Methods, media and program products for evaluating and predicting the impact of ultra-low-level jets on offshore wind power generation

Abstract

The present invention discloses a method, medium, and program product for evaluating and predicting the impact of ultra-low-altitude jets on offshore wind power generation, including meteorological-wind power multi-source special gradient data fusion and preprocessing; ultra-low-altitude jet identification and the establishment of a typical case library; the development of a WRF-WFP model suitable for simulating climate characteristics and wind power conversion characteristics at the land-sea boundary from dimensions such as axial induction factor, air density, and impeller equivalent wind speed; and the classification and quantification of the comprehensive impact of ultra-low-altitude jets on offshore wind power generation through numerical scheme design, sensitivity numerical experiments, and statistical analysis, to predict potential power generation fluctuations and mechanical risks. This method can evaluate and predict the comprehensive impact of ultra-low-altitude jets on wind speed disturbances, turbulent diffusion, and power generation efficiency of offshore wind farms under complex underlying surface conditions at the land-sea boundary, help deepen the understanding of the interaction mechanism between atmospheric dynamics and renewable energy power generation, and provide prediction results and production scheduling recommendations for wind farm operations.

Description

Technical Field

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

[0002] With the large-scale development trend of offshore wind power, the installed capacity and rotor diameter of wind turbines are constantly increasing, and the height of wind energy utilization is gradually closely related to the frequent ultra-low-altitude jets in coastal areas. Significantly different from the exponential wind profile widely used in the wind energy field, the ultra-low-altitude jet wind profile exhibits a distinct "nose" structure with complex and severe vertical shear and strong transport characteristics. The above characteristics not only affect the efficiency of wind power generation, but may even cause damage to wind turbines. The unstable power output caused by them will also bring huge challenges to the operation of the power grid. Therefore, ultra-low-altitude 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-altitude jets on the power generation efficiency of offshore wind farms has become one of the key issues facing the promotion of high-quality development of new energy.

[0003] In the field of wind energy utilization, an ultra-low-altitude jet (ULJ) refers to a strong, narrow band of intense winds occurring within the lower 50-500 meters of the atmospheric boundary layer. Currently, research on ULLJs has largely focused on land-based conditions, primarily utilizing limited specialized observations and mesoscale numerical simulations to analyze the climatic characteristics and formation mechanisms of ULLJs. In recent years, with the rapid development of offshore wind power, research on the comprehensive impacts of ULLJs on offshore wind farm wind speeds, turbine power generation, and blade and tower fatigue loads has become a hot topic. Based on the aerodynamic performance of wind turbines, relevant studies have evaluated and analyzed the potential impacts of ULLJs on turbine loads and blade stress characteristics. However, at the regional scale, research on the impacts of ULLJs on wind speed perturbations, turbulent diffusion, and wind power generation efficiency in wind farms is still in its infancy. To date, only a few exploratory studies, based on limited jet case data from the North Sea in Europe and New York Bay in the United States, have examined the potential impacts of ULLJs on wind speed attenuation and wind power generation in wind farms. Other studies have shown that when the jet stream is above the turbine hub, the wake may recover faster, helping to improve energy production efficiency; whereas when the jet stream is below or in the middle of the turbine hub, the wake may recover more slowly, adversely affecting energy production. However, the supporting data used in these studies is scarce, and universal wind farm parameterization models still have room for optimization. Furthermore, substantial progress has not yet been made in understanding the response of offshore wind farm wind flow characteristics and turbulent diffusion to ultra-low-altitude jet streams.

[0004] Chinese invention patent application CN118757346A discloses a method and device for warning low-level jets for large wind turbines. By measuring wind speeds at different tower heights, the method determines the risk level of low-level jets and controls the operating status of the wind turbine. This method is limited to warnings for a single wind turbine, does not evaluate the comprehensive impact of low-level jets on wind farms at a regional scale, and does not consider the simulation of wind power conversion characteristics under the complex climate background of the land-sea interface. For example, Chinese invention patent CN105468899B discloses a method for automatically identifying and mapping low-level jets based on MICAPS wind field information. This method uses clustering and fitting steps to automatically identify low-level jets. However, this method mainly targets land underlying surface conditions and focuses on the identification and mapping of low-level jets. It does not involve the assessment of the impact of low-level jets on wind turbines and lacks applicability to offshore wind farms.

[0005] In summary, the assessment and prediction of the combined impact of ultra-low-level jets (ULLJs), an emerging meteorological event with a high impact on wind power generation, on wind speed disturbances, turbulent diffusion, and wind energy generation efficiency at offshore wind farms, still face numerous technical challenges. Particularly given the complex underlying surface conditions at the land-sea interface in my country, scientifically and accurately assessing the combined impact of ULLJs on offshore wind power generation has become a pressing technical challenge in the current field of wind energy utilization and the safe operation of offshore wind power. Summary of the Invention

[0006] (1) Purpose of the invention

[0007] 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 and the establishment of a typical case library based on the wind speed difference method and wind shear method; comprehensively considering 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, a meteorological-wind power coupling numerical model suitable for simulating the climate characteristics and wind power conversion characteristics of my country's land-sea boundary is developed; through numerical scheme design, sensitivity numerical experiments and statistical analysis, the comprehensive impact of ultra-low-altitude jets on offshore wind power generation is classified and quantified with the changes in power generation and turbulent kinetic energy as indicators, and potential power generation fluctuations and mechanical risks are predicted. The method for assessing and predicting the impact of ultra-low-level jets on offshore wind power generation, established based on the above steps, provides a practical approach for evaluating and predicting the comprehensive impact of ultra-low-level jets, an emerging high-impact meteorological event for wind power generation, on wind speed disturbances, turbulent diffusion, and wind energy generation at offshore wind farms under complex underlying conditions at the land-sea interface. This method not only helps deepen our understanding of the interaction between atmospheric dynamics and renewable energy generation, but also provides wind farm operators with forecast results and production scheduling recommendations, thereby effectively addressing the challenges posed by high-impact meteorological events and enhancing the climate adaptability and resilience of renewable energy systems.

[0008] (2) Technical solution

[0009] 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 purpose of the invention is as follows:

[0010] The first 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. The method is based on a coupled numerical simulation of offshore wind farms and ultra-low-altitude jets, and comprises at least the following steps:

[0011] SS1. Collection of meteorological-wind power special gradient data:

[0012] Collect multi-source gradient wind observation data in coastal areas, perform quality control and integration, and form a basic wind data set; collect offshore wind farm wind turbine SCADA data and basic wind turbine parameters and perform quality control;

[0013] SS2. Ultra-low-altitude jet stream identification and case library construction:

[0014] The basic wind dataset was screened to identify cases of ultra-low-altitude jets whose centers were within the swept area of the wind turbines and establish a case library. Based on the relative relationship between the height of the jet center and the hub height of the wind turbine, the jets were subdivided into jets below the hub height, at the hub height, and above the hub height. Cases where the wind profiles followed an exponential law were screened from the basic dataset to establish a standard reference flow field case library corresponding to the jet case library, which served as a benchmark for evaluating the impact of ultra-low-altitude jets on the wind flow structure of wind farms.

[0015] SS3. Meteorological-wind power physical modeling and numerical analysis based on impact mechanism analysis:

[0016] Based on an analysis of the impact of wind farm operation on the atmospheric boundary layer energy balance and the aerodynamic characteristics of wind turbines under a real climate environment, a standard Fitch scheme was modified using the axial induction factor, transient air density, and rotor equivalent wind speed. A wind farm numerical parameterization scheme (WFP) was developed that is suitable for the complex climate conditions at the land-sea interface and the large-scale development of wind turbines. This scheme was coupled with a mesoscale meteorological model (Weather Research and Forecasting Model, WRF) to form a WRF-WFP model. SCADA data was used to verify the coupled model and optimize its parameters.

[0017] SS4. Design of a mesoscale numerical scheme for simulating ultra-low-level jet streams in the land-sea interface region:

[0018] The mesoscale WRF model is used to simulate the ultra-low-level jet under the underlying surface conditions at the land-sea interface. The sea-land cover ratio, boundary layer parameterization scheme, land surface parameterization scheme and vertical grid setting are compared and optimized within the spatial range to form the optimal parameterization scheme combination of the mesoscale WRF model for the ultra-low-level jet at the land-sea interface.

[0019] SS5. Simulation and Quantification of the Impact of Ultra-Low-Level Jet Streams on Offshore Wind Power Generation:

[0020] By using the WRF-WFP coupling model and combining it with the optimal parameterization scheme combination of the mesoscale WRF model, and taking the ultra-low-level jet case as the meteorological condition driving field, we carried out classified sensitivity numerical experiments, statistically quantified the comprehensive impact of the ultra-low-level jet on offshore wind power generation, and predicted potential power generation fluctuations and mechanical risks.

[0021] The second object of the present invention is to provide a computer program product comprising computer instructions for executing the above-mentioned method for evaluating and predicting the impact of ultra-low-altitude jets on offshore wind power generation.

[0022] The third object of the present invention is to provide a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the method for evaluating and predicting the impact of the ultra-low-altitude jet on offshore wind power generation is implemented.

[0023] (3) Technical effects

[0024] Compared with the prior art, the method, medium, and program product for evaluating and predicting the impact of ultra-low-altitude jets on offshore wind power generation described in the present invention have the following beneficial effects:

[0025] (1) The present invention establishes an evaluation and prediction method based on the fusion analysis of meteorological and wind power multi-source special gradient data, the identification and clustering of ultra-low-altitude jets, the development of meteorological and wind power coupling numerical models, and the simulation and quantification of the impact of ultra-low-altitude jets on offshore wind power generation. It can comprehensively consider the large-scale development characteristics 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 the dimensions of axial induction factor, air density and impeller equivalent wind speed, so as to more accurately simulate the comprehensive impact of ultra-low-altitude jets on offshore wind power. This method provides a practical method for evaluating and predicting the comprehensive 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.

[0026] (2) The assessment and prediction method proposed in this paper focuses on the complex coupling relationship between ultra-low-level jets and renewable energy generation. Based on the fusion analysis of multi-source big data, the development of meteorological-energy professional physical models, and high-precision sensitivity numerical simulation experiments, a tightly connected and highly efficient collaborative algorithm chain is constructed to accurately capture and predict the changes in ultra-low-level jets and their impact on offshore wind power generation. This method not only helps to deepen the understanding of the interaction mechanism between atmospheric dynamics and renewable energy 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. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 Schematic diagram of the implementation process of the method for evaluating and predicting the impact of ultra-low-altitude jets on offshore wind power generation according to the present invention.

[0028] Figure 2 Schematic diagram of meteorological-wind power multi-source special gradient observation.

[0029] Figure 3 Schematic diagrams of three typical wind profiles of ultra-low-altitude jets in the present invention: (a) jet below hub height; (b) jet at hub height; (c) jet above hub height. DETAILED DESCRIPTION

[0030] The present invention aims to provide a method, medium, and program product for assessing and predicting the impact of ultra-low-altitude jet streams on offshore wind power generation. To further clarify the objectives, technical solutions, and advantages of the present invention, the following describes the technical solutions in the embodiments of the present invention in more detail, in conjunction with the accompanying drawings. The described embodiments are only a partial embodiment of the present invention, not the complete embodiment.

[0031] Example 1

[0032] 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:

[0033] SS1. Collection of meteorological-wind power special gradient data:

[0034] Extensive collection of multi-source gradient wind data in coastal areas, including boundary layer wind profiler radar wind measurements, L-band radar second-level wind measurements, Doppler lidar wind measurements, wind tower gradient observation data, and high-temporal and spatial resolution wind energy resource data; eliminating false values exceeding a reasonable threshold and rigid values that remain unchanged for multiple consecutive time periods, and then using machine learning algorithms to fuse the above data to obtain a basic data set covering at least the past 10 years, hourly, with a horizontal resolution of no less than 9 km and at least 10 layers within an altitude of 500 m; collecting SCADA data and basic wind turbine parameter data from offshore wind farms, and performing quality control on the raw SCADA data based at least on the operating status and power generation of the wind turbines;

[0035] SS2. Ultra-low-altitude jet identification and typical case library construction:

[0036] In view of the complex climate characteristics of the land-sea interface and the characteristics of offshore wind power applications, the differences between the wind speed difference method and the wind shear method in identifying ultra-low-altitude jet streams were compared, and the dominant algorithm was selected. The multi-source fusion basic data set described in SS1 was screened to select typical jet stream cases with the jet stream center located within the wind turbine sweep range. Based on the relative positional relationship between the jet stream center and the hub height of the wind turbine, the ultra-low-altitude jet streams were further subdivided into three categories: jet streams below the hub height, jet streams at the hub height, and jet streams above the hub height. At the same time, a "standard" reference flow field case library (i.e., wind profiles following an exponential law) corresponding to the jet stream case library was screened and established.

[0037] SS3. Meteorological-wind power physical modeling and numerical analysis based on impact mechanism analysis:

[0038] Taking into account the trend of large-scale offshore wind power development and the problem of poor wind power simulation results caused by the standard Fitch scheme's rough depiction of processes such as wind turbine subgrid effects, transient air density, and rotor equivalent wind speed, this paper optimizes and improves the physical processes of the wind farm model based on the analysis of the impact mechanism of wind farm operation on the energy balance of the atmospheric boundary layer and the aerodynamic characteristics of wind turbines under a real climate background. From the dimensions of axial induction factor, air density, and rotor equivalent wind speed, a wind farm numerical parameterization scheme (WFP) suitable for the complex climate background and large-scale development characteristics of wind turbines in my country's coastal areas is constructed and coupled with the mesoscale WRF model. SCADA data is used to test the WRF-WFP coupling model and optimize its parameters.

[0039] SS4. Design of a mesoscale numerical scheme for simulating ultra-low-level jet streams in the land-sea interface region:

[0040] For the case of ultra-low-level jets under the underlying conditions of land and sea, the mesoscale WRF model was used to simulate and emulate them. The sea-land cover ratio, boundary layer parameterization scheme, land surface parameterization scheme, vertical grid, etc. within the simulation space were compared and optimized. The optimal parameterization scheme combination suitable for mesoscale WRF simulation of ultra-low-level jets in coastal areas of my country was designed.

[0041] SS5. Simulation and Quantification of the Impact of Ultra-Low-Level Jet Streams on Offshore Wind Power Generation:

[0042] Using the WRF-WFP meteorological wind farm coupling numerical model built in step SS3, and taking the ultra-low-level jet typical case library established in step SS2 as the meteorological condition driving field, sensitivity numerical experiments are carried out in a classified manner; on this basis, the comprehensive impact of the ultra-low-level jet on offshore wind power generation is statistically quantified, and potential power generation fluctuations and mechanical risks are predicted.

[0043] Example 2

[0044] Based on Example 1, this Example 2 further refines each implementation step therein.

[0045] 1. In step SS1 above, meteorological-wind power special gradient data collection

[0046] The multi-source gradient wind observation data collected include at least boundary layer wind profiler 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 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; data fusion uses a machine learning algorithm, and the basic wind data set formed by the fusion has a refined data structure with at least the past 10 years, hourly, horizontal resolution of not less than 9km, and at least 10 layers within a vertical height of 500m.

[0047] When quality control is performed on the collected original wind observation data, false values exceeding the reasonable threshold are first removed from the original observation data through the climate threshold of each variable, and then a spatiotemporal consistency check is performed. For rigid values that remain unchanged for multiple consecutive detection times, the calculation formula is used to calculate the consistency. Compare the detection values at adjacent moments to see if they fluctuate within a certain range for screening and elimination, where: u t+1 represents the detection value at time t+1, u t represents the detection value at time t, ε The permissible fluctuation range is expressed and assigned a value based on 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.

[0048] For gradient observation data such as boundary layer wind profiler radar wind measurement, L-band radar second-level wind measurement, Doppler lidar wind measurement, wind tower, etc. Figure 2 ), random forest or support vector machine machine learning algorithms are used to fuse multi-source data. According to the vertical distribution of wind speed, the horizontal distribution of wind direction and the spatiotemporal variation trend of wind speed, wind profiles that can represent coastal areas are screened out. 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 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.

[0049] When performing quality control on wind turbine SCADA data, in addition to selecting valid wind speed data based on the turbine's operating status and power generation, we also screen for wind speed data that meets actual meteorological conditions based on environmental parameters, including ambient temperature, humidity, and pressure, to improve the reliability and accuracy of wind speed data. Furthermore, using the turbine's standard power curve as a constraint, we eliminate outliers beyond two standard deviations to perform quality control on the raw SCADA data.

[0050] 2. In the above step SS2, ultra-low-altitude jet identification and typical case library construction

[0051] Compare the differences between the wind speed difference method and the wind shear method in identifying ultra-low-altitude jet cases, select the dominant algorithm, screen the multi-source fusion basic data set constructed in step SS1, select typical jet cases where the jet center is within the wind turbine sweep range, and establish an ultra-low-altitude jet case library:

[0052] (I) Wind speed difference method: below the center of the jet stream, the horizontal wind speed increases by at least 1 m / s with height 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 by at least 1 m / s with height and is not less than 10% of the wind speed at the center of the jet stream.

[0053] (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 the wind speed is at least -0.01 s -1 . The purpose of this technical solution is to more accurately reflect the vertical variation law of the wind speed and improve the recognition accuracy of ultra-low altitude jet stream events.

[0054] For the fan types of a specific wind farm, compare the values of the hub height (hub_height) of the fan with the height (core_height) where the ultra-low altitude jet stream center is located one by one, and further divide the ultra-low altitude jet stream into three categories ( Figure 3 ): If the hub height hub_height < core_height, it is determined as the jet stream above the hub height; if the hub height hub_height = core_height, it is determined as the jet stream at the hub height; if hub_height > core_height, it is determined as the jet stream below the hub height.

[0055] In order to establish a standard flow field reference system that matches the jet stream case library, further screen and construct a series of standard flow field case libraries with wind profiles strictly following the exponential law from the multi-source fusion basic dataset constructed in step SS1, as the benchmark for the actual flow field deviating from the ideal state, and evaluate the impact of the ultra-low altitude jet stream on the wind flow structure of the wind farm.

[0056] In the above step SS3, based on the impact mechanism analysis, meteorological-wind power physical modeling and numericalization

[0057] Characterize the impact of air density on power generation and drag force through the transient air density optimization scheme. The correction formula for the wind speed is , U is the wind speed after air density correction, U std is the wind speed corresponding to the standard power curve of the fan, ρ is the actual air density, ρ std is the standard air density, with a value of 1.23 kg / m³; β ( U std ) is the density correction coefficient, which is a function of the average wind speed;

[0058] The non-uniform characteristics of the wind speed in the impeller surface are characterized by the impeller equivalent wind speed optimization scheme, in which the equivalent wind speed in the impeller swept surface is v The calculation formula is , v is the equivalent wind speed within the impeller swept surface, S is the impeller swept area, U ( z , r ) is the impeller in-plane 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;

[0059] At the same time, the axial induction factor is introduced to correct the wind speed to the free flow, where the axial correction factor a The calculation formula is:

[0060] Where, C T is the thrust coefficient of the fan, S is the impeller swept area, D is the blade diameter, dx is the horizontal scale of the grid points of the mesoscale model, δ is the deviation angle between the impeller normal and the grid dominant wind direction; U ∞ is the free stream wind speed and ;

[0061] After multiple corrections such as the above-mentioned air density, impeller equivalent wind speed and axial induction factor, an optimized meteorological-wind power coupling numerical model (WRF-WFP) can be established.

[0062] The wind turbine SCADA wind speed and power generation data obtained after quality control in step SS1 were used to verify the wind farm wind speed, wind direction, and power generation simulated by the WRF-WFP coupling model. Based on this data, the default input parameters of the WRF-WFP coupling model, particularly the wind power curve and drag coefficient curve, were continuously optimized through careful and systematic adjustments, enabling the coupling model to most accurately simulate the wind flow characteristics of the specific wind farm.

[0063] 4. Design of a mesoscale numerical scheme for simulating ultra-low-level jet streams in the land-sea boundary region in step SS4

[0064] For the simulation of low-level jets under the underlying surface conditions at the land-sea interface, 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 conditions of stable atmospheric stratification at night; at the same time, by comparing and optimizing the boundary layer parameterization scheme, land surface parameterization scheme, vertical grid setting, etc., the optimal parameter combination of the mesoscale WRF simulation of ultra-low-level jets in my country's coastal areas is designed to realize the simulation of the ultra-low-level jet case in step SS2.

[0065] 5. Simulation and quantification of the impact of ultra-low-level jets on offshore wind power generation in step SS5 above

[0066] Based on the optimal combination of scale models determined in step SS4 and the WRF-WFP meteorological wind farm coupling numerical model established in step SS3, four numerical test scenarios were designed using the typical case library established in step SS2: three ultra-low-level jet scenarios and a "standard" reference flow field scenario. Global reanalysis data (such as ERA5 or CFRv2) were introduced as initial fields and boundary conditions, and four scenario sensitivity numerical experiments were conducted. Using changes in power generation and turbulent kinetic energy as indicators, the comprehensive impact of ultra-low-level jets on offshore wind power generation was categorized and quantified, and potential power generation fluctuations and mechanical risks were predicted:

[0067]

[0068]

[0069] In the formula, the subscript k There are three ultra-low-level jet scenarios and k =1~3, subscript ct is the reference flow field scenario, Δ P and Δ TKE represent the changes in power generation and turbulent kinetic energy, respectively.

[0070] Furthermore, if observational data conditions permit, the measured power generation data of SACDA wind turbines corresponding to the three ultra-low-level jet scenarios described in SS1 and the "standard" reference flow field scenario will be compared and analyzed to quantify the changes in power generation caused by jet events, and the above-mentioned evaluation and prediction results based on the WRF-WFP wind farm coupled response model will be revised and uncertainty analysis will be performed.

[0071] This embodiment is merely a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present invention should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection 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 from coastal areas, perform quality control, and integrate them 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. Screen the basic wind dataset to identify ultra-low-altitude jet streams whose centers are within the wind turbine's swept area and establish a case library. Based on the jet stream center's altitude, these cases are subdivided into jet streams below hub height, at hub height, and above hub height. Select cases from the basic dataset where wind profiles follow an exponential law and establish a standard reference flow field case library corresponding to the jet stream case library. SS3. The WFP parameterization scheme was modified based on the axial induction factor, transient air density, and impeller equivalent wind speed. This scheme was then coupled with the mesoscale WRF model to form a WRF-WFP model. SCADA data was used to verify the coupled model and optimize its parameters. The modification process was as follows: The transient air density optimization scheme is used to characterize the effect of air density on power generation and drag force. The impeller equivalent wind speed optimization scheme is used to characterize the non-uniform characteristics of the wind speed within the impeller surface. At the same time, the axial induction factor is introduced to correct the wind speed to the free flow, where: The correction formula for 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 in-plane 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 stream wind speed and ; SS4. Simulate the ultra-low-level jet stream at the land-sea interface using the mesoscale WRF model. Compare and optimize the land-sea cover ratio, boundary layer parameterization scheme, land surface parameterization scheme, and vertical grid settings within the spatial scope to develop the optimal mesoscale WRF model parameterization scheme for the ultra-low-level jet stream at the land-sea interface. SS5. Using the WRF-WFP model and the optimal parameterization scheme combination of the mesoscale WRF model, with the ultra-low-level jet case as the driving meteorological condition, we conduct classified sensitivity numerical experiments to statistically quantify the comprehensive impact of the ultra-low-level 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 includes boundary layer wind profiler 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 eliminating 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 with at least the past 10 years, hourly, 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, characterized in that: In the above step SS1, machine learning algorithms such as random forest or support vector machine are used for multi-source data fusion. According to the vertical distribution of wind speed, the horizontal distribution of wind direction, and the temporal and spatial variation trends of wind speed, the wind profile representing the coastal area is screened out. And during the fusion process, different weight coefficients are set for wind observation data from different sources, with different precisions and different spatio-temporal resolutions, a unified standard for multi-constrained data is constructed, and through the mutual calibration relationship between multi-source data, a correction coefficient matrix between data is established by the least squares method, further screening out data with higher stability and reliability, and finally forming a basic wind data set that meets the quality and precision requirements.

4. 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: When performing quality control on the wind turbine SCADA data, first, the effective wind speed data are screened out according to the operating state and power generation of the wind turbine. Then, according to the temperature, humidity, and pressure of the atmosphere around the wind turbine, the wind speed data that conform to the actual meteorological conditions are further screened out. Finally, with the standard power curve of the wind turbine as a constraint, the outliers beyond 2 times the standard deviation 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, characterized in that: In step SS2, the wind speed difference method or the wind shear method is used to identify the ultra-low-altitude jet. The judgment criteria for the wind speed difference method are: 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 at 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 at the center of the jet; the judgment criteria for the wind shear method are: below the center of the jet, the vertical wind shear 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, characterized in that: In the above step SS2, for the wind turbine types in a specific wind farm, the low-level jet is further subdivided by comparing the magnitude of the hub height hub_height of the wind turbine with the height core_height where the low-level jet center is located: if hub_height < core_height, it is determined as the jet above the hub height; if hub_height = core_height, it is determined as the jet at the hub height; if hub_height > core_height, it is determined as the 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, 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 wind turbine SCADA wind speed and power generation data. Based on this, by optimizing the input parameters in the coupling model, which at least includes the wind power curve and the drag coefficient curve, the coupling model can accurately simulate the wind flow characteristics of a specific wind farm.

8. 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 SS4, for the simulation of the low-level jet under the land-sea junction 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, an optimal parameterization scheme combination of the mesoscale WRF model suitable for the low-level jet at the land-sea junction is designed to achieve the simulation of the low-level jet case in step SS2.

9. 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 coupling model established in step SS3, the ultra-low-altitude jet case library established in step SS2 is used to design three ultra-low-altitude jet scenarios below the hub height, at the hub height, and above the hub height, as well as a standard reference flow field scenario; global reanalysis data are introduced as the initial field and boundary conditions, and four scenario sensitivity numerical experiments are carried out respectively. Using the changes in power generation and turbulent kinetic energy as indicators, the comprehensive impact of ultra-low-altitude jets on offshore wind power generation is classified and quantified, and potential power generation fluctuations and mechanical risks are predicted, among which: In the formula, the subscript k There are three ultra-low-level jet scenarios and k =1~3, subscript ct For the reference flow field scenario, and represent the changes in power generation and turbulent kinetic energy, respectively.

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: 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 revising and performing uncertainty analysis on the evaluation and prediction results based on the WRF-WFP model.

11. 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 10.

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