Extreme wind forecasting method, system, device and medium for new energy station

Abstract

The application discloses a kind of extreme gale forecasting methods, systems, equipment and medium for new energy station, comprising: obtaining the basic information of new energy station and grid numerical weather prediction conventional data;According to the basic information of the new energy station and grid numerical weather prediction conventional data, calculate the wind speed u h At each integration time step on the hub height h of wind turbine of new energy station in forecast period;According to the wind speed u h At each integration time step on the hub height h of wind turbine of new energy station in forecast period, calculate the extreme gale wind speed u gust At each integration time step on the hub height h of wind turbine of new energy station in forecast period;According to the extreme gale wind speed u gust At each integration time step on the hub height h of wind turbine of new energy station in forecast period, determine the forecast wind speed of extreme gale of new energy station in forecast period, which can accurately and reliably forecast extreme gale for new energy station.

Description

TECHNICAL FIELD

[0001] The application belongs to the field of extreme gale prediction for new energy stations, and relates to an extreme gale prediction method, system, device and medium for new energy stations. BACKGROUND

[0002] Under the background of global climate change intensification, the frequent occurrence of extreme disastrous weather events has a significant adverse impact on new energy stations, especially extreme gale events. Extreme gale often causes wind turbine shutdown due to large wind cutting, which not only affects power generation efficiency, but also may lead to instability of the power grid; and even may cause damage to wind turbine blades, tower drums and towers, and further cause electrical system failure and control system failure. Extreme gale may increase the safety risk of new energy stations and significantly increase the maintenance and repair cost. Therefore, how to accurately predict and warn extreme gale for new energy stations directly affects the economy and safety of new energy station operation. However, the current extreme gale prediction technology is usually based on atmospheric neutral assumption, and uses logarithmic wind profile or power exponential wind profile for interpolation to estimate the wind speed at the hub height of the wind turbine. These methods fail to fully consider the influence of the atmosphere on the vertical wind profile under different stability conditions. In addition, the existing technology often ignores the complex influence of local topography and the contribution of turbulent vertical transport to extreme gale events, and therefore has poor accuracy and reliability in extreme gale prediction. SUMMARY

[0003] The application aims to overcome the above-mentioned shortcomings of the prior art, and provides an extreme gale prediction method, system, device and medium for new energy stations, which can accurately and reliably predict extreme gale for new energy stations.

[0004] To achieve the above-mentioned purpose, the application discloses an extreme gale prediction method for new energy stations, comprising:

[0005] obtaining basic information of a new energy station and grid-based numerical weather prediction regular data;

[0006] calculating the wind speed u h at the hub height h of the wind turbine at each integration time step in the prediction period according to the basic information of the new energy station and the grid-based numerical weather prediction regular data;

[0007] calculating the extreme gale wind speed u gust at the hub height h of the wind turbine of the new energy station at each integration time step in the prediction period according to the wind speed u h at the hub height h of the wind turbine at each integration time step in the prediction period;

[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 described in the present invention is:

[0010] Furthermore, during the calculation forecast period, the wind speed u at the hub height h at each integral time step is h 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 station center, respectively, and Ψ is the atmospheric stability function.

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

[0014]

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

[0016] Furthermore, the extreme wind speed u at the hub height h of the new energy station wind turbine at each integral time step during the forecast period is gust 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 wind speed u at the hub height h of the new energy station wind turbine 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] an acquisition module, configured to acquire basic information of a new energy station and grid-based numerical weather prediction regular data;

[0023] a first calculation module, configured to calculate, according to the basic information of the new energy station and the grid-based numerical weather prediction regular data, a wind speed u h at a hub height h of a wind turbine of the new energy station at each integral time step in a prediction period;

[0024] a second calculation module, configured to calculate, according to the wind speed u h at the hub height h of the wind turbine of the new energy station at each integral time step in the prediction period, an extreme gale wind speed u gust at the hub height h of the wind turbine of the new energy station at each integral time step in the prediction period;

[0025] a determination module, configured to determine, according to the extreme gale wind speed u gust at the hub height h of the wind turbine of the new energy station at each integral time step in the prediction period, a predicted wind speed of an extreme gale of the new energy station in the prediction period.

[0026] The extreme gale prediction system for the new energy station according to the application has the further improvement that:

[0027] Further, the wind speed u h at the hub height h of the wind turbine of the new energy station at each integral time step in the prediction period is expressed as:

[0028]

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

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

[0031]

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

[0033] Further, the extreme gale wind speed u gust at the hub height h of the wind turbine of the new energy station at each integral time step in the prediction period is expressed as:

[0034]

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

[0036] Furthermore, the extreme wind speed u at the hub height h of the new energy station wind turbine 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;

[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, which stores a computer program. When the computer program is executed by a processor, the steps of the extreme gale forecasting method for new energy stations 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 specifically operated 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 , thus taking into account 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 new energy station wind turbine 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 not only helps to reduce structural damage and mechanical failures of wind turbines and station equipment, but also effectively reduces downtime and power generation losses, and improves the overall operational efficiency and safety of the station. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] The accompanying drawings, which constitute part of the present invention, are intended to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are intended to explain the present invention and do not constitute an undue 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 System structure diagram of the present application. DETAILED DESCRIPTION

[0045] 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. Obviously, the described embodiments are some of the embodiments of the present application but not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative efforts belong to the scope of the present application.

[0046] In the description of the present application, it should be understood that the terms "comprising" and "including" indicate the presence of described features, integers, steps, operations, elements, and / or components, but do not exclude one or more other features, integers, steps, operations, elements, components, and / or groups thereof.

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

[0048] It should be further understood that the term "and / or" used in the specification and the appended claims of the present application means 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 three cases of A alone, A and B together, and B alone. In addition, the character " / " in the present application generally represents an "or" relationship between the front and rear associated objects.

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

[0050] Depending on the context, the word "if" as used herein can be interpreted as meaning "when" or "while" or "in response to determining" or "in response to detecting". Similarly, depending on the context, the phrase "if determined" or "if detecting (a stated condition or event)" can be interpreted as meaning "when determined" or "in response to determining" or "when detecting (a stated condition or event)" or "in response to detecting (a stated condition or event)".

[0051] In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the following will be combined with the drawings of the embodiments of the present application to make a clear and complete description of the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. The components of the embodiments of the present application described and shown in the drawings herein can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the claimed present application, but only represents selected embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work are within the scope of protection of the present application.

[0052] Various structural schematic diagrams according to the disclosed embodiments of the present application are shown in the drawings. These diagrams are not drawn to scale, in which some details are enlarged for the purpose of clear expression, and some details can be omitted. The shapes of various regions, layers and their relative size and positional relationship shown in the drawings are only exemplary, and in actuality, there can be deviations due to manufacturing tolerances or technical limitations, and regions / layers with different shapes, sizes and relative positions can be additionally designed by those skilled in the art according to actual needs.

[0053] Embodiment one

[0054] Reference Figure 1 The extreme wind forecasting method for new energy station according to the present application comprises the following steps:

[0055] 1) Obtain the basic information of the new energy station and the gridded numerical weather prediction data;

[0056] The basic information of the new energy station includes the latitude and longitude of the station, the hub height of the wind turbine and the topography of the region. The matching relationship between the latitude and longitude information of the new energy station and the gridded numerical weather prediction data is established, and the numerical weather prediction data of the corresponding grid points is extracted, covering the 10-meter height wind speed, friction speed, roughness and M-0 length.

[0057] The process of step 1) is:

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

[0059] 12) Extract the numerical weather prediction regular data corresponding to the grid of the new energy station, based on the grid numerical weather prediction data covering the new energy station, according to the latitude and longitude information of the center point of the station in step 11), the matching relationship between the new energy station and the grid numerical weather prediction data is established, and the wind speed u 10 , the friction velocity u * , the surface roughness z0 and the M-O length L.

[0060] It should be noted that, in fact, the above-mentioned regular data is a time series array, that is, a plurality of time series data in a prediction valid period, and the size of the time series depends on the prediction time of the numerical weather prediction and the time resolution. The method and model proposed in the present application need to be calculated for each integral time step of the numerical weather prediction.

[0061] 2) Construct a reference wind speed diagnostic model for the hub height of the wind turbine of the new energy station;

[0062] Since the logarithmic wind profile and the power exponent wind profile both have the assumption of neutral atmosphere, which obviously does not match the actual situation. Moreover, extreme gales often occur under unstable atmospheric conditions, so it is impossible to calculate the wind speed at the hub height of the wind turbine by using the previous logarithmic interpolation or power exponent interpolation. The present application applies the Monin-Obukhov similarity theory to consider the influence of different atmospheric stability conditions on the vertical wind profile, and constructs a reference wind speed diagnostic model for the hub height of the wind turbine of the new energy station.

[0063] The specific process is as follows:

[0064] 21) Construct a wind speed diagnostic model at the hub height of the wind turbine based on the similarity theory. Through weather analysis, it can be known that the extreme gale weather that can cut off the new energy station usually occurs under unstable atmospheric conditions. Therefore, it is impossible to use the logarithmic wind profile or the power exponent wind profile under the assumption of neutral atmosphere to interpolate and calculate the wind speed at the hub height of the wind turbine of the station. The present application applies the Monin-Obukhov similarity theory, fully considers the influence of atmospheric stability conditions on the vertical wind profile, and proposes a wind speed diagnostic model at the hub height of the wind turbine, wherein the wind speed u h at each integral time step at the hub height h of the wind turbine is:

[0065]

[0066] Wherein, Ψ is the atmospheric stability function.

[0067] 22) The stability function calculation under different atmospheric conditions, in the complex terrain of new energy station, although most of the extreme wind occurs in unstable atmospheric conditions, but also possible in stable and neutral atmospheric conditions. Therefore, the present application gives the calculation method of atmospheric stability function Ψ under different atmospheric conditions, the atmospheric stability function is related to M-O length L and height h, therefore the atmospheric stability function Ψ is expressed as:

[0068]

[0069] Wherein, kappa is Karman constant, about 0.4, beta is empirical coefficient, for adjusting the strength of stability function, generally take value in [3, 5], different atmospheric conditions are divided according to the value of L, when L < 0, it is unstable atmosphere, when L ≈ 0, it is neutral atmosphere, when L > 0, it is stable atmosphere.

[0070] Through formula (2), the wind speed profile diagnostic equation, that is, formula (1), can be dynamically adjusted according to the stability state of the atmosphere. Through the atmospheric stability function, the wind speed at the hub height of the new energy station fan can be more accurately diagnosed.

[0071] 3) Establish an extreme wind forecasting model considering local terrain and turbulence contribution, since the new energy station is usually built in the mountainous area far away from the town, the complex terrain conditions and turbulence activities have certain contribution and influence on the occurrence of extreme wind. The present application adopts the turbulence contribution term considering the surface roughness and atmospheric stability, establishes an extreme wind forecasting method and system for the hub height of the fan, and carries out calculation for each integral time step in the forecasting period, and finally calculates the maximum value in the forecasting period as the wind speed of the extreme wind.

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

[0073] 31) Construct an extreme wind forecasting model considering turbulence contribution. When the extreme wind occurs in the new energy station, due to the influence of complex terrain and turbulence activities, the wind speed will be further increased in the vertical turbulence transport. Therefore, the friction velocity u * And the turbulence contribution term represented by M-O length L is proposed, and it is added to the calculation of extreme wind, then the extreme wind speed u gust At the hub height of the new energy station fan is expressed as:

[0074]

[0075] Wherein, c turb Represents the turbulence mixing parameter, the value range is [7.2, 7.71], z iis the height of the mixing layer, which is set to 1000 meters 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). gust , and select the maximum value as the forecast wind speed of extreme strong winds at new energy stations 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 stabilities on vertical wind profiles, and constructs a wind speed diagnostic 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 calculating the maximum value of the forecast period, it is possible to achieve accurate forecast and early warning of extreme strong winds for new energy stations. The present invention significantly improves the accuracy of extreme strong wind forecasts, 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, achieve accurate prediction of extreme strong wind events, and provide solid technical support and guarantee for the safe operation and equipment protection of new energy stations.

[0079] Example 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 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 according to the wind turbine hub height h during the forecast period. h Calculate the extreme 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 ;

[0084] The determination module is used to determine the extreme 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 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 hub height h at each integral time step is h 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 station center, 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 strength 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 wind speed u at the hub height h of the new energy station wind turbine at each integral time step during the forecast period is gust 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 wind speed u at the hub height h of the new energy station wind turbine 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 the modules in the embodiments of the present application is illustrative, and is only a logical function division. In actual implementation, another division manner can be used. In addition, each function module in each embodiment of the present application can be integrated in one processor, or can be physically separated, or two or more modules can be integrated in one module. The integrated module can be realized in the form of hardware or in the form of a software function module.

[0097] Embodiment three

[0098] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the steps of the extreme wind forecast method for a new energy station when executing the computer program, for example, including: obtaining basic information of a new energy station and gridded numerical weather forecast regular data; calculating wind speed u h at the hub height h of the wind turbine of the new energy station at each integral time step in a forecast period according to the basic information of the new energy station and the gridded numerical weather forecast regular data; calculating extreme wind speed u h at the hub height h of the wind turbine of the new energy station at each integral time step in the forecast period according to the wind speed u gust at the hub height h of the wind turbine of the new energy station at each integral time step in the forecast period; and determining a forecast wind speed of the extreme wind in the forecast period of the new energy station according to the extreme wind speed u gust at the hub height h of the wind turbine of the new energy station at each integral time step in the forecast period. The memory can include a memory, for example, a high-speed random access memory, and can also include a non-volatile memory, for example, at least one disk memory, etc.; the processor, the network interface, and the memory are connected to each other through an internal bus, which can be an industry standard architecture bus, a peripheral component interconnect standard bus, an extended industry standard architecture bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. The memory is used to store a program, specifically, the program can include program code, and the program code includes computer operation instructions. The memory can include a memory and a non-volatile memory, and provides instructions and data to the processor.

[0099] Embodiment four

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

[0101] Those skilled in the art will appreciate that embodiments of the present application can be provided as methods, systems, or computer program products. Accordingly, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application can take the form of a computer program product on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROMs, optical storage, etc.) embodying computer usable program code.

[0102] The present application is described with reference to flowcharts and / or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, as well as combinations of flows and / or blocks in the flowcharts and / or block diagrams, 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 apparatus to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing apparatus produce a means for implementing the functions specified in the flowcharts and / or block diagrams. Figure 1 The flow or multiple flows and / or blocks Figure 1 The means for implementing the functions specified in the flowcharts and / or block diagrams.

[0103] These computer program instructions can also be stored in a computer readable memory that can direct the computer or other programmable data processing apparatus to work in a specific manner, so that the instructions stored in the computer readable memory produce a product including instruction means, which implements the functions specified in the flowcharts and / or block diagrams. Figure 1 The flow or multiple flows and / or blocks Figure 1 The means for implementing the functions specified in the flowcharts and / or block diagrams.

[0104] These computer program instructions can also be loaded into a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 Figure 1

[0105] Other embodiments of the application will be apparent to those skilled in the art from consideration of the specification and practice of the application disclosed herein. It is intended that the specification and examples be considered as exemplary only, with the true scope and spirit of the application being indicated by the following claims.

[0106] It is to be understood that the application is not limited to the precise details of design or construction set forth above, and that various modifications and changes in the exact embodiments thereof can be resorted to, without departing from the scope of the application. The scope of the application is to be determined by the terms of the following claims.

[0107] The above description is the preferred embodiment of the application. Various modifications and changes can be made thereto without departing from the scope of the application, which is indicated by the foregoing description, and examples with reference to the drawings. The scope of the application encompasses not only the preferred embodiments, but also all equivalent variations that fall within the spirit and scope of the application.​​

Claims

1. A method for forecasting extreme gale winds for new energy stations, characterized in that: include: Obtain basic information on new energy stations and gridded numerical weather forecast 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 each integral time step at the hub height h during the forecast period, h Calculate the extreme 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 ; 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 gale at the new energy station during the forecast period; During the calculation forecast period, the wind speed u at the hub height h at each integral time step h Expressed as: Among them, u 10 , z0 and L are the wind speed, surface roughness and MO length at a height of 10 m at the grid point corresponding to the station center, respectively, and Ψ is the atmospheric stability function; The atmospheric stability function Ψ is expressed as: Among them, κ is the Karman constant, and β is the empirical coefficient used to adjust the strength 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; According to the extreme 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 Expressed as: Among them, c turb represents the turbulent mixing parameter, z i is the height of the mixing layer.

2. The extreme gale forecasting method for new energy stations according to claim 1 is characterized in that: According to the extreme 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 , the forecast wind speed of extreme gale at the new energy station during the forecast period is determined as: 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.

3. An extreme gale forecasting system for new energy stations, characterized by: include: The acquisition module is used to obtain 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 according to the wind turbine hub height h during the forecast period. h Calculate the extreme 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 ; The determination module is used to determine the extreme 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; During the calculation forecast period, the wind speed u at the hub height h at each integral time step h Expressed as: Among them, u 10 , z0 and L are the wind speed, surface roughness and MO length at a height of 10 m at the grid point corresponding to the station center, respectively, and Ψ is the atmospheric stability function; The atmospheric stability function Ψ is expressed as: Among them, κ is the Karman constant, and β is the empirical coefficient used to adjust the strength 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; According to the extreme 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 Expressed as: Among them, c turb represents the turbulent mixing parameter, z i is the height of the mixing layer.

4. The extreme gale forecasting system for new energy stations according to claim 3 is characterized in that: According to the extreme 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 , the forecast wind speed of extreme gale at the new energy station during the forecast period is determined as: 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.

5. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the extreme gale forecasting method for new energy stations as described in any one of claims 1-2 are implemented.

6. 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 1-2 are implemented.

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