Offshore wind farm-tornado coupling risk quantitative evaluation method and terminal

Abstract

The application provides a kind of offshore wind farm-taiwan coupling risk quantitative evaluation method and terminal, by building a set of control experiment without offshore wind farm and a set of sensitive experiment with wind farm parameterization, ensure that the difference between the experiments is caused by the influence of wake and its terrain coupling. Compare the simulation results of the two experiments to extract macro variables, conduct difference analysis based on macro variables, analyze the atmospheric vertical structure changes caused by offshore wind farm wake, identify gravity waves downstream of the wake, analyze the transfer process of momentum disturbance caused by wind farm in the vertical and horizontal directions, and quantify the vertical motion difference caused by the wake through the terrain. Finally, the offshore wind farm-taiwan coupling risk modulation index is calculated, the index is used to determine the risk level of secondary enhancement of typhoon, and the evaluation results of the influence of offshore wind farm wake and wake direction on corresponding terrain on typhoon are formed. In this way, the "secondary enhancement" area can be quickly identified in the early stage of typhoon.

Description

Technical Field

[0001] This invention relates to the technical field of wind farm assessment, and in particular to a method and terminal for quantitative assessment of the coupled risk of offshore wind farms and typhoons. Background Technology

[0002] Wind energy, as a clean and renewable energy source, has enormous potential for development. Compared to onshore wind power, offshore wind energy resources are abundant. However, the large-scale development of offshore wind farms is accompanied by increasingly significant impacts on the surrounding environment, necessitating a comprehensive and systematic assessment of these impacts.

[0003] The presence of numerous wind turbines within a wind farm causes a reduction in wind speed. This reduction extends downstream, and due to air mixing, the wind speed downstream slowly recovers. This recovery distance is called the recovery / wake length, and for a wind farm, the wake length is much greater than the wake length of a single turbine. Under stable atmospheric conditions, the wake length can extend downstream for over fifty kilometers. The length and structure of the wake are closely related to the underlying surface, specifically the roughness length. A greater roughness length generally corresponds to greater turbulence intensity, resulting in a faster recovery rate behind the wind farm and thus a shorter wake length. At sea, however, roughness length and turbulence intensity are typically lower than onshore, leading to longer wake lengths for offshore wind farms compared to onshore wind farms.

[0004] Current research on the wake impact of wind farms mainly focuses on assessing downstream wind speed, turbulence, and power generation. However, existing wind farm impact assessment methods and typhoon forecasting lack awareness and early warning capabilities regarding the potential "secondary intensification" risk that specific downstream areas (especially those with complex terrain) may face during typhoon passage. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to provide a method and terminal for quantitative assessment of the coupling risk of offshore wind farms and typhoons, which can assess the potential impact of offshore wind farm wakes and their interaction with terrain on typhoons, and improve the early warning capability for possible "secondary enhancement" during the passage of typhoons.

[0006] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows: A method for quantitatively assessing the coupled risks of offshore wind farms and typhoons includes: The first experiment was set up based on typhoon data excluding offshore wind farms, and the second experiment was set up based on typhoon data including offshore wind farms. By comparing the simulation results of the first experiment and the second experiment, macroscopic variables for evaluating the impact of offshore wind farm wakes on typhoons are obtained, and difference analysis is performed on the macroscopic variables to obtain the first analysis result; Based on the macroscopic variables, the analysis results include the vertical structural changes caused by the wake of the offshore wind farm, the identification of the fluctuations generated downstream of the wake, the analysis of the transmission process of momentum disturbances caused by the wind farm in the vertical and horizontal directions, and the quantification of the vertical motion differences caused by the wake passing through the terrain. By integrating the first and second analysis results, the offshore wind farm-typhoon coupling risk modulation index is calculated. Based on the index, the typhoon secondary enhancement risk level is determined, and the assessment results of the impact of the offshore wind farm wake and the terrain corresponding to the wake direction on the typhoon are obtained.

[0007] To solve the above-mentioned technical problems, another technical solution adopted by the present invention is as follows: A terminal for quantitative assessment of risk coupling between offshore wind farms and typhoons includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the various steps of the aforementioned quantitative assessment method for risk coupling between offshore wind farms and typhoons.

[0008] The beneficial effects of this invention are as follows: By constructing two sets of numerical experiments—a first experiment without offshore wind farms and a second experiment incorporating wind farm parameterization—the differences in the experiments are ensured to stem from the influence of wake and topographic coupling. Macroscopic variables are extracted by comparing the simulation results of the two experiments. Based on these macroscopic variables, difference analysis is performed, analyzing the vertical structural changes caused by the wake of the offshore wind farm, identifying the fluctuations generated downstream of the wake, analyzing the transmission process of momentum disturbances caused by the wind farm in the vertical and horizontal directions, and quantifying the vertical motion differences caused by the wake passing through the terrain. Finally, the results are integrated to obtain the assessment results of the impact of the offshore wind farm wake and the corresponding terrain on typhoons. In this way, the potential modulation intensity of the offshore wind farm wake and topographic coupling on typhoons can be reflected, enabling rapid identification of "secondary enhancement" areas in the early stages of typhoons. This facilitates targeted early warning and disaster prevention decisions, thereby improving risk identification and response capabilities during typhoon passage. Attached Figure Description

[0009] Figure 1 This is a flowchart of a method for quantitatively assessing the coupled risks of offshore wind farms and typhoons, according to an embodiment of the present invention. Figure 2 This is a flowchart illustrating the diagnostic mechanism of the impact of offshore wind farm wake and terrain coupling on typhoons according to an embodiment of the present invention. Figure 3 This is a schematic diagram of a terminal for quantitative assessment of the coupling risk of offshore wind farm and typhoon, according to an embodiment of the present invention. Detailed Implementation

[0010] Definitions:

[0011] To explain in detail the technical content, objectives, and effects of the present invention, the following description is provided in conjunction with the embodiments and accompanying drawings.

[0012] In existing technologies, typical offshore wind farms are densely distributed with large spacing, and the turbines operate on high-altitude, low-roughness sea surfaces, resulting in wake lengths far exceeding those of onshore wind farms. Meanwhile, the energy absorption and structural evolution of typhoons in the low-level convection region have a decisive impact on their intensity and path. Current typhoon forecasting models and wind farm impact assessment methods largely focus on a single scale: typhoon models emphasize large-scale thermodynamics and dynamics, while wind power assessments focus on turbine power and local wind speed attenuation, largely ignoring the mesoscale effects of wake on low-level divergence, vertical shear, and coupling with complex downstream topography (such as islands and mountains). The lack of a systematic assessment of the multi-scale coupling mechanism of wake-topography-typhoon makes it difficult to predict the risk of "secondary intensification" during typhoon passage, posing hidden dangers to wind power operation and coastal disaster prevention.

[0013] To address at least the aforementioned issues, this study compares meteorological numerical experiments with and without wind farms, extracts macroscopic variables, and diagnoses vertical structural changes, fluctuations, momentum transfer, and topographically induced vertical motions caused by wakes. Ultimately, this allows for the assessment of the impact of wind farm wakes and their corresponding topography on typhoons. This provides a systematic and operable risk quantification basis for offshore wind farm planning and typhoon early warning, enhancing the prediction and disaster prevention capabilities for secondary typhoon intensification.

[0014] The following details a method for quantitatively assessing the coupled risk of offshore wind farms and typhoons, as described in this invention. Please refer to [link / reference]. Figure 1 The method 100 includes steps 101 to 103: Step 101: Set up the first experiment based on typhoon data without offshore wind farms, and set up the second experiment based on typhoon data with offshore wind farms.

[0015] Among them, a high-resolution simulation system with three or more nested grids was established using a mesoscale meteorological data model (with a large-scale low-resolution coarse grid as the base grid, and smaller, higher-resolution fine grid sub-regions embedded layer by layer within the target study area, so that the multi-layer grids are completely superimposed in space and the data is transmitted in real time). In the high-resolution simulation system, a first experiment without offshore wind farms and a second experiment with offshore wind farms were set up.

[0016] Specifically, a typical weather process with significant topography downstream of the wind farm wake and interaction between the typhoon and the background wind field (such as the northeast monsoon) is selected as the target case. A high-resolution simulation system with three or more nested grids is established using a mesoscale meteorological numerical model (such as WRF). A first experiment (i.e., the control experiment) with "no wind farm" and a second experiment (i.e., the sensitive experiment) with "wind farm parameterization" are set up respectively. The sensitive experiment includes wind turbine parameters, including turbine spacing, hub height, blade diameter, thrust coefficient, and power coefficient.

[0017] Step 102: Compare the simulation results of the first experiment and the second experiment to obtain macroscopic variables for evaluating the impact of offshore wind farm wakes on typhoons, and perform difference analysis on the macroscopic variables to obtain the first analysis result.

[0018] Step 102 includes: comparing the simulation results in the first experiment and the second experiment to obtain macroscopic variables including maximum wind speed, low-level wind field and divergence field.

[0019] Specifically, the simulation results of the first and second experiments were compared, and key macroscopic variables such as typhoon center pressure, maximum wind speed, low-level wind field and divergence field were extracted. Through difference analysis, it was confirmed whether the wake of the wind farm has a significant modulating effect on the intensity, asymmetric structure or evolution process of the typhoon.

[0020] For example, the area with the maximum wind speed at 900 hPa altitude for a typhoon is located on its southwest side. After 170 hours, it can be observed that the typhoon intensity in the controlled experiment began to weaken rapidly, while the typhoon intensity in the sensitive experiment, although also weakening, weakened more slowly. After 189 hours, the wind speed on the southwest side of the typhoon in the controlled experiment was only 12 m / s, while the wind speed on the southwest side of the typhoon in the sensitive experiment was still 16 m / s. Overall, the evolution of weak typhoons in the Beibu Gulf is significantly influenced by offshore wind farms in the northern upstream waters.

[0021] Step 103: Based on the macroscopic variables, analyze the vertical structural changes caused by the wake of the offshore wind farm, identify the fluctuations generated downstream of the wake, analyze the transmission process of momentum disturbances caused by the wind farm in the vertical and horizontal directions, and quantify the vertical motion differences caused by the wake passing through the terrain to obtain the second analysis result; integrate the first analysis result and the second analysis result to calculate the offshore wind farm-typhoon coupling risk modulation index, and determine the secondary enhancement risk level of the typhoon based on the index to obtain the assessment result of the impact of the wake of the offshore wind farm and the terrain corresponding to the wake direction on the typhoon.

[0022] Specifically, the study calculates stability parameters such as Richardson number and vertical wind shear in the lower atmosphere to analyze vertical structural changes caused by the wake. Through cross-spectral analysis of perturbation vorticity and divergence, it identifies whether mesoscale waves such as gravity waves are generated downstream of the wake and determines their wavelength, phase, and propagation characteristics. Based on the zonal momentum budget equation, it diagnoses the transmission process of momentum perturbations caused by wind farms in the vertical and horizontal directions. It introduces topographic gradients and the model's bottom-layer wind field (specifically, the lowest vertical grid layer directly in contact with the sea / land surface, which is the core layer for calculating near-surface boundary conditions) to calculate the creep and its resulting vertical velocity changes, quantifying the differences in vertical motion caused by topographic forcing of the wake. Combining the above analysis results and the difference analysis results, it obtains the assessment results of the wake of offshore wind farms and the impact of the corresponding topography on typhoons.

[0023] As described above, this method extracts macroscopic variables such as maximum wind speed, low-level wind field, and divergence field by comparing high-resolution mesoscale simulations with and without wind farms, thereby quantifying the modulation of typhoon intensity and structure by the coupling of wake and topography. Using diagnostic methods such as Richardson number, vertical wind shear, and cross-spectrum analysis of disturbance vorticity and divergence, the method can identify wake-induced gravity waves and their propagation characteristics. Based on the zonal momentum budget equation and creep calculation, the method further assesses the impact of the wake on vertical momentum and vertical velocity. The integrated analysis results form a multi-dimensional quantitative assessment, providing a scientific and actionable decision-making basis for offshore wind farm planning, typhoon early warning, and risk management.

[0024] In some embodiments, after step 101, the method further includes: simulating the first experiment and the second experiment in the high-resolution simulation system with the same experimental initial field (which is the initial spatiotemporal state dataset of all physical quantities predefined for the simulation of the global three-dimensional mesh before the start-up time integration of the numerical simulation system, and is the starting point for the calculation of the control equations in the mode iteration, directly determining the accuracy and stability of the simulation results) and boundary conditions.

[0025] Specifically, the Weather Research and Forecasting (WRF) numerical model uses a three-layer nesting structure with horizontal grid spacings of 27 km, 9 km, and 3 km, respectively. The innermost layer represents the typhoon activity area. Vertically, there are 32 eta layers, four of which cover the wind turbine blade area (the vertical resolution of the blade area can be refined based on computational resources). The physical parameterization schemes include the Single-Moment 6-class microphysics scheme; the Grell-Devenyi ensemble cumulus convection parameterization scheme, but not used for the innermost layer of WRF; the fast radiative transfer model (a mathematical and physical model based on the physical laws of electromagnetic radiation and medium interaction, quantitatively describing the entire process of propagation, absorption, scattering, emission, and transmission of solar shortwave radiation and atmospheric and sea surface longwave radiation in the Earth's atmosphere-ocean medium) and the Dudhia scheme, respectively used to calculate longwave radiation (the Earth system's own thermal radiation exchange) and shortwave radiation (external energy transported from the Sun to the Earth); the Mellor-Yamada-Nakanishi-Niino 2.5-order planetary boundary layer scheme and the thermal diffusion land surface scheme, as well as the MYNN near-surface layer scheme, were selected for predictable subgrid turbulent kinetic energy terms (used to quantitatively describe the entire process of kinetic energy generation, dissipation, transport, and transformation of subscale turbulent motion smaller than the grid resolution that cannot be directly analyzed by numerical grids).

[0026] Please refer to Figure 2 The following parameters need to be set: turbine spacing (5 to 10 times the blade diameter), hub height, blade diameter, thrust coefficient, and power coefficient (related to wind speed). The wind farm location is in a preset sea area (providing the latitude and longitude of the turbines), and the initial field and boundary field are provided for the model.

[0027] An experiment without a wind farm needs to be set up as the control experiment and compared with an experiment with a wind farm. Both experiments are 240 hours long; the first 24 hours are considered the model spin-up period and are not compared. The model results are output at 1-hour intervals in NetCDF format for easy data reading and calculation.

[0028] As described above, by employing a three-layer nesting mechanism and a 32-layer eta resolution, coupled with a high-precision physics scheme, a detailed wake-topography coupling simulation can be achieved in typhoon activity areas. This process can quantify the modulation of typhoon intensity, structure, and extreme wind speeds by wind farms, providing an actionable risk assessment basis for offshore wind farm planning and typhoon early warning.

[0029] In some embodiments, step 102 includes steps 1021 to 1022: Step 1021: Based on the simulation results of the first experiment and the second experiment, the simulation results are converted to an isobaric surface by interpolation, and the zonal and longitudinal wind speeds on the isobaric surface are calculated to obtain the horizontal wind speed at each grid point; the horizontal wind speeds at all grid points are traversed, and the maximum value is taken to obtain the maximum wind speed.

[0030] Specifically, the NetCDF output file does not contain isobaric surface variables. A script written in the NCL language (NCL's built-in functions are very convenient for processing WRF model output files) can be used to interpolate based on the model's original vertical layer data to calculate the zonal and longitudinal wind speeds (u, v) on each isobaric surface, thus calculating the horizontal wind speed at each grid point. ), iterate through the horizontal wind speeds of all grid points within the region, and take the maximum value as the maximum wind speed.

[0031] Step 1022: Based on the simulation results of the first experiment and the second experiment, calculate the divergence field according to the latitude and longitude wind speeds and their corresponding spatial coordinates.

[0032] Specifically, calculate the divergence between the downstream lower and upper layers:

[0033] In the formula, Denotes divergence, Indicates zonal wind speed. Represents latitudinal spatial coordinates. Indicates meridional wind speed. Represents the meridional spatial coordinates.

[0034] Compared to the controlled experiment, which only showed low-level convergence (a horizontal atmospheric motion phenomenon occurring within the atmospheric boundary layer, referring to the process of horizontal airflow converging from all directions towards a central region) but lacked high-level divergence (a horizontal atmospheric motion phenomenon occurring in the upper troposphere, referring to the process of horizontal airflow spreading from a central region outwards), the divergence in the sensitive experiment clearly exhibited a more pronounced low-level convergence and high-level divergence distribution, which is more conducive to typhoon development. Furthermore, although both experiments showed convergence at the low level (900 hPa), differences existed. Only the sensitive experiment displayed a very distinct "negative-positive-negative" oscillation phenomenon.

[0035] In some embodiments, step 103 includes steps 1031 to 1035: Step 1031: Calculate the vertical wind shear based on the meridional and zonal wind speeds in the low-level wind field, obtain the Richardson number by calculating the temperature gradient and the vertical wind shear, and evaluate whether the vertical wind shear induces gravity waves based on the Richardson number to obtain the vertical structural changes caused by the wake of the offshore wind farm.

[0036] Specifically, the Richardson number of the downstream lower atmosphere is calculated. The Richardson number is an important parameter for assessing gravity waves induced by vertical wind shear (VWS), and its expression is:

[0037] In the formula, Represents the Richardson number. This is a virtual temperature. Represents gravitational acceleration. The value represents the vertical altitude. The numerator represents the Brunt-Vaisala frequency, used to measure atmospheric stability. The VWS is represented by the denominator. The Brunt-Vaisala frequencies from the two experiments are difficult to distinguish, requiring a focus on analyzing the lower-level VWS. At the 170th hour of model integration, a relatively large local VWS region exists in the control experiment, reaching 1.2 × 10⁻⁶. -5 s -2 This area corresponds to the region of maximum typhoon wind speed. However, the VWS in this region was much smaller in the sensitivity experiment, with a maximum of approximately 0.6 × 10⁻⁶. -5 s -2 The time series data for this region shows low... ( The proportion of <1) increases before 170 hours, when energy shifts from mean airflow to unstable airflow, and gravity waves are in their early stages. In sensitive experiments, low The proportion began to decline rapidly after 170 hours, dropping to 33% by 180 hours. At this point, VWS began to decrease, leading to... The increase, i.e., low The reduced proportion causes energy in the unstable airflow to be continuously converted into disturbance kinetic energy, providing energy for the formation of gravity waves. Therefore, the controlled experiment no longer generates gravity waves during this period, while the wind farm still generates gravity waves locally, providing the driving force for the development of the typhoon.

[0038] Further, the low-level vertical wind shear of the control experiment and the sensitive experiment is calculated, and the low-level vertical wind shear is calculated based on the wind speed difference between the 800hPa and 900hPa isobaric surfaces; based on the difference in vertical wind shear between the control experiment and the sensitive experiment, combined with the vertical wind shear of the control experiment, the vertical wind shear attenuation rate is calculated.

[0039] Specifically, the difference in vertical wind shear at lower levels between the two experiments. It can be represented as:

[0040] In the formula, the subscripts 800 and 900 represent the corresponding isobaric surfaces. Furthermore, since the background wind field is easterly, the values ​​in the formula are not considered. The components, then:

[0041] In this case, the eastward cold air mass in the northern South China Sea is also a low-level jet stream, characterized vertically by maximum wind speeds around 900 hPa. Wind farms cannot change the altitude at which maximum wind speeds occur in the short term; therefore, wind speeds at 800 hPa are typically lower than those at 900 hPa. Thus, the difference in zonal wind speeds between the two experiments at 900 hPa and 800 hPa approximates the difference in VWS (velocity-weighted wind speed) at 900 hPa. At sea, the zonal wind difference between the two experiments ranges from 1000 hPa to 600 hPa, with a maximum of only 1.6 m / s and a relatively small vertical gradient. However, in the mountainous areas of Hainan Island and the Changshan Mountains, the vertical gradient of the zonal wind difference is larger, with the largest difference at 900 hPa, reaching 4 m / s, while the difference at 800 hPa is only 1.2–2 m / s. This indicates that in the mountainous areas downstream of the wind farm, the difference in VWS at 900 hPa mainly depends on the difference in zonal wind at 900 hPa.

[0042] Step 1032: Based on the full data of zonal and meridional winds output from the low-level wind field, the spatial average decomposition method is used. The large-scale wind field of the outer coarse grid is used as the background basic quantity to calculate the zonal and meridional wind disturbance quantities corresponding to the inner high-resolution grid. Based on the disturbance quantities, the vertical vorticity and horizontal divergence of the disturbance are calculated. Cross-spectral analysis is performed on the vertical vorticity and horizontal divergence of the disturbance to obtain the characteristic wavelengths corresponding to the coherence coefficient, phase difference, and power spectrum peak. Based on the characteristic wavelengths corresponding to the coherence coefficient, phase difference, and power spectrum peak, it is identified whether the wave excited downstream of the wake is a gravity wave.

[0043]

[0044]

[0045] In the formula, This represents the vertical vorticity of the disturbance. Indicates the horizontal divergence of the disturbance. This represents the amount of zonal wind speed disturbance. This represents the amount of meridional wind speed disturbance.

[0046] Cross-spectral analysis of the vertical vorticity and horizontal divergence of the disturbance yields three core indicators: coherence coefficient (statistical correlation of the wave signal), phase difference (verification of whether it conforms to the polarization properties of gravity waves), and the characteristic wavelength corresponding to the peak power spectrum. Gravity wave discrimination criteria: coherence coefficient > 0.5, phase difference meets the following requirements. Gravity wave polarization requirements of approximately 2 / 3 and characteristic wavelengths in the 30-60 km range are both met for a signal to be considered valid. Cross-spectral analysis showed that after 170 hours, the high-value region in the controlled experiment was only located west of the cross section, with wavelengths less than 40 km. The vertical vorticity and horizontal divergence of the disturbance were essentially in phase or out of phase, which does not conform to the polarization properties of gravity waves. However, in the wind power experiment, a significant high-value region covered the entire cross section. All these oscillations were within the influence domain, with phase differences satisfying the polarization properties of gravity waves, and wavelengths of approximately 30-60 km, indicating that these oscillations are very likely gravity waves. Gravity waves cause upward and downward atmospheric motion, thereby inducing local divergence and convergence.

[0047] Step 1033: Construct a zonal momentum equation based on the low-level wind field; analyze the transmission process of momentum disturbance caused by the wind farm in the vertical and horizontal directions based on the zonal momentum equation.

[0048] Specifically, this study analyzes which physical processes (income and expenditure items) are affected by the wind farm based on the zonal momentum equation.

[0049] The form of the zonal momentum equation in (isobaric) coordinates is:

[0050] In the formula, Indicates air density, Represents the vertical velocity in isobaric coordinates. Indicates air pressure. Indicates the Coriolis parameter, This represents the gravitational potential. Except for the Coriolis parameter, which is a fixed geographic parameter for the target area, all other physical quantities are grid-analytical scale variables that can be directly output by the numerical model.

[0051] The first term on the right-hand side of the above equation represents the horizontal momentum term, the second term represents the vertical momentum term, the third term is the Coriolis force momentum term, and the fourth term is the potential gradient term. Considering both sides of the above equation from the perspective of time... arrive After integrating, and since the initial conditions of the two experiments are the same, the difference in air density is ignored. Therefore, at any given time... The difference in zonal wind between the two experiments can be expressed as the difference in the time integral of the balance term. The transformed zonal momentum balance term can be used to diagnose land and ocean regions. In both regions, the Coriolis force momentum term is negligible in its contribution to the zonal wind variation caused by wind farms due to its small magnitude. In mountainous regions, the zonal wind variation caused by wind farms is largely attributable to the change in the vertical momentum term caused by wind farms.

[0052] Step 1034: Obtain the terrain gradient corresponding to the wake direction of the offshore wind farm; calculate the vertical velocity brought by the climb based on the low-level wind field and the terrain gradient, and quantify the vertical motion difference caused by the wake passing through the terrain through the vertical velocity.

[0053] Specifically, the terrain gradient data of the control experiment and the sensitive experiment, as well as the zonal and meridional wind speeds at the lowest level of the model, are obtained; the zonal and meridional components of the runoff generated by terrain forcing in the two sets of experiments are calculated respectively; based on the dot product of the runoff components and the terrain gradient, the low-level vertical velocities of the control experiment and the sensitive experiment are obtained; the vertical velocity difference between the sensitive experiment and the control experiment is calculated; and the vertical velocity difference is divided by the vertical velocity in the control experiment to obtain the vertical velocity difference rate.

[0054] Although wind farms only significantly affect atmospheric motion within the boundary layer, horizontal wind speeds can significantly influence vertical atmospheric motion through topography. Therefore, it is necessary to analyze the extent to which changes in horizontal wind speed caused by the wake effect of a wind farm can induce changes in low-level vertical velocity as it passes through downstream mountainous areas. The wind field at the lowest level of the model can be decomposed into around-flow and creep, where creep is related to vertical velocity, calculated as follows:

[0055]

[0056]

[0057] in, For terrain gradient, and These represent the zonal and meridional wind speeds at the bottom layer of the model, respectively. and These represent the latitudinal and longitudinal components of the creeping current, respectively. The vertical velocity introduced by the creeping current can be expressed as:

[0058] When horizontal wind fields pass through terrain, they cause local variations in the vertical velocity near the ground. On Hainan Island, these variations can reach 0.02–0.03 m / s, and further downstream in the Changshan Mountains, they can reach 0.06 m / s, approaching the magnitude of the vertical velocity at the near-surface level itself. This demonstrates that the wake of wind farms has a significant impact on the vertical velocity in mountainous regions.

[0059] Step 1035: After completing the divergence field difference calculation, gravity wave discrimination, Richardson number analysis, vertical wind shear conversion, zonal momentum budget diagnosis, and creeping and vertical velocity calculations in the aforementioned operational steps, the diagnostic results from each dimension need to be systematically integrated to form a comprehensive conclusion that can directly serve risk assessment and practical applications. The specific steps are as follows: (1) The first-level calculation formula is as follows:

[0060] in, This is the offshore wind farm-typhoon coupling risk modulation index, with a final output range of 0 to 10. The higher the value, the stronger the wind farm's enhancement modulation effect on the typhoon, and the higher the risk of downstream "secondary enhancement". For the first The weight coefficients of each secondary diagnostic indicator, with all weights summing to 1, are assigned based on the correlation between the physical importance of atmospheric dynamics and engineering risk. For the first The standardized normalized values ​​of each secondary diagnostic indicator, ranging from 0 to 1, are calculated from the diagnostic analysis results in the original scheme.

[0061] (2) Secondary indicator system, normalization method and weight allocation:

[0062] (3) Engineering risk level classification standard

[0063] Taking the weak typhoon case in the Beibu Gulf as an example, the normalized values ​​of the six secondary indicators were calculated as follows: =0.82、 =0.75、 =0.68、 =0.71、 =0.65、 =0.58, calculated using preset weights. =7.42, corresponding to a high-risk level, indicating that the wake of the offshore wind farm is coupled with the downstream topography, which significantly optimizes the dynamic conditions for typhoon development. There is a clear risk of secondary typhoon intensification in the downstream area, and a special emergency plan needs to be activated.

[0064] According to another aspect of the invention, Figure 3 This is a schematic diagram illustrating a quantitative risk assessment terminal 300 for offshore wind farm-typhoon coupling according to an embodiment of the present invention. (Refer to...) Figure 3 The terminal 300 includes a memory 301, a processor 302, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the various steps of a method for quantitative assessment of the coupling risk of offshore wind farms and typhoons as described above.

[0065] The above description is merely an embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent modifications made based on the content of the present invention specification and drawings, or direct or indirect applications in related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A method for quantitatively assessing the coupled risk of offshore wind farms and typhoons, characterized in that, include: The first experiment was set up based on typhoon data excluding offshore wind farms, and the second experiment was set up based on typhoon data including offshore wind farms. By comparing the simulation results of the first experiment and the second experiment, macroscopic variables for evaluating the impact of offshore wind farm wakes on typhoons are obtained, and difference analysis is performed on the macroscopic variables to obtain the first analysis result; Based on the macroscopic variables, the analysis results include the vertical structural changes caused by the wake of the offshore wind farm, the identification of the fluctuations generated downstream of the wake, the analysis of the transmission process of momentum disturbances caused by the wind farm in the vertical and horizontal directions, and the quantification of the vertical motion differences caused by the wake passing through the terrain. By integrating the first and second analysis results, the offshore wind farm-typhoon coupling risk modulation index is calculated. Based on the index, the typhoon secondary enhancement risk level is determined, and the assessment results of the impact of the offshore wind farm wake and the terrain corresponding to the wake direction on the typhoon are obtained.

2. The method according to claim 1, characterized in that, The first experiment was conducted using typhoon data excluding offshore wind farms, and the second experiment was conducted using typhoon data including offshore wind farms, including: A high-resolution simulation system with three or more nested grids was established using a mesoscale meteorological data model. A first experiment without offshore wind farms and a second experiment with offshore wind farms were set up in the high-resolution simulation system. The second experiment includes fan parameters, which include fan spacing, hub height, blade diameter, thrust coefficient, and power coefficient.

3. The method according to claim 2, characterized in that, The first experiment was conducted using typhoon data excluding offshore wind farms, and the second experiment was conducted using typhoon data including offshore wind farms. The following experiments were then conducted: The first and second experiments were simulated in the high-resolution simulation system with the same initial experimental field and boundary conditions.

4. The method according to claim 2, characterized in that, By comparing the simulation results of the first and second experiments, macroscopic variables for assessing the impact of offshore wind farm wakes on typhoons were obtained, including: By comparing the simulation results in the first experiment and the second experiment, macroscopic variables including maximum wind speed, low-level wind field and divergence field are obtained. These macroscopic variables are the calculation inputs for the offshore wind farm-typhoon coupling risk modulation index.

5. The method according to claim 4, characterized in that, By comparing the simulation results in the first and second experiments, the maximum wind speed was obtained, including: Based on the simulation results of the first and second experiments, the simulation results are converted to an isobaric surface by interpolation, and the zonal and longitudinal wind speeds on the isobaric surface are calculated to obtain the horizontal wind speed at each grid point. Iterate through the horizontal wind speeds of all grid points, take the maximum value, and obtain the maximum wind speed; By comparing the simulation results in the first experiment and the second experiment, the divergence field is obtained, including: Based on the simulation results of the first and second experiments, the divergence field is calculated according to the latitude and longitude wind speeds and their corresponding spatial coordinates.

6. The method according to claim 4, characterized in that, The analysis of vertical structural changes caused by the wake of offshore wind farms based on the aforementioned macroscopic variables includes: The vertical wind shear is calculated based on the meridional and zonal wind speeds in the lower-level wind field. The Richardson number is calculated using the temperature gradient and the vertical wind shear. The Richardson number is used to assess whether the vertical wind shear induces gravity waves. The analysis results of the vertical structural changes caused by the wake of the offshore wind farm are obtained and used to calculate the offshore wind farm-typhoon coupling risk modulation index.

7. The method according to claim 4, characterized in that, Identifying downstream-induced fluctuations based on the aforementioned macroscopic variables includes: Based on the full zonal and meridional wind data output from the lower-level wind field, the spatial average decomposition method is used. The large-scale wind field of the outer coarse grid is used as the background basic quantity to calculate the zonal and meridional wind disturbance quantities corresponding to the inner high-resolution grid. Based on the zonal and meridional wind disturbance quantities, the calculated vertical vorticity and horizontal divergence of the disturbance are obtained. Cross-spectral analysis is performed on the vertical vorticity and horizontal divergence of the disturbance to obtain the characteristic wavelengths corresponding to the coherence coefficient, phase difference, and power spectrum peak. Based on the characteristic wavelengths corresponding to the coherence coefficient, phase difference, and power spectrum peak, it is determined whether the wave excited downstream of the wake is a gravity wave, and the gravity wave identification result is obtained for the calculation of the offshore wind farm-typhoon coupling risk modulation index.

8. The method according to claim 4, characterized in that, The analysis of the momentum disturbance caused by the wind farm in the vertical and horizontal directions based on the aforementioned macroscopic variables includes: Based on the aforementioned low-level wind field, a zonal momentum equation is constructed. Based on the zonal momentum equation, the transmission process of momentum disturbance caused by the wind farm in the vertical and horizontal directions is analyzed to obtain momentum balance diagnosis results, which are used to calculate the offshore wind farm-typhoon coupling risk modulation index.

9. The method according to claim 4, characterized in that, The vertical motion differences caused by the wake passing through the terrain are quantified based on the macroscopic variables, including: Obtain the terrain gradient corresponding to the wake direction of the offshore wind farm; The vertical velocity brought about by the climb current is calculated based on the low-level wind field and the terrain gradient. The vertical velocity is used to quantify the vertical motion difference caused by the wake passing through the terrain, and the vertical motion quantification result is obtained, which is used to calculate the offshore wind farm-typhoon coupling risk modulation index.

10. The method according to claim 1, characterized in that, Integrating the results of the first and second analyses, the offshore wind farm-typhoon coupling risk modulation index is calculated, including: From the first and second analysis results, six secondary indicators were extracted: typhoon low-level convergence-high-level divergence coupling anomaly degree, wake-induced gravity wave effective intensity, vertical wind shear modulation rate, topographically forced vertical motion anomaly rate, momentum disturbance vertical transfer efficiency, and typhoon maximum wind speed change rate. Each secondary indicator is standardized and normalized in the range of 0 to 1, and then weighted and summed with preset weight coefficients to obtain the offshore wind farm-typhoon coupling risk modulation index in the range of 0 to 10. Based on the numerical range of the index, the risk levels for secondary intensification of typhoons are classified into three levels: low, medium, and high.

11. A terminal for quantitative assessment of the coupled risk of offshore wind farms and typhoons, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements each step of the method for quantitative assessment of the coupling risk of offshore wind farm-typhoon as described in any one of claims 1 to 10.

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