Extreme gale forecasting method, system and equipment for new energy station and medium

Abstract

The invention discloses a new energy station-oriented extreme strong wind forecasting method, system and device and a medium. The method comprises the steps of obtaining basic information of a new energy station and gridded numerical weather forecasting conventional data; according to the basic information of the new energy station and the gridded numerical weather forecast conventional data, calculating the wind speed uh of the fan hub height h on each integral time step length in a forecast period; according to the wind speed uh of the fan hub height h on each integral time step in the forecasting period, calculating the extreme gale wind speed ugust at the fan hub height h of the new energy station on each integral time step in the forecasting period; according to the extreme strong wind speed ugust at the new energy station fan hub height h on each integral time step in the forecasting period, the forecast wind speed of the extreme strong wind of the new energy station in the forecasting period is determined, and the method, the system, the equipment and the medium can accurately and reliably forecast the extreme strong wind for the new energy station.

Description

Technical Field

[0001] The present invention belongs to the field of extreme gale forecasting for new energy stations, and relates to an extreme gale forecasting method, system, equipment and medium for new energy stations. Background Art

[0002] Against the backdrop of intensified global climate change, the frequent occurrence of extreme catastrophic weather events has had a significant adverse impact on new energy sites, especially extreme gale events. Extreme gale often causes wind turbines to shut down due to gale, which not only affects power generation efficiency, but may also lead to instability of the power grid; it may even cause damage to wind turbine blades, towers, and towers, leading to electrical system failures and control system failures. Extreme gale may increase the safety risks of new energy sites, and significantly increase maintenance and repair costs. Therefore, how to accurately forecast and warn of extreme gale for new energy sites directly affects the economy and safety of new energy site operations. However, current extreme gale forecasting techniques are usually based on the atmospheric neutrality assumption, using logarithmic wind profiles or power exponential wind profiles for interpolation to estimate the wind speed at the hub height of the wind turbine. These methods fail to fully consider the impact of the atmosphere on the vertical wind profile under different stability conditions. In addition, existing technologies often ignore the complex effects of local topography and the contribution of turbulent vertical transport to extreme gale events, so the accuracy and reliability of extreme gale forecasts are poor. Summary of the invention

[0003] The purpose of the present invention is to overcome the shortcomings of the above-mentioned prior art and to provide a method, system, device and medium for forecasting extreme strong winds for new energy stations. The method, system, device and medium can accurately and reliably forecast extreme strong winds for new energy stations.

[0004] To achieve the above object, the present invention discloses an extreme gale forecasting method for a new energy station, comprising:

[0005] Obtain basic information of new energy stations and gridded numerical weather forecast routine data;

[0006] According to the basic information of the new energy station and the gridded numerical weather forecast routine data, the wind speed u at the hub height h of the wind turbine at each integral time step during the forecast period is calculated. h ;

[0007] According to the wind speed u at the hub height h of the wind turbine at each integral time step during the forecast period, h , calculate the extreme wind speed u at the hub height h of the wind turbine at the new energy station at each integral time step during the forecast period gust ;

[0008] According to the extreme strong wind speed u at the hub height h of the wind turbine of the new energy station at each integral time step during the forecast period gust , determine the forecast wind speed of extreme strong winds at new energy sites during the forecast period.

[0009] The further improvement of the extreme gale forecasting method for new energy stations of the present invention is:

[0010] Furthermore, during the calculation forecast period, the wind speed u at the wind turbine hub height h at each integral time step is h It is expressed as:

[0011]

[0012] Among them, u 10 , z0 and L are the wind speed, surface roughness and MO length at a height of 10 meters at the grid point corresponding to the center of the station, respectively, and Ψ is the atmospheric stability function.

[0013] Furthermore, the atmospheric stability function Ψ is expressed as:

[0014]

[0015] Among them, k is the Karman constant, β is the empirical coefficient, which is used to adjust the intensity of the atmospheric stability function. When L < 0, it is an unstable atmosphere; when L ≈ 0, it is a neutral atmosphere; when L > 0, it is a stable atmosphere.

[0016] Furthermore, the extreme high wind speed u at the hub height h of the wind turbine of the new energy station at each integral time step during the forecast period is gust It is expressed as:

[0017]

[0018] Among them, c turb represents the turbulent mixing parameter, z i is the height of the mixing layer.

[0019] Furthermore, the extreme high wind speed u at the hub height h of the wind turbine of the new energy station at each integral time step during the forecast period is gust , determine the forecast wind speed of extreme gale at the new energy station during the forecast period;

[0020] Determine the extreme high wind speed u at the hub height h of the new energy station wind turbine at each integral time step gust The maximum value is used as the forecast wind speed of extreme strong winds at the new energy station during the forecast period.

[0021] The present invention discloses an extreme gale forecasting system for new energy stations, comprising:

[0022] The acquisition module is used to obtain the basic information of new energy stations and gridded numerical weather forecast routine data;

[0023] The first calculation module is used to calculate the wind speed u at the hub height h of the wind turbine at each integral time step during the forecast period based on the basic information of the new energy station and the gridded numerical weather forecast routine data. h ;

[0024] The second calculation module is used to calculate the wind speed u at each integral time step at the hub height h of the wind turbine during the forecast period. h , calculate the extreme wind speed u at the hub height h of the wind turbine at the new energy station at each integral time step during the forecast period gust ;

[0025] A determination module is used to determine the extreme high wind speed u at the hub height h of the wind turbine of the new energy station at each integral time step during the forecast period. gust , determine the forecast wind speed of extreme strong winds at new energy sites during the forecast period.

[0026] The further improvement of the extreme gale forecasting system for new energy stations of the present invention is:

[0027] Furthermore, during the calculation forecast period, the wind speed u at the wind turbine hub height h at each integral time step is h It is expressed as:

[0028]

[0029] Among them, u 10 , z0 and L are the wind speed, surface roughness and M-0 length at a height of 10 meters at the grid point corresponding to the center of the station, respectively, and Ψ is the atmospheric stability function.

[0030] Furthermore, the atmospheric stability function Ψ is expressed as:

[0031]

[0032] Among them, K is the Karman constant and β is the empirical coefficient, which is used to adjust the intensity of the atmospheric stability function. When L < 0, it is an unstable atmosphere; when L ≈ 0, it is a neutral atmosphere; when L > 0, it is a stable atmosphere.

[0033] Furthermore, the extreme high wind speed u at the hub height h of the wind turbine of the new energy station at each integral time step during the forecast period is gust It is expressed as:

[0034]

[0035] Among them, cturb represents the turbulent mixing parameter, z i is the height of the mixing layer.

[0036] Furthermore, the extreme high wind speed u at the hub height h of the wind turbine of the new energy station at each integral time step during the forecast period is gust , determine the forecast wind speed of extreme gale at the new energy site during the forecast period;

[0037] Determine the extreme high wind speed u at the hub height h of the new energy station wind turbine at each integral time step gust The maximum value is used as the forecast wind speed of extreme strong winds at the new energy station during the forecast period.

[0038] The present invention discloses a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the extreme gale forecasting method for new energy stations are implemented.

[0039] The present invention discloses a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the extreme gale forecasting method for new energy sites are implemented.

[0040] The present invention has the following beneficial effects:

[0041] The extreme gale forecasting method, system, device and medium for new energy stations of the present invention are used to calculate the wind speed u at each integral time step at the hub height h of the wind turbine during the forecast period according to the basic information of the new energy station and the gridded numerical weather forecast routine data. h , thus considering the complex influence of local topography and the contribution of turbulent vertical transport to extreme wind events, and then according to the extreme wind speed u at the hub height h of the wind turbine of the new energy station at each integral time step during the forecast period gust , determine the forecast wind speed of extreme strong winds at new energy stations during the forecast period, and achieve accurate and reliable forecast of extreme strong winds, which will not only help reduce structural damage and mechanical failures of wind turbines and station equipment, but also effectively reduce downtime and power generation losses, and improve the overall operational efficiency and safety of the station. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] The accompanying drawings constituting a part of the present invention are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the accompanying drawings:

[0043] Figure 1 is a flow chart of the method of the present invention;

[0044] Figure 2 It is a system structure diagram of the present invention. DETAILED DESCRIPTION

[0045] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0046] In the description of the present invention, it should be understood that the terms “include” and “comprises” indicate the presence of described features, wholes, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or collections thereof.

[0047] It should also be understood that the terms used in the present specification are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the present specification and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0048] It should be further understood that the term "and / or" used in the present specification and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes these combinations. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in the present invention generally indicates that the associated objects are in an "or" relationship.

[0049] It should be understood that, although the terms first, second, third, etc. may be used to describe preset ranges, etc. in the embodiments of the present invention, these preset ranges should not be limited to these terms. These terms are only used to distinguish preset ranges from each other. For example, without departing from the scope of the embodiments of the present invention, the first preset range may also be referred to as the second preset range, and similarly, the second preset range may also be referred to as the first preset range.

[0050] The word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrases "if it is determined" or "if (stated condition or event) is detected" may be interpreted as "when it is determined" or "in response to determining" or "when detecting (stated condition or event)" or "in response to detecting (stated condition or event)", depending on the context.

[0051] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. The components of the embodiments of the present invention described and shown in the drawings here can usually be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0052] Various structural schematic diagrams of the embodiments disclosed in the present invention are shown in the accompanying drawings. These figures are not drawn to scale, and some details are magnified and some details may be omitted for the purpose of clear expression. The shapes of various regions and layers shown in the figures and the relative sizes and positional relationships therebetween are only exemplary, and may deviate in practice due to manufacturing tolerances or technical limitations, and those skilled in the art may additionally design regions / layers with different shapes, sizes, and relative positions according to actual needs.

[0053] Embodiment 1

[0054] refer to Figure 1 The extreme gale forecasting method for new energy stations of the present invention comprises the following steps:

[0055] 1) Obtain basic information of new energy stations and gridded numerical weather forecast routine data;

[0056] The basic information of the new energy station includes the longitude and latitude of the station, the height of the wind turbine hub and the terrain where it is located. A matching relationship between the longitude and latitude information of the new energy station and the gridded numerical weather forecast data is established, and the conventional numerical weather forecast data of the corresponding grid points are extracted, covering the wind speed at a height of 10 meters, friction speed, roughness and M-0 length.

[0057] The process of step 1) is:

[0058] 11) Obtaining basic information of the new energy station, the basic information of the new energy station includes the longitude and latitude of the center point of the new energy station, the height of the wind turbine hub, and the topography of the area where it is located, wherein the longitude and latitude of the center point of the station are x and y respectively, and the height of the wind turbine hub is h.

[0059] 12) Extract the gridded numerical weather forecast conventional data corresponding to the new energy station. Based on the gridded numerical weather forecast data covering the new energy station, according to the latitude and longitude information of the center point of the station in step 11), establish a matching relationship between the new energy station and the gridded numerical weather forecast data, and extract the wind speed u at a height of 10 meters corresponding to the grid point of the center point of the new energy station. 10 , friction speed u * , surface roughness z0 and MO length L.

[0060] It should be noted that, in fact, the above conventional data is an array of time series, that is, data of multiple time series within a forecast validity period, and the size of the time series depends on the forecast timeliness and time resolution of the numerical weather forecast. The method and model proposed in the present invention need to be calculated for each integral time step of the numerical weather forecast.

[0061] 2) Construct a benchmark wind speed diagnostic model at the hub height of wind turbines for new energy stations;

[0062] Since both the logarithmic wind profile and the power exponential wind profile assume that the atmosphere is neutral, this is obviously inconsistent with the actual situation. Moreover, extreme strong winds often occur under unstable atmospheric conditions, so it is impossible to use the previous logarithmic interpolation or power exponential interpolation to calculate the wind speed at the hub height of the wind turbine. The present invention applies the Monin-Obukhov similarity theory, considers the impact of different atmospheric stability conditions on the vertical wind profile, and constructs a benchmark wind speed diagnostic model at the hub height of the wind turbine in the new energy station.

[0063] The specific process is:

[0064] 21) Construct a wind speed diagnostic model at the height of the wind turbine hub based on similarity theory. Through meteorological analysis, it is known that extreme windy weather that can cause strong winds to occur in new energy stations usually occurs under unstable atmospheric conditions. Therefore, it is impossible to use the logarithmic wind profile or power wind profile properties that assume the atmosphere is neutral to interpolate and calculate the wind speed at the height of the station's wind turbine hub. The present invention applies the Monin-Obukhov similarity theory and proposes a wind speed diagnostic model at the height of the wind turbine hub, based on full consideration of the influence of atmospheric stability conditions on the vertical wind profile, wherein the wind speed u at each integral time step at the wind turbine hub height h is h for:

[0065]

[0066] Where Ψ is the atmospheric stability function.

[0067] 22) Calculation of stability function under different atmospheric conditions. In new energy stations with complex topography, although extreme gales mostly occur under unstable atmospheric conditions, they may also occur under stable and neutral atmospheric conditions. Therefore, the present invention provides a calculation method for the atmospheric stability function Ψ under different atmospheric conditions. Using this method, the atmospheric stability function is related to the MO length L and the height h, so the atmospheric stability function Ψ is expressed as:

[0068]

[0069] Among them, κ is the Karman constant, which is about 0.4, and β is an empirical coefficient used to adjust the strength of the stability function. It generally takes a value between [3,5]. Different atmospheric conditions are divided according to the value of L. When L<0, it is an unstable atmosphere; when L≈0, it is a neutral atmosphere; when L>0, it is a stable atmosphere.

[0070] Formula (2) can be used to dynamically adjust the wind speed profile diagnostic equation according to the atmospheric stability state, that is, Formula (1). The wind speed at the hub height of the wind turbine in the new energy station can be diagnosed more accurately through the atmospheric stability function.

[0071] 3) Establish an extreme gale forecast model that takes into account local topography and turbulence contributions. Since new energy sites are usually built in mountainous areas far away from towns, complex topography and turbulence activities have certain contributions and influences on the occurrence of extreme gales. The present invention adopts turbulence contribution items that take into account surface roughness and atmospheric stability, establishes an extreme gale forecast method and system facing the height of the wind turbine hub, and performs calculations for each integral time step during the forecast period, and finally calculates the maximum value during the forecast period as the wind speed of the extreme gale.

[0072] The specific process of step 3) is:

[0073] 31) Construct an extreme wind forecast model that takes into account the contribution of turbulence. When extreme winds occur at new energy sites, the wind speed will be further increased in vertical turbulent transport due to the influence of complex topography and turbulent activity. Therefore, it is proposed to use the friction velocity u * and the turbulence contribution represented by the MO length L, and added to the calculation of extreme strong winds, the extreme strong wind speed u at the hub height of the wind turbine in the new energy station is gust express:

[0074]

[0075] Among them, c turb represents the turbulent mixing parameter, with a value range of [7.2, 7.71], z iis the mixing layer height, which is set to 1000 m here, representing the typical height of the atmospheric boundary layer, where turbulent activities mainly occur.

[0076] 32) Iteratively calculate the maximum wind speed for each integral time step during the forecast period;

[0077] Based on the time series data of numerical weather forecast of new energy stations, the extreme wind speed u at the hub height of the wind turbine of the new energy station at each integral time step is calculated according to formula (3) to obtain gust , and select the maximum value as the forecast wind speed of extreme strong winds at new energy sites during the forecast period.

[0078] It should be noted that the present invention is based on the basic information of new energy stations and gridded numerical weather forecast data, adopts the Monin-Obukhov similarity theory, considers the impact of different atmospheric stability on vertical wind profiles, and constructs a wind speed diagnosis model under different atmospheric stability conditions. By adding turbulence contribution terms that describe local topography and turbulent activity, and iteratively calculating at each integral time step, and finally statistically calculating the maximum value of the forecast period, the accurate forecast and early warning of extreme gale for new energy stations is realized. The present invention significantly improves the accuracy of extreme gale forecasts, which not only helps to reduce structural damage and mechanical failures of wind turbines and station equipment, but also can effectively reduce downtime and power generation losses, improve the overall operational efficiency and safety of the station, and achieve accurate prediction of extreme gale events, providing solid technical support and guarantee for the safe operation and equipment protection of new energy stations.

[0079] Embodiment 2

[0080] refer to Figure 2 The extreme gale forecasting system for new energy stations of the present invention comprises:

[0081] The acquisition module is used to obtain the basic information of new energy stations and gridded numerical weather forecast routine data;

[0082] The first calculation module is used to calculate the wind speed u at the hub height h of the wind turbine at each integral time step during the forecast period based on the basic information of the new energy station and the gridded numerical weather forecast routine data. h ;

[0083] The second calculation module is used to calculate the wind speed u at each integral time step at the hub height h of the wind turbine during the forecast period. h , calculate the extreme wind speed u at the hub height h of the wind turbine at the new energy station at each integral time step during the forecast period gust ;

[0084] A determination module is used to determine the extreme high wind speed u at the hub height h of the wind turbine of the new energy station at each integral time step during the forecast period. gust , determine the forecast wind speed of extreme strong winds at new energy sites during the forecast period.

[0085] In this embodiment, during the calculation forecast period, the wind speed u at the wind turbine hub height h at each integral time step is h It is expressed as:

[0086]

[0087] Among them, u 10 , z0 and L are the wind speed, surface roughness and M-0 length at a height of 10 meters at the grid point corresponding to the center of the station, respectively, and Ψ is the atmospheric stability function.

[0088] In this embodiment, the atmospheric stability function Ψ is expressed as:

[0089]

[0090] Among them, K is the Karman constant and β is the empirical coefficient, which is used to adjust the intensity of the atmospheric stability function. When L < 0, it is an unstable atmosphere; when L ≈ 0, it is a neutral atmosphere; when L > 0, it is a stable atmosphere.

[0091] In this embodiment, the extreme high wind speed u at the hub height h of the wind turbine of the new energy station at each integral time step during the forecast period is gust It is expressed as:

[0092]

[0093] Among them, c turb represents the turbulent mixing parameter, z i is the height of the mixing layer.

[0094] In this embodiment, the extreme high wind speed u at the hub height h of the wind turbine of the new energy station at each integral time step during the forecast period is gust , determine the forecast wind speed of extreme gale at the new energy station during the forecast period;

[0095] Determine the extreme high wind speed u at the hub height h of the new energy station wind turbine at each integral time step gust The maximum value is used as the forecast wind speed of extreme strong winds at the new energy station during the forecast period.

[0096] The division of modules in the embodiments of the present application is schematic and is only a logical function division. There may be other division methods in actual implementation. In addition, each functional module in each embodiment of the present application may be integrated into a processor, or may exist physically separately, or two or more modules may be integrated into one module. The above-mentioned integrated modules may be implemented in the form of hardware or in the form of software functional modules.

[0097] Embodiment 3

[0098] A computer device comprises a memory, a processor and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the extreme gale forecasting method for new energy stations are implemented, for example, including: obtaining basic information of the new energy station and gridded conventional numerical weather forecast data; calculating the wind speed u at the hub height h of the wind turbine at each integral time step during the forecast period according to the basic information of the new energy station and the gridded conventional numerical weather forecast data. h ; According to the wind speed u at the hub height h of the wind turbine at each integral time step during the forecast period h , calculate the extreme wind speed u at the hub height h of the wind turbine at the new energy station at each integral time step during the forecast period gust ; According to the extreme high wind speed u at the hub height h of the new energy station wind turbine at each integral time step during the forecast period gust , determine the forecast wind speed of extreme gale at the new energy station during the forecast period. Wherein, the memory may include a memory, such as a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk memory, etc.; the processor, the network interface, and the memory are interconnected through an internal bus, and the internal bus may be an industrial standard architecture bus, a peripheral component interconnection standard bus, an extended industrial standard structure bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. The memory is used to store programs. Specifically, the program may include a program code, and the program code includes computer operation instructions. The memory may include a memory and a non-volatile memory, and provides instructions and data to the processor.

[0099] Embodiment 4

[0100] A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method for generating and sending the credible state alarm information are implemented, for example, including: obtaining basic information of a new energy station and gridded conventional numerical weather forecast data; calculating the wind speed u at the hub height h of the wind turbine at each integral time step during the forecast period according to the basic information of the new energy station and the gridded conventional numerical weather forecast data.h ; According to the wind speed u at the hub height h of the wind turbine at each integral time step during the forecast period h , calculate the extreme wind speed u at the hub height h of the wind turbine at the new energy station at each integral time step during the forecast period gust ; According to the extreme high wind speed u at the hub height h of the new energy station wind turbine at each integral time step during the forecast period gust , determine the forecast wind speed of extreme gale at the new energy station during the forecast period. Specifically, the computer-readable storage medium includes, but is not limited to, for example, volatile memory and / or non-volatile memory. The volatile memory may include random access memory (RAM) and / or cache memory (cache), etc. The non-volatile memory may include read-only memory (ROM), hard disk, flash memory, optical disk, magnetic disk, etc.

[0101] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0102] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0103] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0104] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0105] Those skilled in the art will readily appreciate other embodiments of the present invention after considering the specification and disclosure of the invention. This application is intended to cover any variations, uses or adaptations of the present invention that follow the general principles of the present invention and include common knowledge or customary techniques in the art that are not disclosed by the present invention. The specification and examples are to be considered exemplary only, and the true scope and spirit of the present invention are indicated by the following claims.

[0106] It should be understood that the present invention is not limited to the exact construction that has been described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the present invention is limited only by the appended claims.

[0107] The above description is only a preferred embodiment of the present invention and does not limit the present invention in any way. Any simple modification, change and equivalent structural change made to the above embodiment based on the technical essence of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A method for forecasting extreme gale winds for new energy stations, characterized in that: include: Obtain basic information of new energy stations and gridded numerical weather forecast routine data; According to the basic information of the new energy station and the gridded numerical weather forecast routine data, the wind speed u at the hub height h of the wind turbine at each integral time step during the forecast period is calculated. h ; According to the wind speed u at the hub height h of the wind turbine at each integral time step during the forecast period, h , calculate the extreme wind speed u at the hub height h of the wind turbine at the new energy station at each integral time step during the forecast period gust ; According to the extreme strong wind speed u at the hub height h of the wind turbine of the new energy station at each integral time step during the forecast period gust , determine the forecast wind speed of extreme strong winds at new energy sites during the forecast period.

2. The extreme gale forecasting method for new energy stations according to claim 1 is characterized in that: During the calculation forecast period, the wind speed u at the fan hub height h at each integral time step h It is expressed as: Among them, u 10 , z0 and L are the wind speed, surface roughness and MO length at a height of 10 meters at the grid point corresponding to the center of the station, respectively, and Ψ is the atmospheric stability function.

3. The extreme gale forecasting method for new energy stations according to claim 2 is characterized in that: The atmospheric stability function Ψ is expressed as: Among them, κ is the Karman constant and β is the empirical coefficient, which is used to adjust the intensity of the atmospheric stability function. When L<0, it is an unstable atmosphere; when L≈0, it is a neutral atmosphere; when L>0, it is a stable atmosphere.

4. The extreme gale forecasting method for new energy stations according to claim 3 is characterized in that: According to the extreme high wind speed u at the hub height h of the wind turbine in the new energy station at each integral time step during the forecast period gust It is expressed as: Among them, c turb represents the turbulent mixing parameter, z i is the height of the mixing layer.

5. The extreme gale forecasting method for new energy stations according to claim 1 is characterized in that: According to the extreme high wind speed u at the hub height h of the wind turbine in the new energy station at each integral time step during the forecast period gust , determine the forecast wind speed of extreme gale at the new energy site during the forecast period; Determine the extreme high wind speed u at the hub height h of the new energy station wind turbine at each integral time step gust The maximum value is used as the forecast wind speed of extreme strong winds at the new energy station during the forecast period.

6. An extreme gale forecasting system for new energy stations, characterized in that: include: The acquisition module is used to obtain the basic information of new energy stations and gridded numerical weather forecast routine data; The first calculation module is used to calculate the wind speed u at the hub height h of the wind turbine at each integral time step during the forecast period based on the basic information of the new energy station and the gridded numerical weather forecast routine data. h ; The second calculation module is used to calculate the wind speed u at each integral time step at the hub height h of the wind turbine during the forecast period. h , calculate the extreme wind speed u at the hub height h of the wind turbine at the new energy station at each integral time step during the forecast period gust ; A determination module is used to determine the extreme high wind speed u at the hub height h of the wind turbine of the new energy station at each integral time step during the forecast period. gust , determine the forecast wind speed of extreme strong winds at new energy sites during the forecast period.

7. The extreme gale forecasting system for new energy stations according to claim 6 is characterized in that: During the calculation forecast period, the wind speed u at the fan hub height h at each integral time step h It is expressed as: Among them, u 10 , z0 and L are the wind speed, surface roughness and MO length at a height of 10 meters at the grid point corresponding to the center of the station, respectively, and Ψ is the atmospheric stability function.

8. The extreme gale forecasting system for new energy stations according to claim 7 is characterized in that: The atmospheric stability function Ψ is expressed as: Among them, κ is the Karman constant and β is the empirical coefficient, which is used to adjust the intensity of the atmospheric stability function. When L<0, it is an unstable atmosphere; when L≈0, it is a neutral atmosphere; when L>0, it is a stable atmosphere.

9. The extreme gale forecasting system for new energy stations according to claim 8, characterized in that: According to the extreme high wind speed u at the hub height h of the wind turbine in the new energy station at each integral time step during the forecast period gust It is expressed as: Among them, c turb represents the turbulent mixing parameter, z i is the height of the mixing layer.

10. The extreme gale forecasting system for new energy stations according to claim 6, characterized in that: According to the extreme high wind speed u at the hub height h of the wind turbine in the new energy station at each integral time step during the forecast period gust , determine the forecast wind speed of extreme gale at the new energy site during the forecast period; Determine the extreme high wind speed u at the hub height h of the new energy station wind turbine at each integral time step gust The maximum value is used as the forecast wind speed of extreme strong winds at the new energy station during the forecast period.

11. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the extreme wind forecasting method for new energy stations as described in any one of claims 1-5 are implemented.

12. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the extreme gale forecasting method for new energy stations as described in any one of claims 1 to 5 are implemented.

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