A typhoon intensity self-adaptive correction method for offshore wind power

Abstract

The application discloses a typhoon intensity self-adaptive correction method for offshore wind power, relates to the technical field of offshore wind power prediction and typhoon early warning, and the method firstly fuses marine monitoring data and a typhoon prediction initial field, recursively updates a typhoon center position and a maximum wind speed; then a mapping relationship between a typhoon wind field and a geographical position of a wind power plant is constructed, fine wind speeds of each machine position are obtained, and wind power generators are divided into three types of operating states, i.e., low constant speed, maximum power tracking and high constant speed, according to the wind speeds; aggregated parameters are dynamically adjusted according to different operating states, a wind energy conversion efficiency coefficient is corrected on line, and collaborative optimization is formed; when electrical parameters are detected to be abnormal, the method is switched to a correction parameter in a fault ride-through state; and the application realizes closed-loop feedback of typhoon intensity prediction and wind power prediction, and significantly improves the offshore wind power prediction accuracy and the power grid operation safety during the influence of a typhoon.

Description

Technical Field

[0001] This invention relates to the field of offshore wind power prediction and typhoon early warning technology, and in particular to an adaptive correction method for typhoon intensity for offshore wind power. Background Technology

[0002] Offshore wind power, as an important component of renewable energy, has developed rapidly in recent years. However, offshore wind farms are mostly located in typhoon-prone areas, and extreme wind conditions during typhoons pose a serious threat to the safe operation of wind turbines and power forecasting. Accurately forecasting typhoon intensity and timely correcting wind power forecasts are of great significance for ensuring the safe operation of wind farms and the stability of the power grid.

[0003] In the prior art, Chinese patent CN115600361A discloses an online parameter adaptive correction method for eliminating steady-state and transient errors in offshore wind farm aggregation models. This method groups wind turbines using a wind speed aggregation module and dynamically adjusts aggregation parameters based on the turbine operating status. However, this technology mainly focuses on electrical aggregation modeling within the wind farm and lacks a solution for improving the accuracy of typhoon intensity forecasts themselves.

[0004] In addition, existing typhoon forecasting technologies have the following specific shortcomings when applied to offshore wind power: First, there is an information gap between typhoon forecasts and wind power predictions. In existing technologies, typhoon intensity forecasts are directly used as inputs for wind power predictions, without considering the spatial distribution characteristics of wind speeds in wind farm areas and the effects of air-sea interaction during the approach of a typhoon. This leads to a significant increase in power prediction errors during the typhoon's influence.

[0005] Secondly, the aggregated parameters of wind turbines lack dynamic correlation with changes in typhoon intensity. Existing wind farm aggregated models still use fixed aggregated parameters based on rated operating conditions during typhoon impacts. When changes in typhoon intensity cause wind turbines to switch between different operating modes such as low constant speed, MPPT, and high constant speed, the aggregated parameters cannot dynamically follow, resulting in steady-state errors in wind power prediction.

[0006] Third, the wind energy utilization coefficient is not dynamically adjusted according to typhoon conditions. During typhoons, wind speeds far exceed rated wind speeds, and wind turbines enter a high constant speed and constant power operation state. The wind energy utilization coefficient changes drastically with the tip speed ratio and blade pitch angle. In existing technologies, the wind energy utilization coefficient is usually taken as a fixed value, which leads to systematic deviations in power calculations during typhoons.

[0007] Fourth, there is a lack of an adaptive correction mechanism for typhoon intensity under power grid fault conditions. Typhoons are often accompanied by faults such as voltage drops in the power grid. In existing technologies, the aggregated model still uses normal operating parameters under fault conditions and does not identify online whether the unit has entered the fault ride-through state, which further amplifies the transient simulation error.

[0008] To address the aforementioned issues, this invention proposes an adaptive correction method for typhoon intensity for offshore wind power. By constructing a closed-loop feedback mechanism between typhoon intensity forecasting and wind power prediction, the accuracy of wind power prediction during typhoon impact periods is significantly improved. Summary of the Invention

[0009] The purpose of this invention is to provide an adaptive correction method for typhoon intensity for offshore wind power, in order to solve the problems in the prior art such as the disconnect between typhoon forecast and wind power prediction, the inability of aggregated parameters to dynamically follow changes in typhoon intensity, the fixed wind energy utilization coefficient, and the lack of an adaptive correction mechanism under fault conditions.

[0010] To achieve the above objectives, this invention provides an adaptive typhoon intensity correction method for offshore wind power, comprising the following steps: Step 1: Obtain real-time monitoring data of ocean surface flow, fuse it with the initial field of typhoon forecast, and iteratively update the typhoon center position and maximum wind speed through recursive filtering to obtain the adaptive forecast result of typhoon intensity. Step 2: Based on the typhoon intensity adaptive forecast results obtained in Step 1, construct the mapping relationship between the typhoon wind field and the geographical location of the wind farm, and use the spatial interpolation method to convert the wind speed at the center of the typhoon into a refined wind speed sequence at each turbine location of the wind farm, which will serve as the input condition for subsequent steps. Step 3: Based on the refined wind speed sequence obtained in Step 2, and combined with the preset speed-power characteristic curve of the wind turbine, the wind turbines corresponding to each turbine location are divided into three operating states: low constant speed operating zone, maximum power point tracking operating zone, and high constant speed operating zone. Step 4: Based on the operational status classification results identified in Step 3, dynamically adjust the aggregated parameters of the wind turbines corresponding to each status category. These aggregated parameters include wind turbine capacity, blade rotation radius, gear ratio, moment of inertia, and control parameters, ensuring that the output characteristics of the aggregated model match the actual output characteristics of each turbine. The aggregated model receives the refined wind speed sequence from Step 2 and the operational status classification results from Step 3. It generates equivalent turbine parameters for each operational status category based on the aggregated expressions for wind turbine capacity, blade rotation radius, gear ratio, moment of inertia, and control parameters. These parameters are used to replace the power output calculations of all actual turbines within that category. The aggregated model equates multiple wind turbines within the same operational status category to a single turbine, dynamically adjusting the aggregated parameters to match the output characteristics of the equivalent turbine with the sum of the actual output characteristics of each individual turbine. Step 5: Based on the refined wind speed sequence obtained in Step 2 and the operating status identified in Step 3, the wind energy conversion efficiency coefficient is corrected online through the online correction model. The corrected wind energy conversion efficiency coefficient is then fed back to the aggregation parameter adjustment process in Step 4 to form a synergistic optimization between the aggregation parameter and the wind energy conversion efficiency. Step 6: Monitor the electrical parameters of the grid connection point in real time during the typhoon's impact. When an abnormality in the electrical parameters is detected, switch the aggregated parameters determined in Step 4 to the corrected values ​​under the fault ride-through state, and use the corrected aggregated parameters for the power output calculation of the wind farm's grid connection point.

[0011] Preferably, the fusion processing in step 1 adopts a sequential estimation method, using the typhoon intensity parameter as the variable to be estimated, and its state update expression is: ; ; in, For the first Adaptive correction values ​​for the typhoon intensity parameter vector of each forecast sample. For the first The typhoon intensity parameter vector of each forecast sample. These are monitoring values ​​of ocean surface flow conditions. To observe the projection operator, Here is the gain matrix. For the prediction error covariance matrix, To monitor the error covariance matrix, the optimal estimate of the typhoon center location and maximum wind speed is obtained by weighted combination of multiple forecast samples.

[0012] Preferably, in step 2, the spatial interpolation method uses radial basis functions to construct a typhoon wind field distribution model, and the wind farm's first... Precise wind speed at individual machine locations Represented as: ; In the formula, The maximum wind speed at the center of the typhoon. This represents the distance between the location of the aircraft and the center of the typhoon. This is a characteristic scale of the typhoon's wind circle. The wind direction asymmetry coefficient, For the first The azimuth angle of each location relative to the center of the typhoon. This represents the typhoon's direction of movement.

[0013] Preferably, in step 4, the aggregation parameters are dynamically adjusted based on the classification results of the operating status, including the wind turbine capacity. The adjustment expression is: ; In the formula, The number of generating units belonging to the same operating state. For the first Rated capacity of the unit These are the state adaptation coefficients, with the following values: ; Among them, the coefficient of low constant speed operating zone The calculation expression is: ; In the formula, For the first The output power of the unit, For the first The corresponding conversion wind speed for the unit. The optimal operating ratio coefficient; Maximum Power Point Tracking (MPPT) Operating Range Coefficient The calculation expression is: ; In the formula, The rated power of the unit, The wind energy conversion efficiency coefficient. Where is the radius of the wind turbine. air density, This is the loss correction factor. This is the upper limit of wind speed in the low constant speed range. This is the lower limit wind speed in the high constant speed zone; High constant speed operating range coefficient The value can be: .

[0014] Preferably, the wind energy conversion efficiency coefficient in step 5 The online correction model is as follows: ; ; In the formula, For the tip speed ratio, The pitch angle; Under typhoon conditions, when the wind speed exceeds the rated wind speed, the pitch angle... Adjust dynamically based on wind speed: ; In the formula, The initial pitch angle, The maximum pitch angle, The pitch rate coefficient is... Rated wind speed, This represents the real-time actual wind speed of the wind turbines during the typhoon's impact.

[0015] Preferably, in step 6, abnormal electrical parameters are determined by comparing theoretical power with measured power. The calculation expression is: ; in, This indicates the real-time refined wind speed corresponding to the wind farm turbine location; when the relative deviation between the measured power and the theoretical power exceeds a preset threshold, and the relative deviation between the measured current and the theoretical current also exceeds a preset threshold, the system is determined to enter the fault ride-through state.

[0016] Preferably, under fault-crossing conditions, the modified expression for the aggregation parameter is: ; ; In the formula, This represents the equivalent aggregate capacity of the wind turbine under fault ride-through conditions. This is the fault ride-through attenuation coefficient. The equivalent impedance of the wind turbine under fault ride-through conditions. The voltage at the grid connection point. This represents the total power loss of the collector line. This refers to the capacitance between the collector line and ground. For the first Line correction factor for the Taiwanese generator set.

[0017] Preferably, the typhoon intensity adaptive correction method also includes an adaptive correction step for typhoon intensity forecasts, which uses forecast residual analysis to estimate forecast bias in real time. ; ; in, For the first Step-forward residual vector, These are monitoring values ​​of ocean surface flow conditions. To predict the projected values, To monitor the estimated value of the error covariance matrix, when the root mean square value of the forecast residual sequence exceeds a set threshold, the online correction of the typhoon intensity forecast is triggered, and the corrected typhoon intensity parameters are fed back to step 1 to form a closed-loop correction mechanism.

[0018] Preferably, in step 4, the blade rotation radius The dynamic adjustment expression is: ; In the formula, For the first The blade rotation radius of the tactical unit; Gear ratio The dynamic adjustment expression is: ; In the formula, For the first Gear ratio of the unit; Moment of inertia The dynamic adjustment expression is: ; In the formula, For the first Moment of inertia of the unit.

[0019] Preferably, the dynamic adjustment expression for the control parameters in step 4 is: ; ; ; ; In the formula, , These are the proportional and integral coefficients of the aggregated outer loop control, respectively. , These are the proportional and integral coefficients of the outer loop control before aggregation. , These are the proportional and integral coefficients of the aggregated inner-loop control, respectively. , These are the proportional and integral coefficients of the inner loop control before aggregation, respectively.

[0020] Therefore, the above-mentioned adaptive typhoon intensity correction method for offshore wind power, adopted in this invention, has the following beneficial effects: (1) By adaptive correction of typhoon intensity and refined wind speed mapping of wind farm, the problem of disconnect between typhoon forecast and power prediction in traditional methods is solved, and the wind power prediction error during typhoon passage is significantly reduced.

[0021] (2) Based on the operating status of the wind turbine in different wind speed ranges, the aggregate parameters such as capacity, inertia, and control parameters are dynamically adjusted to avoid steady-state errors caused by fixed parameters and improve the realism of the aggregate model response.

[0022] (3) Under typhoon conditions, the wind energy conversion efficiency coefficient is dynamically corrected based on the blade tip speed ratio and blade pitch angle, which overcomes the systematic power deviation caused by the traditional fixed coefficient and improves the accuracy of power calculation.

[0023] (4) By identifying abnormal electrical parameters, the aggregated parameters are automatically switched to the correction value under fault ride-through state to enhance the transient simulation accuracy and grid connection stability of wind farms during typhoon faults.

[0024] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0025] Figure 1 This is a flowchart of a typhoon intensity adaptive correction method for offshore wind power according to the present invention; Figure 2 This is a comparison chart of the results of an embodiment of the present invention. Detailed Implementation

[0026] The following detailed description of embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0027] Example I. Simulation Environment and Parameter Settings: This embodiment selects a typical offshore wind farm in southeastern coastal China as the simulation object. The wind farm has an installed capacity of 100MW and consists of 20 wind turbine generators with a single unit capacity of 5MW. The geographical coordinates of the center of the wind farm are 24.5°N, 118.5°E, and the distance from the shore is about 15 kilometers.

[0028] Typhoon No. 7 "Hagupit" in 2008 was selected as the simulation condition. This typhoon affected the target wind farm area on July 18, 2008. The actual maximum wind speed at the typhoon center was 33 m / s, and the typhoon's directional angle was 315° (moving from southeast to northwest). The characteristic scale of the typhoon's wind circle was [not specified]. The value is taken as 30km, and the wind direction asymmetry coefficient is used. The value is 0.25.

[0029] The simulation started when the typhoon center was 25 km from the wind farm, at which point the initial average wind speed in the wind farm area was 12 m / s. As the typhoon center approached, the wind speed in the wind farm area continued to rise, reaching a peak of 28 m / s when the typhoon center was 10 km from the wind farm. The entire simulation lasted 6 hours, with a time resolution of 10 minutes and a total of 36 time sampling points.

[0030] The key parameters for the wind turbine are set as follows: rated wind speed is 12 m / s, cut-out wind speed is 25 m / s, and the upper limit wind speed in the low constant speed range. The lower limit wind speed in the high constant speed zone is 8 m / s. The wind speed is 20 m / s. Wind turbine radius. 63m, air density The standard value is taken as 1.225 kg / m. 3 Initial pitch angle 0°, maximum pitch angle 25°, pitch rate coefficient Take 2. The rated voltage at the grid connection point is 110kV, and the capacitance to ground of the collector line is... Take 0.15 μF / km.

[0031] Traditional comparison methods use the same initial typhoon forecast value (maximum wind speed at the typhoon center 40 m / s) and do not assimilate marine monitoring data; wind speed mapping uses a simple distance attenuation model; aggregation parameters use fixed values, i.e., the capacity coefficient is always 1; and the wind energy conversion efficiency coefficient... The value is always 0.45; no parameter switching is performed under fault conditions.

[0032] II. The implementation process of the method of the present invention is as follows: Figure 1 As shown: Step 1: Typhoon Intensity Adaptive Correction: By integrating ocean surface flow monitoring data, a sequential estimation method was used to adaptively correct the typhoon intensity. After correction, the maximum wind speed at the typhoon center was reduced from the initial forecast value of 40 m / s to 34.5 m / s. Compared with the actual value of 33 m / s, the relative error was only 4.5%, while the comparison method directly used the initial forecast value, and the error was as high as 21.0%.

[0033] Step 2, Refined Wind Speed ​​Mapping: A typhoon wind field distribution model is constructed using radial basis functions to convert the typhoon center wind speed into a refined wind speed sequence at each turbine location in the wind farm. Using this spatial interpolation method, the average wind speed mapping error of the wind farm is 5.8%, while the comparison method, which uses a simple distance attenuation model, has a mapping error as high as 21.5%.

[0034] Step 3: Operational Status Classification: Based on the refined wind speed sequence and the preset speed-power characteristic curve of the wind turbine, the wind turbines at each location are classified into three operational statuses: Low constant speed operation zone (wind speed ≤ 8m / s): 2 units; Maximum power point tracking (MPPT) operating range (8~20m / s): 5 units; High constant speed operating range (≥20m / s): 13 units.

[0035] Step 4: Dynamic Adjustment of Aggregate Parameters: Based on the operational status classification results, dynamically adjust the aggregate parameters of the wind turbine corresponding to each status category. This includes the state adaptive coefficient. Values ​​are assigned based on the running status: Low constant speed operating range: ; Maximum power point tracking operating range: ; High constant speed operating range: ; Meanwhile, the blade rotation radius, gear transmission ratio, moment of inertia, and control parameters are all dynamically adjusted according to the corresponding formulas.

[0036] Step 5, Online Correction of Wind Energy Conversion Efficiency: Under typhoon conditions, when the wind speed exceeds the rated wind speed, the blade pitch angle is dynamically adjusted according to the wind speed. ; In the formula, The initial pitch angle, The maximum pitch angle, The pitch rate coefficient is... Rated wind speed, This represents the real-time actual wind speed of the wind turbines during the typhoon's impact.

[0037] Wind energy conversion efficiency coefficient Real-time calculations are performed using an online correction model: ; In the high wind speed area (wind speed 28m / s, blade pitch angle 15°). The dynamic correction value is approximately 0.12, while the comparison method uses a fixed value of 0.45, resulting in a deviation as high as 275%.

[0038] Step 6, Fault Ride Detection and Parameter Switching: During the typhoon's impact, the electrical parameters of the grid connection point are monitored in real time, and fault ride-through is detected by comparing the theoretical power with the measured power. ; When the relative deviation between the measured power and the theoretical power exceeds 15%, and the relative deviation between the measured current and the theoretical current simultaneously exceeds 12%, the system is determined to have entered fault ride-through state, and the aggregation parameters are switched to the correction values ​​for fault ride-through state. ; in, For wind turbine capacity, This is the fault ride-through attenuation factor, which is 0.7 here.

[0039] III. Results Comparison Tables: Table 1 and below Figure 2 Both methods demonstrate the changes in power prediction error between the proposed method and the comparative method during the typhoon's impact period (36 time points in total, one sampling point every 10 minutes): Table 1: Comparison of power prediction errors between the method of the present invention and the comparative method

[0040] The key indicators are summarized in Table 2: Table 2: Summary Table of Key Indicators

[0041] IV. Conclusion and Analysis: This embodiment fully demonstrates the implementation process and quantification results of the six steps of the method of the present invention under the same typhoon conditions and wind farm conditions. Through typhoon intensity adaptive correction (step 1), refined wind speed mapping (step 2), operation state-driven aggregate parameter adjustment (steps 3-4), online correction of wind energy conversion efficiency (step 5), and fault ride-through parameter switching (step 6), the power prediction error of the method of the present invention is significantly lower than that of the traditional comparative method throughout the entire time period.

[0042] The main conclusions are as follows: Typhoon intensity forecast accuracy improved: By sequentially estimating and assimilating ocean monitoring data, the forecast error for maximum typhoon wind speed decreased from 11.1% to 1.8%, an improvement of approximately 83.8%.

[0043] Improved wind speed mapping accuracy: By using radial basis function spatial interpolation, the wind speed mapping error of the wind farm was reduced from 21.5% to 5.8%, an improvement of approximately 73.0%.

[0044] Power prediction errors have been significantly reduced: the average power prediction error during typhoons decreased from 13.3% to 4.3%, and the maximum power prediction error decreased from 20.2% to 5.6%, representing reductions of approximately 67.7% and 72.3%, respectively.

[0045] The above results fully verify the effectiveness and industrial applicability of the present invention in predicting offshore wind power during typhoon impacts.

[0046] Therefore, this invention employs the aforementioned adaptive typhoon intensity correction method for offshore wind power. First, it acquires ocean surface flow monitoring data and integrates it with the initial typhoon forecast field. Sequential estimation is used to recursively update the typhoon center position and maximum wind speed to obtain adaptive forecast results. Second, a spatial distribution model of the typhoon wind field is constructed based on radial basis functions. The typhoon center wind speed is interpolated into a refined wind speed sequence for each turbine location. Based on the speed-power curve, the turbines are classified into three operating states: low constant speed, maximum power point tracking, and high constant speed. Then, for different states, aggregated parameters such as turbine capacity, blade radius, transmission ratio, inertia, and control parameters are dynamically adjusted, and the wind energy conversion efficiency coefficient is corrected online to achieve collaborative optimization. Finally, the electrical parameters at the grid connection point are monitored in real time. When the power and current deviations exceed thresholds, a fault ride-through state is determined, and the aggregated parameters are switched to fault correction values ​​for grid connection point power calculation. This method forms a closed-loop feedback mechanism for typhoon intensity forecasting and wind power prediction, significantly improving forecast accuracy and system robustness during typhoon impact periods.

[0047] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the technical solutions of the present invention, and these modifications or equivalent substitutions cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for adaptive correction of typhoon intensity for offshore wind power, characterized in that, Includes the following steps: Step 1: Obtain real-time monitoring data of ocean surface flow, fuse it with the initial field of typhoon forecast, and iteratively update the typhoon center position and maximum wind speed through recursive filtering to obtain the adaptive forecast result of typhoon intensity. Step 2: Based on the adaptive typhoon intensity forecast results obtained in Step 1, construct the mapping relationship between the typhoon wind field and the geographical location of the wind farm, and use the spatial interpolation method to convert the wind speed at the center of the typhoon into a refined wind speed sequence at each turbine location of the wind farm. Step 3: Based on the refined wind speed sequence obtained in Step 2, and combined with the preset speed-power characteristic curve of the wind turbine, the wind turbines corresponding to each turbine location are divided into three operating states: low constant speed operating zone, maximum power point tracking operating zone, and high constant speed operating zone. Step 4: Based on the operation status classification results identified in Step 3, dynamically adjust the wind turbine aggregation parameters corresponding to each status category. The aggregation parameters include wind turbine capacity, blade rotation radius, gear ratio, moment of inertia, and control parameters, so that the output characteristics of the aggregation model match the actual output characteristics of each unit. Step 5: Based on the refined wind speed sequence obtained in Step 2 and the operating status identified in Step 3, the wind energy conversion efficiency coefficient is corrected online through the online correction model. The corrected wind energy conversion efficiency coefficient is then fed back to the aggregation parameter adjustment process in Step 4 to form a synergistic optimization between the aggregation parameter and the wind energy conversion efficiency. Step 6: Monitor the electrical parameters of the grid connection point in real time during the typhoon's impact. When an abnormality in the electrical parameters is detected, switch the aggregated parameters determined in Step 4 to the corrected values ​​under the fault ride-through state, and use the corrected aggregated parameters for the power output calculation of the wind farm's grid connection point.

2. The typhoon intensity adaptive correction method for offshore wind power according to claim 1, characterized in that, In step 1, the fusion processing uses a sequential estimation method, taking the typhoon intensity parameter as the variable to be estimated. Its state update expression is: ； ； in, For the first Adaptive correction values ​​for the typhoon intensity parameter vector of each forecast sample. For the first The typhoon intensity parameter vector of each forecast sample. These are monitoring values ​​of ocean surface flow conditions. To observe the projection operator, Here is the gain matrix. For the prediction error covariance matrix, To monitor the error covariance matrix, the optimal estimate of the typhoon center location and maximum wind speed is obtained by weighted combination of multiple forecast samples.

3. The typhoon intensity adaptive correction method for offshore wind power according to claim 2, characterized in that, In step 2, the spatial interpolation method uses radial basis functions to construct a typhoon wind field distribution model. The wind farm's first... Precise wind speed at individual machine locations Represented as: ； In the formula, The maximum wind speed at the center of the typhoon. This represents the distance between the location of the aircraft and the center of the typhoon. This is a characteristic scale of the typhoon's wind circle. The wind direction asymmetry coefficient, For the first The azimuth angle of each location relative to the center of the typhoon. This represents the typhoon's direction of movement.

4. The typhoon intensity adaptive correction method for offshore wind power according to claim 3, characterized in that, In step 4, the aggregation parameters are dynamically adjusted based on the classification results of the operating status, including the wind turbine capacity. The adjustment expression is: ； In the formula, The number of generating units belonging to the same operating state. For the first The rated capacity of the unit These are the state adaptation coefficients, with the following values: ； Among them, the coefficient of low constant speed operating zone The calculation expression is: ； In the formula, For the first The output power of the unit, For the first The corresponding conversion wind speed for the unit. The optimal operating ratio coefficient; Maximum Power Point Tracking (MPPT) Operating Range Coefficient The calculation expression is: ； In the formula, The rated power of the unit, The wind energy conversion efficiency coefficient. Where is the radius of the wind turbine. air density, This is the loss correction factor. This is the upper limit of wind speed in the low constant speed range. This is the lower limit wind speed in the high constant speed zone; High constant speed operating range coefficient The value can be: 。 5. The typhoon intensity adaptive correction method for offshore wind power according to claim 4, characterized in that, Wind energy conversion efficiency coefficient in step 5 The online correction model is as follows: ； ； In the formula, For the tip speed ratio, The pitch angle; Under typhoon conditions, when the wind speed exceeds the rated wind speed, the pitch angle... Adjust dynamically based on wind speed: ； In the formula, The initial pitch angle, The maximum pitch angle, The pitch rate coefficient is... Rated wind speed, This represents the real-time actual wind speed of the wind turbines during the typhoon's impact.

6. The typhoon intensity adaptive correction method for offshore wind power according to claim 5, characterized in that: In step 6, abnormal electrical parameters are identified by comparing the theoretical power with the measured power. Theoretical power... The calculation expression is: ； in, This indicates the real-time refined wind speed corresponding to the wind farm turbine location; when the relative deviation between the measured power and the theoretical power exceeds a preset threshold, and the relative deviation between the measured current and the theoretical current also exceeds a preset threshold, the system is determined to enter the fault ride-through state.

7. The typhoon intensity adaptive correction method for offshore wind power according to claim 6, characterized in that, Under fault-crossing conditions, the corrected expression for the aggregation parameter is: ； ； In the formula, This represents the equivalent aggregate capacity of the wind turbine under fault ride-through conditions. This is the fault ride-through attenuation coefficient. The equivalent impedance of the wind turbine under fault ride-through conditions. The voltage at the grid connection point. This represents the total power loss of the power collection line. This refers to the capacitance between the collector line and ground. For the first Line correction factor for the Taiwanese generator set.

8. The typhoon intensity adaptive correction method for offshore wind power according to claim 7, characterized in that: The typhoon intensity adaptive correction method also includes an adaptive correction step for typhoon intensity forecasts, which uses forecast residual analysis to estimate forecast bias in real time. ； ； in, For the first Step-forward residual vector, These are monitoring values ​​of ocean surface flow conditions. To predict the projected values, To monitor the estimated value of the error covariance matrix, The desired outcome is that when the root mean square value of the forecast residual sequence exceeds a set threshold, online correction of the typhoon intensity forecast is triggered, and the corrected typhoon intensity parameters are fed back to step 1, forming a closed-loop correction mechanism.

9. A method for adaptive correction of typhoon intensity for offshore wind power according to claim 8, characterized in that, Blade rotation radius in step 4 The dynamic adjustment expression is: ； In the formula, For the first The blade rotation radius of the tactical unit; Gear ratio The dynamic adjustment expression is: ； In the formula, For the first Gear ratio of the machine unit; Moment of inertia The dynamic adjustment expression is: ； In the formula, For the first Moment of inertia of the unit.

10. A method for adaptive correction of typhoon intensity for offshore wind power according to claim 9, characterized in that, The dynamic adjustment expression for the control parameters in step 4 is: ； ； ； ； In the formula, , These are the proportional and integral coefficients of the aggregated outer loop control, respectively. , These are the proportional and integral coefficients of the outer loop control before aggregation. , These are the proportional and integral coefficients of the aggregated inner-loop control, respectively. , These are the proportional and integral coefficients of the inner loop control before aggregation, respectively.

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