Method for evaluating and predicting influence of ultra-low altitude torrent on offshore wind power generation, medium and program product

Abstract

The invention discloses a method for evaluating and predicting the influence of ultra-low-altitude torrent on offshore wind power generation, a medium and a program product. The method comprises the following steps: fusing and preprocessing meteorological-wind power multi-source special gradient data; carrying out ultra-low-altitude jet stream identification and typical case library construction; a WRF-WFP model suitable for sea-land junction climate characteristic and wind power conversion characteristic simulation is researched and developed from the dimensions of axial induction factors, air density, impeller equivalent wind speed and the like; by means of numerical scheme design, sensitivity numerical test, statistical analysis and the like, comprehensive influence of ultra-low altitude torrent on offshore wind power generation is classified and quantified, and potential power generation fluctuation and mechanical risks are predicted. According to the method, comprehensive influence evaluation and prediction of ultra-low altitude torrent on wind speed disturbance, turbulence diffusion and power generation efficiency of the offshore wind power plant under the condition of a complex underlying surface of a sea-land junction can be realized, understanding of an interaction mechanism of aerodynamics and new energy power generation can be deepened, and a prediction result and a production scheduling suggestion are provided for operation of the wind power plant.

Description

Technical Field

[0001] The present invention belongs to the technical field of meteorological data analysis and wind energy utilization evaluation, and relates to the impact evaluation and prediction of ultra-low-level jets on offshore wind power generation. In particular, it relates to an evaluation and prediction method, medium and program product established based on the collection, fusion and preprocessing of meteorological-wind power special gradient data, the identification of ultra-low-level jets and the construction of a typical case library, the research and development of a meteorological-wind power coupled numerical model, and the simulation and quantification of the impact of ultra-low-level jets on offshore wind power generation, which is used to quantify the comprehensive impact of ultra-low-level jets on the wind speed disturbance, turbulent diffusion and power generation efficiency of offshore wind farms, predict potential power generation fluctuations and mechanical risks, and improve the climate adaptability and operation efficiency of offshore wind power systems. Background Art

[0002] Under the trend of large-scale development of offshore wind power, the installed capacity and impeller diameter of wind turbines are increasing continuously, and the height of wind energy utilization is gradually closely related to the frequently occurring ultra-low-level jets in coastal areas. Significantly different from the exponential law wind profile widely used in the field of wind energy, the ultra-low-level jet wind profile shows an obvious "nose" structure, with complex and intense vertical shear and strong transport characteristics. These characteristics will not only affect the wind energy power generation efficiency, but may even cause damage to the wind turbines, and the unstable power output caused by them will also pose a huge challenge to the operation of the power grid. Therefore, ultra-low-level jets have become one of the high-impact meteorological events in the field of wind power generation. Under this premise, how to scientifically evaluate the comprehensive impact of ultra-low-level jets on the power generation benefits of offshore wind farms has become one of the key issues facing the high-quality development of new energy.

[0003] In the field of wind energy utilization, the ultra-low-level jet refers to a strong and narrow band of strong wind currents occurring within the height range of 50 - 500 m in the lower layer of the atmospheric boundary layer. Currently, most research on ultra-low-level jets focuses on the land surface conditions, mainly using limited special observation and mesoscale numerical simulation techniques to analyze the climatic characteristics and formation mechanisms of ultra-low-level jets. In recent years, with the rapid development of offshore wind power, the research on the comprehensive impacts of ultra-low-level jets on the wind speed of offshore wind farms, the power generation of wind turbines, and the fatigue loads of blades and towers has gradually become a hot topic. Starting from the aerodynamic performance of wind turbines, relevant research has evaluated and analyzed the potential impacts of ultra-low-level jets on the loads of wind turbines and the force characteristics of blades. However, at the regional scale, the research on the impacts of ultra-low-level jets on wind speed disturbance, turbulent diffusion, and wind energy generation efficiency in wind farms is still in its infancy. Up to now, only a small number of exploratory studies have locally analyzed the possible impacts of ultra-low-level jets on wind speed attenuation and wind energy generation based on limited jet case data in the North Sea of Europe and the New York Bight of the United States. Some studies have also pointed out that when the jet is above the hub height of the wind turbine, the wake recovery may be faster, which helps to improve the energy production efficiency; while when the jet is below or in the middle of the hub height of the wind turbine, the wake recovery may be slower, having an adverse impact on energy production. However, the supporting data used in the research is scarce, there is still room for optimization of the general wind farm parameterization model, and no substantial progress has been made in the response mechanism of the wind flow characteristics and turbulent diffusion of offshore wind farms to ultra-low-level jets.

[0004] Domestic research on the impact of ultra-low-level jets on wind energy utilization lags behind. Existing research mostly focuses on the impacts of ultra-low-level jets on precipitation, aircraft takeoff and landing, sand and dust activities, and pollutant diffusion. There is a lack of research on the role and impact of low-level jets on wind energy utilization, which greatly restricts the scientific and technological support level for the high-quality development of China's offshore wind power. For example, Chinese patent application CN118757346A discloses a method and device for warning of low-level jets in large wind turbines. By measuring the wind speed at different heights of the tower, the risk level of low-level jets is judged and the operating state of the wind turbine is controlled. This method is limited to the warning of a single wind turbine, does not evaluate the comprehensive impact of low-level jets on the wind farm at the regional scale, and does not consider the simulation of wind power conversion characteristics under complex sea-land boundary climate backgrounds. Another example is Chinese patent CN105468899B, which discloses an automatic identification and mapping method of low-level jets based on MICAPS wind field information. The automatic identification of low-level jets is achieved through steps such as clustering and fitting. However, this method mainly targets land surface conditions, focuses on the identification and mapping of low-level jets, does not involve the impact assessment of low-level jets on wind turbines, and lacks applicability to offshore wind farms.

[0005] In summary, as an emerging high-impact meteorological event for wind power generation, the evaluation and prediction of the comprehensive impact of ultra-low-altitude jets on wind speed disturbance, turbulent diffusion and wind power generation efficiency in offshore wind farms still face many technical difficulties. Especially under the complex underlying surface conditions at the sea-land junction in my country, how to scientifically and accurately evaluate the comprehensive impact of ultra-low-altitude jets on offshore wind power has become a technical problem that needs to be solved urgently in the current field of wind energy utilization and safe operation of offshore wind power. Summary of the invention

[0006] 1. Purpose of the invention In response to the technical problems faced in the development of the above-mentioned industries, the present invention proposes a method, medium and program product for evaluating and predicting the impact of ultra-low-altitude jets on offshore wind power generation, the method includes the collection, fusion and preprocessing of meteorological-wind power special gradient data; ultra-low-altitude jet identification based on wind speed difference method and wind shear method and the establishment of a typical case library; comprehensive consideration of the large-scale development characteristics of offshore wind turbines and the aerodynamic characteristics of wind turbines under the real climate background, from the dimensions of axial induction factor, air density and impeller equivalent wind speed, develop a meteorological-wind power coupling numerical model suitable for simulating the climate characteristics and wind power conversion characteristics of the sea-land boundary in my country; through numerical scheme design, sensitivity numerical experiments and statistical analysis, with the changes in power generation and turbulent kinetic energy as indicators, classify and quantify the comprehensive impact of ultra-low-altitude jets on offshore wind power generation, and predict potential power generation fluctuations and mechanical risks. The impact assessment and prediction method of ultra-low-altitude jets on offshore wind power generation established based on the above steps provides a practical method for the comprehensive assessment and prediction of the impact of ultra-low-altitude jets, an emerging high-impact meteorological event for wind power generation, on wind speed disturbances, turbulent diffusion and wind power generation in offshore wind farms under the complex underlying surface conditions at the sea-land interface. This method not only helps to deepen the understanding of the interaction mechanism between atmospheric dynamics and renewable energy power generation, but also provides wind farm operators with prediction results and production scheduling recommendations, thereby effectively responding to the challenges brought by high-impact meteorological events and improving the climate adaptability and resilience of renewable energy systems.

[0007] (II) Technical solution The present invention aims to solve the problem of comprehensive response evaluation and prediction method of offshore wind farms under the background of ultra-low altitude jet streams. The technical solution adopted to achieve the above-mentioned invention object is: The first invention object of the present invention is to provide a method for evaluating and predicting the impact of ultra-low-altitude jets on offshore wind power generation, wherein the evaluation and prediction method is carried out by aiming at the coupled numerical simulation of offshore wind farms and ultra-low-altitude jets, and the method comprises at least the following steps: SS1. Meteorological-wind power special gradient data collection: Collect multi-source gradient wind observation data in coastal areas and perform quality control and fusion to form a basic wind data set; collect offshore wind farm wind turbine SCADA data and basic wind turbine parameters and perform quality control; SS2. Ultra-low-level jet identification and case library construction: Screen the basic wind data set, identify ultra-low-level jet cases where the jet center is within the swept area of the wind turbine and establish a case library; according to the relative relationship between the height where the jet center is located and the hub height of the wind turbine, subdivide it into jets below the hub height, at the hub height, and above the hub height; and screen cases with wind profiles following the exponential law from the basic data set to establish a standard reference flow field case library corresponding to the jet case library as a benchmark for evaluating the impact of ultra-low-level jets on the wind farm's wind flow structure; SS3. Meteorological-wind power physical modeling and numericalization based on impact mechanism analysis: Based on the impact mechanism analysis of wind farm operation on the energy balance of the atmospheric boundary layer and the aerodynamic characteristics of wind turbines under real climate backgrounds, modify the standard Fitch scheme from the axial induction factor, transient air density, and impeller equivalent wind speed, and build a wind farm numerical parameterization scheme (WindFarm Parameterization, WFP) suitable for the complex climate conditions at the land-sea boundary and the development characteristics of large-scale wind turbines. Coupled with the mesoscale meteorological model (Weather Research and Forecasting Model, WRF) to form the WRF-WFP model, and at the same time use SCADA data to test and optimize the parameters of the coupled model; SS4. Mesoscale numerical scheme design for simulating ultra-low-level jets in the land-sea boundary region: For ultra-low-level jets under the land-sea boundary underlying surface conditions, conduct simulation and emulation through the mesoscale WRF model, and conduct comparison and optimization for the sea-land coverage ratio, boundary layer parameterization scheme, land surface parameterization scheme, and vertical grid settings within the spatial range to form an optimal parameterization scheme combination of the mesoscale WRF model suitable for ultra-low-level jets in the land-sea boundary; SS5. Simulation and quantification of the impact of ultra-low-level jets on offshore wind power generation: Using the WRF-WFP coupled model and combining the optimal parameterization scheme combination of the mesoscale WRF model, with ultra-low-level jet cases as the meteorological condition driving field, conduct sensitivity numerical tests by classification, statistically quantify the comprehensive impact of ultra-low-level jets on offshore wind power generation, and predict potential power generation fluctuations and mechanical risks.

[0008] The second object of the present invention is to provide a computer program product, including computer instructions for executing the above method for evaluating and predicting the impact of ultra-low-level jets on offshore wind power generation.

[0009] The third object of the present invention is to provide a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the method for evaluating and predicting the impact of the ultra-low-level jet on offshore wind power is realized.

[0010] (III) Technical effects Compared with the prior art, the method, medium and program product for evaluating and predicting the impact of the ultra-low-level jet on offshore wind power according to the present invention have the following beneficial effects: (1) The evaluation and prediction method established by the present invention based on the fusion analysis of meteorological-wind power multi-source special gradient data, the identification and clustering of ultra-low-level jets, the research and development of meteorological-wind power coupled numerical models, and the simulation and quantification of the impact of ultra-low-level jets on offshore wind power can comprehensively consider the characteristics of the large-scale development of offshore wind turbines and the aerodynamic characteristics of wind turbines under the real climate background, and construct a digital twin platform for the aerodynamic response of wind farms from dimensions such as axial induction factor, air density and equivalent wind speed of the impeller, so as to more accurately simulate the comprehensive impact of ultra-low-level jets on offshore wind power. This method provides a practical method for the comprehensive impact assessment and prediction of the emerging high-impact meteorological event of ultra-low-level jets on the wind speed disturbance, turbulent diffusion and wind energy generation of offshore wind farms under the complex underlying surface conditions of the land-sea boundary.

[0011] (2) The evaluation and prediction method proposed by the present invention focuses on the complex coupling relationship between ultra-low-level jets and new energy power generation. Based on the fusion analysis of multi-source big data, the research and development of meteorological-energy professional physical models, and high-precision sensitivity numerical simulation experiments, a closely connected and highly collaborative algorithm chain is constructed, realizing the accurate capture and forward prediction of the changes of ultra-low-level jets and their impact on offshore wind power. This method not only helps to deepen the understanding of the interaction mechanism between atmospheric dynamics and new energy power generation, but also can provide prediction results and production scheduling suggestions for wind farm operators, so as to effectively respond to the challenges brought by high-impact meteorological events and improve the climate adaptability and resilience of new energy systems. Description of the drawings

[0012] Figure 1 It is a schematic diagram of the implementation process of the method for evaluating and predicting the impact of the ultra-low-level jet on offshore wind power according to the present invention.

[0013] Figure 2 It is a schematic diagram of multi-source special gradient observations of meteorology and wind power.

[0014] Figure 3 It is a schematic diagram of three typical wind profiles of the ultra-low-level jet according to the present invention: (a) Jet below the hub height; (b) Jet at the hub height; (c) Jet above the hub height. Detailed implementation manners

[0015] The present invention aims to provide a method, medium and program product for evaluating and predicting the impact of ultra-low-altitude jets on offshore wind power generation. In order to make the purpose, technical solution and advantages of the implementation of the present invention clearer, the technical solution in the embodiment of the present invention will be described in more detail below in conjunction with the accompanying drawings in the embodiment of the present invention. The described embodiment is a part of the embodiment of the present invention, not all of the embodiments.

[0016] Example 1 like Figure 1 As shown, the present invention establishes an impact assessment and prediction method for ultra-low-altitude jets on offshore wind power generation based on multi-source meteorological-wind power special gradient data fusion analysis, ultra-low-altitude jet identification and clustering, meteorological-wind power coupling numerical model development, and simulation and quantification of the impact of ultra-low-altitude jets on offshore wind power generation. The construction includes at least the following steps: SS1. Meteorological-wind power special gradient data collection: Collect multi-source gradient wind data in coastal areas, including boundary layer wind profile radar wind measurement, L-band radar second-level wind measurement, Doppler lidar wind measurement, wind tower gradient observation data and high temporal and spatial resolution wind energy resource data; remove false values ​​exceeding reasonable thresholds and rigid values ​​that remain unchanged for multiple consecutive time periods, and then use machine learning algorithms to fuse the above data to obtain a basic data set of at least 10 years, hourly, with a horizontal resolution of not less than 9 km and at least 10 layers within 500 m above the ground; collect SCADA data and basic parameter data of wind turbines in offshore wind farms, and perform quality control on the original SCADA data at least based on the operating status and power generation of the wind turbines; SS2. Ultra-low-altitude jet identification and typical case library construction: In view of the complex climate characteristics of the sea-land border area and the characteristics of offshore wind power application, the differences between the wind speed difference method and the wind shear method in identifying ultra-low-altitude jet cases are compared, and the superior algorithm is selected; the multi-source fusion basic data set described in SS1 is screened to select typical jet cases where the jet center is located within the wind sweep range of the wind turbine; based on the relative position relationship between the height of the jet center and the height of the wind turbine hub, the ultra-low-altitude jet is further subdivided into three categories, namely, the jet below the hub height, the jet at the hub height, and the jet above the hub height; at the same time, a "standard" reference flow field (i.e., the wind profile follows the exponential law) case library corresponding to the jet case library is screened and established; SS3. Meteorological-wind power physical modeling and digitization based on impact mechanism analysis: Considering the large-scale development trend of offshore wind power and the problem that the standard Fitch scheme crudely depicts processes such as the sub-grid effect of wind turbines, transient air density, and equivalent wind speed of the impeller, resulting in poor wind power simulation effects, based on the analysis of the influence mechanism of wind farm operation on the energy balance of the atmospheric boundary layer and the aerodynamic characteristics of wind turbines under real climate backgrounds, optimize and improve the physical processes of the wind farm model. From dimensions such as the axial induction factor, air density, and equivalent wind speed of the impeller, build a wind farm numerical parameterization scheme (WFP) suitable for the complex climate backgrounds along the coast of China and the development characteristics of large-scale wind turbines, and couple it with the mesoscale WRF model; use SCADA data to test and optimize the parameters of the WRF-WFP coupled model; SS4. Design of a mesoscale numerical scheme for simulating ultra-low-level jets in the land-sea boundary region: For the ultra-low-level jet case under the land-sea boundary surface conditions, conduct simulation and emulation through the mesoscale WRF model, and compare and optimize the sea-land coverage ratio, boundary layer parameterization scheme, land surface parameterization scheme, vertical grid, etc. within the simulation space range, and design and give the optimal parameterization scheme combination for mesoscale WRF simulation of ultra-low-level jets in the coastal areas of China; SS5. Simulation and quantification of the impact of ultra-low-level jets on offshore wind power generation: Using the WRF-WFP meteorological wind farm coupled numerical model built in step SS3, with the typical ultra-low-level jet case library established in step SS2 as the meteorological condition driving field, conduct classified sensitivity numerical experiments; on this basis, statistically quantify the comprehensive impact of ultra-low-level jets on offshore wind power generation, and predict potential power generation fluctuations and mechanical risks.

[0017] Example 2 Based on Example 1, this Example 2 further refines each implementation step.

[0018] 1. In the above step SS1, collection of meteorological-wind power special gradient data The collected multi-source gradient wind observation data includes at least boundary layer wind profile radar wind measurement, L-band radar second-level wind measurement, Doppler lidar wind measurement, gradient observation data of the meteorological tower, and high spatio-temporal resolution wind energy resource data; the quality control of the multi-source gradient wind observation data includes eliminating false values exceeding reasonable thresholds and stagnant values that remain unchanged for consecutive multiple time steps; data fusion uses machine learning algorithms, and the formed basic wind data set has a refined data structure with at least nearly 10 years, hourly, horizontal resolution not lower than 9 km, and at least 10 layers within a vertical height of 500 m.

[0019] When performing quality control on the collected original wind observation data, first eliminate the spurious values exceeding the reasonable thresholds in the original observation data through the climate thresholds of each variable, and then perform spatio-temporal consistency checks. For the fixed values with the detection values remaining unchanged for multiple consecutive time steps, use the calculation formula to compare whether the detection values at adjacent moments fluctuate within a certain range for screening and elimination. Among them, u t+1 represents the detection value at the (t + 1)th moment, u t represents the detection value at the tth moment, ε represents the allowable fluctuation range and is assigned values according to the statistical characteristics of the actual observation data or expert experience. By performing quality control on the original observation data, the randomness and uncertainty of the wind speed data can be further reduced, and the stability and credibility of the wind speed data can be improved.

[0020] For gradient observation data such as boundary layer wind profiler wind measurement, L-band radar second-level wind measurement, Doppler lidar wind measurement, and wind measurement towers ( Figure 2 ), use machine learning algorithms such as random forest or support vector machine for multi-source data fusion. According to the vertical distribution of wind speed, the horizontal distribution of wind direction, and the spatio-temporal variation trend of wind speed, screen out the wind profiles that can represent the coastal area; and during the fusion process, set differential weight coefficients for wind observation data from different sources, with different precisions and different spatio-temporal resolutions, construct a unified standard for multi-constrained data, and through the mutual calibration relationship between multi-source data, establish a deviation correction coefficient matrix between data by the least squares method, further screen out data with higher stability and reliability, so as to reduce the randomness and uncertainty of the observation data, and finally form a basic wind data set that meets the quality and accuracy requirements.

[0021] When performing quality control on the wind turbine SCADA data, in addition to screening out effective wind speed data according to the operating status and power generation power of the wind turbine, at least screen out wind speed data that conforms to the actual meteorological conditions according to environmental parameters including the temperature, humidity, and pressure of the atmosphere around the wind turbine, so as to improve the reliability and accuracy of the wind speed data. At the same time, with the standard power curve of the wind turbine as a constraint, eliminate the outliers outside 2 times the standard deviation, and perform quality control on the original SCADA data.

[0022] 2. In the above step SS2, identification of ultra-low-level jet and construction of a typical case library Compare the differences between the following wind speed difference method and wind shear method in identifying ultra-low-level jet cases, select the superior algorithm, screen the multi-source fusion basic data set constructed in step SS1, select typical jet cases with the jet center within the swept range of the wind turbine, and establish an ultra-low-level jet case library: (I) Wind speed difference method: Below the center of the jet stream, the horizontal wind speed increases with height by at least 1 m / s and is not less than 10% of the wind speed at the center of the jet stream; and above it, the horizontal wind speed decreases with height by at least 1 m / s and is not less than 10% of the wind speed at the center of the jet stream.

[0023] (II) Wind shear method: Below the center of the jet stream, the vertical shear of the wind speed is at least 0.01 s -1 , and above it, the vertical shear of wind speed is at least -0.01 s -1 The purpose of this technical solution is to more accurately reflect the vertical variation of wind speed and improve the recognition accuracy of ultra-low-altitude jet events.

[0024] For the wind turbine types in a specific wind farm, the hub height (hub_height) of the wind turbine is compared with the height of the center of the ultra-low-altitude jet (core_height) one by one, and the ultra-low-altitude jet is further subdivided into three categories: Figure 3 ): If the fan hub height hub_height <core_height,则判定为轮毂高度之上的急流;若风机轮毂高度hub_height=core_height,则判定为轮毂高度处的急流;若hub_height> core_height, it is determined to be a rapid below the hub height.

[0025] In order to establish a standard flow field reference system that matches the jet case library, a series of standard flow field case libraries whose wind profiles strictly follow the exponential law are further screened and constructed from the multi-source fusion basic data set constructed in step SS1 as a benchmark for the deviation of the actual flow field from the ideal state, and the impact of the ultra-low-altitude jet on the wind flow structure of the wind farm is evaluated.

[0026] In the above step SS3, meteorological-wind power physical modeling and digitization based on impact mechanism analysis The effect of air density on power generation and drag force is characterized by the transient air density optimization scheme, and the correction formula for wind speed is: , U is the wind speed corrected for air density, U std is the wind speed corresponding to the standard power curve of the wind turbine, ρ is the actual air density, ρ std is the standard air density, which is 1.23 kg / m³; β ( U std ) is the density correction factor, which is a function of the mean wind speed; The non-uniform characteristics of the wind speed on the impeller surface are characterized by the impeller equivalent wind speed optimization scheme, where the equivalent wind speed on the impeller swept surface is v The calculation formula is , v is the equivalent wind speed within the impeller swept area, S is the impeller swept area, U ( z , r ) is the height within the impeller plane z and the radial position r at which the actual wind speed is located, θ ( z , r ) is the angle between the local wind direction and the normal direction of the impeller; Meanwhile, an axial induction factor is introduced to correct the wind speed to the free stream. Among them, the axial correction factor a has the following calculation formula: In the formula, C T is the thrust coefficient of the wind turbine, S is the impeller swept area, D is the blade diameter, dx is the horizontal scale of the grid point of the mesoscale model, δ is the deviation angle between the normal direction of the impeller and the dominant wind direction of the grid; U ∞ is the free stream wind speed and ; After the above multi-dimensional corrections of air density, impeller equivalent wind speed, axial induction factor, etc., an optimized meteorological-wind power coupling numerical model (WRF-WFP) can be established.

[0027] Using the wind turbine SCADA wind speed and power generation data quality-controlled in step SS1, the wind speed, wind direction, and power generation of the wind farm simulated by the WRF-WFP coupling model are tested; based on this, through careful and systematic adjustments, the default input parameters of the WRF-WFP coupling model are continuously optimized, especially the wind power curve and drag coefficient curve, so that the coupling model can most accurately simulate the wind flow characteristics of a specific wind farm.

[0028] 4. In step SS4, the design of the mesoscale numerical scheme for the simulation of the ultra-low-level jet in the land-sea boundary region For the simulation of the low-level jet under the land-sea boundary surface conditions, by adjusting the land-sea coverage ratio within the simulation space of the mesoscale WRF model, the model can better capture the inertial oscillation characteristics of the non-geostrophic airflow in the boundary layer under the condition of a stable nocturnal atmospheric stratification; meanwhile, through the comparison and optimization of the boundary layer parameterization scheme, land surface parameterization scheme, vertical grid setting, etc., the optimal parameter combination for the mesoscale WRF simulation of the ultra-low-level jet in the coastal areas of China is designed to achieve the simulation of the ultra-low-level jet case in step SS2.

[0029] 5. In step SS5, simulation and quantification of the impact of the low-level jet on offshore wind power generation Based on the optimal combination of scale patterns determined in step SS4 and the WRF-WFP meteorological wind farm coupled numerical model established in step SS3, using the typical case library established in step SS2, four numerical test scenarios are designed, namely three low-level jet scenarios and a "standard" reference flow field scenario; introducing global reanalysis data (such as ERA5 or CFRv2, etc.) as the initial field and boundary conditions, and conducting four scenario sensitivity numerical tests respectively. Taking the changes in power generation and turbulent kinetic energy as indicators, classify and quantify the comprehensive impact of the low-level jet on offshore wind power generation, and predict potential power generation fluctuations and mechanical risks: In the formula, the subscript k represents three low-level jet scenarios and k = 1 to 3, the subscript ct represents the reference flow field scenario, Δ P and Δ TKE respectively represent the changes in power generation and turbulent kinetic energy.

[0030] Furthermore, if the observational data conditions permit, compare and analyze the measured power generation data of the SACDA wind turbines corresponding to the three low-level jet scenarios and the "standard" reference flow field scenario described in SS1, quantify the power generation changes caused by the jet events, and correct and conduct uncertainty analysis on the evaluation and prediction results based on the above WRF-WFP wind farm coupled response model.

[0031] This embodiment is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by those skilled in the art within the technical scope disclosed by the present invention should be covered by the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.

Claims

1. A method for evaluating and predicting the impact of ultra-low-altitude jets on offshore wind power generation, characterized in that: The method comprises at least the following steps: SS1. Collect multi-source gradient wind observation data in coastal areas and perform quality control and integration to form a basic wind data set; collect SCADA data and basic parameters of wind turbines in offshore wind farms and perform quality control; SS2. Screen the basic wind data set, identify the ultra-low-altitude jet cases where the jet center is located within the wind turbine sweep range, and establish a case library; subdivide the jets into jets below the hub height, at the hub height, and above the hub height according to the height of the jet center; screen the cases where the wind profile follows the exponential law from the basic data set, and establish a standard reference flow field case library corresponding to the jet case library; SS3. Based on the axial induction factor, transient air density and impeller equivalent wind speed correction standard Fitch scheme, a WFP parameterization scheme suitable for the complex climate conditions at the sea-land interface and the large-scale development characteristics of wind turbines is constructed, and coupled with the mesoscale WRF model to form a WRF-WFP model. At the same time, SCADA data is used to test the coupled model and optimize the parameters; SS4. For the ultra-low-altitude jet stream under the underlying surface conditions at the sea-land interface, the mesoscale WRF model is used to simulate and emulate it, and the sea-land coverage ratio, boundary layer parameterization scheme, land surface parameterization scheme and vertical grid setting are compared and optimized in the spatial range to form the optimal parameterization scheme combination of the mesoscale WRF model suitable for the ultra-low-altitude jet stream at the sea-land interface; SS5. Using the WRF-WFP model and the optimal parameterization scheme combination of the mesoscale WRF model, the ultra-low-altitude jet case is used as the driving field of meteorological conditions, and sensitivity numerical experiments are carried out in a classified manner to statistically quantify the comprehensive impact of the ultra-low-altitude jet on offshore wind power generation and predict potential power generation losses and mechanical risks.

2. The method for evaluating and predicting the impact of ultra-low-altitude jets on offshore wind power generation according to claim 1 is characterized in that: In the above step SS1, the multi-source gradient wind observation data at least include boundary layer wind profile radar wind measurement, L-band radar second-level wind measurement, Doppler lidar wind measurement, wind tower gradient observation data and high temporal and spatial resolution wind energy resource data; the quality control of the multi-source gradient wind observation data includes the elimination of false values ​​exceeding a reasonable threshold and rigid values ​​that remain unchanged for multiple consecutive time periods; the data fusion adopts a machine learning algorithm, and the basic wind data set formed by the fusion has a refined data structure of at least nearly 10 years, hourly, with a horizontal resolution of not less than 9 km, and at least 10 layers within a vertical height of 500 m.

3. The method for evaluating and predicting the impact of ultra-low-altitude jets on offshore wind power generation according to claim 2 is characterized in that: In the above step SS1, a random forest or support vector machine-type machine learning algorithm is used to fuse multi-source data, and the wind profile that can represent the coastal area is screened out according to the vertical distribution of wind speed, the horizontal distribution of wind direction and the spatiotemporal variation trend of wind speed; and in the fusion process, differentiated weight coefficients are set for wind observation data from different sources, different accuracies and different spatiotemporal resolutions, and a unified standard for multiple constrained data is constructed. Through the mutual calibration relationship between multi-source data, the least squares method is used to establish a correction coefficient matrix between data, and further screen out data with higher stability and reliability, and finally form a basic wind data set that meets the quality and accuracy requirements.

4. The method for evaluating and predicting the impact of ultra-low-altitude jets on offshore wind power generation according to claim 1 is characterized in that: When performing quality control on the SCADA data of a wind turbine, first, the effective wind speed data is selected according to the operating state and power generation of the wind turbine. Then, the wind speed data that conforms to the actual meteorological conditions is further selected according to the temperature, humidity, and pressure of the atmosphere around the wind turbine. Finally, with the standard power curve of the wind turbine as a constraint, the outliers beyond 2 standard deviations of the wind speed-power relationship are removed.

5. The method for evaluating and predicting the impact of ultra-low-altitude jets on offshore wind power generation according to claim 1 is characterized in that: In the above step SS2, the wind speed difference method or the wind shear method is used to identify the ultra-low-altitude jet. The judgment standard of the wind speed difference method is: below the center of the jet, the horizontal wind speed increases with height by at least 1 m / s and is not less than 10% of the wind speed in the center of the jet, and above it, the horizontal wind speed decreases with height by at least 1 m / s and is not less than 10% of the wind speed in the center of the jet; the judgment standard of the wind shear method is: below the center of the jet, the vertical shear of the wind speed is at least 0.01 s -1 , and above it, the vertical wind shear is at least -0.01 s -1 .

6. The method for evaluating and predicting the impact of ultra-low-altitude jets on offshore wind power generation according to claim 1 is characterized in that: In the above step SS2, for the wind turbine type in a specific wind farm, the low-level jet is further subdivided by comparing the magnitude of the hub height of the wind turbine with the height of the low-level jet center. If hub_height < core_height, it is determined as a jet above the hub height; if hub_height = core_height, it is determined as a jet at the hub height; if hub_height > core_height, it is determined as a jet below the hub height.

7. The method for evaluating and predicting the impact of ultra-low-altitude jets on offshore wind power generation according to claim 1 is characterized in that: In the above step SS3, the impact of air density on power generation and drag force is characterized by the transient air density optimization scheme, the non-uniform characteristics of the wind speed within the impeller plane are characterized by the impeller equivalent wind speed optimization scheme, and the axial induction factor is introduced to correct the wind speed to the free stream, where: The correction formula of transient air density to wind speed is: Equivalent wind speed within the impeller swept surface v The calculation formula is: Axial correction factor a The calculation formula is: In the above formulas, U is the wind speed corrected for air density, U std is the wind speed corresponding to the standard power curve of the wind turbine, ρ std is the standard air density; ρ is the actual air density; β ( U std ) is the density correction factor, which is a function of the mean wind speed; v is the equivalent wind speed within the impeller swept surface, S is the impeller swept area, U ( z , r ) is the impeller inner surface height z and radial position r The actual wind speed at θ ( z , r ) is the angle between the local wind direction and the impeller normal; C t is the thrust coefficient of the fan, D is the blade diameter, dx is the horizontal scale of the grid points of the mesoscale WRF model, δ is the deviation angle between the impeller normal and the grid dominant wind direction; U ∞ is the free-flow wind speed and .

8. The method for evaluating and predicting the impact of ultra-low-altitude jets on offshore wind power generation according to claim 1 is characterized in that: In the above step SS3, the wind speed, wind direction, and power generation of the wind farm simulated by the WRF-WFP model are tested using the quality-controlled SCADA wind speed and power generation data of the wind turbine. Based on this, by optimizing the input parameters in the coupled model, including at least the wind power curve and the drag coefficient curve, the coupled model can accurately simulate the wind flow characteristics of a specific wind farm.

9. The method for evaluating and predicting the impact of ultra-low altitude jets on offshore wind power generation according to claim 1 is characterized in that: In the above step SS4, for the simulation of the low-level jet under the land-sea interface underlying surface conditions, by adjusting the land-sea coverage ratio within the spatial range simulated by the mesoscale WRF model, the model can accurately capture the inertial oscillation characteristics of the boundary layer ageostrophic flow under the condition of a stable nocturnal atmospheric boundary layer. At the same time, through the ratio selection and optimization of the boundary layer parameterization scheme, the land surface parameterization scheme, and the vertical grid settings, the optimal parameterization scheme combination of the mesoscale WRF model applicable to the low-level jet over the land-sea interface is designed to realize the simulation of the low-level jet case in step SS2.

10. The method for evaluating and predicting the impact of ultra-low-altitude jets on offshore wind power generation according to claim 1, characterized in that: In the above step SS5, based on the optimal parameterization scheme combination of the mesoscale WRF model determined in step SS4 and the WRF-WFP coupled model established in step SS3, using the low-level jet case library established in step SS2, three low-level jet scenarios and a standard reference flow field scenario below the hub height, at the hub height, and above the hub height are designed. The global reanalysis data (ERA5 or CFRv2, etc.) is introduced as the initial field and boundary conditions, and four scenario sensitivity numerical experiments are carried out respectively. Taking the changes in power generation and turbulent kinetic energy as indicators, the comprehensive impact of the low-level jet on offshore wind power generation is classified and quantified, and potential power generation fluctuations and mechanical risks are predicted, where: In the formula, the subscript k There are three ultra-low-altitude jet scenarios and k =1~3, subscript ct is the reference flow field scenario, Δ P and Δ TKE Represent the changes of power generation and turbulent kinetic energy respectively.

11. The method for evaluating and predicting the impact of ultra-low-altitude jets on offshore wind power generation according to claim 1, characterized in that: The above step SS5 further includes comparing and analyzing the measured power generation data of the SACDA wind turbine corresponding to the three ultra-low-altitude jet scenarios and the standard reference flow field scenario, quantifying the power generation changes caused by the jet event, and correcting and analyzing the uncertainty of the evaluation and prediction results based on the WRF-WFP model.

12. A computer program product comprising computer instructions, characterized in that: Used to implement the method for evaluating and predicting the impact of ultra-low-altitude jets on offshore wind power generation as described in any one of claims 1 to 11.

13. A computer program product comprising computer instructions, characterized in that: Used to implement the method for evaluating and predicting the impact of ultra-low-altitude jets on offshore wind power generation as described in any one of claims 1 to 11.

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