Wind field dynamic optimization method and system based on real-time data adaptive regulation and control

Abstract

The invention discloses a wind field dynamic optimization method and system based on real-time data adaptive regulation and control, and relates to the technical field of wind field optimization, and the method comprises the steps: obtaining wind field environment data and wind turbine generator state data, mapping the data to a state space model, and carrying out the alignment in the same time step; the method comprises the following steps: constructing a wind field dynamic optimization target, calculating uncertainty, dynamic importance and a target collaborative contribution rate, constructing a fuzzy analytic hierarchy process matrix, solving to obtain a weight of the wind field dynamic optimization target, and constructing a dynamic optimization target function; constructing a wind field constraint, solving a dynamic optimization objective function, generating an optimal control parameter set, inputting a safety barrier function for verification, and performing dynamic optimization of the wind field after verification is passed. By constructing a wind field dynamic optimization target, power coordination optimization in the whole field range is achieved, and wake flow damage is reduced; a safety barrier function and a fault-tolerant control mechanism are introduced, and the overall optimization efficiency and reliability of the wind power plant are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of wind farm optimization, and particularly to a wind farm dynamic optimization method and system based on real-time data adaptive regulation. Background Art

[0002] As an important renewable energy source, the development of wind power is limited by the uncertainty and complexity of wind farm operation. The operating efficiency of wind turbines depends not only on environmental factors such as wind speed and wind direction, but also on the state of the turbines themselves. To achieve the overall optimal power generation efficiency of a wind farm, dynamic optimization and regulation of the turbine group are required. However, the current wind farm optimization and regulation methods mainly have the following problems: Lack of coordination in single-machine optimization. Traditional wind turbine control mainly relies on single-machine control strategies, that is, each wind turbine is optimized and adjusted based on its own sensor data, such as adjusting the pitch angle and yaw angle to maximize power output. However, this method ignores the interaction between wind turbines, especially the wake effect of wind turbines, which may lead to a decrease in the power generation efficiency of downstream wind turbines and even exacerbate the fatigue loss of wind turbine components. Uncertainty in wind farm operating conditions. Environmental factors such as wind speed, wind direction, and turbulence intensity have significant time-varying characteristics, and the state variables of wind turbines (such as bearing loads and drivetrain torques) also change with operating conditions. Current optimization methods often rely on static models and are difficult to adapt to changes in wind farm operating conditions in real time, resulting in optimization schemes that are difficult to be effective in the long term. Difficulty in balancing optimization objectives. The optimization of a wind farm is not only about increasing power output, but also about reducing the fatigue load of wind turbines and reducing wake interference. However, there are conflicts between different objectives. For example, increasing power output may exacerbate the load on wind turbine blades and affect the lifespan of wind turbines; excessive reduction of wake effects may affect the power generation efficiency of wind turbines. Current optimization methods are difficult to effectively balance these optimization objectives, resulting in unstable or difficult-to-implement optimization results. Summary of the Invention

[0003] Based on the above-mentioned disadvantages of the prior art, the object of the present invention is to provide a wind farm dynamic optimization method and system based on real-time data adaptive regulation to solve the above technical problems.

[0004] To achieve the above object, the present invention provides the following technical solution: A wind farm dynamic optimization method based on real-time data adaptive regulation, comprising: Obtain wind farm environmental data and wind turbine status data, map the data to a state space model, and align them within the same time step; Construct a wind farm dynamic optimization objective, calculate the uncertainty degree, dynamic importance degree, and target collaborative contribution rate based on the wind farm dynamic optimization objective, construct a fuzzy analytic hierarchy process matrix according to the target collaborative contribution rate, solve to obtain the weight of the wind farm dynamic optimization objective, and construct a dynamic optimization objective function based on the wind farm dynamic optimization objective and the weight; Construct a wind farm constraint, solve the dynamic optimization objective function under the condition of satisfying the wind farm constraint, and generate an optimal control parameter set; Input the optimal control parameter set into the safety barrier function for verification. After passing the verification, execute the optimal control parameter set for wind farm dynamic optimization.

[0005] The present invention is further configured that the wind farm environment data includes wind speed, wind direction, wind shear index, turbulence intensity, wake wind speed, and wake diffusion coefficient; the wind turbine status data includes wind turbine power output, rotational speed, tip speed ratio, pitch angle, yaw angle, bearing load, and drive train torque.

[0006] The present invention is further configured to map the data to a state space model and align it within the same time step, including: Construct an initial state vector and covariance matrix according to the wind farm environment data and wind turbine status data; Perform state prediction based on the initial state vector and output a predicted state vector; Calculate an adaptive state correction coefficient according to the covariance matrix; Update the data according to the predicted state vector and the adaptive state correction coefficient to obtain the wind farm environment data and wind turbine status data aligned within the same time step.

[0007] The present invention is further configured that the calculation logic of the predicted state vector is: , is the data at time's predicted state vector, is the state transition matrix, is the data at time's predicted update vector, is the control input matrix, is the control input vector; The calculation logic of the adaptive state correction coefficient is: , is the adaptive state correction coefficient, is the predicted error covariance matrix, is the observation matrix, is the transpose of the observation matrix, is the measurement noise covariance matrix; The calculation logic for updating the data is: , is the data at time's predicted update vector, is the data at Measured value at a moment.

[0008] The present invention is further configured such that the wind farm dynamic optimization objectives include maximizing the wind turbine power, minimizing the fatigue load, and minimizing the wake interference. Converting the maximization of the wind turbine power into the minimization of the negative wind turbine power, the calculation logic for maximizing the wind turbine power is: , is the number of wind turbines, is the wind turbine power mapping function, , is the air density, is the swept area of the wind turbine, is the power coefficient, is the tip speed ratio, is the pitch angle, is the wind speed; The calculation logic for minimizing the fatigue load is: , is the fatigue load mapping function, , and are empirical parameters, is the wind turbine acceleration, is the torque fluctuation of the drive train; The calculation logic for minimizing the wake interference is: , is the wake effect mapping function, , is the induction factor, is the wake diffusion coefficient, is the wind turbine spacing, is the wind turbine diameter.

[0009] The present invention is further configured such that the calculation logic for the uncertainty degree is: , is the uncertainty degree of the th objective function, is the number of data groups, is the probability density, , is the value of the th objective function in the th group of data; The calculation logic for the dynamic importance degree is: , is the dynamic importance degree of the th objective function; The calculation logic for the target collaborative contribution rate is: , is the th and the The target collaborative contribution rate of the objective function, and is the value of the -th and the -th objective functions, and is the preset threshold.

[0010] The present invention is further configured to construct a fuzzy analytic hierarchy process matrix according to the target collaborative contribution rate , , and solve the eigenvector corresponding to the maximum eigenvalue of the fuzzy analytic hierarchy process matrix : , , is the maximum eigenvalue of the fuzzy analytic hierarchy process matrix, is the final target weight vector, , , and are the weights of the objective functions, and are normalized to construct a dynamic optimization objective function: , is the dynamic optimization objective function.

[0011] The present invention is further configured to that the wind field constraint includes control input constraint and wind turbine state variable constraint, the control input constraint includes pitch angle control constraint and yaw angle control constraint, the wind turbine state variable constraint includes wind turbine rotational speed constraint, fatigue load constraint and wake interference constraint, and the dynamic optimization objective function is solved by controlling the pitch angle and the yaw angle within the constraints to obtain an optimal control parameter set.

[0012] The present invention is further configured to that the construction logic of the safety barrier function is: , is the safety barrier function, is the maximum eigenvalue of the wind turbine, is the influence of the control input on the wind turbine state. When , through verification, the optimal control parameter set is executed for wind field dynamic optimization; When , the fault-tolerant control mechanism is entered for adjustment, , is the adjusted control parameter set, is the current control parameter set, is the optimal control parameter set, is the constraint condition, and within the feasible region, the adjusted control parameter set is solved by the projected gradient descent method, and the adjusted control parameter set is input into the safety barrier function for verification, and is executed if the verification is passed.

[0013] The present invention also provides a wind farm dynamic optimization system based on real-time data adaptive regulation for implementing the above-mentioned wind farm dynamic optimization method based on real-time data adaptive regulation. The system includes: Data acquisition module: Acquire wind farm environment data and wind turbine status data, map the data to a state space model, and align them within the same time step; Objective construction module: Construct a wind farm dynamic optimization objective, calculate the uncertainty degree, dynamic importance degree, and objective collaborative contribution rate based on the wind farm dynamic optimization objective, construct a fuzzy analytic hierarchy process matrix according to the objective collaborative contribution rate, solve to obtain the weight of the wind farm dynamic optimization objective, and construct a dynamic optimization objective function based on the wind farm dynamic optimization objective and the weight; Optimal generation module: Construct wind farm constraints, solve the dynamic optimization objective function under the condition of satisfying the wind farm constraint conditions, and generate an optimal control parameter set; Safety verification module: Input the optimal control parameter set into a safety barrier function for verification. After passing the verification, execute the optimal control parameter set to perform wind farm dynamic optimization.

[0014] The present invention provides a wind farm dynamic optimization method and system based on real-time data adaptive regulation. The method includes acquiring wind farm environment data and wind turbine status data, mapping the data to a state space model, and aligning them within the same time step; constructing a wind farm dynamic optimization objective, calculating the uncertainty degree, dynamic importance degree, and objective collaborative contribution rate based on the wind farm dynamic optimization objective, constructing a fuzzy analytic hierarchy process matrix according to the objective collaborative contribution rate, solving to obtain the weight of the wind farm dynamic optimization objective, and constructing a dynamic optimization objective function based on the wind farm dynamic optimization objective and the weight; constructing wind farm constraints, solving the dynamic optimization objective function under the condition of satisfying the wind farm constraint conditions, and generating an optimal control parameter set; inputting the optimal control parameter set into a safety barrier function for verification. After passing the verification, execute the optimal control parameter set to perform wind farm dynamic optimization. The beneficial effects include: 1. Improve the overall power generation efficiency of the wind farm: By constructing a wind farm dynamic optimization objective, comprehensively considering the wind turbine power, fatigue load, and wake effect, ensure that the wind turbine group generates electricity in the optimal operating state; adopt a state space modeling and real-time data adaptive alignment method to enable the wind turbine control to accurately match the real-time changes in the wind farm environment, thereby improving the power output; by maximizing the optimization objective function of the wind turbine power, avoid the local optimum problem caused by the traditional single-machine optimization method, and achieve power coordination optimization within the entire field; 2. Solve the wake effect and improve the power generation capacity of downstream wind turbines: The wake effect between wind turbines will affect the wind energy utilization efficiency of downstream wind turbines. The present invention reduces wake damage and improves the power generation capacity of downstream wind turbines during the collaborative optimization of a wind turbine group by minimizing the optimization objective function of wake interference; adopts a wake diffusion model, considers wind speed attenuation and wind turbine spacing during the optimization process, reasonably adjusts the control parameters of the wind turbines, makes the wind energy utilization more balanced, and improves the overall efficiency of the wind farm; 3. Intelligent collaborative calculation for objective optimization to improve optimization efficiency: By calculating the objective collaborative contribution rate and dynamically adjusting the optimization objective weights, it ensures that the optimization direction automatically adjusts with environmental changes, improving the calculation efficiency; adopts an uncertainty calculation method to evaluate the importance of each optimization objective under different wind farm environments, avoiding the problem of optimization failure caused by fixed weights, and improving the intelligence level of the optimization strategy; 4. Introduce a safety barrier function to improve system stability and security: While optimizing the control parameters, introduce a safety barrier function to ensure that the optimization scheme meets the safe operation conditions of the wind turbines, avoiding wind turbine failures or abnormalities caused by optimization mistakes; adopt a fault-tolerant control mechanism, when the optimization scheme does not meet the safety requirements, automatically adjust the control input to make adjustments within the feasible region, improving the reliability of wind turbine operation.

[0015] The above description is only an overview of the technical solution of this application. In order to be able to understand the technical means of this application more clearly, it can be implemented according to the content of the specification. And in order to make the above and other purposes, features and advantages of this application more obvious and understandable, the following specifically gives the specific implementation manners of this application. Brief Description of the Drawings

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings. In the drawings: Figure 1 It is a flowchart of a wind farm dynamic optimization method based on real-time data adaptive regulation shown in an exemplary embodiment of the present invention; Figure 2 It is a schematic structural diagram of a wind farm dynamic optimization system based on real-time data adaptive regulation shown in an exemplary embodiment of the present invention. Detailed Description of the Invention

[0017] The embodiments of the present invention will be described below with reference to the accompanying drawings and preferred embodiments. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments. Various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be understood that the preferred embodiments are only for illustrating the present invention and not for limiting the protection scope of the present invention.

[0018] It should be noted that the diagrams provided in the following embodiments only illustrate the basic concept of the present invention in a schematic manner. Therefore, only the components related to the present invention are shown in the diagrams, rather than being drawn according to the number, shape, and size of the components in actual implementation. The type, quantity, and ratio of each component in actual implementation can be arbitrarily changed, and the component layout type may also be more complex.

[0019] In the following description, a large number of details are explored to provide a more thorough explanation of the embodiments of the present invention. However, it is obvious to those skilled in the art that the embodiments of the present invention can be implemented without these specific details. In other embodiments, well-known structures and devices are shown in the form of block diagrams rather than in detail to avoid making the embodiments of the present invention difficult to understand.

[0020] Embodiment 1 A wind farm dynamic optimization method based on real-time data adaptive regulation, as Figure 1 shown, includes: Obtain wind farm environmental data and wind turbine status data, map the data to a state space model, and align them within the same time step; Construct a wind farm dynamic optimization target, calculate the uncertainty degree, dynamic importance degree, and target collaborative contribution rate based on the wind farm dynamic optimization target, construct a fuzzy analytic hierarchy process matrix according to the target collaborative contribution rate, solve to obtain the weight of the wind farm dynamic optimization target, and construct a dynamic optimization objective function according to the wind farm dynamic optimization target and the weight; Construct wind farm constraints, and solve the dynamic optimization objective function under the condition of satisfying the wind farm constraint conditions to generate an optimal control parameter set; Input the optimal control parameter set into a safety barrier function for verification. After passing the verification, execute the optimal control parameter set to perform wind farm dynamic optimization.

[0021] The present invention is further configured such that the wind farm environmental data includes wind speed, wind direction, wind shear index, turbulence intensity, wake wind speed, and wake diffusion coefficient; the wind turbine status data includes the power output of the wind turbine, rotational speed, tip speed ratio, pitch angle, yaw angle, bearing load, and drive train torque. Specifically, the wind speed refers to the air flow velocity within the wind farm. The wind speed directly affects the power output of the wind turbine. The higher the wind speed, the greater the wind energy. However, an excessively high wind speed may cause the wind turbine to enter the protection mode; the wind direction refers to the direction of wind flow, which affects the yaw control of the wind turbine to ensure that the wind turbine always faces the optimal wind energy direction; the wind shear index describes the rate of change of wind speed with height and is represented by the logarithmic wind speed distribution formula: , is the wind speed at height , is the reference height at which the wind speed is measured, is the wind shear index, and its value range is [0.1, 0.3]; the turbulence intensity measures the degree of fluctuation of the wind speed, , is the standard deviation of the wind speed, is the average wind speed; the wake wind speed refers to the wind speed behind the wind turbine blades. Since the wind turbine extracts energy from the wind, the wind speed in the wake area is usually lower than the incoming wind speed, , is the induction factor, is the wake diffusion coefficient, is the wind turbine spacing, is the wind turbine diameter; the wake diffusion coefficient represents the degree of diffusion of the wake in the wind farm and is related to the terrain, wind speed, and turbulence intensity. The wake diffusion trend is calculated using historical data; the power output of the wind turbine refers to the actual output power of the wind turbine at a certain moment; the rotational speed refers to the rotational speed of the wind turbine impeller; the tip speed ratio refers to the ratio of the blade tip speed to the incoming wind speed; the pitch angle refers to the rotational angle of the wind turbine blade; the yaw angle refers to the deflection angle of the wind turbine nacelle relative to the wind direction; the bearing load refers to the torque borne by the main bearing of the wind turbine; the drive train torque refers to the torque borne by the drive train.

[0022] The present invention is further configured to map the data to a state space model and align it within the same time step, including: Construct an initial state vector and covariance matrix based on the wind farm environmental data and the wind turbine status data; specifically, the state vector is used to describe the current key operating parameters of the wind turbine and is jointly composed of the wind farm environmental data and the wind turbine status data; the covariance matrix is used to describe the uncertainty and correlation between state variables. The size of the covariance matrix determines the sensitivity of the system to measurement errors; in the covariance matrix, the diagonal elements represent the variances of the state variables, measuring the uncertainty of the variables, and the non-diagonal elements represent the covariances between the variables, reflecting the correlation between the variables; Perform state prediction based on the initial state vector and output the predicted state vector. The calculation logic of the predicted state vector is as follows: , is the data at the predicted state vector at time, is the state transition matrix, is the data at the predicted update vector at time, is the control input matrix, is the control input vector; specifically, based on the initial state vector, use the state transition matrix to predict the state of the wind turbine to obtain the predicted state vector, thereby providing an estimate of the future state of the wind turbine. This process allows the wind farm optimization control system to take measures in advance to improve the wind energy utilization efficiency, reduce wake interference, and optimize the fatigue load of the wind turbine; starting from the initial state vector , obtain the current state information of the wind turbine such as wind speed, power, and rotational speed; based on the state transition matrix , calculate the predicted state of the wind turbine at the next time, consider the control input , and combine the control input matrix to adjust the predicted state. Finally, obtain the predicted update vector , which provides input for subsequent optimization calculations; Calculate the adaptive state correction coefficient according to the covariance matrix. The calculation logic of the adaptive state correction coefficient is as follows: , is the adaptive state correction coefficient, is the predicted error covariance matrix, is the observation matrix, is the transpose of the observation matrix, is the measurement noise covariance matrix; specifically, during the wind farm optimization control process, the measurement data is often affected by sensor noise, environmental disturbances, and wind farm uncertainties. To ensure more accurate state estimation, it is necessary to calculate the adaptive state correction coefficient to dynamically balance the credibility of the predicted state and the measured state, thereby obtaining a more robust state estimation. Calculate the predicted error covariance matrix , estimate the error range of the system state through the predicted state equation, and measure the uncertainty of the system in different state variables. Calculate the observation matrix and the measurement noise covariance matrix . The observation matrix is used to describe the mapping relationship between the measurement variables and the system state variables, and the measurement noise covariance matrix reflects the error level of the sensor measurement. Calculate the adaptive state correction coefficient , and use to perform state correction to balance the influence of the predicted state and the measured state. If the measurement data is more credible (i.e., small) tend to trust the measurement data. If the measurement data has a large noise (i.e., large) tend to trust the prediction data; Update the data according to the predicted state vector and the adaptive state correction coefficient to obtain the aligned wind field environment data and wind turbine state data within the same time step. The calculation logic for updating the data is: , is the data at the predicted update vector at time is the data at the measured value at time. Specifically, in the wind farm optimization control, the wind turbine state data is collected by multiple sensors. However, due to problems such as different sampling frequencies, data delays, and measurement noises, different data may not be directly aligned, affecting the accuracy of the optimization calculation. To align the wind field environment data and the wind turbine state data within the same time step, data update is required; through the prediction model, calculate the predicted value of the wind turbine state, that is, the optimal estimate obtained based on the historical state and control input when there is no measurement data in the system. The measurement data may contain noises and needs to be corrected in combination with the predicted state to improve the accuracy of the data. Calculate the measurement residual to measure the deviation between the predicted value and the measured value. Use the adaptive state correction coefficient to correct the predicted value to make it closer to the more reliable data. Finally, obtain the updated state to ensure that all wind turbine states are aligned within the same time step.

[0023] The present invention is further set such that the wind farm dynamic optimization objectives include maximizing the wind turbine power, minimizing the fatigue load, and wake interference; Convert the maximization of the wind turbine power into the minimization of the negative wind turbine power. The calculation logic for maximizing the wind turbine power is: , is the number of wind turbines, is the wind turbine power mapping function, , is the air density, is the swept area of the wind turbine, is the power coefficient, is the tip speed ratio, is the pitch angle, is the wind speed; specifically, in wind farm optimization control, maximizing the power output of the wind turbine is one of the core objectives. To unify the format, maximizing the wind turbine power is converted to minimizing the negative wind turbine power. The wind turbine power mapping function of the wind turbine is calculated based on the aerodynamic model. represents the wind energy that the wind turbine can receive, which is related to the air density and the swept area. is the power coefficient, which describes the efficiency of converting wind energy into mechanical power. is the cube of the wind speed, indicating the influence of the wind speed on the power. The calculation logic for minimizing the fatigue load is: , is the fatigue load mapping function. , and are empirical parameters. is the wind turbine acceleration. is the torque fluctuation of the drive train; specifically, during the operation of the wind turbine, components such as the blades, main shaft, and drive train are subjected to alternating loads, and long-term action will cause fatigue damage, affecting the lifespan of the wind turbine. The optimization objective of minimizing the fatigue load is to reduce fatigue damage by controlling the operating parameters of the wind turbine, improving the structural stability and service life of the wind turbine. The calculation logic for minimizing the wake interference is: , is the wake effect mapping function. , is the induction factor. is the wake diffusion coefficient. is the wind turbine spacing. is the wind turbine diameter. Specifically, during the operation of the wind turbine, it extracts energy from the wind, which causes the wind speed behind it to decrease, forming a wake effect. The wake will reduce the incoming wind speed of the downstream wind turbine, affecting the power generation efficiency, and increasing the uneven load between the wind turbines, resulting in fatigue damage. To reduce the wake effect, it is necessary to minimize the difference between the wake wind speed and the free wind speed . The wake wind speed is the wind speed of the wind turbine after being affected by the upstream wind turbine. Since the upstream wind turbine extracts energy from the wind, the downstream wind speed decreases, affecting its power generation efficiency. First, calculate the free wind speed , that is, the incoming wind speed under no interference conditions. Calculate the induction factor of the upstream wind turbine to quantify the degree of wind energy extraction by the wind turbine. Calculate the degree of wake diffusion, that is, the process of the wake gradually recovering with the wind turbine spacing , and calculate the wake wind speed to estimate the affected degree of the downstream wind turbine.

[0024] The present invention is further set as, the calculation logic of the uncertainty degree is: , is the uncertainty degree of the th objective function, is the number of data groups, is the probability density, , is the th objective function value in the th data group; specifically, in wind farm optimization control, the weights of each optimization objective, including maximizing wind turbine power, minimizing fatigue loads, and minimizing wake interference, will vary due to data fluctuations. To quantify the instability of these objective functions, it is necessary to calculate the uncertainty degree, which is used to describe the distribution of objective function values in different data groups and dynamically adjust the objective weights during the optimization process; The calculation logic of the dynamic importance degree is: , is the dynamic importance degree of the th objective function; specifically, in wind farm optimization control, the dynamic importance degree is used to measure the relative weight of each objective during the optimization process to ensure that the optimization strategy can adapt to the operating state of the wind farm; The calculation logic of the objective collaborative contribution rate is: , is the objective collaborative contribution rate of the th and the th objective functions, and are the th and the th objective function values, and are the preset thresholds. Specifically, obtain the numerical values and of the objective functions in the current optimization state, evaluate the importance of each objective in the current optimization task and , if , it indicates that there is almost no interaction between the th and the th objective functions, and the collaborative contribution rate is set to 0. If , it indicates that there is a certain synergistic effect between the two objectives, and calculate its strength , if , it indicates that the synergistic effect between the two objectives is very obvious, and the collaborative contribution rate is set to 1. A high collaborative contribution rate indicates that the two objectives can promote each other, and their weights should be increased simultaneously during optimization.

[0025] The present invention is further configured to construct a fuzzy analytic hierarchy process matrix according to the objective collaborative contribution rate , , according to the fuzzy analytic hierarchy process matrix , solve for the eigenvector corresponding to its maximum eigenvalue : , is the maximum eigenvalue of the fuzzy analytic hierarchy process matrix, is the final target weight vector, , 、 and are the weights of the objective function. After normalization, a dynamic optimization objective function is constructed: , is the dynamic optimization objective function. Specifically, in wind farm optimization, three objectives need to be optimized simultaneously: maximizing the wind turbine power, minimizing the fatigue load, and minimizing the wake interference. However, due to the interaction between different objectives, directly assigning fixed weights results in poor optimization effects. Therefore, it is necessary to dynamically calculate the objective weights based on the objective collaborative contribution rate through the fuzzy analytic hierarchy process matrix and construct an optimization objective function; The present invention is further configured such that the wind farm constraints include control input constraints and wind turbine state variable constraints. The control input constraints include pitch angle control constraints and yaw angle control constraints. The wind turbine state variable constraints include wind turbine speed constraints, fatigue load constraints, and wake interference constraints. By controlling the pitch angle and yaw angle within the constraints, the dynamic optimization objective function is solved to obtain an optimal control parameter set. Specifically, the purpose of wind farm optimization control is to maximize the wind turbine power output while minimizing the fatigue load and wake interference to ensure the stable operation of the wind turbine group under complex wind conditions. However, during the optimization process, the physical constraints of the wind turbine and the control input limitations must be satisfied. Therefore, the present invention introduces wind farm constraints, including: control input constraints (limiting the adjustable range of control variables): pitch angle control constraints, yaw angle control constraints; wind turbine state variable constraints (ensuring that the wind turbine operates within a safe range): wind turbine speed constraints, fatigue load constraints, wake interference constraints; Finally, by optimizing the pitch angle and yaw angle within the constraints, the dynamic optimization objective function is solved to obtain an optimal control parameter set to ensure the operation of the wind turbine under efficient and safe conditions.

[0026] The present invention is further configured such that the construction logic of the safety barrier function is: , is the safety barrier function, is the maximum eigenvalue of the wind turbine, is the influence of the control input on the wind turbine state. When When verified, the optimal control parameter set is executed to perform dynamic optimization of the wind field. Specifically, in the wind field optimization control, the safety of the wind turbine operation is of crucial importance. The optimized control strategies (such as pitch angle and yaw angle adjustment) must ensure that the wind turbine operates within a safe range, avoiding mechanical damage or efficiency reduction caused by overload or unstable operation. Therefore, a safety barrier function is needed to verify the feasibility of the optimization results and ensure that the wind turbine control strategy meets the safety constraints. The safety barrier function is used to measure whether the current state of the wind turbine and the control input meet the safety requirements. Control input The influence gradient on the wind turbine state reflects whether the current control strategy conforms to the safety constraints; when the control strategy passes the safety verification, the optimal control parameter set is executed to achieve dynamic optimization of the wind field; When it enters the fault-tolerant control mechanism for adjustment. , is the adjusted control parameter set, is the current control parameter set, is the optimal control parameter set, is the constraint condition. In the feasible region, the adjusted control parameter set is solved by the projected gradient descent method, and the adjusted control parameter set is input into the safety barrier function for verification. If the verification passes, it is executed. Specifically, in the wind field optimization control, if the safety barrier function is less than 0, the current control parameters may cause the wind turbine to operate unstably or exceed the safety limit. Therefore, a fault-tolerant control mechanism is needed to adjust the control input to meet the safety constraints. This adjustment process uses the projected gradient descent method to optimize the control parameters to make them as close as possible to the optimal control parameter set and meet the safety barrier function constraints. Finally, the adjusted control parameters pass the safety barrier function verification, ensuring that the wind turbine operates within a safe range and executing this optimized control strategy. If it does not pass, the above steps are repeated until the safety barrier function verification passes. This fault-tolerant control mechanism automatically detects the safety of the control parameters and makes minimal adjustments in the risk state to restore the safe operation of the wind turbine, ultimately ensuring that the wind turbine can achieve optimal control under different wind conditions, improving the long-term economic benefits and operation stability of the wind field.

[0027] Embodiment 2 Please refer to Figure 2 which is an exemplary wind field dynamic optimization system based on real-time data adaptive regulation for implementing the above-mentioned wind field dynamic optimization method based on real-time data adaptive regulation. The system includes: Data acquisition module: Acquire wind field environment data and wind turbine state data, map the data to the state space model, and align them within the same time step. Target construction module: Construct the dynamic optimization target of the wind farm, calculate the uncertainty degree, dynamic importance degree and target collaborative contribution rate based on the dynamic optimization target of the wind farm, construct a fuzzy analytic hierarchy process matrix according to the target collaborative contribution rate, solve to obtain the weight of the dynamic optimization target of the wind farm, and construct a dynamic optimization objective function based on the dynamic optimization target of the wind farm and the weight; Optimal generation module: Construct the wind farm constraints, and solve the dynamic optimization objective function under the condition of satisfying the wind farm constraint conditions to generate an optimal control parameter set; Safety verification module: Input the optimal control parameter set into the safety barrier function for verification. After passing the verification, execute the optimal control parameter set to perform dynamic optimization of the wind farm.

[0028] It should be noted that a wind farm dynamic optimization system based on real-time data adaptive regulation provided by the above embodiments and a wind farm dynamic optimization method based on real-time data adaptive regulation provided by the above embodiments belong to the same concept. The specific ways in which each module and unit perform operations have been described in detail in the method embodiments and will not be repeated here. In practical applications, a wind farm dynamic optimization system based on real-time data adaptive regulation provided by the above embodiments can, according to needs, allocate the above functions to different functional modules, that is, divide the internal structure of the system into different functional modules to complete all or part of the functions described above. This is not limited here either.

[0029] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions described in the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center by wire (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that the computer can access or a data storage device such as a server or data center that contains one or more collections of available media. The available medium can be a magnetic medium (such as a floppy disk, hard disk, magnetic tape), an optical medium (such as a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0030] It should be understood that the term "and / or" in this text is merely a description of the association relationship between associated objects, indicating that three relationships can exist. For example, A and / or B can represent three situations: A exists alone, A and B exist simultaneously, and B exists alone. Here, A and B can be singular or plural. Additionally, the character " / " in this text generally represents an "or" relationship between the associated objects before and after, but it may also represent an "and / or" relationship. The specific meaning can be understood by referring to the context before and after.

[0031] In this application, "at least one" means one or more, and "a plurality" means two or more. "At least one of the following items" or its similar expressions refer to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b, or c can represent: a, b, c, a - b, a - c, b - c, or a - b - c, where a, b, and c can be single or multiple.

[0032] It should be understood that in various embodiments of this application, the magnitude of the serial numbers of the above processes does not mean the sequence of execution. The execution sequence of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of this application.

[0033] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed in this text can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods for each specific application to implement the described functions, but such implementation should not be considered to exceed the scope of this application.

[0034] Those skilled in the art can clearly understand that for the convenience and conciseness of description, the specific working processes of the systems, devices, and units described above can refer to the corresponding processes in the foregoing method embodiments, and will not be elaborated herein.

[0035] In several embodiments provided in this application, it should be understood that the disclosed system can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division. In actual implementation, there can be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling or direct coupling or communication connection between each other can be through some interfaces. The indirect coupling or communication connection of the devices or units can be in an electrical, mechanical, or other form.

[0036] The unit described as a separation component may or may not be physically separated. The component shown as a unit may or may not be a physical unit, that is, it may be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0037] In addition, each functional unit in various embodiments of the present application can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit.

[0038] If the above function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art or a 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 and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present application. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs that can store program codes.

[0039] The above is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present application can easily think of changes or substitutions, which should all be covered by the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A wind farm dynamic optimization method based on real-time data adaptive control, characterized in that: include: Obtain wind farm environment data and wind turbine status data, map the data to the state space model, and align them within the same time step; Construct the dynamic optimization target of the wind farm, calculate the uncertainty, dynamic importance and target synergy contribution rate based on the dynamic optimization target of the wind farm, construct the fuzzy hierarchical analysis matrix according to the target synergy contribution rate, solve and obtain the weight of the dynamic optimization target of the wind farm, and construct the dynamic optimization objective function according to the dynamic optimization target and weight of the wind farm; Construct wind farm constraints, solve the dynamic optimization objective function under the conditions of satisfying wind farm constraints, and generate the optimal control parameter set; The optimal control parameter set is input into the safety barrier function for verification. After verification, the optimal control parameter set is executed to perform dynamic optimization of the wind farm.

2. A wind farm dynamic optimization method based on real-time data adaptive control according to claim 1, characterized in that: Wind farm environment data include wind speed, wind direction, wind shear index, turbulence intensity, wake wind speed and wake diffusion coefficient; wind turbine status data include wind turbine power output, speed, tip speed ratio, pitch angle, yaw angle, bearing load and transmission chain torque.

3. A wind farm dynamic optimization method based on real-time data adaptive control according to claim 2, characterized in that: Mapping data to a state-space model, aligned within the same time step, includes: Construct the initial state vector and covariance matrix according to the wind farm environment data and wind turbine status data; Perform state prediction based on the initial state vector and output the predicted state vector; Calculate adaptive state correction coefficients based on the covariance matrix; The data is updated according to the predicted state vector and the adaptive state correction coefficient to obtain the aligned wind farm environment data and wind turbine state data within the same time step.

4. A wind farm dynamic optimization method based on real-time data adaptive control according to claim 3, characterized in that: The calculation logic of the predicted state vector is: , For data exist The predicted state vector at time , is the state transfer matrix, For data exist The moment prediction update vector, is the control input matrix, is the control input vector; The calculation logic of the adaptive state correction coefficient is: , is the adaptive state correction coefficient, is the forecast error covariance matrix, is the observation matrix, is the transpose of the observation matrix, is the measurement noise covariance matrix; The calculation logic for updating the data is: , For data exist The moment prediction update vector, For data exist The measured value at the moment.

5. The method for dynamic optimization of a wind farm based on real-time data adaptive control according to claim 2, characterized in that: Wind farm dynamic optimization objectives include maximizing wind turbine power, minimizing fatigue loads and wake interference; Convert the maximum wind turbine power into the minimum negative wind turbine power. The calculation logic of the maximum wind turbine power is: , is the number of fans, is the wind turbine power mapping function, , is the air density, is the fan swept area, is the power coefficient, is the tip speed ratio, is the pitch angle, is the wind speed; The calculation logic for minimizing fatigue load is: , is the fatigue load mapping function, , and is an empirical parameter, is the fan acceleration, is the torque fluctuation of the transmission chain; The calculation logic for minimizing wake interference is: , is the wake effect mapping function, , As the inducing factor, is the wake diffusion coefficient, is the fan spacing, is the fan diameter.

6. A wind farm dynamic optimization method based on real-time data adaptive control according to claim 5, characterized in that: The calculation logic of uncertainty is: , For the The uncertainty of the objective function is is the number of data sets, is the probability density, , For the The objective function is The value of the group data; The calculation logic of dynamic importance is: , For the The dynamic importance of the objective function; The calculation logic of the target synergy contribution rate is: , For the and The target synergy contribution rate of the objective function is and For the and The value of the objective function, and is the preset threshold.

7. A wind farm dynamic optimization method based on real-time data adaptive control according to claim 6, characterized in that: Constructing fuzzy hierarchical analysis matrix based on target collaborative contribution rate , , according to the fuzzy hierarchical analysis matrix , solve the eigenvector corresponding to its maximum eigenvalue : , is the maximum eigenvalue of the fuzzy hierarchical analysis matrix, is the final target weight vector, , , and Normalize the weight of the objective function and construct a dynamic optimization objective function: , It is a dynamic optimization objective function.

8. The method for dynamic optimization of a wind farm based on real-time data adaptive control according to claim 1, characterized in that: Wind farm constraints include control input constraints and wind turbine state variable constraints. Control input constraints include pitch angle control constraints and yaw angle control constraints. Wind turbine state variable constraints include wind turbine speed constraints, fatigue load constraints and wake interference constraints. The dynamic optimization objective function is solved within the constraint range by controlling the pitch angle and yaw angle to obtain the optimal control parameter set.

9. The method for dynamic optimization of a wind farm based on real-time data adaptive control according to claim 1, characterized in that: The construction logic of the safety barrier function is: , is the safety barrier function, is the maximum characteristic value of the fan, To control the influence of input on the fan status, When the optimal control parameter set is verified, the wind farm is dynamically optimized; when When the fault-tolerant control mechanism is activated, , is the adjusted control parameter set, is the current control parameter set, is the optimal control parameter set, As constraints, the adjusted control parameter set is solved by projected gradient descent method in the feasible domain, and the adjusted control parameter set is input into the safety barrier function for verification, which is then executed.

10. A wind farm dynamic optimization system based on real-time data adaptive control, used to implement a wind farm dynamic optimization method based on real-time data adaptive control as claimed in any one of claims 1 to 9, characterized in that: include: Data acquisition module: obtains wind farm environment data and wind turbine status data, maps the data to the state space model, and aligns them within the same time step; Target construction module: construct the dynamic optimization target of the wind farm, calculate the uncertainty, dynamic importance and target synergy contribution rate based on the dynamic optimization target of the wind farm, construct the fuzzy hierarchical analysis matrix according to the target synergy contribution rate, solve and obtain the weight of the dynamic optimization target of the wind farm, and construct the dynamic optimization target function according to the dynamic optimization target and weight of the wind farm; Optimal generation module: constructs wind field constraints, solves the dynamic optimization objective function under the conditions of satisfying wind field constraints, and generates the optimal control parameter set; Safety verification module: The optimal control parameter set is input into the safety barrier function for verification. After verification, the optimal control parameter set is executed to perform dynamic optimization of the wind farm.

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