Method for calculating wind speed, power and electric quantity of wind power plant

Abstract

The invention discloses a wind power plant wind speed, power and electric quantity calculation method, and relates to the technical field of wind power plant wind speed survey, and the method comprises the steps: obtaining high-resolution meteorological data of a wind power plant region, processing the data, correcting the wind speed data based on the parameters of a convection boundary layer, and generating corrected wind speed data; constructing a dynamic wind speed prediction model based on a time sequence, and predicting the corrected wind speed data; constructing a wind energy utilization rate optimization model based on the wake effect and the layout synergistic effect between the wind turbine generators; for extreme weather conditions, a robustness power prediction model is constructed based on historical weather data and an anomaly detection mechanism, prediction model parameters are dynamically adjusted in combination with an online learning mechanism, and calculation of the power of the wind power plant is completed. The invention provides a systematic solution from wind speed correction to power prediction, and is suitable for wind power plant operation management under complex environment and dynamic operation conditions.

Description

Technical Field

[0001] The present invention relates to the technical field of wind speed survey of wind farms, and in particular to a method for calculating wind speed, power and electricity of a wind farm. Background Art

[0002] At present, as an important form of renewable energy utilization, the accurate prediction and optimization of wind farm power generation capacity is of vital importance. However, the dynamic changes in wind speed, the wake effect between wind turbines, and the uncertainty of wind speed and power under extreme weather conditions make the existing wind speed prediction and wind energy utilization methods have large deviations in complex environments. The traditional wind speed profile model is mainly based on static parameters, ignoring dynamic influencing factors such as turbulence intensity and mixing layer height, resulting in insufficient accuracy of wind speed calculation. In addition, the layout design of wind turbines mostly does not fully consider the wake effect and the synergy between wind turbines, which can easily lead to reduced wind energy utilization. For wind speed and power prediction under extreme weather conditions, the existing methods are difficult to adapt to the rapidly changing environmental characteristics, resulting in low reliability of power prediction models under abnormal conditions. Therefore, there is an urgent need for a comprehensive method that combines dynamic wind speed correction, optimized layout of wind turbines, and robust power prediction to improve the efficiency and stability of wind farm operation. Summary of the invention

[0003] In view of the above existing problems, the present invention is proposed.

[0004] Therefore, the present invention provides a method for calculating wind speed, power and electricity in a wind farm, and proposes solutions to the three key problems in the existing wind farm operation technology. First, in the wind speed calculation, how to accurately describe the influence of factors such as turbulence intensity and mixing layer height on wind speed through the dynamic wind speed correction method, and realize the dynamic adjustment of wind speed with height and time. Secondly, in the layout optimization of wind turbines, how to optimize the layout parameters of the units based on the wake effect and the synergy of the units, improve the efficiency of wind energy utilization, and reduce the power loss caused by the wake. Finally, under extreme weather conditions, how to use real-time data and historical samples to construct a robust power prediction model, accurately predict the relationship between wind speed and power, and dynamically adjust the model to adapt to rapidly changing extreme environments. By comprehensively solving the above problems, the present invention realizes comprehensive optimization from wind speed correction to power prediction.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions: a method for calculating wind speed, power and electricity in a wind farm, comprising: Obtain high-resolution meteorological data in the wind farm area, clean the data, process outliers, and align the data in time and space, and correct the wind speed data based on the parameters of the convective boundary layer to generate corrected wind speed data; Construct a dynamic wind speed prediction model based on time series to predict the corrected wind speed data; Construct a wind energy utilization optimization model based on the wake effect and layout synergy effect between wind turbines; In response to extreme weather conditions, a robust power prediction model is built based on historical weather data and anomaly detection mechanism. The prediction model parameters are dynamically adjusted in combination with the online learning mechanism to complete the calculation of wind farm power.

[0006] As a preferred solution of the method for calculating wind speed, power and electricity of a wind farm described in the present invention, the high-resolution meteorological data includes wind speed, wind direction, air pressure and temperature data, and the data sources include satellite remote sensing, ground meteorological station observations and data collected by radar systems, and the data are aligned in time and space through an interpolation algorithm.

[0007] As a preferred solution of the method for calculating wind speed, power and electricity of a wind farm according to the present invention, the correction of wind speed data based on the parameters of the convective boundary layer includes the expression of the wind speed profile model being: , in, is the height Basic wind speed at , without turbulence correction; represents the friction speed; represents the Karman constant; Indicates the surface roughness; Introducing turbulence intensity and mixing layer height ,right To make dynamic adjustments: , The effect of turbulence on wind speed is corrected by an exponential function, and the correction amplitude is proportional to the turbulence intensity and inversely proportional to the height of the mixing layer. The cumulative effect of turbulence on wind speed depends not only on height but also on shear stress. and turbulence variance The role of is expressed in integral form as: , in, is the similarity function, Calculation formula to express height Stability length The nonlinear effect of represents the turbulent shear stress, combined with the friction velocity and aerodynamic parameters change dynamically.

[0008] As a preferred solution of the method for calculating wind speed, power and electricity in a wind farm according to the present invention, the correction of wind speed data based on the parameters of the convective boundary layer includes: and turbulence integral effects After integration, the improved dynamic correction wind speed formula is obtained: , in, For height and time Lower corrected wind speed; is the friction speed; is the Karman constant is the surface roughness; Measuring height for wind speed; For time; is the turbulence intensity correction factor; is the height of the mixing layer, and Dynamic correlation; is the shear stress; is the air density; is the turbulence effect adjustment coefficient; is the turbulence field at height The variance at ; is the stability length at time t; is the integral variable, representing the height from the ground surface To target height location.

[0009] As a preferred solution of the method for calculating wind speed, power and electricity of a wind farm described in the present invention, wherein: the construction of a dynamic wind speed prediction model based on time series includes, based on a long short-term memory network LSTM or Transformer structure in deep learning, by processing the corrected wind speed time series data, combining local terrain characteristics and seasonal climate patterns, obtaining a complex dynamic relationship of wind speed changes; Define the input feature embedding matrix as , where all features are normalized to a uniform range of ; Time step and The correlation between them is calculated through the self-attention mechanism

[0010] , in: Query Matrix: Key Matrix: Value matrix:

[0011] Weight Matrix is a learnable parameter, is the dimension of the key vector; For LSTM, the time step The hidden state is updated to: , in, represents the Sigmoid activation function, is the learnable parameter of the network; Final wind speed forecast The output of LSTM or Transformer is mapped through a fully connected layer as follows: , in, are the weights and biases of the fully connected layer.

[0012] As a preferred solution of the method for calculating wind speed, power and electricity of a wind farm described in the present invention, wherein: the construction of the wind energy utilization optimization model includes, based on the wake effect and synergy effect of the wind turbines, comprehensively calculating the influence of the airflow interaction between the wind turbines on the overall power generation capacity, wherein the wake effect in the wind energy utilization optimization model is described by the Jensen model or the Gaussian wake model, wherein the Jensen model is based on the linear assumption of wake velocity attenuation, and its wake velocity attenuation formula is: , in, It represents the wind speed at a point in the wake area; is the wind speed not affected by the wake, is the induction factor of the unit, is the wake expansion coefficient, is the horizontal distance between the unit and the wake impact point. The Gaussian model simulates the spatial attenuation characteristics of the wake through Gaussian distribution, and the expression is:

[0013] , in, It represents the wind speed at a point in the wake region in three-dimensional space; It represents the horizontal lateral coordinate, which is used to describe the lateral distribution of the wake; is the position of the wake center axis, is the wake expansion width parameter, reflecting the spatial distribution of the wake; The synergistic effect is used to optimize the layout of wind turbines through genetic algorithms, and the objective function is defined as the total power generated by the group, expressed as

[0014] , in, It represents the total power generation of all wind turbines in the wind farm; Represents the total number of wind turbines in the wind farm; For the The generating capacity of each unit, is the corrected wind speed at the location of the unit, is the wind direction angle of the unit.

[0015] As a preferred solution of the method for calculating wind speed, power and electricity of a wind farm described in the present invention, the construction of a robust power prediction model includes constructing multiple random splitting trees, mapping samples to leaf nodes by eigenvalue partitioning, and the abnormal score formula of the isolation forest is defined as: , in, For sample The anomaly score, for The average path length required to be separated among all random trees, is the expected path length of the adjustment factor, which represents the standard path length value when the number of sample points in the forest is n; is the total number of samples in the isolation forest; when (threshold), determine the current data point Extreme weather conditions trigger the switch of forecast mode; is the abnormality determination threshold of the isolation forest; After detecting extreme weather, the robust power prediction model switches from the standard prediction mode to the extreme weather prediction mode, keeps the real-time data stream input uninterrupted, and reloads the parameterized model under extreme weather conditions according to the current environmental variables; The extreme weather prediction model uses random forest or Bayesian regression algorithms. For random forest, the input is the features after anomaly detection. , respectively represent wind speed, wind direction, temperature and air pressure. The nonlinear relationship between wind speed and power is calculated by splitting multiple decision trees. The power prediction value is given by the average output of all trees.

[0016] , in, represents the power prediction value; For the A tree, is the total number of decision trees; for Bayesian regression, define the regression model for power output: , in, is the weight vector, The input feature vector is the noise term, and the Bayesian formula is used to update the posterior distribution of the weights: , in, and are the input and output samples respectively, is the prior distribution.

[0017] As a preferred solution of the method for calculating wind speed, power and electricity in a wind farm described in the present invention, the method of dynamically adjusting the prediction model parameters in combination with the online learning mechanism includes dynamically updating the model with the latest real-time data through a sliding window, and the window length Determined by the characteristics of the current wind speed fluctuation, when the input data arrives, the splitting parameters of the random forest or the prior weights of the Bayesian regression are adjusted through the online learning mechanism, and the formula is

[0018] , in, is the learning rate, is the updated i-th weight value, is the i-th weight value before updating, is the gradient update amount of the current window, ensuring that the model has dynamic adaptability under extreme weather conditions.

[0019] A computer device comprises a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of a method for calculating wind speed, power and electricity in a wind farm when executing the computer program.

[0020] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of a method for calculating wind speed, power and electricity in a wind farm.

[0021] Beneficial effects of the present invention: The present invention comprehensively improves the operating efficiency and adaptability of wind farms by combining dynamic wind speed correction, optimized layout of wind turbines and robust power prediction. In terms of wind speed correction, dynamic parameters such as turbulence intensity and mixing layer height are introduced based on the wind speed profile model, which significantly improves the accuracy and spatiotemporal adaptability of wind speed calculation. In terms of optimized layout of wind turbines, the position and parameters of the units are reasonably adjusted through wake effect modeling and genetic algorithm optimization, which improves the utilization rate of wind energy and reduces the impact of wake. In terms of extreme weather power prediction, anomaly detection is performed using the isolation forest algorithm, and high-precision prediction is achieved through random forest and Bayesian regression models, which enhances the stability and robustness of the power prediction model under extreme conditions. The present invention provides a systematic solution from wind speed correction to power prediction, which is suitable for wind farm operation and management under complex environments and dynamic operating conditions, and lays a solid foundation for the efficient and intelligent utilization of wind energy. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0023] Figure 1 A schematic flow chart of a method for calculating wind speed, power and electricity in a wind farm provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0024] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0025] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0026] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0027] The present invention is described in detail with reference to schematic diagrams. When describing the embodiments of the present invention, for the sake of convenience, the cross-sectional diagrams showing the device structure will not be partially enlarged according to the general scale, and the schematic diagrams are only examples, which should not limit the scope of protection of the present invention. In addition, in actual production, the three-dimensional dimensions of length, width and depth should be included.

[0028] At the same time, in the description of the present invention, it should be noted that the directions or positional relationships indicated by the terms "upper, lower, inner and outer" are based on the directions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore cannot be understood as limiting the present invention. In addition, the terms "first, second or third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0029] In the present invention, unless otherwise clearly specified and limited, the terms "install, connect, connect" should be understood in a broad sense, for example: it can be a fixed connection, a detachable connection or an integral connection; it can also be a mechanical connection, an electrical connection or a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0030] Example 1, reference Figure 1 , which is the first embodiment of the present invention, provides a method for calculating wind speed, power and electricity in a wind farm, including: S1: Obtain high-resolution meteorological data in the wind farm area, clean the data, process outliers, and align the data in time and space, and correct the wind speed data based on the parameters of the convective boundary layer to generate corrected wind speed data.

[0031] S2: Construct a dynamic wind speed prediction model based on time series to predict the corrected wind speed data.

[0032] S3: Construct a wind energy utilization optimization model based on the wake effect and layout synergy effect between wind turbines.

[0033] S4: For extreme weather conditions, a robust power prediction model is built based on historical weather data and anomaly detection mechanism. The prediction model parameters are dynamically adjusted in combination with the online learning mechanism to complete the calculation of wind farm power.

[0034] The high-resolution meteorological data include wind speed, wind direction, air pressure and temperature data. The data sources include satellite remote sensing, ground meteorological station observations and data collected by radar systems, and the data are aligned in time and space through interpolation algorithms.

[0035] The correction of wind speed data based on the parameters of the convective boundary layer includes: the expression of the wind speed profile model is: , in, is the height Basic wind speed at , without turbulence correction; represents the friction speed; represents the Karman constant; Indicates the surface roughness; Introducing turbulence intensity and mixing layer height ,right To make dynamic adjustments: , The effect of turbulence on wind speed is corrected by an exponential function, and the correction amplitude is proportional to the turbulence intensity and inversely proportional to the height of the mixing layer. The cumulative effect of turbulence on wind speed depends not only on height but also on shear stress. and turbulence variance The role of is expressed in integral form as: , in, is the similarity function, Calculation formula to express height Stability length The nonlinear effect of represents the turbulent shear stress, combined with the friction velocity and aerodynamic parameters change dynamically.

[0036] The method of correcting the wind speed data based on the parameters of the convective boundary layer includes: and turbulence integral effects After integration, the improved dynamic correction wind speed formula is obtained: , in, For height and time Lower corrected wind speed; is the friction speed; is the Karman constant; is the surface roughness; Measuring height for wind speed; For time; is the turbulence intensity correction factor; is the height of the mixing layer, and time Dynamic correlation; is the shear stress; is the air density; is the turbulence effect adjustment coefficient; is the turbulence field at height The variance at ; is the stability length at time t; is the integral variable, representing the height from the ground surface To target height location.

[0037] The construction of a dynamic wind speed prediction model based on time series includes, based on a long short-term memory network LSTM or Transformer structure in deep learning, processing the corrected wind speed time series data, combining local terrain characteristics and seasonal climate patterns, to obtain a complex dynamic relationship of wind speed changes; Define the input feature embedding matrix as , where all features are normalized to a uniform range of ; Time step and The correlation between them is calculated through the self-attention mechanism

[0038] , in: Query Matrix: Key Matrix: Value matrix:

[0039] Weight Matrix is a learnable parameter, is the dimension of the key vector; For LSTM, the time step The hidden state is updated to: , in, represents the Sigmoid activation function, is the learnable parameter of the network; Final wind speed forecast The output of LSTM or Transformer is mapped through a fully connected layer as follows: , in, are the weights and biases of the fully connected layer.

[0040] The wind energy utilization optimization model is constructed by comprehensively calculating the influence of airflow interaction between wind turbines on the overall power generation capacity based on the wake effect and synergy effect of wind turbines, wherein the wake effect in the wind energy utilization optimization model is described by the Jensen model or the Gaussian wake model, wherein the Jensen model is based on the linear assumption of wake velocity attenuation, and its wake velocity attenuation formula is: , in, It represents the wind speed at a point in the wake area; is the wind speed not affected by the wake, is the induction factor of the unit, is the wake expansion coefficient, is the horizontal distance between the unit and the wake impact point. The Gaussian model simulates the spatial attenuation characteristics of the wake through Gaussian distribution, and the expression is:

[0041] , in, It represents the wind speed at a point in the wake region in three-dimensional space; It represents the horizontal lateral coordinate, which is used to describe the lateral distribution of the wake; is the position of the wake center axis, is the wake expansion width parameter, reflecting the spatial distribution of the wake; the synergistic effect is used to optimize the layout of wind turbines through genetic algorithms, and the objective function is defined as the total power generated by the group, expressed as

[0042] , in, It represents the total power generation of all wind turbines in the wind farm; Represents the total number of wind turbines in the wind farm; For the The generating capacity of each unit, is the corrected wind speed at the location of the unit, is the wind direction angle of the unit.

[0043] The construction of the robust power prediction model includes constructing multiple random splitting trees, mapping samples to leaf nodes by eigenvalue partitioning, and the anomaly score formula of the isolation forest is defined as: , in, For sample The anomaly score, for The average path length required to be separated among all random trees, is the expected path length of the adjustment factor, which represents the standard path length value when the number of sample points in the forest is n; is the total number of samples in the isolation forest; when (threshold), determine the current data point Extreme weather conditions trigger the switch of forecast mode; is the abnormality determination threshold of the isolation forest; After detecting extreme weather, the robust power prediction model switches from the standard prediction mode to the extreme weather prediction mode, keeps the real-time data stream input uninterrupted, and reloads the parameterized model under extreme weather conditions according to the current environmental variables; The extreme weather prediction model uses random forest or Bayesian regression algorithms. For random forest, the input is the features after anomaly detection. , respectively represent wind speed, wind direction, temperature and air pressure. The nonlinear relationship between wind speed and power is calculated by splitting multiple decision trees. The power prediction value is given by the average output of all trees.

[0044] , in, represents the power prediction value; For the A tree, is the total number of decision trees; for Bayesian regression, define the regression model for power output: , in, is the weight vector, is the input feature vector is the noise term, and the Bayesian formula is used to update the posterior distribution of the weights: , in, and are the input and output samples respectively, is the prior distribution.

[0045] The dynamic adjustment of prediction model parameters by combining online learning mechanism includes that the model dynamically updates the most recent real-time data through a sliding window, and the window length Determined by the characteristics of the current wind speed fluctuation, when the input data arrives, the splitting parameters of the random forest or the prior weights of the Bayesian regression are adjusted through the online learning mechanism, and the formula is

[0046] , in, is the learning rate, is the updated i-th weight value, is the i-th weight value before updating, is the gradient update amount of the current window, ensuring that the model has dynamic adaptability under extreme weather conditions.

[0047] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

[0048] Embodiment 2, the second embodiment of the present invention, is different from the previous embodiment in that: If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program codes.

[0049] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0050] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0051] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0052] Although the preferred embodiments of the present application have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0053] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. A method for calculating wind speed, power and electricity in a wind farm, characterized by: include, Obtain high-resolution meteorological data in the wind farm area, clean the data, process outliers, and align the data in time and space, and correct the wind speed data based on the parameters of the convective boundary layer to generate corrected wind speed data; Construct a dynamic wind speed prediction model based on time series to predict the corrected wind speed data; Construct a wind energy utilization optimization model based on the wake effect and layout synergy effect between wind turbines; In response to extreme weather conditions, a robust power prediction model is built based on historical weather data and anomaly detection mechanism. The prediction model parameters are dynamically adjusted in combination with the online learning mechanism to complete the calculation of wind farm power.

2. A method for calculating wind speed, power and electricity in a wind farm according to claim 1, characterized in that: The high-resolution meteorological data include wind speed, wind direction, air pressure and temperature data. The data sources include satellite remote sensing, ground meteorological station observations and data collected by radar systems, and the data are aligned in time and space through interpolation algorithms.

3. A method for calculating wind speed, power and electricity in a wind farm according to claim 2, characterized in that: The correction of wind speed data based on the parameters of the convective boundary layer includes: the expression of the wind speed profile model is: ， in, is the height Basic wind speed at , without turbulence correction; represents the friction speed; represents the Karman constant; Indicates the surface roughness; Introducing turbulence intensity and mixing layer height ,right To make dynamic adjustments: ， The effect of turbulence on wind speed is corrected by an exponential function, and the correction amplitude is proportional to the turbulence intensity and inversely proportional to the height of the mixing layer. The cumulative effect of turbulence on wind speed depends not only on height but also on shear stress. and turbulence variance The role of is expressed in integral form as: ， in, is the similarity function, Calculation formula to express height Stability length The nonlinear effect of represents the turbulent shear stress, combined with the friction velocity and aerodynamic parameters change dynamically.

4. A method for calculating wind speed, power and electricity in a wind farm according to claim 3, characterized in that: The method of correcting the wind speed data based on the parameters of the convective boundary layer includes: and turbulence integral effects After integration, the improved dynamic correction wind speed formula is obtained: ， in, For height and time Lower corrected wind speed; is the friction speed; is the Karman constant is the surface roughness; Measuring height for wind speed; For time; is the turbulence intensity correction factor; is the height of the mixing layer, and time Dynamic correlation; is the shear stress; is the air density; is the turbulence effect adjustment coefficient; is the turbulence field at height The variance at is the stability length at time t; is the integral variable, representing the height from the ground surface To target height location.

5. A method for calculating wind speed, power and electricity in a wind farm according to claim 4, characterized in that: The construction of a dynamic wind speed prediction model based on time series includes, based on a long short-term memory network LSTM or Transformer structure in deep learning, processing the corrected wind speed time series data, combining local terrain characteristics and seasonal climate patterns, to obtain a complex dynamic relationship of wind speed changes; Define the input feature embedding matrix as , where all features are normalized to a uniform range of ; Time step and The correlation between them is calculated through the self-attention mechanism: ， in: Query Matrix: ; Key Matrix: ; Value matrix: ; Weight Matrix is a learnable parameter, is the dimension of the key vector; For LSTM, the time step The hidden state is updated to: ， in, represents the Sigmoid activation function, is the learnable parameter of the network; Final wind speed forecast The output of LSTM or Transformer is mapped through a fully connected layer as follows: ， in, are the weights and biases of the fully connected layer.

6. A method for calculating wind speed, power and electricity in a wind farm according to claim 5, characterized in that: The wind energy utilization optimization model is constructed by comprehensively calculating the influence of airflow interaction between wind turbines on the overall power generation capacity based on the wake effect and synergy effect of wind turbines, wherein the wake effect in the wind energy utilization optimization model is described by the Jensen model or the Gaussian wake model, wherein the Jensen model is based on the linear assumption of wake velocity attenuation, and its wake velocity attenuation formula is: ， in, It represents the wind speed at a point in the wake area; is the wind speed not affected by the wake, is the induction factor of the unit, is the wake expansion coefficient, is the horizontal distance between the unit and the wake impact point. The Gaussian model simulates the spatial attenuation characteristics of the wake through Gaussian distribution, and the expression is: ， in, It represents the wind speed at a point in the wake region in three-dimensional space; It represents the horizontal lateral coordinate, which is used to describe the lateral distribution of the wake; is the position of the wake center axis, is the wake expansion width parameter, reflecting the spatial distribution of the wake; The synergistic effect optimizes the layout of wind turbines through genetic algorithms, and defines the objective function as the total power generated by the group, which is expressed as: ， in, It represents the total power generation of all wind turbines in the wind farm; Represents the total number of wind turbines in the wind farm; For the The generating capacity of each unit, is the corrected wind speed at the location of the unit, is the wind direction angle of the unit.

7. A method for calculating wind speed, power and electricity in a wind farm according to claim 6, characterized in that: The construction of the robust power prediction model includes constructing multiple random splitting trees, mapping samples to leaf nodes by eigenvalue partitioning, and the anomaly score formula of the isolation forest is defined as: ， in, For sample The anomaly score, for The average path length required to be separated among all random trees, is the expected path length of the adjustment factor, which represents the standard path length value when the number of sample points in the forest is n; is the total number of samples in the isolation forest; when When the current data point Extreme weather conditions trigger the switch of forecast mode; is the abnormality determination threshold of the isolation forest; After detecting extreme weather, the robust power prediction model switches from the standard prediction mode to the extreme weather prediction mode, keeps the real-time data stream input uninterrupted, and reloads the parameterized model under extreme weather conditions according to the current environmental variables; The extreme weather prediction model uses random forest or Bayesian regression algorithms. For random forest, the input is the features after anomaly detection. , respectively represent wind speed, wind direction, temperature and air pressure. The nonlinear relationship between wind speed and power is calculated by splitting multiple decision trees. The power prediction value is given by the average output of all trees: ， in, represents the power prediction value; For the A tree, is the total number of decision trees; for Bayesian regression, define the regression model for power output: ， in, is the weight vector, is the input feature vector is the noise term, and the Bayesian formula is used to update the posterior distribution of the weights: ， in, and are the input and output samples respectively, is the prior distribution.

8. A method for calculating wind speed, power and electricity in a wind farm according to claim 7, characterized in that: The dynamic adjustment of prediction model parameters by combining online learning mechanism includes that the model dynamically updates the most recent real-time data through a sliding window, and the window length Determined by the characteristics of the current wind speed fluctuation, when the input data arrives, the splitting parameters of the random forest or the prior weights of the Bayesian regression are adjusted through the online learning mechanism, and the formula is: ， in, is the learning rate, is the updated i-th weight value, is the i-th weight value before updating, is the gradient update amount of the current window, ensuring that the model has dynamic adaptability under extreme weather conditions.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 8 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 8 are implemented.

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