A method for obtaining wind speed and direction of a wind turbine generator set

Abstract

This application relates to the field of wind power generation and discloses a method for acquiring wind speed and direction data of wind turbine generators, comprising the following steps: 1) acquiring wind speed and direction data in the operating area of ​​the wind turbine generator in real time through sensors; 2) eliminating high-frequency disturbances based on a turbulence model; 3) correcting the wind speed and direction data based on a wind shear model to compensate for wind speed gradients; 4) constructing a wind field optimization model and dynamically optimizing the wind speed and direction data using a global optimization method; 5) generating final wind speed and direction data based on multi-scale decomposition and fusion of macroscopic trends and local details; 6) outputting high-precision corrected wind speed and direction data. By introducing turbulence correction and a wind shear model, the main measurement errors caused by turbulence disturbances and height differences are eliminated, addressing the complex wind field characteristics within the operating area of ​​the wind turbine generator. This hierarchical correction method effectively reduces the randomness and bias of wind speed and direction data, enabling the output data to have high reliability and accuracy in various wind field environments.

Description

Technical Field

[0001] This invention relates to the field of wind power generation technology, specifically a method for obtaining wind speed and direction of wind turbine generators. Background Technology

[0002] The efficient operation of wind turbines relies heavily on the accurate acquisition of wind speed and direction data. While wind speed and direction measurement technologies have made some progress, traditional methods primarily rely on mechanical anemometers and wind vanes. With increasing measurement demands, newer devices such as lidar and ultrasonic sensors are gradually being introduced. These devices enable real-time monitoring of wind speed and direction, improving measurement accuracy and environmental adaptability to some extent. Furthermore, some methods attempt to address turbulence interference by introducing mathematical models or compensate for wind shear effects through simple gradient correction, further enhancing data reliability. These technologies provide fundamental support for the operation and control of wind turbines. However, in complex wind farm environments, these technologies still have room for improvement when dealing with the dynamic characteristics and complex changes in wind speed and direction.

[0003] Current technologies still face several limitations in addressing key issues in wind speed and direction measurement. First, existing methods have limited capacity to handle high-frequency turbulent disturbances, especially in scenarios with severe turbulence, where measurement data is easily affected by random fluctuations and lacks stability. Second, wind shear effects are mostly handled using fixed correction factors, which are ill-suited to varying wind speed gradients at different wind turbine heights and operating areas. Furthermore, processing wind speed and direction data struggles to balance global wind field continuity with capturing local dynamic characteristics; existing technologies often prioritize local corrections, leading to insufficient overall data consistency. Finally, while optimized models can improve measurement results in some scenarios, existing models have weak dynamic adaptability and fail to adequately address real-time changes in the wind field environment. Therefore, further breakthroughs are needed to address complex wind field environments to better meet the multiple demands for high accuracy, dynamic adaptability, and global consistency. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a method for obtaining wind speed and direction data for wind turbines, which solves the problems of measurement errors and insufficient dynamic adaptability caused by turbulence disturbances, wind shear effects, and complex wind field environments during the acquisition of wind speed and direction data within the operating area of ​​the turbine.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a method for obtaining wind speed and direction of a wind turbine generator, comprising the following steps:

[0006] 1) Real-time acquisition of wind speed and direction data in the wind turbine operating area via sensors;

[0007] 2) The collected wind speed and direction data are corrected based on the turbulence model to remove high-frequency disturbances caused by turbulence;

[0008] 3) Based on the wind shear model, wind speed and direction data are corrected to compensate for wind speed gradients caused by height differences;

[0009] 4) Construct a wind field optimization model and combine it with a global optimization method to dynamically optimize wind speed and wind direction data;

[0010] 5) Based on the multi-scale decomposition method, the wind speed and wind direction data are fused to integrate the macro trend and local details, generating the final corrected wind speed and wind direction data;

[0011] 6) Output corrected, high-precision wind speed and direction data.

[0012] Preferably, the sensors include lidar and ultrasonic sensors, and the wind speed and direction data are represented in the form of a three-dimensional velocity vector, including the three directional components of the wind speed.

[0013] Preferably, in step 2), the turbulence correction includes the following process:

[0014] A dynamic model of turbulence is constructed, and by describing the characteristics of turbulent energy transfer and dissipation, the random disturbance of turbulence to wind speed and direction data is estimated.

[0015] Using a closed turbulence model, we calculate the turbulence disturbance components and remove these disturbances to correct the wind speed and direction data.

[0016] Preferably, the turbulence dynamics model uses the Reynolds average method to analyze turbulence disturbances, and the turbulence closure model eliminates the influence of turbulence on wind speed and wind direction data by calculating the relationship between turbulent kinetic energy generation and dissipation.

[0017] Preferably, in step 3), the wind shear model is used to describe the gradient change of wind speed at different heights. The wind shear model dynamically adjusts the wind speed correction factor according to the wind speed difference in the area surrounding the wind turbine to compensate for the wind speed deviation caused by the height difference.

[0018] Preferably, in step 4), the objectives of constructing the wind field optimization model include the following:

[0019] Data fitting constraints are applied to ensure consistency between wind field data and measurement data.

[0020] Smoothness constraints ensure the global continuity of wind field data;

[0021] Turbulent energy constraints limit the impact of turbulent disturbances on wind speed and direction data.

[0022] Preferably, in step 4), the wind field optimization model is solved by a global optimization method, and the obtained corrected wind speed and wind direction data satisfy the optimization results of data fitting constraints and smoothness constraints, and are dynamically adjusted in combination with boundary conditions.

[0023] Preferably, in step 5), the multi-scale decomposition and fusion are iteratively calculated by dynamically adjusting the fusion coefficients of the macro-trend component and the local detail component, wherein:

[0024] The macro trend component is used to describe the overall distribution characteristics of the wind field;

[0025] Local detail components are used to capture the dynamic characteristics of turbulence and wind shear;

[0026] The fusion process adjusts the multi-scale decomposition results by dynamically updating the fusion coefficients, thereby completing the multi-scale reconstruction of the wind field distribution.

[0027] Preferably, the fusion coefficient of the macro trend component is calculated based on the smoothness constraint of the spatial distribution of global wind speed and direction in the wind field optimization model, and the fusion coefficient of the local detail component is calculated based on the sensitivity constraint of turbulence disturbance and wind shear characteristics in the wind field optimization model. The dynamic adjustment of the multi-scale decomposition results is completed through iterative calculation.

[0028] Preferably, in step 5), the multi-scale decomposition and dynamic fusion process is combined with the iterative calculation process of the wind field optimization model to adjust the fusion parameters of the macro trend component and the local detail component in real time, thereby performing multi-scale optimization of the dynamic distribution of wind speed and wind direction data.

[0029] This invention provides a method for obtaining wind speed and direction in wind turbine generators. It has the following beneficial effects:

[0030] 1. This invention, by introducing turbulence correction and a wind shear model, addresses the complex wind field characteristics within the wind turbine operating area, eliminating the main measurement errors caused by turbulence disturbances and height differences. This layered correction method effectively reduces the randomness and bias of wind speed and direction data, ensuring high reliability and accuracy of the output data in various wind field environments.

[0031] 2. This invention employs a multi-scale decomposition and fusion method to divide wind speed and direction data into macroscopic trend components and local detail components, and reconstructs the wind field data by dynamically adjusting the fusion coefficients. The macroscopic trend ensures the continuity of the global wind field, while the local details capture the dynamic characteristics of the wind field, such as turbulence and wind shear. The fused data possesses both the integrity of a global perspective and the flexibility to adapt to the complex needs of local changes.

[0032] 3. By constructing a wind field optimization model and combining it with a global optimization method, this invention can dynamically adjust wind speed and direction data to simultaneously satisfy data fitting constraints, global smoothness constraints, and turbulent energy constraints. This optimization method ensures the rationality of wind speed and direction data in terms of spatial distribution, and at the same time has a strong dynamic adaptability in variable wind field environments, providing reliable data support for the efficient operation of wind turbine units. Attached Figure Description

[0033] Figure 1 This is a flowchart of the method steps of the present invention. Detailed Implementation

[0034] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0035] Please see the appendix Figure 1 This invention provides a method for obtaining wind speed and direction of a wind turbine, comprising the following steps:

[0036] 1) Real-time acquisition of wind speed and direction data in the wind turbine operating area via sensors;

[0037] 2) The collected wind speed and direction data are corrected based on the turbulence model to remove high-frequency disturbances caused by turbulence;

[0038] 3) Based on the wind shear model, wind speed and direction data are corrected to compensate for wind speed gradients caused by height differences;

[0039] 4) Construct a wind field optimization model and combine it with a global optimization method to dynamically optimize wind speed and wind direction data;

[0040] 5) Based on the multi-scale decomposition method, the wind speed and wind direction data are fused to integrate the macro trend and local details, generating the final corrected wind speed and wind direction data;

[0041] 6) Output corrected, high-precision wind speed and direction data.

[0042] Specifically, the first step is to collect wind speed and direction data in real time using sensors, which forms the foundation of the entire method. The collected data includes the three directional components of wind speed, represented as a three-dimensional vector. This design comprehensively reflects the dynamic characteristics of wind speed and direction within the wind turbine's operating area. The sensors employed are lidar and ultrasonic sensors; their combination improves data acquisition accuracy and spatial coverage. The raw data collected is affected by turbulence and wind shear effects, making it unsuitable for direct use in subsequent analysis; therefore, further correction and processing are required.

[0043] After turbulence and wind shear corrections were completed, the data entered the wind field optimization stage. The wind field optimization model was constructed based on a global optimization method, with objectives including data fitting, smoothness, and turbulence energy constraints. The optimization process not only ensured the consistency between the data and actual measurements but also improved the continuity of the global wind field distribution through smoothness constraints. This step both locally adjusted individual data points and corrected the overall wind field distribution pattern, ensuring the overall consistency and rationality of the data.

[0044] Next, the data enters the multi-scale decomposition and fusion stage. Using a multi-scale decomposition method, wind speed and direction data are split into two components: macroscopic trend and local detail. The macroscopic trend component describes the global distribution characteristics of the wind field, while the local detail component captures the dynamic changes of turbulence and wind shear. The two parts of the data are then recombined by dynamically adjusting the fusion coefficient. In this process, the fusion coefficient is dynamically calculated based on the results of the wind field optimization model, ensuring that the decomposed data can balance global trends and local dynamic changes. This step increases the data resolution to multiple levels, making it more adaptable to complex wind field environments.

[0045] Finally, the corrected high-precision wind speed and direction data are output. This data possesses high precision, global continuity, and consistency in local dynamic changes, and can be directly used for applications such as wind turbine operation monitoring and control strategy adjustment. The entire method, from data acquisition to processing and final output, forms a tightly linked technical loop, with each step contributing to improved data quality and laying the foundation for subsequent processing.

[0046] In step 1), the sensors include lidar and ultrasonic sensors, and the wind speed and direction data are represented in the form of a three-dimensional velocity vector, which includes the three directional components of the wind speed.

[0047] Specifically, this embodiment employs two different types of devices for data acquisition: lidar and ultrasonic sensors. LiDAR is used for wind speed measurement over long distances, while the ultrasonic sensors focus on high-precision short-range measurements. This sensor combination design can cover wind speed and direction changes over a wide area while also capturing spatial details. The acquired data is represented as a three-dimensional velocity vector, specifically including wind speed within... , , Components in three directions:

[0048]

[0049] in, Indicates time, , , These represent the coordinates of the measurement point in space.

[0050] As an alternative, lidar can be installed on the downwind side of the wind turbine, typically at a certain distance from the turbine on the ground or near the nacelle, to measure wind speeds at multiple altitudes through vertical scanning. Ultrasonic sensors, on the other hand, can be mounted directly on the top of the nacelle to collect wind speed and direction information for a localized area around the turbine. This installation method reduces signal interference between devices and ensures measurement stability.

[0051] Specifically, in some embodiments, the lidar's measurement range can cover the entire height range from the ground to the blade tip, with a height resolution set between 5 and 10 meters. For example, for a wind turbine with a height of 150 meters, the lidar can collect wind speed and direction data in at least 15 layers. Each layer of data contains wind speed vector information on a horizontal plane, representing the horizontal and vertical components of the wind speed.

[0052] In another possible implementation, the measurement data from ultrasonic sensors is primarily used to supplement the insufficient response of lidar under high-frequency variations. Ultrasonic sensors can sample at frequencies up to 10 Hz, enabling them to capture rapid wind speed changes caused by turbulence. This high-frequency data can achieve measurement accuracy within 0.1 m / s in the area surrounding the wind turbine nacelle.

[0053] Typically, data from lidar and ultrasonic sensors need to be spatially synchronized. LiDAR's long-range data is usually output in the form of fixed-height slices, while ultrasonic sensor data is based on the three-dimensional spatial coordinates of the cabin. The two data are fused using coordinate transformation methods to obtain a unified three-dimensional wind speed representation.

[0054] In some embodiments, to improve data real-time performance, sensors can be deployed in a distributed manner. For example, lidar and ultrasonic sensors are connected to the unit control center via optical fibers, and each sensor module completes preliminary data analysis and calibration through an independent processing unit. Then, the analyzed data is transmitted to the centralized processing system at the back end via a communication interface.

[0055] To further improve data stability and anti-interference capabilities, the sensor sampling data in this embodiment also undergoes preliminary filtering processing. Specifically:

[0056] The data filtering employed a Kalman filtering method. This method dynamically adjusts the filter gain based on the covariance of the measured and estimated values ​​to reduce the impact of measurement noise on the data.

[0057] In another possible implementation, the multi-frame data output by the sensor can be smoothed using a sliding window averaging method. The time span of the sliding window can be set to 1–5 seconds to balance the real-time performance and stability of the data.

[0058] Furthermore, in this embodiment, the collected wind speed and direction data are stored in a time-series format and sent to subsequent modules in three-dimensional vector form for turbulence correction, wind shear compensation, and wind field optimization. The interface design between the acquisition module and the correction module ensures efficient data transmission while reducing error accumulation caused by data latency.

[0059] In step 2), turbulence correction includes the following process:

[0060] A dynamic model of turbulence is constructed, and by describing the characteristics of turbulent energy transfer and dissipation, the random disturbance of turbulence to wind speed and direction data is estimated.

[0061] Using a closed turbulence model, the turbulence disturbance components are calculated and these disturbances are removed to correct the wind speed and direction data;

[0062] The turbulence dynamics model uses the Reynolds average method to analyze turbulence disturbances, and the turbulence closure model eliminates the influence of turbulence on wind speed and direction data by calculating the relationship between turbulent kinetic energy generation and dissipation.

[0063] Specifically, in wind turbine operating areas, turbulence is often one of the main factors causing instability in wind speed and direction measurement data. The presence of turbulence causes random disturbances in wind speed and direction data, interfering with the acquisition of actual wind field characteristics. Generally, the generation of turbulence is closely related to energy transfer processes, including various complex factors such as wind turbine blade wakes and terrain. In step 1) above, the collected wind speed and direction data contain high-frequency components of turbulence disturbances, which need to be removed through turbulence correction. This step corrects the errors caused by turbulence in the collected data by constructing a turbulence dynamics model and combining it with the turbulence closure method, providing more reliable input data for subsequent wind field optimization and multi-scale decomposition.

[0064] In this embodiment, turbulence correction first decomposes the wind speed and direction data based on the Reynolds averaging method. Specifically, the measurement data can be divided into an average component and a turbulence disturbance component:

[0065]

[0066] in, This refers to the actual measured wind speed vector. This represents the average component of the wind field. The mean component represents the overall trend of the wind field, while the turbulent component exhibits high-frequency variations.

[0067] As an alternative, this embodiment employs a turbulence model to model and correct turbulent disturbances. Turbulent kinetic energy The expression is:

[0068]

[0069] in, , , Turbulent disturbances at , , Directional component. Turbulent kinetic energy. This indicates the energy level of the turbulent disturbance.

[0070] Under normal circumstances, the energy dissipation rate of turbulence This represents the rate at which turbulent energy is transferred to smaller scales and ultimately converted into heat energy, and its expression is:

[0071]

[0072] in, These are constant parameters in the turbulence model. This represents the characteristic length scale of turbulence.

[0073] Specifically, in this embodiment, turbulence correction is achieved through the following steps:

[0074] First, turbulent kinetic energy is calculated based on measurement data. and dissipation rate Turbulent kinetic energy is calculated from the fluctuating component of the measured wind speed, while the dissipation rate is calculated using the turbulence closure equation.

[0075] Secondly, construct the Reynolds stress tensor of turbulence. Its expression is:

[0076]

[0077] in, Represents the turbulent viscosity coefficient. Let S be the strain rate tensor of the average velocity. For Kroneck's symbol.

[0078] Finally, by removing high-frequency components from the wind speed and direction data using the calculated turbulence disturbance components, the corrected wind speed vector is:

[0079]

[0080] In one possible implementation, the turbulent viscosity coefficient Calculated using the following relationship:

[0081]

[0082] This expression shows that the turbulent viscosity coefficient is determined by both the turbulent kinetic energy and the dissipation rate, and is directly related to the strength of the turbulence.

[0083] In step 3), the wind shear model is used to describe the gradient change of wind speed at different heights. The wind shear model dynamically adjusts the wind speed correction factor according to the wind speed difference in the area surrounding the wind turbine to compensate for the wind speed deviation caused by the height difference.

[0084] Specifically, in this embodiment, wind shear correction uses a wind shear model to describe the wind speed gradient changes at different heights, and dynamically adjusts the correction factor to adapt to complex wind field characteristics. Specifically, the basic expression of the wind shear model is as follows:

[0085]

[0086] in, For height Wind speed at the location; For reference height Wind speed at the location; The wind shear index is used to reflect the nonlinear characteristics of wind speed as a function of altitude.

[0087] Generally, reference height The hub height of the wind turbine can be selected. Alternatively, the reference height can also be dynamically determined based on the measurement conditions of the wind farm area, such as selecting the height of the middle layer within the lidar measurement range.

[0088] In this embodiment, the wind shear index It is not a fixed value, but rather dynamically adjusted based on the actual characteristics of the wind field. Specifically, the wind shear index is calculated in relation to turbulence parameters, and its expression is:

[0089]

[0090] in, For turbulent kinetic energy, The formula uses turbulent kinetic energy as a reference. It shows that when the turbulent kinetic energy is large, the variation range of the wind shear index gradually decreases, thus better adapting to the height distribution characteristics under strong turbulent conditions.

[0091] As one possible implementation, the wind shear correction step first calculates the wind speed data at different heights to obtain a height correction factor. Specifically, the correction factor can be calculated using the following formula:

[0092]

[0093] in, This represents the dynamic change in the wind shear index. Subsequently, the corrected wind speed can be expressed as:

[0094]

[0095] In some embodiments, to improve the adaptability of wind shear correction, multi-layer data from lidar can be combined to achieve more accurate height compensation by calculating the wind shear index and correction factor in layers.

[0096] In certain special scenarios, such as offshore wind farms, the spatial characteristics of wind shear can be significantly affected by humidity and temperature difference. This embodiment can extend the wind shear model by introducing additional correction factors (such as temperature difference coefficient and humidity correction coefficient). Specifically, the temperature difference correction coefficient and humidity correction coefficient can be calculated using the following formulas:

[0097]

[0098]

[0099] in, Indicates the amount of temperature change. For reference temperature; Indicates the amount of change in humidity. This is for reference humidity.

[0100] In step 4), the objectives of constructing the wind field optimization model include the following:

[0101] Data fitting constraints are applied to ensure consistency between wind field data and measurement data.

[0102] Smoothness constraints ensure the global continuity of wind field data;

[0103] Turbulent energy constraints limit the impact of turbulent disturbances on wind speed and direction data;

[0104] In step 4), the wind field optimization model is solved by a global optimization method. The obtained corrected wind speed and wind direction data satisfy the optimization results of data fitting constraints and smoothness constraints, and are dynamically adjusted in combination with boundary conditions.

[0105] Specifically, in this embodiment, the objective function for wind field optimization is designed with minimizing the overall error of wind speed and direction data as its core, while also considering global smoothness constraints and turbulence disturbance energy limitations. The mathematical expression of the objective function is:

[0106]

[0107] in, This represents the optimized wind speed and direction vector; These are the corrected measurement data; The spatial domain of the wind field; This represents the gradient distribution of the wind field; and These are the smoothness weighting coefficient and the turbulence energy weighting coefficient.

[0108] Item 1 : Ensure that the optimized wind speed and direction data have a high degree of fit with the corrected measurement data;

[0109] Item 2 Constrain the global smoothness of the wind field and reduce spatial abrupt changes in wind speed and direction data;

[0110] Item 3 Limit the energy of turbulent disturbances to ensure that the optimized wind speed and direction data are physically reasonable.

[0111] In this embodiment, the constraints of the wind field optimization model mainly include the boundary conditions of the wind field and physical continuity constraints.

[0112] Boundary conditions:

[0113] During the optimization process, wind speed and direction data need to meet the flux-free condition at the boundary of the wind field.

[0114] In general, weighting coefficients and The value needs to be adjusted according to the characteristics of the actual wind field. For example, for scenarios with high turbulence intensity, it can be appropriately increased. The weights are adjusted to enhance the turbulence suppression effect.

[0115] In step 5), the multi-scale decomposition and fusion are iteratively calculated by dynamically adjusting the fusion coefficients of the macro-trend component and the local detail component, wherein:

[0116] The macro trend component is used to describe the overall distribution characteristics of the wind field;

[0117] Local detail components are used to capture the dynamic characteristics of turbulence and wind shear;

[0118] The fusion process adjusts the multi-scale decomposition results by dynamically updating the fusion coefficients, thereby completing the multi-scale reconstruction of the wind field distribution.

[0119] The fusion coefficient of the macro trend component is calculated based on the smoothness constraint of the spatial distribution of global wind speed and direction in the wind field optimization model, and the fusion coefficient of the local detail component is calculated based on the sensitivity constraint of turbulence disturbance and wind shear characteristics in the wind field optimization model. The dynamic adjustment of the multi-scale decomposition results is completed through iterative calculation.

[0120] In step 5), the multi-scale decomposition and dynamic fusion process is combined with the iterative calculation process of the wind field optimization model to adjust the fusion parameters of the macro trend component and the local detail component in real time, thereby performing multi-scale optimization of the dynamic distribution of wind speed and wind direction data.

[0121] Specifically, in this embodiment, the multi-scale decomposition method decomposes wind speed and direction data into macroscopic trend components and local detail components. The macroscopic trend component describes the overall trend of wind field changes, while the local detail component focuses on capturing local dynamic changes in turbulence and wind shear. The mathematical form of the decomposition can be expressed as:

[0122]

[0123] in, As a component of macroeconomic trends, For local detail components.

[0124] In this implementation, wavelet transform is used for multi-scale decomposition. By decomposing the time and frequency domain characteristics of the data, wavelet transform can simultaneously obtain the global low-frequency components and local high-frequency components of wind speed and direction data. The specific decomposition formula is as follows:

[0125]

[0126]

[0127] in, These are the wavelet decomposition coefficients. These are wavelet basis functions.

[0128] As an alternative, the basis functions can be Daubechies wavelets to describe the changing characteristics of wind speed and direction. In another possible implementation, the Fast Fourier Transform (FFT) can also be used to decompose the high-frequency and low-frequency components of the wind speed and direction data, where the low-frequency components represent trend characteristics and the high-frequency components correspond to local disturbances.

[0129] After decomposing the data into macro-trend components and local detail components, this embodiment introduces a dynamic weight adjustment mechanism to achieve effective fusion of the two. The mathematical expression of the fused wind speed and direction data is as follows:

[0130]

[0131] in, and These represent the weights of the trend component and the detail component, respectively, satisfying... + =1.

[0132] In general, weight and The dynamic adjustment is based on the global smoothness parameter and the local dynamic sensitivity parameter of the wind field. Specifically, the weight is calculated using the following formula:

[0133]

[0134]

[0135] in, To adjust the parameters, This represents the critical value for global smoothness.

[0136] Local dynamic sensitivity parameters The calculation formula is:

[0137]

[0138] In this embodiment, multi-scale decomposition and fusion are implemented using an iterative optimization method. Each iteration includes the following steps:

[0139] The fused wind speed and direction data are calculated using the current trend component and detail component.

[0140] and ;

[0141] Determine whether the objective function satisfies the convergence condition.

[0142] The mathematical form of the objective function is:

[0143]

[0144] in, For smoothness weights, This represents the spatial domain of the wind field.

[0145] In general, when the objective function When the value is less than the preset convergence threshold, the iteration process terminates, and the final output is the fused wind speed and direction data.

[0146] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for obtaining wind speed and direction of a wind turbine generator set, characterized in that, Includes the following steps: 1) Real-time acquisition of wind speed and direction data in the wind turbine operating area via sensors; 2) The collected wind speed and direction data are corrected based on the turbulence model to remove high-frequency disturbances caused by turbulence; 3) Based on the wind shear model, wind speed and direction data are corrected to compensate for wind speed gradients caused by height differences; 4) Construct a wind field optimization model and combine it with a global optimization method to dynamically optimize wind speed and wind direction data; 5) Based on the multi-scale decomposition method, the wind speed and wind direction data are fused to integrate the macro trend and local details, generating the final corrected wind speed and wind direction data; 6) Output corrected, high-precision wind speed and direction data; In step 2), turbulence correction includes the following process: constructing a dynamic model of turbulence, estimating the random disturbances of turbulence to wind speed and direction data by describing the characteristics of turbulent energy transfer and dissipation; using a turbulence closure model, calculating the turbulence disturbance components and removing these disturbances to correct the wind speed and direction data. The turbulence dynamics model uses the Reynolds average method to analyze turbulence disturbances, and the turbulence closure model eliminates the influence of turbulence on wind speed and direction data by calculating the relationship between turbulence kinetic energy generation and dissipation. In step 3), the wind shear model is used to describe the gradient change of wind speed at different heights. The wind shear model dynamically adjusts the wind speed correction factor according to the wind speed difference in the area around the wind turbine to compensate for the wind speed deviation caused by the height difference. In step 4), the objectives of constructing the wind field optimization model include the following: data fitting constraints to ensure the consistency between wind field data and measurement data; smoothness constraints to ensure the global continuity of wind field data; and turbulence energy constraints to limit the impact of turbulence disturbances on wind speed and wind direction data. In step 4), the wind field optimization model is solved by a global optimization method. The obtained corrected wind speed and wind direction data satisfy the optimization results of data fitting constraints and smoothness constraints, and are dynamically adjusted in combination with boundary conditions. In step 5), the multi-scale decomposition and fusion are iteratively calculated by dynamically adjusting the fusion coefficients of the macro-trend component and the local detail component, wherein: The macro trend component is used to describe the overall distribution characteristics of the wind field; the local detail component is used to capture the dynamic changes in turbulence and wind shear. The fusion process adjusts the multi-scale decomposition results by dynamically updating the fusion coefficients, thereby completing the multi-scale reconstruction of the wind field distribution. The fusion coefficient of the macro trend component is calculated based on the smoothness constraint of the spatial distribution of global wind speed and direction in the wind field optimization model, and the fusion coefficient of the local detail component is calculated based on the sensitivity constraint of turbulence disturbance and wind shear characteristics in the wind field optimization model. The dynamic adjustment of the multi-scale decomposition results is completed through iterative calculation.

2. The method for obtaining wind speed and direction of a wind turbine according to claim 1, characterized in that, In step 1), the sensors include lidar and ultrasonic sensors, and the wind speed and direction data are represented in the form of a three-dimensional velocity vector, which includes the three directional components of the wind speed.

3. The method for obtaining wind speed and direction of a wind turbine generator according to claim 1, characterized in that, In step 5), the multi-scale decomposition and dynamic fusion process is combined with the iterative calculation process of the wind field optimization model to adjust the fusion coefficients of the macro trend component and the local detail component in real time, thereby performing multi-scale optimization of the dynamic distribution of wind speed and wind direction data.

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