Fan generation power control method and related equipment
By improving the gray wolf algorithm to optimize the dynamic model of wind turbines and generators, and combining it with predictive control algorithms, the problem of unstable power generation of offshore wind turbines was solved, achieving higher control accuracy and service life.
Patent Information
- Application Number
- CN202511152612.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-08-18
- Publication Date
- 2025-11-18
AI Technical Summary
Offshore wind turbines have unstable power generation. Frequent adjustments to control signals due to wind field changes can cause system resonance, affecting the lifespan of the turbines. Existing control models have low accuracy.
An improved gray wolf algorithm is used to optimize the dynamic model of the wind turbine and generator. The initial state space equation is determined through coupled calculation. The optimal control parameters are calculated by combining the predictive control algorithm, and the wind turbine and generator are adjusted to improve the control accuracy.
It improves the accuracy of wind turbine power generation control, reduces system resonance, and extends the service life of wind turbines.
Smart Images
Figure CN120969040A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of wind turbine control technology, and in particular to a wind turbine power generation control method and related equipment. Background Technology
[0002] Offshore wind resources are abundant and can be used for wind power generation; however, due to the influence of climate and environment, the working environment of offshore wind power is harsh, and changes in the offshore wind field lead to unstable wind turbine power generation. At the same time, the wind turbine main control system is frequently adjusted by the wind field, causing the wind turbine system to resonate and affecting the service life of the wind turbine. Existing technology controls the wind turbine system through control models, but the control accuracy is low due to the influence of the accuracy of the model training data. Summary of the Invention
[0003] The main objective of this application is to propose a wind turbine power generation control method and related equipment, which can improve control accuracy.
[0004] To achieve the above objectives, one aspect of this application proposes a method for controlling the power generation control of a wind turbine, the method comprising: Obtain current system status parameters and historical operating data of the wind turbine; The wind turbine dynamic model is determined by optimization calculations based on the historical operating data of the wind turbine and the improved Grey Wolf algorithm; the generator dynamic model is determined by reasoning calculations based on the preset generator structure data. The dynamic models of the wind turbine and the generator are coupled and calculated to determine the initial state space equations; The optimal control parameters are determined by performing optimization calculations based on the initial state space equations, the current system state parameters, and the preset predictive control algorithm; and the wind turbine and generator are adjusted according to the optimal control parameters.
[0005] In some embodiments, the step of determining the wind turbine dynamic model by performing optimization calculations based on the wind turbine's historical operating data and the improved Grey Wolf algorithm specifically includes: A mapping analysis is performed on the historical operating data of the wind turbine to determine a first objective function; wherein, the first objective function represents the mapping relationship between wind speed and wind direction and wind turbine yaw angle and wind turbine deflection angle; The improved gray wolf algorithm is analyzed to determine the convergence factor. The convergence factor is then correlated and corrected with the historical operating data of the wind turbine to determine the corrected convergence factor. The improved Grey Wolf algorithm is position-corrected based on the modified convergence factor, the first objective function, and the historical wind turbine operating data to determine the position update function. The position update function is subjected to nonlinear processing to determine the dynamic model of the wind turbine.
[0006] In some embodiments, the step of determining the optimal control parameters by performing optimization calculations based on the initial state-space equations, the current system state parameters, and a preset predictive control algorithm specifically includes: Based on the initial state space equation and the current system state parameters, a prediction output sequence is determined. Rolling optimization is performed based on the predicted output sequence and the preset predictive control algorithm to determine the optimal control sequence, and the optimal control sequence is then filtered to determine the optimal control parameters.
[0007] In some embodiments, the step of predicting based on the initial state-space equation and the current system state parameters to determine the predicted output sequence specifically includes: Discretize the initial state-space equations to determine the prediction model; Based on the prediction model and the current system state parameters, a prediction state sequence is determined. The prediction output sequence is determined by making predictions based on the predicted state sequence, the prediction model, and the preset prediction step size.
[0008] In some embodiments, the step of performing rolling optimization based on the predicted output sequence and a preset predictive control algorithm to determine the optimal control sequence specifically includes: The system error sequence is determined by calculating the difference between the preset standard data sequence and the predicted output sequence; The target optimization function is determined by substituting the system error sequence into the preset optimization function; and the output constraints are determined based on the operating conditions of the wind turbine system. The target control sequence is determined by calculation based on the objective optimization function and the constraints.
[0009] In some embodiments, the method further includes: Obtain the target state parameters, and calculate the difference between the target state parameters and the predicted output sequence to determine the prediction error; wherein, the target state parameters represent the wind turbine system state parameters after adjustment according to the optimal control parameters; The optimal control sequence is adjusted based on the prediction error.
[0010] To achieve the above objectives, another aspect of this application proposes a control system for wind turbine power generation, the system comprising: The acquisition module is used to acquire current system status parameters and historical operating data of the wind turbine; The construction module is used to perform optimization calculations based on the historical operating data of the wind turbine and the improved Grey Wolf algorithm to determine the dynamic model of the wind turbine; and to perform inference calculations based on the preset generator structure data to determine the dynamic model of the generator. The coupling module is used to perform coupled calculations on the wind turbine dynamic model and the generator dynamic model to determine the initial state space equations. The control module is used to perform optimization calculations based on the initial state space equation, the current system state parameters, and the preset predictive control algorithm to determine the optimal control parameters; and to adjust the wind turbine and generator respectively according to the optimal control parameters.
[0011] To achieve the above objectives, another aspect of this application provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the method described above.
[0012] To achieve the above objectives, another aspect of the embodiments of this application proposes a computer-readable storage medium storing a computer program that, when executed by a processor, implements the methods described above.
[0013] To achieve the above objectives, another aspect of this application provides a computer program product, including a computer program that, when executed by a processor, implements the methods described above. The embodiments of this application include at least the following beneficial effects: This application provides a method, system, electronic device, storage medium, and program product for controlling the power generation of a wind turbine. This solution obtains the current system's state parameters and the wind turbine's historical operating data, analyzes and optimizes the historical operating data based on an improved gray wolf algorithm, and calculates the wind turbine's dynamic model. It also performs inference calculations on the generator's structural data in the wind turbine to determine the corresponding generator dynamic model. The wind turbine dynamic model and the generator dynamic model are coupled and discretized to construct an initial state space equation. Based on the obtained initial state space equation, the obtained current system state parameters, and the predictive control algorithm, optimization calculations are performed to determine the optimal control parameters. The wind turbine system and generator are then adjusted accordingly based on the optimal control parameters. A wind turbine dynamic model and a generator dynamic model are established based on the wind turbine's historical data, and a predictive control algorithm is used to predict the output. The prediction results are then optimized to determine the control parameters, thereby improving control accuracy. Attached Figure Description
[0014] Figure 1 This is a flowchart of a wind turbine power generation control method provided in an embodiment of this application; Figure 2 yes Figure 1 The flowchart of step S102 in the document; Figure 3 yes Figure 1 The flowchart of step S104 in the process; Figure 4 yes Figure 3 The flowchart of step S301 in the process; Figure 5 yes Figure 3 The flowchart of step S302 in the document; Figure 6 This is a flowchart illustrating feedback correction in a wind turbine power generation control method provided in this application embodiment; Figure 7 This is a flowchart of a specific embodiment provided in this application; Figure 8 This is an optimized flowchart in a specific embodiment provided in this application; Figure 9 This is a schematic diagram of the structure of a wind turbine power generation control system provided in an embodiment of this application; Figure 10 This is a schematic diagram of the hardware structure of the electronic device provided in the embodiments of this application. Detailed Implementation
[0015] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to limit it. In the following description, when referring to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with those of this application; they are merely examples of apparatuses and methods consistent with some aspects of the embodiments of this application as detailed in the appended claims.
[0016] It is understood that the terms “first,” “second,” etc., used in this application may be used herein to describe various concepts, but unless otherwise stated, these concepts are not limited by these terms. These terms are only used to distinguish one concept from another. For example, without departing from the scope of the embodiments of this application, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Depending on the context, the words “if,” “when,” or “in response to a determination” as used herein may be interpreted as “when…” or “when…” or “in response to a determination.”
[0017] As used in this application, the terms "at least one", "multiple", "each", "any", etc., "at least one" includes one, two or more, "multiple" includes two or more, "each" refers to each of the corresponding multiples, and "any" refers to any one of the multiples.
[0018] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.
[0019] Before providing a detailed description of the embodiments of this application, some of the nouns and terms involved in the embodiments of this application will be explained first. The nouns and terms involved in the embodiments of this application are subject to the following interpretations.
[0020] MPC (Model Predictive Control) is an advanced control method based on a system dynamic model. It predicts future system behavior using a mathematical model and optimizes the control input in each control cycle to achieve the desired objective. MPC selects the optimal control action by solving an optimization problem, typically taking into account system constraints (such as physical limitations), and executes only the previous step of the optimization result each time. With the arrival of new measurement data, MPC recalculates the control strategy.
[0021] Yaw and Pitch Angle: Yaw refers to the rotation angle of the wind turbine blades and tower relative to the wind direction. It is typically used to adjust the turbine's orientation to maximize wind power utilization efficiency. When the wind direction changes, the turbine rotates the tower through the yaw control system to ensure the blades always face the wind. Pitch Angle, on the other hand, refers to the relative angle between the turbine blades and the wind. Adjusting the pitch angle changes the angle of attack of the blades, thereby controlling the turbine's speed and power output. Both are important parts of the wind turbine control system, used to optimize the turbine's performance and stability.
[0022] Electromagnetic torque: The electromagnetic torque (Te) of the generator in a doubly-fed wind turbine is the torque generated by electromagnetic force.
[0023] The Improved Grey Wolf Optimizer (IGWO) is a variant of the Grey Wolf Optimizer. It enhances the original algorithm's global search capability and convergence speed by introducing new strategies or mechanisms (such as dynamically adjusting parameters, increasing local search ability, and incorporating chaos theory). Its basic idea is to mimic the hunting behavior of a grey wolf pack, finding the optimal solution by simulating the interaction between the pack leader and other members. In applications that identify system models based on input and output data, the Improved Grey Wolf Optimizer exhibits strong global search capabilities, effectively avoiding getting trapped in local optima while improving identification accuracy.
[0024] Dynamic mathematical models are models that use mathematical equations and algorithms to describe the behavior and state changes of physical objects over time. These models are typically based on physical laws (such as Newton's laws and the laws of thermodynamics), using variables and parameters to represent the dynamic characteristics of an object, such as motion, force, and energy changes. By establishing the system's dynamic equations, dynamic mathematical models can predict the reactions and behaviors of physical objects under different conditions. They are commonly used in engineering, control systems, robotics, and mechanical design to help analyze and optimize the performance of physical systems.
[0025] Figure 1 This is an optional flowchart of a wind turbine power generation control method provided in an embodiment of this application. Figure 1 The method may include, but is not limited to, steps S101 to S104.
[0026] Step S101: Obtain the current system status parameters and historical operating data of the wind turbine; Step S102: Optimize and calculate based on historical wind turbine operating data and the improved Grey Wolf algorithm to determine the wind turbine dynamic model; perform inference calculations based on preset generator structure data to determine the generator dynamic model. Step S103: Perform coupled calculations on the wind turbine dynamic model and the generator dynamic model to determine the initial state space equations; Step S104: Optimize the system based on the initial state space equations, current system state parameters, and preset predictive control algorithm to determine the optimal control parameters; and adjust the wind turbine and generator according to the optimal control parameters.
[0027] In steps S101 to S104 of this embodiment, the main control system acquires historical operating data of the wind turbine system, including wind speed, wind direction, generator speed, set reference power output, measured yaw angle, deflection angle, and generator rotor current recorded at historical moments. Simultaneously, it acquires current-moment state parameters of the wind turbine system, including yaw angle, deflection angle, wind speed, and wind direction signals, through data acquisition devices or sensors installed in the wind turbine system. The main control system uses an improved gray wolf algorithm to analyze and calculate the historical operating data, determining the mapping relationship between wind direction, wind speed, yaw angle, and deflection angle, and uses this as the wind turbine dynamic model. Based on the physical structure of the generator in the wind turbine system, the main control system performs inference calculations to construct the generator's power output and rotor current. The mapping relationship between currents is used as the dynamic model of the generator. The main control system couples and discretizes the dynamic models of the wind turbine and the generator to establish the initial state space equation of the wind turbine system. Based on the constructed initial state space equation, the main control system uses a predictive control algorithm and the current state parameters of the wind turbine system to predict the output and determine the predicted output result of the wind turbine system at the current moment. Then, according to the preset optimization objective function and the corresponding output constraints, the predicted output result is rolled to optimize and determine the corresponding control sequence to adjust the wind turbine system and the generator to control the generator's power generation to stabilize at the preset power generation. Based on historical operating data, the improved gray wolf algorithm and predictive control algorithm are used to obtain the corresponding control parameters, reducing the interference of real-time collected training data on the model parameters and improving control accuracy.
[0028] Please see Figure 2 In some embodiments, step S102 may include, but is not limited to, steps S201 to S204: Step S201: Perform mapping analysis on the historical operating data of the wind turbine to determine the first objective function; wherein, the first objective function represents the mapping relationship between wind speed and wind direction and wind turbine yaw angle and wind turbine deflection angle; Step S202: Analyze the improved Grey Wolf algorithm to determine the convergence factor, and then correlate and correct the convergence factor with the historical operating data of the wind turbine to determine the corrected convergence factor. Step S203: Based on the corrected convergence factor, the first objective function, and historical wind turbine operating data, the improved Grey Wolf algorithm is position-corrected to determine the position update function; Step S204: Perform nonlinear processing on the position update function to determine the dynamic model of the wind turbine.
[0029] In step S201 of some embodiments, the main control system analyzes the acquired historical operating data to obtain the mapping relationship between the yaw angle and the deflection angle of the wind turbine system, and uses it as the first objective function. The specific expression formula is as follows: , in, This represents the mapping relationship between yaw angle and deviation angle. The first prediction by the Grey Wolf algorithm Yaw angle at time For the first The yaw angle measured at all times. The first prediction of the Grey Wolf algorithm model Deflection angle at all times For the first The deflection angle measured at all times. Changes in yaw angle between adjacent time points The change in deflection angle at adjacent time points , These are the prediction error weights, , These are the smoothing penalty weights.
[0030] In step S202 of some embodiments, the convergence factor is determined by analyzing the improved Grey Wolf algorithm; the wind speed data in the historical operating data is correlated and corrected with the convergence factor to obtain the corrected convergence factor, and the specific expression formula is as follows: , in, The convergence factor is For wind speed, The rated wind speed is used; the main control system intervenes in the improved gray wolf algorithm to search for the wind turbine's yaw angle, deflection angle, wind speed, and wind direction based on the convergence factor; in this embodiment, the higher the wind speed, the earlier the improved gray wolf algorithm enters the local search.
[0031] In step S203 of some embodiments, the position update formula of the improved Grey Wolf algorithm is modified according to the corrected convergence factor, the established first objective function, and historical running data. The wind direction gradient function is determined based on the historical running data, and then incorporated into the position update formula of the improved Grey Wolf algorithm to obtain the following position update function: , in, For the first Position vector at time , , Yaw angle It is an angle of deflection. For the first objective function on the position vector gradient, For the first Constantly enhance the search direction sensitive to wind direction. For the first target position vectors , For the first One interference factor, These represent the optimal, suboptimal, and third-best solutions of the Grey Wolf algorithm, respectively.
[0032] In step S204 of some embodiments, the corrected position update function is processed using a nonlinear polynomial model to obtain the yaw angle and deflection angle models of the wind turbine system, specifically expressed by the following formulas: , in, , These are the yaw angle model and the deflection angle model, respectively. For wind speed, For the parameters to be identified, The solution obtained by optimizing the solution using the Grey Wolf algorithm is as follows: The established yaw angle model and deflection angle model are used as the dynamic model of the wind turbine.
[0033] Please see Figure 3 In some embodiments, step S104 may include, but is not limited to, steps S301 to S302: Step S301: Make a prediction based on the initial state space equation and the current system state parameters to determine the predicted output sequence; Step S302: Perform rolling optimization based on the predicted output sequence and the preset predictive control algorithm to determine the optimal control sequence, and then filter the optimal control sequence to determine the optimal control parameters.
[0034] In step S301 of some embodiments, the main control module analyzes the obtained initial state space equations, defines state vectors and control quantities, and determines the state transition equations based on the state vectors and control quantities; it then performs statistical analysis based on the obtained state transition equations, state vectors, and control quantities to determine the standard state space equation form, the specific expression of which is as follows: , , in, For the first The state vector at time t, For the first The state vector at time t, For the first The amount of control at any given moment For the first Predicted output at time step The coefficient matrices are as follows: , , , in, This is the coupling coefficient between the yaw angle and the rotational speed. This is the coupling coefficient between the deflection angle and the rotational speed. This is the damping coefficient of the rotational speed with respect to itself. This is the current-induced suppression coefficient for rotational speed. is the coupling coefficient between wind speed and rotational speed. for , , The system can be identified using the Grey Wolf algorithm or obtained from empirical data; the current system state parameters are then substituted into the state-space equations obtained above to perform prediction calculations and determine the corresponding prediction output sequence.
[0035] In step S302 of some embodiments, the predicted output sequence is input into a preset optimization objective function, and the output constraints are combined to determine the minimization optimization function; the minimization optimization function is solved to determine the corresponding optimal control sequence; the first term in the obtained optimal control sequence is used as the optimal control parameter to control the wind turbine system and the generator.
[0036] Please see Figure 4 In some embodiments, step S301 may include, but is not limited to, steps S401 to S403: Step S401: Discretize the initial state space equations to determine the prediction model; Step S402: Make predictions based on the prediction model and the current system state parameters to determine the predicted state sequence; Step S403: Make predictions based on the predicted state sequence, the prediction model, and the preset prediction step size to determine the predicted output sequence.
[0037] In step S401 of some embodiments, the obtained initial state space equations are discretized, and state vectors and control quantities are defined based on the discretized initial state space equations. A state transition equation is then determined based on the state vectors and control quantities, serving as the prediction model for the predictive control algorithm. The expression of this prediction model is as follows: , in, Sampling time, For the generator's moment of inertia, This refers to the electromagnetic torque of the generator, which is related to the rotor current. For torque and Strongly coupled terms The coupling coefficient can be identified using the Grey Wolf algorithm.
[0038] In step S402 of some embodiments, the current system state parameters and the state parameters fed back by the system are substituted into the obtained prediction model for prediction calculation to determine the state sequence data within a prediction step in the future, so as to predict the system output sequence; wherein, the state parameters fed back by the system are collected from the wind turbine system by sensors or data acquisition devices.
[0039] In step S403 of some embodiments, the predicted state sequence data is substituted into a preset model to predict the output, and the output sequence data within the prediction step size of the future is determined. The main control system can perform rolling optimization based on the predicted output sequence data to determine the corresponding control sequence. At the same time, the prediction model of the system can be corrected based on the predicted output sequence and the actual collected output data to improve the prediction accuracy.
[0040] Please see Figure 5 In some embodiments, step S302 may include, but is not limited to, steps S501 to S503: Step S501: Calculate the difference between the preset standard data sequence and the predicted output sequence to determine the system error sequence; Step S502: Substitute the system error sequence into the preset optimization function to determine the target optimization function; and determine the output constraint conditions based on the wind turbine system operating conditions. Step S503: Calculate and determine the target control sequence based on the objective optimization function and constraints.
[0041] In step S501 of some embodiments, error calculation is performed based on the obtained predicted output sequence and the standard output sequence preset in the main control system to determine system error data; the main control system optimizes and corrects the control data output to the wind turbine and generator based on the calculated system error data.
[0042] In step S502 of some embodiments, the main control system performs a high-dimensional transformation on the constructed objective optimization function to obtain the corresponding cost function, substitutes the calculated system error data into the cost function, and applies a minimization constraint to obtain the corresponding objective optimization function. The specific expression is as follows: , in, To minimize the cost function, For the first The optimal control sequence at time 1. For the first Systematic error at time, The corresponding weight matrix is shown below. The main control system constructs corresponding output constraints and control constraints based on the actual output conditions of the wind turbine system. The output constraints are as follows: , , in, This indicates that the deflection angle is affected by the coupling effect of the generator speed. This indicates that the yaw angle is affected by wind speed coupling, and the maximum and minimum values are limited by the actual physical constraints of the system. The control constraints are as follows: , in, For the first Yaw angle adjustment rate at any given time For the first The rate of adjustment of the deflection angle at any given time. This represents the maximum value of the joint adjustment rate.
[0043] In step S503 of some embodiments, the main control system solves the constructed constraints and the established minimization objective optimization function using the interior point method to determine the optimal control increment; and adjusts the wind turbine and generator according to the optimal control increment.
[0044] Please see Figure 6 In some embodiments, the wind turbine power generation control method provided in this application may include, but is not limited to, steps S601 to S602: Step S601: Obtain the target state parameters, and calculate the difference between the target state parameters and the predicted output sequence to determine the prediction error; wherein, the target state parameters represent the wind turbine system state parameters after adjustment based on the optimal control parameters; Step S602: Adjust the optimal control sequence based on the prediction error.
[0045] In step S601 of some embodiments, after the main control system adjusts the yaw angle and deflection angle of the wind turbine system and the rotor current of the generator according to the obtained optimal control parameters, the adjusted state parameters, including the current wind speed, wind direction, yaw, deflection angle, generator rotor speed, and rotor current, are obtained by sensors or data acquisition devices installed on the wind turbine system. The difference between the collected state data and the predicted output at the current moment is calculated to determine the prediction error of the current system.
[0046] In step S602 of some embodiments, the main control system adjusts the output optimal control sequence based on the calculated prediction error to correct the system state, so that the current state of the wind turbine system is close to the set reference value, thereby eliminating the influence of model uncertainty or external interference and ensuring the performance of the control system.
[0047] The following is a detailed description and explanation of the solutions in the embodiments of the present invention, using specific application examples: Please see Figure 7 Historical data of the wind turbine is acquired, including wind speed v, wind direction θ, generator speed ωr, reference power output Pref, measured yaw angle γ, deflection angle β, and rotor current iq. An improved gray wolf optimization algorithm is used to calculate the mapping relationship between wind speed, wind direction, yaw angle, and deflection angle in the historical data, determining this mapping relationship as the wind turbine dynamic model. Based on the physical structure of the doubly-fed generator, the relationship between generator speed and electromagnetic torque is determined, deriving the mathematical relationship between generator electromagnetic torque and rotor current, and further deriving the mathematical relationship between generator power output and rotor current, as the generator dynamic model. The wind turbine dynamic model and the generator dynamic model are coupled and discretized, defining the objective function as the optimization object of the predictive control algorithm. The main control module of the wind turbine... Figure 8 The control flowchart shown optimizes the wind turbine system. The main control model focuses on tracking the reference power and smoothing the control input, and sets constraints. It uses the interior point method to solve the determined optimization objective function, outputs the optimal control increment through rolling optimization, and controls the wind turbine and generator based on the optimal control increment. It also collects the current state data of the controlled wind turbine system in real time to calculate the prediction error, and corrects the prediction results based on the prediction error to eliminate the influence of model uncertainty or external interference, and stabilize the power output of the generator. Furthermore, by controlling the yaw angle and deflection angle of the wind turbine, it reduces the physical resonance of the system and improves the service life of the wind turbine system.
[0048] The embodiments of this application include at least the following beneficial effects: This application provides a method, system, electronic device, storage medium, and program product for controlling the power generation of a wind turbine. This solution obtains the current system's state parameters and the wind turbine's historical operating data, analyzes and optimizes the historical operating data based on an improved gray wolf algorithm, and calculates the wind turbine's dynamic model. It also performs inference calculations on the generator's structural data in the wind turbine to determine the corresponding generator dynamic model. The wind turbine dynamic model and the generator dynamic model are coupled and discretized to construct an initial state space equation. Based on the obtained initial state space equation, the obtained current system state parameters, and the predictive control algorithm, optimization calculations are performed to determine the optimal control parameters. The wind turbine system and generator are then adjusted accordingly based on the optimal control parameters. A wind turbine dynamic model and a generator dynamic model are established based on the wind turbine's historical data, and a predictive control algorithm is used to predict the output. The prediction results are then optimized to determine the control parameters, thereby improving control accuracy.
[0049] Please see Figure 9 This application also provides a wind turbine power generation control system that can implement the above method. The system includes: The acquisition module is used to acquire current system status parameters and historical operating data of the wind turbine; The construction module is used to perform optimization calculations based on the historical operating data of the wind turbine and the improved Grey Wolf algorithm to determine the dynamic model of the wind turbine; and to perform inference calculations based on the preset generator structure data to determine the dynamic model of the generator. The coupling module is used to perform coupled calculations on the wind turbine dynamic model and the generator dynamic model to determine the initial state space equations. The control module is used to perform optimization calculations based on the initial state space equation, the current system state parameters, and the preset predictive control algorithm to determine the optimal control parameters; and to adjust the wind turbine and generator respectively according to the optimal control parameters.
[0050] It is understood that the content of the above method embodiments is applicable to the present device embodiments. The specific functions implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.
[0051] This application also provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the above-described method. This electronic device can be any smart terminal, including tablet computers, in-vehicle computers, etc.
[0052] It is understood that the content of the above method embodiments is applicable to this device embodiment. The specific functions implemented by this device embodiment are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.
[0053] Please see Figure 10 , Figure 10 The hardware structure of an electronic device according to another embodiment is illustrated. The electronic device includes: The processor 1001 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this application. The memory 1002 can be implemented as a read-only memory (ROM), static storage device, dynamic storage device, or random access memory (RAM). The memory 1002 can store the operating system and other applications. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 1002 and is called and executed by the processor 1001 using the methods described in the embodiments of this application. Input / output interface 1003 is used to implement information input and output; The communication interface 1004 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.). Bus 1005 transmits information between various components of the device (e.g., processor 1001, memory 1002, input / output interface 1003, and communication interface 1004); The processor 1001, memory 1002, input / output interface 1003 and communication interface 1004 are connected to each other within the device via bus 1005.
[0054] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method.
[0055] It is understood that the content of the above method embodiments is applicable to this storage medium embodiment. The specific functions implemented in this storage medium embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments.
[0056] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.
[0057] It is understood that the content of the above method embodiments is applicable to the embodiments of this program product. The specific functions implemented by the embodiments of this program product are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.
[0058] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.
[0059] The embodiments described in this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided by the embodiments of this application. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems.
[0060] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of this application, and may include more or fewer steps than shown, or combine certain steps, or different steps.
[0061] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.
[0062] Those skilled in the art will understand that all or some of the steps in the methods disclosed above, as well as the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, or suitable combinations thereof.
[0063] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.
[0064] It should be understood that in this application, "at least one (item)" means one or more, and "more than" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.
[0065] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of the units described above is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.
[0066] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.
[0067] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.
[0068] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes multiple instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing programs, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.
[0069] The preferred embodiments of the present application have been described above with reference to the accompanying drawings, but this does not limit the scope of the claims of the present application. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and substance of the embodiments of the present application shall be within the scope of the claims of the present application.
Claims
1. A method for controlling the power generation capacity of a wind turbine, characterized in that, The method includes: Obtain current system status parameters and historical operating data of the wind turbine; The wind turbine dynamic model is determined by optimization calculations based on the historical operating data of the wind turbine and the improved Grey Wolf algorithm; the generator dynamic model is determined by reasoning calculations based on the preset generator structure data. The dynamic models of the wind turbine and the generator are coupled and calculated to determine the initial state space equations; The optimal control parameters are determined by performing optimization calculations based on the initial state space equations, the current system state parameters, and the preset predictive control algorithm; and the wind turbine and generator are adjusted according to the optimal control parameters.
2. The method according to claim 1, characterized in that, The step of optimizing and calculating based on the historical operating data of the wind turbine and the improved Grey Wolf algorithm to determine the dynamic model of the wind turbine specifically includes: A mapping analysis is performed on the historical operating data of the wind turbine to determine a first objective function; wherein, the first objective function represents the mapping relationship between wind speed and wind direction and wind turbine yaw angle and wind turbine deflection angle; The improved gray wolf algorithm is analyzed to determine the convergence factor. The convergence factor is then correlated and corrected with the historical operating data of the wind turbine to determine the corrected convergence factor. The improved Grey Wolf algorithm is position-corrected based on the modified convergence factor, the first objective function, and the historical wind turbine operating data to determine the position update function. The position update function is subjected to nonlinear processing to determine the dynamic model of the wind turbine.
3. The method according to claim 1, characterized in that, The step of optimizing and calculating the optimal control parameters based on the initial state space equations, the current system state parameters, and a preset predictive control algorithm specifically includes: Based on the initial state space equation and the current system state parameters, a prediction output sequence is determined. Rolling optimization is performed based on the predicted output sequence and the preset predictive control algorithm to determine the optimal control sequence, and the optimal control sequence is then filtered to determine the optimal control parameters.
4. The method according to claim 3, characterized in that, The step of predicting and determining the predicted output sequence based on the initial state space equation and the current system state parameters specifically includes: Discretize the initial state-space equations to determine the prediction model; Based on the prediction model and the current system state parameters, a prediction state sequence is determined. The prediction output sequence is determined by making predictions based on the predicted state sequence, the prediction model, and the preset prediction step size.
5. The method according to claim 3, characterized in that, The step of performing rolling optimization based on the predicted output sequence and a preset predictive control algorithm to determine the optimal control sequence specifically includes: The system error sequence is determined by calculating the difference between the preset standard data sequence and the predicted output sequence; The target optimization function is determined by substituting the system error sequence into the preset optimization function; and the output constraints are determined based on the operating conditions of the wind turbine system. The target control sequence is determined by calculation based on the objective optimization function and the constraints.
6. The method according to claim 3, characterized in that, The method further includes: Obtain the target state parameters, and calculate the difference between the target state parameters and the predicted output sequence to determine the prediction error; wherein, the target state parameters represent the wind turbine system state parameters after adjustment according to the optimal control parameters; The optimal control sequence is adjusted based on the prediction error.
7. A control system for wind turbine power generation, characterized in that, The system includes: The acquisition module is used to acquire current system status parameters and historical operating data of the wind turbine; The construction module is used to perform optimization calculations based on the historical operating data of the wind turbine and the improved Grey Wolf algorithm to determine the dynamic model of the wind turbine; and to perform inference calculations based on the preset generator structure data to determine the dynamic model of the generator. The coupling module is used to perform coupled calculations on the wind turbine dynamic model and the generator dynamic model to determine the initial state space equations. The control module is used to perform optimization calculations based on the initial state space equation, the current system state parameters, and the preset predictive control algorithm to determine the optimal control parameters; and to adjust the wind turbine and generator respectively according to the optimal control parameters.
8. An electronic device, characterized in that, include: At least one processor; At least one memory for storing at least one program; When the at least one program is executed by the at least one processor, the at least one processor implements the method as described in any one of claims 1-6.
9. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the method of any one of claims 1 to 6.
10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the method of any one of claims 1 to 6.