A wind speed data reconstruction method, system, device and storage medium of a wind farm

Abstract

The present application relates to the field of wind power generation technology, and particularly relates to a wind speed data reconstruction method, system, device and storage medium of a wind farm. The present application aims to solve the technical problem that the data accuracy is low and the actual operation state of the wind turbine is difficult to reflect in real time due to the neglect of wake effect, dynamic change of wind field and other factors in the prior art. The present application comprises: obtaining radar wind speed data measured in real time in the wind farm by using a single wind measurement laser radar device; obtaining operation data of a wind turbine generator set in the wind farm; calculating corresponding interpolation wind speed data by using an interpolation lookup table method based on the operation data; constructing a neural network model for predicting a wind speed attenuation coefficient, inputting the radar wind speed data and the interpolation wind speed data into the neural network model to obtain the wind speed attenuation coefficient; and weighting and fusing the radar wind speed data and the interpolation wind speed data by using the wind speed attenuation coefficient to obtain reconstructed wind speed data.

Description

Technical Field

[0001] This invention belongs to the field of wind power generation technology, and in particular relates to a method, system, equipment and storage medium for reconstructing wind speed data of a wind farm. Background Technology

[0002] With the increasing global demand for clean energy, wind power, as an important renewable energy source, has received widespread attention. In wind farm operation, accurate wind speed data is crucial for optimizing power generation efficiency, predicting power output, and ensuring equipment safety.

[0003] Traditional wind speed data acquisition methods typically rely on multi-point anemometer towers or distributed sensor networks, which suffer from high equipment costs, complex maintenance, and insufficient spatial resolution. Furthermore, existing wind speed reconstruction methods based on single-point measurements often suffer from low data accuracy and difficulty in reflecting the actual operating status of wind turbines in real time due to neglecting wake effects and dynamic changes in the wind field. While some methods attempt to combine wind turbine operating parameters for wind speed estimation, their applicability and robustness are limited by aerodynamic model simplification errors and the influence of external disturbances. Therefore, there is an urgent need for an efficient, low-cost, and high-precision wind speed data reconstruction technology to overcome the limitations of existing methods. Summary of the Invention

[0004] This invention provides a method, system, device, and storage medium for reconstructing wind speed data in a wind farm, aiming to solve the technical problems in the prior art, such as low data accuracy and difficulty in reflecting the actual operating status of wind turbines in real time due to neglecting factors such as wake effect and dynamic changes in the wind farm.

[0005] The technical solution of this invention to solve the above-mentioned technical problems is as follows: A method for reconstructing wind speed data of a wind farm, comprising:

[0006] Real-time radar wind speed data within the wind farm is obtained using a single wind-measuring lidar device;

[0007] Acquire operational data of wind turbine generators within the wind farm;

[0008] Based on the aforementioned operational data, the corresponding interpolated wind speed data is calculated using the interpolation lookup table method.

[0009] A neural network model for predicting wind speed attenuation coefficient is constructed. The radar wind speed data and the interpolated wind speed data are input into the neural network model to obtain the wind speed attenuation coefficient.

[0010] The radar wind speed data and the interpolated wind speed data are weighted and fused using the wind speed attenuation coefficient to obtain reconstructed wind speed data.

[0011] Furthermore, the aforementioned operating data includes: generator speed, impeller speed, blade pitch angle, electromagnetic torque, equivalent intermediate shaft torque, and aerodynamic torque.

[0012] Furthermore, the calculation of the corresponding interpolated wind speed data based on the aforementioned operational data using the interpolation lookup table method specifically includes:

[0013] Construct an aerodynamic system model for a wind turbine generator set;

[0014] Construct an ESO extended state observer, calculate the gain between the observed values ​​and actual measured values ​​of the aerodynamic system model, and obtain the aerodynamic torque;

[0015] Based on the obtained pitch angle and tip speed ratio, the power coefficient interpolation is calculated based on the power coefficient curve;

[0016] Based on the power coefficient interpolation, the updated power of the wind turbine is calculated, and the updated tip speed ratio is calculated based on the updated power and the aerodynamic torque.

[0017] Using the aforementioned aerodynamic system model, interpolated wind speed data are calculated based on the updated power and the updated tip speed ratio.

[0018] Furthermore, the above power coefficient interpolation is shown in the following formula:

[0019] Cp = Cp insert (β,λ)=insert2(β vector ,TSR vector ,Cp data ,β,λ,'spline')

[0020] Among them, Cp insert Let β represent the interpolation function, β represent the blade pitch angle, and λ represent the tip speed ratio. vector The vector representing the pitch angle, TSR vector The vector representing the tip speed ratio, Cp data This indicates known Cp data, and 'spline' specifies the cubic spline interpolation method to be used.

[0021] The updated power output of the wind turbine is calculated using the following formula:

[0022]

[0023] Among them, Cp new The updated power coefficient interpolation is represented by ρ, air density, A, swept area, ω, angular velocity, and Δλ, which represents the increment of the tip speed ratio.

[0024] The updated tip velocity is calculated as shown in the following formula:

[0025]

[0026] Where, λ new T represents the updated tip speed ratio. aero Indicates aerodynamic torque.

[0027] Furthermore, the aerodynamic system model of the aforementioned wind turbine generator set is specifically as follows:

[0028]

[0029] Where P represents the output power of the wind turbine, and C p (λ,β) represents the wind energy utilization coefficient, λ represents the tip speed ratio, β represents the blade pitch angle (in degrees), and ρ represents the air density (in kg / m³). 3 S represents the area of ​​the plane swept by the wind turbine, in meters (m²). 2 v represents the interpolated wind speed data, in m / s; the wind energy utilization coefficient is shown in the following formula:

[0030]

[0031] Where R represents the rotation radius of the wind turbine impeller, in meters (m); ω represents the wind turbine rotation speed, in rad / s.

[0032] Furthermore, the formula for the ESO extended state observer described above is shown in the following equation:

[0033]

[0034] Where e1 represents the observation error, and y represents the first input, selected as the wind turbine speed ω. r The unit is rad / s; z1 represents the observed rotational speed, in rad / s; z2 represents the observed change in rotational speed, in rad / (s). 2 z3 represents the observed perturbation value, dimensionless; k represents the current time point, in seconds; h represents the sampling time interval, in seconds; β 01 The gain, β, represents the rotational speed. 02 Gain, β, representing the change in rotational speed 03 The gain represents the disturbance value, b represents the coefficient, u represents the second input, chosen as the wind turbine pitch angle β, in degrees; fal(e1α1θ) represents the nonlinear function, defined as follows:

[0035]

[0036] Where α represents a constant coefficient and θ represents a threshold.

[0037] Furthermore, the aforementioned neural network model includes an input layer, an LSTM hidden layer, and an output layer, with an architecture of a dual-input, single-output neural network.

[0038] Secondly, to solve the above-mentioned technical problems, the present invention also provides a wind speed data reconstruction system for a wind farm, comprising:

[0039] The radar wind speed acquisition module is used to acquire real-time radar wind speed data within the wind farm using a single wind-measuring lidar device.

[0040] The wind turbine data acquisition module is used to acquire the operating data of wind turbine generators in the wind farm;

[0041] The fan speed calculation module is used to calculate the corresponding interpolated wind speed data based on the operating data using the interpolation lookup table method;

[0042] The wind speed attenuation calculation module is used to construct a neural network model for predicting the wind speed attenuation coefficient. The radar wind speed data and the interpolated wind speed data are input into the neural network model to obtain the wind speed attenuation coefficient.

[0043] The wind speed reconstruction module is used to perform weighted fusion of the radar wind speed data and the interpolated wind speed data using the wind speed attenuation coefficient to obtain reconstructed wind speed data.

[0044] Thirdly, in order to solve the above-mentioned technical problems, the present invention also provides an electronic device, which 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 wind speed data reconstruction method for wind farms of the present application.

[0045] Fourthly, in order to solve the above-mentioned technical problems, the present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the wind speed data reconstruction method for wind farms of the present application.

[0046] Compared with the prior art, the present invention has the following advantages:

[0047] 1. This invention combines radar wind speed data measured by a single lidar device with interpolated wind speed data calculated based on the operating status of the generator set, and then calculates the wind speed attenuation coefficient based on a neural network model, which can more accurately obtain the actual wind conditions in the wind farm.

[0048] 2. This invention uses an ESO extended state observer to dynamically compensate for disturbance errors and combines neural networks to fuse multi-source data, thereby significantly improving the accuracy of wind speed reconstruction.

[0049] 3. Based on the time-series modeling capability of LSTM, this invention can quickly respond to dynamic changes in the wind field and meet real-time control requirements.

[0050] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures pointed out in the description, claims and drawings. Attached Figure Description

[0051] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0052] Figure 1 A flowchart illustrating a wind speed data reconstruction method for a wind farm according to an embodiment of the present invention is shown.

[0053] Figure 2 A schematic diagram of a wind speed data reconstruction system for a wind farm according to an embodiment of the present invention is shown.

[0054] Figure 3 A schematic diagram of an electronic device structure according to an embodiment of the present invention is shown. Detailed Implementation

[0055] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of 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, 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.

[0056] Figure 1 A flowchart illustrating a wind speed data reconstruction method for a wind farm according to an embodiment of the present invention is shown, as follows: Figure 1 As shown, an embodiment of the present invention provides a method for reconstructing wind speed data in a wind farm, comprising:

[0057] Real-time radar wind speed data within the wind farm is obtained using a single wind-measuring lidar device;

[0058] Acquire operational data of wind turbine generators within the wind farm;

[0059] Based on the aforementioned operational data, the corresponding interpolated wind speed data is calculated using the interpolation lookup table method.

[0060] A neural network model for predicting wind speed attenuation coefficient is constructed. The radar wind speed data and the interpolated wind speed data are input into the neural network model to obtain the wind speed attenuation coefficient.

[0061] The radar wind speed data and the interpolated wind speed data are weighted and fused using the wind speed attenuation coefficient to obtain reconstructed wind speed data.

[0062] Optionally, the operating data includes: generator speed, impeller speed, blade pitch angle, electromagnetic torque, equivalent intermediate shaft torque, and aerodynamic torque.

[0063] Optionally, based on the operational data, calculating the corresponding interpolated wind speed data using the interpolation lookup table method specifically includes:

[0064] Construct an aerodynamic system model for a wind turbine generator set;

[0065] Construct an ESO extended state observer, calculate the gain between the observed values ​​and actual measured values ​​of the aerodynamic system model, and obtain the aerodynamic torque;

[0066] Based on the obtained pitch angle and tip speed ratio, the power coefficient interpolation is calculated based on the power coefficient curve;

[0067] Based on the power coefficient interpolation, the updated power of the wind turbine is calculated, and the updated tip speed ratio is calculated based on the updated power and the aerodynamic torque.

[0068] Using the aforementioned aerodynamic system model, interpolated wind speed data are calculated based on the updated power and the updated tip speed ratio.

[0069] Optionally, the power coefficient interpolation is shown in the following formula:

[0070] Cp = Cp insert (β,λ)=insert2(β vector ,TSR vector ,Cp data ,β,λ,'spline')

[0071] Among them, Cp insert Let β represent the interpolation function, β represent the blade pitch angle, and λ represent the tip speed ratio. vector The vector representing the pitch angle, TSR vector The vector representing the tip speed ratio, Cp data This indicates known Cp data, and 'spline' specifies the cubic spline interpolation method to be used.

[0072] The updated power output of the wind turbine is calculated using the following formula:

[0073]

[0074] Among them, Cp new The updated power coefficient interpolation is represented by ρ, air density, A, swept area, ω, angular velocity, and Δλ, which represents the increment of the tip speed ratio.

[0075] The updated tip velocity is calculated as shown in the following formula:

[0076]

[0077] Where, λ new T represents the updated tip speed ratio. aero Indicates aerodynamic torque.

[0078] Optionally, the aerodynamic system model of the wind turbine generator set is specifically as follows:

[0079]

[0080] Where P represents the output power of the wind turbine, and C p (λ,β) represents the wind energy utilization coefficient, λ represents the tip speed ratio, β represents the blade pitch angle (in degrees), and ρ represents the air density (in kg / m³). 3 S represents the area of ​​the plane swept by the wind turbine, in meters (m²). 2 v represents the interpolated wind speed data, in m / s; the wind energy utilization coefficient is shown in the following formula:

[0081]

[0082] Where R represents the rotation radius of the wind turbine impeller, in meters (m); ω represents the wind turbine rotation speed, in rad / s.

[0083] Optionally, the formula for the ESO extended state observer is as follows:

[0084]

[0085] Where e1 represents the observation error, and y represents the first input, selected as the wind turbine speed ω. r The unit is rad / s; z1 represents the observed rotational speed, in rad / s; z2 represents the observed change in rotational speed, in rad / (s). 2 z3 represents the observed perturbation value, dimensionless; k represents the current time point, in seconds; h represents the sampling time interval, in seconds; β 01 The gain, β, represents the rotational speed. 02Gain, β, representing the change in rotational speed 03 The gain represents the disturbance value, b represents the coefficient, u represents the second input, chosen as the wind turbine pitch angle β, in degrees; fal(e1α1θ) represents the nonlinear function, defined as follows:

[0086]

[0087] Where α represents a constant coefficient and θ represents a threshold.

[0088] Optionally, the neural network model includes an input layer, an LSTM hidden layer, and an output layer, with an architecture of a dual-input single-output neural network.

[0089] In this embodiment, the specific process can be represented as follows:

[0090] (1) Preprocess the radar wind speed data and the interpolated wind speed data;

[0091] (2) Construct a dual-input single-output neural network architecture;

[0092] (3) The input layer is determined to be the radar wind speed sequence and the interpolated wind speed sequence, the hidden layer adopts the LSTM network architecture, and the output layer is the wind speed attenuation coefficient.

[0093] (4) Use 70% of the data as training data, 15% as validation data, and 15% as testing data;

[0094] (5) Select the appropriate number of network layers and select the corresponding data to train the basic model, and finally obtain the trained neural network model.

[0095] Based on and Figure 1 Using the same principle as the method shown, this embodiment of the invention also provides a wind speed data reconstruction system for wind farms, such as... Figure 2 As shown, it includes:

[0096] The radar wind speed acquisition module is used to acquire real-time radar wind speed data within the wind farm using a single wind-measuring lidar device.

[0097] The wind turbine data acquisition module is used to acquire the operating data of wind turbine generators in the wind farm;

[0098] The fan speed calculation module is used to calculate the corresponding interpolated wind speed data based on the operating data using the interpolation lookup table method;

[0099] The wind speed attenuation calculation module is used to construct a neural network model for predicting the wind speed attenuation coefficient. The radar wind speed data and the interpolated wind speed data are input into the neural network model to obtain the wind speed attenuation coefficient.

[0100] The wind speed reconstruction module is used to perform weighted fusion of the radar wind speed data and the interpolated wind speed data using the wind speed attenuation coefficient to obtain reconstructed wind speed data.

[0101] The wind speed data reconstruction system for wind farms in this embodiment of the invention can execute the wind speed data reconstruction method for wind farms provided in this embodiment of the invention. The implementation principles are similar. The actions performed by each module and unit in the wind speed data reconstruction system for wind farms in each embodiment of the invention correspond to the steps in the wind speed data reconstruction method for wind farms in each embodiment of the invention. For detailed functional descriptions of each module in the wind speed data reconstruction system for wind farms, please refer to the descriptions in the corresponding wind speed data reconstruction methods for wind farms shown above, which will not be repeated here.

[0102] The wind speed data reconstruction system of the aforementioned wind farm can be a computer program (including program code) running on a computer device. For example, the wind speed data reconstruction system of the wind farm is an application software. The application software can be used to execute the corresponding steps in the method provided in the embodiments of the present invention.

[0103] In some embodiments, the wind speed data reconstruction system for wind farms provided in this invention can be implemented using a combination of hardware and software. As an example, the wind speed data reconstruction system for wind farms provided in this invention can be a processor in the form of a hardware decoding processor, which is programmed to execute the wind speed data reconstruction method for wind farms provided in this invention. For example, the processor in the form of a hardware decoding processor can be one or more application-specific integrated circuits (ASICs), DSPs, programmable logic devices (PLDs), complex programmable logic devices (CPLDs), field-programmable gate arrays (FPGAs), or other electronic components.

[0104] The modules described in the embodiments of the present invention can be implemented in software or hardware. The names of the modules are not, in some cases, limiting the scope of the module itself.

[0105] Based on the same principles as the methods shown in the embodiments of the present invention, the embodiments of the present invention also provide an electronic device, which may include, but is not limited to: a processor and a memory; the memory for storing computer programs; and the processor for executing the methods shown in any embodiment of the present invention by invoking the computer programs.

[0106] In one alternative embodiment, an electronic device is provided, such as Figure 3 As shown, Figure 3 The illustrated electronic device includes a processor and a memory. The processor and memory are connected, for example, via a bus. Optionally, the electronic device may also include a transceiver, which can be used for data interaction between the electronic device and other electronic devices, such as sending and / or receiving data. It should be noted that in practical applications, the transceiver is not limited to one unit, and the structure of this electronic device does not constitute a limitation on the embodiments of the present invention.

[0107] The processor can be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this invention. The processor can also be a combination that implements computational functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc.

[0108] A bus can include a pathway for transmitting information between the aforementioned components. The bus can be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of representation, Figure 3 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0109] The memory may be ROM (Read Only Memory) or other types of static storage devices capable of storing static information and instructions, RAM (Random Access Memory) or other types of dynamic storage devices capable of storing information and instructions, or EEPROM (Electrically Erasable Programmable Read Only Memory), CD-ROM (Compact Disc Read Only Memory) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited to these.

[0110] The memory stores application code (computer program) that executes the present invention, and its execution is controlled by a processor. The processor executes the application code stored in the memory to implement the content shown in the foregoing method embodiments.

[0111] Among these, electronic devices can also be terminal devices. Figure 3 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments of the present invention.

[0112] This invention provides a computer-readable storage medium storing a computer program that, when run on a computer, enables the computer to execute the corresponding content in the aforementioned method embodiments.

[0113] According to another aspect of the present invention, a computer program product or computer program is also provided, comprising computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the methods provided in the various embodiments described above.

[0114] Computer program code for performing the operations of this invention can be written in one or more programming languages ​​or a combination thereof. These programming languages ​​include object-oriented programming languages—such as Java, Smalltalk, and C++—and conventional procedural programming languages—such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0115] It should be understood that the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of methods and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0116] The computer-readable storage medium provided in this invention can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0117] The aforementioned computer-readable storage medium carries one or more programs, which, when executed by the electronic device, cause the electronic device to perform the method shown in the above embodiments.

[0118] The above description is merely a preferred embodiment of the present invention and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of disclosure in this invention is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-described concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features with similar functions disclosed in this invention.

Claims

1. A method for reconstructing wind speed data from a wind farm, characterized in that, The method includes: Real-time radar wind speed data within the wind farm is obtained using a single wind-measuring lidar device; Acquire operational data of wind turbine generators within the wind farm; Based on the aforementioned operational data, the corresponding interpolated wind speed data is calculated using the interpolation lookup table method. A neural network model for predicting wind speed attenuation coefficient is constructed. The radar wind speed data and the interpolated wind speed data are input into the neural network model to obtain the wind speed attenuation coefficient. The radar wind speed data and the interpolated wind speed data are weighted and fused using the wind speed attenuation coefficient to obtain reconstructed wind speed data. The operating data includes: generator speed, impeller speed, blade pitch angle, electromagnetic torque, equivalent intermediate shaft torque, and aerodynamic torque; Based on the aforementioned operational data, the corresponding interpolated wind speed data is calculated using the interpolation lookup table method, specifically including: Construct an aerodynamic system model for a wind turbine generator; Construct an ESO extended state observer, calculate the gain between the observed values ​​and actual measured values ​​of the aerodynamic system model, and obtain the aerodynamic torque; Based on the obtained pitch angle and tip speed ratio, the power coefficient interpolation is calculated based on the power coefficient curve; Based on the power coefficient interpolation, the updated power of the wind turbine is calculated, and the updated tip speed ratio is calculated based on the updated power and the aerodynamic torque. Using the aforementioned aerodynamic system model, interpolated wind speed data are calculated based on the updated power and updated tip speed ratio; The neural network model includes an input layer, an LSTM hidden layer, and an output layer, and its architecture is a dual-input, single-output neural network architecture.

2. The wind speed data reconstruction method for a wind farm according to claim 1, characterized in that, The power factor interpolation is shown in the following formula: Among them, Cp insert Represents the interpolation function. β Indicates the pitch angle. λ Indicates the tip speed ratio, β vector The vector representing the pitch angle, TSR vector The vector representing the tip speed ratio, Cp data This indicates known Cp data, and 'spline' specifies the cubic spline interpolation method to be used. The updated power output of the wind turbine is calculated using the following formula: in, This represents the updated power coefficient interpolation. Indicates air density, and A represents the swept area. ω Δλ represents the angular velocity, Δλ represents the increment of the tip speed ratio, and R represents the rotation radius of the fan impeller. The updated tip velocity is calculated as shown in the following formula: in, T represents the updated tip speed ratio. aero Indicates aerodynamic torque. P represents the output power of the wind turbine.

3. The wind speed data reconstruction method for a wind farm according to claim 1, characterized in that, The aerodynamic system model of the wind turbine generator set is as follows: Where P represents the output power of the wind turbine. C p ( λ , β () represents the wind energy utilization coefficient. λ Indicates the tip speed ratio, β This represents the pitch angle, in degrees. This indicates air density, with units of kg / m³. 3 S represents the area of ​​the plane swept by the wind turbine, in meters (m²). 2 v represents the interpolated wind speed data, in m / s; the wind energy utilization coefficient is shown in the following formula: Where R represents the rotation radius of the wind turbine impeller, in meters (m); ω represents the wind turbine rotation speed, in rad / s.

4. The wind speed data reconstruction method for a wind farm according to claim 1, characterized in that, The formula for the ESO extended state observer is shown below: Where e1 represents the observation error, and y represents the first input, selected as the wind turbine speed ω. r The unit is rad / s; z1 represents the observed rotational speed, in rad / s; z2 represents the observed change in rotational speed, in rad / (s). 2 z3 represents the observed perturbation value, dimensionless; k represents the current time point, in seconds; h represents the sampling time interval, in seconds; β 01 The gain, β, represents the rotational speed. 02 Gain, β, representing the change in rotational speed 03 represents the gain of the disturbance value, b represents the coefficient, u represents the second input, selected as the wind turbine pitch angle β, and the unit is °; This represents a nonlinear function, defined as follows: in, α Indicates a constant coefficient. θ This represents the threshold.

5. A wind speed data reconstruction system for a wind farm, characterized in that, include: The radar wind speed acquisition module is used to acquire real-time radar wind speed data within the wind farm using a single wind-measuring lidar device. A wind turbine data acquisition module is used to acquire operating data of wind turbine generator sets within a wind farm; wherein, the operating data includes: generator speed, rotor speed, blade pitch angle, electromagnetic torque, equivalent intermediate shaft torque, and aerodynamic torque; a wind turbine wind speed calculation module is used to calculate the corresponding interpolated wind speed data based on the operating data using an interpolation lookup table method; specifically including: Construct an aerodynamic system model for a wind turbine generator set; Construct an ESO extended state observer, calculate the gain between the observed values ​​and actual measured values ​​of the aerodynamic system model, and obtain the aerodynamic torque; Based on the obtained pitch angle and tip speed ratio, the power coefficient interpolation is calculated based on the power coefficient curve; Based on the power coefficient interpolation, the updated power of the wind turbine is calculated, and the updated tip speed ratio is calculated based on the updated power and the aerodynamic torque. Using the aforementioned aerodynamic system model, interpolated wind speed data are calculated based on the updated power and updated tip speed ratio; The wind speed attenuation calculation module is used to construct a neural network model for predicting the wind speed attenuation coefficient. The radar wind speed data and the interpolated wind speed data are input into the neural network model to obtain the wind speed attenuation coefficient. The neural network model includes an input layer, an LSTM hidden layer, and an output layer, and is structured as a dual-input single-output neural network architecture. The wind speed reconstruction module is used to perform weighted fusion of the radar wind speed data and the interpolated wind speed data using the wind speed attenuation coefficient to obtain reconstructed wind speed data.

6. An electronic device, characterized in that, It includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the method of any one of claims 1-4.

7. A computer storage medium, characterized in that, The computer storage medium stores a computer program, which, when executed by a processor, implements the method of any one of claims 1-4.

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