig. 1 is a schematic flow chart of an offshore wind power output prediction method according to an embodiment of the present application;
fig. 2 is a schematic flowchart of an embodiment of the present application before step S102;
fig. 3 is a schematic structural diagram of an offshore wind power output prediction device according to an embodiment of the present application;
fig. 4 is a schematic structural diagram of a computer device according to an embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments in the present application without making any creative effort belong to the protection scope of the present application.
Referring to fig. 1, fig. 1 is a schematic flow diagram of an output prediction method for offshore wind power according to an embodiment of the present disclosure. The output prediction method for offshore wind power provided by the embodiment of the application can be applied to computer equipment, wherein the computer equipment comprises but is not limited to equipment such as a smart phone, a notebook computer, a tablet computer, a desktop computer, a physical server and a cloud server. As shown in fig. 1, the method for predicting the offshore wind power output of the embodiment includes steps S101 to S103, which are detailed as follows:
step S101, obtaining operation monitoring data and weather forecast data of the offshore wind farm, wherein the weather forecast data comprises forecast data of typhoon weather.
In this step, the operation monitoring data includes, but is not limited to, data of operation conditions of the offshore wind turbine, wind speed of the wind turbine, layout of the wind turbine, and the like. The forecast data is typhoon weather meteorological data observed by a meteorological station.
And S102, predicting fan wind speed data of the offshore wind power plant under the typhoon weather condition according to the operation monitoring data and the weather forecast data by using a preset offshore wind power wind speed prediction model.
In the step, based on wind speed influence factors, deep learning algorithms such as a deep belief network are adopted, and the offshore wind power wind speed prediction is realized through a deep learning model. The deep learning algorithm can select a proper algorithm model according to an actual application scene. For example, in the embodiment, a deep learning algorithm such as a deep belief network is adopted to obtain the offshore wind power wind speed prediction model, and the operation monitoring data and the weather forecast data are input into the offshore wind power wind speed prediction model, so that the current offshore wind power wind speed can be predicted.
According to the embodiment, the problem of high difficulty in predicting the wind speed of the offshore wind power under the typhoon weather condition is solved through the offshore wind power wind speed prediction model.
In some embodiments, the step S102 includes:
predicting wind resource space-time distribution data under typhoon weather conditions based on the weather forecast data;
and predicting the fan wind speed data of the offshore wind power station under the typhoon weather condition according to the wind resource space-time distribution data and the operation monitoring data by using the preset offshore wind power wind speed prediction model.
In this embodiment, for the typhoon characteristic study, the typhoon historical data is used, the wind resource time-space characteristic information under the typhoon weather condition is identified through the association learning algorithm, and the wind resource time-space distribution characteristic under the typhoon weather condition is analyzed, so that the wind resource time-space distribution data under the typhoon weather condition can be predicted by using the wind resource time-space distribution characteristic and combining with the real-time weather forecast data.
Optionally, before predicting the wind resource space-time distribution data, the method further comprises: and correcting the weather forecast data by using a preset weather error correction model to obtain the corrected weather forecast data, wherein the preset weather error correction model is obtained by analyzing and constructing the historical weather forecast data of a weather monitoring station and the local weather monitoring data of the offshore wind farm.
In this embodiment, specifically, according to actual monitoring and weather numerical prediction data such as a local monitoring point of the wind farm and a weather monitoring station during the typhoon, the error distribution characteristics of the numerical weather prediction data in the sea area of the wind farm during the typhoon are researched, characteristics such as the mean, the variance, and the mean absolute error of the numerical weather prediction data errors are analyzed, and a numerical weather prediction data error correction model is constructed. The weather forecast data of the weather monitoring station at the current moment can be corrected through the corrected error correction model. And then, forecasting the wind speed by using the weather forecast data obtained after correction.
And S103, predicting the power generation output data of the offshore wind farm based on the wind speed data of the wind turbine and the operation monitoring data.
In this step, based on the wind speed data of the fan and the operation monitoring data, a gated cycle unit network algorithm can be adopted to realize the prediction of the offshore wind power output under the typhoon weather condition.
Optionally, after obtaining the offshore wind power output prediction result under the typhoon weather condition, the output prediction can be corrected by using the wind farm risk evaluation result. And after the risk or the fault in the wind power plant is determined, the prediction result is correspondingly modified, so that a more accurate prediction result is obtained.
In some embodiments, the step S103 includes:
and determining the power generation output data of the offshore wind farm by utilizing the preset association characteristic relationship between the wind speed data of the fan, the operation monitoring data and the power generation output data.
In this embodiment, the correlation characteristics of the influence factors of the offshore wind power output under the typhoon weather condition are determined according to the historical typhoon meteorological data and the historical operation monitoring data, wherein the influence factors include: fan spatial layout, fan running state and fan wind speed; and predicting the offshore wind power output under the typhoon weather condition according to the correlation characteristics of the influence factors and the wind speed prediction result.
Specifically, historical typhoon meteorological data are historical data monitored by a meteorological monitoring station, historical operation monitoring data are historical operation data of an offshore wind farm, and based on the historical typhoon meteorological data and the historical operation monitoring data, correlation characteristics of offshore wind farm output and complex multi-factor correlation characteristics such as fan spatial layout, fan operation state and wind speed under typhoon weather conditions are researched, so that influence characteristics of factors such as the fan spatial layout, the operation state and the wind speed on the wind power of the offshore wind farm are analyzed.
For example, from the perspective of a fan, the influence of state difference on a fan wind speed-power conversion curve is researched, and a fan running state characteristic evaluation index is established; according to historical monitoring data of the wind power plant, the wake effect characteristics caused by the spatial layout of the wind power plant are researched, and the wake effect characteristics of the wind power plant at different wind speeds and different wind directions are analyzed.
In some embodiments, before step S102, the method further includes:
step S201, obtaining historical operation monitoring data and historical typhoon meteorological data of the offshore wind farm.
In this step, the historical operation monitoring data includes historical operation condition data of the offshore wind turbine, wind speed of the wind turbine, layout of the wind turbine, and the like, and the historical typhoon meteorological data includes historical meteorological data such as wind speed, temperature, pressure, and the like measured by a meteorological station, and historical typhoon data such as typhoon intensity historical data, historical typhoon meteorological monitoring data, historical typhoon radius historical data, historical typhoon transfer path historical data, historical typhoon transfer speed data, and the like.
And S202, extracting wind resource space-time distribution characteristic data of the offshore wind farm under the typhoon weather condition according to the historical typhoon meteorological data.
In the step, based on a typhoon historical data set, the wind resource space-time distribution characteristics under the influence of typhoons with different intensity levels are researched, a multi-dimensional wind resource characteristic description index system under the typhoon weather conditions in which the typhoon intensity, radius, path, transfer speed, turbulence intensity and the like are considered is established, the wind resource characteristics at different stages of the typhoon are analyzed, and the wind resource space-time distribution characteristic data under the typhoon weather conditions are obtained.
In some embodiments, the step S202 includes:
and learning the wind resource space-time distribution characteristics under various typhoon weather conditions by using an associated learning algorithm according to the historical typhoon meteorological data to obtain wind resource space-time distribution characteristic data, wherein the typhoon weather conditions comprise typhoon weather conditions corresponding to various typhoon grades.
In this embodiment, for typhoon characteristic research, typhoon historical data is used, wind resource space-time characteristic information under a typhoon weather condition is identified through an association learning algorithm, and wind resource space-time distribution characteristics under the typhoon weather condition are analyzed. Firstly, typhoon historical data is input into an associated learning algorithm for learning, wind resource space-time distribution characteristics under the influence of typhoons of different intensity levels are researched based on a historical measurement data set, for example, eight-level typhoon intensity, corresponding typhoon radius, typhoon transfer path, typhoon speed and the like are established, a wind resource characteristic description index system under the condition of multi-dimensional typhoon weather in consideration of typhoon intensity, radius, path, transfer speed, turbulence intensity and the like is established, and wind resource characteristics under different stages of typhoons are analyzed.
And S203, extracting fan wind speed influence characteristic data under the typhoon weather condition according to the historical operation monitoring data and the historical typhoon meteorological data.
In the step, the correlation characteristics among different meteorological factors are determined so as to identify the correlation characteristics among complex meteorological factors, wherein the meteorological factors are the temperature, the pressure, the wind speed and the like collected by the meteorological observation station. On the basis, the key influence factors of the wind speed prediction of the offshore wind farm under the typhoon weather condition are extracted by combining historical operation monitoring data and the historical typhoon meteorological data, and the wind speed influence characteristic data of the fan under the typhoon weather condition is determined. The historical typhoon meteorological data comprises historical weather forecast data related to typhoons, and the historical operation monitoring data comprises: historical operating condition data of the offshore wind turbine, historical wind turbine wind speed, historical wind turbine layout and the like.
In some embodiments, the step S203 includes:
and mining association influence characteristics among meteorological factors according to the historical operation monitoring data and the historical typhoon meteorological data by utilizing a time sequence association mining algorithm to obtain wind speed influence characteristic data of the fan under the typhoon weather condition, wherein the meteorological factors comprise at least one of wind speed, temperature and air pressure.
In the embodiment, meteorological monitoring data and wind farm monitoring data are acquired; determining correlation characteristics among meteorological factors through a time sequence correlation mining algorithm according to meteorological monitoring data and wind farm monitoring data; and acquiring wind speed influence factors under the typhoon weather conditions according to the correlation characteristics among the meteorological factors.
Specifically, based on multi-source multi-dimensional historical data such as an offshore wind farm and a weather monitoring station, optionally, the time sequence association mining algorithm is applied to the embodiment, the association characteristics among complex multi-weather factors such as wind speed, temperature and air pressure are mined, and the association characteristics among the complex multi-weather factors are identified.
And S204, performing model training on the wind resource space-time distribution characteristic data and the wind speed influence characteristic data by using a preset deep learning algorithm to generate the preset offshore wind power wind speed prediction model.
In the step, deep learning algorithms such as a deep belief network and the like can be adopted for model training to obtain an offshore wind power wind speed prediction model.
In this embodiment, the extracted wind speed prediction key influence factor takes wind resource characteristics under the influence of typhoons into consideration, optionally, the input data can be trained through a preset deep learning algorithm in combination with wind farm sea area numerical weather forecast, wind farm local monitoring information, typhoon intensity, path, radius and other forecast information, so as to realize offshore wind power wind speed prediction.
In order to implement the output prediction method of the offshore wind power corresponding to the method embodiment, corresponding functions and technical effects are achieved. Referring to fig. 3, fig. 3 is a block diagram illustrating a structure of an offshore wind power generation prediction apparatus according to an embodiment of the present application. For convenience of explanation, only the parts related to the present embodiment are shown, and the offshore wind power output prediction apparatus provided in the embodiment of the present application includes:
the acquisition module 301 is configured to acquire operation monitoring data and weather forecast data of an offshore wind farm, where the weather forecast data includes forecast data of typhoon weather;
the first prediction module 302 is configured to predict wind speed data of a fan of the offshore wind farm under a typhoon weather condition according to the operation monitoring data and the weather forecast data by using a preset offshore wind power wind speed prediction model;
and a second prediction module 303, configured to predict the power generation output data of the offshore wind farm based on the wind speed data of the wind turbine and the operation monitoring data.
In some embodiments, the first prediction module 302 is specifically configured to:
predicting wind resource space-time distribution data under typhoon weather conditions based on the weather forecast data;
and predicting the fan wind speed data of the offshore wind power station under the typhoon weather condition according to the wind resource space-time distribution data and the operation monitoring data by using the preset offshore wind power wind speed prediction model.
In some embodiments, the second prediction module 303 is further specifically configured to:
and correcting the weather forecast data by using a preset weather error correction model to obtain the corrected weather forecast data, wherein the preset weather error correction model is obtained by analyzing and constructing the preset weather error correction model based on historical weather forecast data of a weather monitoring station and local weather monitoring data of the offshore wind farm.
In some embodiments, the second prediction module 303 is specifically configured to:
and determining the power generation output data of the offshore wind farm by utilizing the preset correlation characteristic relationship between the wind speed data of the fan and the operation monitoring data and the power generation output data.
In some embodiments, before the apparatus, further comprising:
the second acquisition module is used for acquiring historical operation monitoring data and historical typhoon meteorological data of the offshore wind farm;
the first extraction module is used for extracting wind resource space-time distribution characteristic data of the offshore wind farm under the typhoon weather condition according to the historical typhoon meteorological data;
the second extraction module is used for extracting fan wind speed influence characteristic data under the typhoon weather condition according to the historical operation monitoring data and the historical typhoon meteorological data;
and the generation module is used for performing model training on the wind resource space-time distribution characteristic data and the wind speed influence characteristic data by using a preset deep learning algorithm to generate the preset offshore wind power wind speed prediction model.
In some embodiments, the first extraction module is specifically configured to:
and learning the wind resource space-time distribution characteristics under various typhoon weather conditions by using an associated learning algorithm according to the historical typhoon meteorological data to obtain wind resource space-time distribution characteristic data, wherein the typhoon weather conditions comprise typhoon weather conditions corresponding to various typhoon grades.
In some embodiments, the second extraction module is specifically configured to:
and mining association influence characteristics among meteorological factors according to the historical operation monitoring data and the historical typhoon meteorological data by utilizing a time sequence association mining algorithm to obtain wind speed influence characteristic data of the fan under the typhoon weather condition, wherein the meteorological factors comprise at least one of wind speed, temperature and air pressure.
The offshore wind power output prediction device can implement the offshore wind power output prediction method of the method embodiment. The alternatives in the above-described method embodiments are also applicable to this embodiment and will not be described in detail here. The rest of the embodiments of the present application may refer to the contents of the above method embodiments, and in this embodiment, details are not described again.
Fig. 4 is a schematic structural diagram of a computer device according to an embodiment of the present application. As shown in fig. 4, the computer device 4 of this embodiment includes: at least one processor 40 (only one shown in fig. 4), a memory 41, and a computer program 42 stored in the memory 41 and executable on the at least one processor 40, the processor 40 implementing the steps of any of the method embodiments described above when executing the computer program 42.
The computer device 4 may be a computing device such as a smart phone, a tablet computer, a desktop computer, and a cloud server. The computer device may include, but is not limited to, a processor 40, a memory 41. Those skilled in the art will appreciate that fig. 4 is merely an example of the computer device 4 and does not constitute a limitation of the computer device 4, and may include more or less components than those shown, or combine certain components, or different components, such as input output devices, network access devices, etc.
The Processor 40 may be a Central Processing Unit (CPU), and the Processor 40 may be other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic device, discrete hardware component, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory 41 may in some embodiments be an internal storage unit of the computer device 4, such as a hard disk or a memory of the computer device 4. The memory 41 may also be an external storage device of the computer device 4 in other embodiments, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like, which are provided on the computer device 4. Further, the memory 41 may also include both an internal storage unit and an external storage device of the computer device 4. The memory 41 is used for storing an operating system, an application program, a BootLoader (BootLoader), data, and other programs, such as program codes of the computer program. The memory 41 may also be used to temporarily store data that has been output or is to be output.
In addition, an embodiment of the present application further provides a computer-readable storage medium, where a computer program is stored, and when the computer program is executed by a processor, the computer program implements the steps in any of the method embodiments described above.
The embodiments of the present application provide a computer program product, which when executed on a computer device, enables the computer device to implement the steps in the above method embodiments.
In several embodiments provided herein, it will be understood that each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved.
The functions, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application or portions thereof that substantially contribute to the prior art may be embodied in the form of a software product stored in a storage medium and including instructions for causing a computer device to perform all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The above-mentioned embodiments are provided to further explain the objects, technical solutions and advantages of the present application in detail, and it should be understood that the above-mentioned embodiments are only examples of the present application and are not intended to limit the scope of the present application. It should be understood that any modifications, equivalents, improvements and the like, which come within the spirit and principle of the present application, may occur to those skilled in the art and are intended to be included within the scope of the present application.
Claims (10)
1. The output prediction method of offshore wind power is characterized by comprising the following steps:
acquiring operation monitoring data and weather forecast data of an offshore wind farm, wherein the weather forecast data comprises forecast data of typhoon weather;
predicting fan wind speed data of the offshore wind power plant under the typhoon weather condition according to the operation monitoring data and the weather forecast data by using a preset offshore wind power wind speed prediction model;
and predicting the power generation output data of the offshore wind farm based on the wind speed data of the wind turbine and the operation monitoring data.
2. The offshore wind power output prediction method of claim 1, wherein the predicting the wind speed data of the wind turbine of the offshore wind farm under typhoon weather conditions according to the operation monitoring data and the weather forecast data by using a preset offshore wind power wind speed prediction model comprises:
predicting wind resource space-time distribution data under typhoon weather conditions based on the weather forecast data;
and predicting the fan wind speed data of the offshore wind power plant under the typhoon weather condition according to the wind resource space-time distribution data and the operation monitoring data by using the preset offshore wind power wind speed prediction model.
3. The offshore wind power contribution prediction method of claim 2, wherein, prior to predicting the wind resource spatio-temporal distribution data under typhoon weather conditions based on the weather forecast data, further comprising:
and correcting the weather forecast data by using a preset weather error correction model to obtain the corrected weather forecast data, wherein the preset weather error correction model is obtained by analyzing and constructing the historical weather forecast data of a weather monitoring station and the local weather monitoring data of the offshore wind farm.
4. The offshore wind power output prediction method of claim 1, wherein said predicting power generation output data of said offshore wind farm based on said wind turbine speed data and said operational monitoring data comprises:
and determining the power generation output data of the offshore wind farm by utilizing the preset correlation characteristic relationship between the wind speed data of the fan and the operation monitoring data and the power generation output data.
5. The offshore wind power output prediction method of claim 1, wherein, before predicting the wind speed data of the wind turbine of the offshore wind farm under typhoon weather conditions according to the operation monitoring data and the weather forecast data by using a preset offshore wind power wind speed prediction model, further comprising:
acquiring historical operation monitoring data and historical typhoon meteorological data of an offshore wind farm;
extracting wind resource space-time distribution characteristic data of the offshore wind farm under the typhoon weather condition according to the historical typhoon meteorological data;
extracting fan wind speed influence characteristic data under the typhoon weather condition according to the historical operation monitoring data and the historical typhoon meteorological data;
and performing model training on the wind resource space-time distribution characteristic data and the wind speed influence characteristic data by using a preset deep learning algorithm to generate the preset offshore wind power wind speed prediction model.
6. The offshore wind power output prediction method of claim 5, wherein said extracting wind resource space-time distribution characteristic data of said offshore wind farm under typhoon weather conditions based on said historical typhoon meteorological data comprises:
and learning the wind resource space-time distribution characteristics under various typhoon weather conditions by using an associated learning algorithm according to the historical typhoon meteorological data to obtain wind resource space-time distribution characteristic data, wherein the typhoon weather conditions comprise typhoon weather conditions corresponding to various typhoon grades.
7. The offshore wind power output prediction method of claim 5, wherein said extracting wind speed influence characteristic data of the wind turbine under typhoon weather conditions from said historical operational monitoring data and said historical typhoon meteorological data comprises:
and mining association influence characteristics among meteorological factors according to the historical operation monitoring data and the historical typhoon meteorological data by utilizing a time sequence association mining algorithm to obtain wind speed influence characteristic data of the fan under the typhoon weather condition, wherein the meteorological factors comprise at least one of wind speed, temperature and air pressure.
8. The output prediction method of the offshore wind power is characterized by comprising the following steps:
the system comprises an acquisition module, a monitoring module and a control module, wherein the acquisition module is used for acquiring operation monitoring data and weather forecast data of an offshore wind farm, and the weather forecast data comprises forecast data of typhoon weather;
the first prediction module is used for predicting fan wind speed data of the offshore wind power plant under the typhoon weather condition according to the operation monitoring data and the weather forecast data by utilizing a preset offshore wind power wind speed prediction model;
and the second prediction module is used for predicting the power generation output data of the offshore wind farm based on the wind speed data of the wind turbine and the operation monitoring data.
9. A computer arrangement comprising a processor and a memory for storing a computer program which, when executed by the processor, implements the method of offshore wind power contribution prediction according to any of claims 1 to 7.
10. A computer-readable storage medium, characterized in that it stores a computer program which, when executed by a processor, implements the method of offshore wind power export prediction according to any of claims 1 to 7.
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| CN117454720A (en) * | 2023-12-21 | 2024-01-26 | 浙江远算科技有限公司 | Method and equipment for simulating and predicting scouring risk of offshore wind farm in extreme weather |
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| CN117454720B (en) * | 2023-12-21 | 2024-03-29 | 浙江远算科技有限公司 | Method and equipment for simulating and predicting scouring risk of offshore wind farm in extreme weather |
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