Photovoltaic-wind power collaborative wind resistance dynamic response optimization method

Abstract

The invention provides a photovoltaic-wind power collaborative wind-resistant dynamic response optimization method, which comprises the steps of collecting photovoltaic, wind power and meteorological data by constructing a monitoring network, establishing a structure coupling and wind-resistant dynamic response model after preprocessing, designing a multi-target collaborative optimization function considering stability, power generation efficiency and equipment service life, and optimizing the wind-resistant dynamic response of the structure coupling and the wind-resistant dynamic response of the structure coupling and the wind-resistant dynamic response of the structure coupling and the wind-resistant dynamic response. A distributed optimization algorithm is adopted to solve and generate a cooperative control strategy, the inclination angle of the photovoltaic panel, the array layout and the operation parameters of the wind turbine generator are adjusted in real time, an optimization model is dynamically updated through an effect evaluation mechanism, and efficient and stable operation and equipment protection of the system under the strong wind condition are achieved. The stability and power generation efficiency of the system under strong wind can be improved, the equipment service life can be prolonged, and safe and efficient operation of the new energy power station is achieved.

Description

Technical Field

[0001] This invention relates to the field of new energy power generation technology, and more specifically, to a method for optimizing the dynamic response of photovoltaic-wind power synergy. Background Technology

[0002] With the rapid development of new energy technologies, photovoltaic and wind power, as important clean energy sources, are playing an increasingly important role in the energy structure. Photovoltaic systems convert solar energy into electricity through the photovoltaic effect, while wind power systems utilize wind power to drive a turbine, which then converts mechanical energy into electrical energy through a generator. However, both systems face the impact of wind loads during operation. Wind loads not only affect the system's power generation efficiency but can also lead to structural damage and reduce equipment lifespan.

[0003] While existing photovoltaic (PV) and wind power systems each have their own wind-resistant measures, these measures are mostly designed for individual systems. PV systems typically reduce the impact of wind loads by fixing the angle and layout of the photovoltaic panels, while wind power systems cope with wind speed changes by adjusting the pitch and yaw angles of the wind turbines. However, these wind-resistant measures for individual systems do not take into account the interaction between PV and wind power systems. In actual operation, there is a complex aerodynamic and structural coupling relationship between the PV array and the wind turbine. This coupling relationship causes wind loads to propagate and interact between the two systems, thereby exacerbating system vibration and fatigue damage.

[0004] In implementing the embodiments of the present invention, the prior art has at least the following problems or defects: the existing wind resistance measures of photovoltaic and wind power systems lack synergy and cannot effectively cope with the wind load coupling effect in complex wind field environments, resulting in problems such as insufficient system stability, reduced power generation efficiency and shortened equipment life under strong wind conditions. Summary of the Invention

[0005] This invention provides a method for optimizing the dynamic response of photovoltaic-wind power synergy against wind, comprising: Step S1: Construct a monitoring network to collect real-time operating parameters of photovoltaic systems, wind power systems, and environmental meteorological data; Step S2: Preprocess the collected data, including data cleaning, outlier removal, and data standardization. Step S3: Based on the preprocessed data, establish a structural coupling relationship model of the photovoltaic-wind power system and analyze the propagation characteristics of wind load between the photovoltaic array and the wind turbine. Step S4: Construct a wind-resistant dynamic response model that considers the aerodynamic-structural coupling effect, wherein the model includes the dynamic characteristics of photovoltaic modules and wind turbines; Step S5: Design a multi-objective collaborative optimization function that simultaneously optimizes system stability, power generation efficiency, and equipment lifespan; Step S6: Solve the multi-objective collaborative optimization function using a distributed optimization algorithm to generate a collaborative control strategy for the photovoltaic system and the wind power system; Step S7: Implement a collaborative strategy to adjust the tilt angle of photovoltaic panels, the layout of photovoltaic arrays, and the operating parameters of wind turbines in real time; Step S8: Establish an effect evaluation mechanism to verify the control effect and dynamically update and optimize the model.

[0006] Further, step S1 specifically includes: Step S1.1: Deploy light intensity sensors, temperature sensors, vibration acceleration sensors, and output power sensors in the photovoltaic field area; Step S1.2: Deploy wind speed sensors, wind direction sensors, blade stress sensors, and turbine status sensors in the wind farm area; Step S1.3: Set up a meteorological monitoring station to collect real-time meteorological data, including air pressure, humidity and temperature gradient; Step S1.4: The data from various sensors is collected and sent to the central processing system through the data acquisition unit.

[0007] Furthermore, establishing the structural coupling relationship model in step S3 includes: Step S3.1: Analyze the spatial relationship between the photovoltaic array and the wind turbine; Step S3.2: Establish a propagation path model for wind field disturbances in the photovoltaic-wind power hybrid system; Step S3.3: Determine the dynamic coupling coefficient between the photovoltaic support and the wind turbine tower.

[0008] Furthermore, the construction of the wind-resistant dynamic response model in step S4 includes: Step S4.1: Establish a mathematical model of wind load considering turbulence effects:

[0009] Where ρ is the air density. The drag coefficient, Let A be the mass coefficient, V(t) be the windward area, V(t) be the time-varying wind speed, and F(t) be the function of wind load over time. This is the derivative of wind speed with respect to time; Step S4.2: Construct the system dynamic equations:

[0010] Where M is the mass matrix, C is the damping matrix, K is the stiffness matrix, and X is the displacement vector. For acceleration vectors, For velocity vectors, For wind load, This refers to the coupling force of the system.

[0011] Furthermore, the design of the multi-objective collaborative optimization function in step S5 includes: Step S5.1: Define the system stability objective function:

[0012] in, Let T be the system stability objective function, T be the integration time, and X be the displacement vector. Let Q be the velocity vector, Q be the displacement weight matrix, and R be the velocity weight matrix. This is the transpose of the displacement vector. This is the transpose of the velocity vector; Step S5.2: Define the objective function for power generation efficiency:

[0013] in, Let T be the objective function for power generation efficiency. The efficiency coefficient of a photovoltaic system. For the output power of the photovoltaic system, The efficiency coefficient of the wind power system. This refers to the output power of the wind power system. Step S5.3: Define the objective function for equipment lifespan:

[0014] in, Let T be the objective function for equipment lifespan, and T be the integration time. and t represents the fatigue damage index of photovoltaic modules and wind turbines, respectively, with t being the time variable.

[0015] Furthermore, the multi-objective collaborative optimization function is integrated into a single-objective optimization problem through a weighted sum method:

[0016] Where J is the integrated single-objective optimization function. For the weighting coefficients, satisfying , Let the system stability objective function be... Let the objective function be power generation efficiency. The objective function is the equipment lifespan.

[0017] Furthermore, the distributed optimization algorithm used in step S6 includes: Step S6.1: Decompose the overall optimization problem into a photovoltaic subsystem optimization problem and a wind power system optimization problem. The photovoltaic subsystem optimization problem mainly optimizes the tilt angle and array layout of the photovoltaic panels, while the wind power system optimization problem mainly optimizes the pitch angle and power generation load of the wind turbine. Step S6.2: Design a coordination mechanism based on the consensus algorithm to achieve collaborative optimization of the two subsystems through alternating iterations, including exchanging boundary state information and control parameters in each optimization cycle; Step S6.3: Set convergence criteria. Stop the calculation when the change in the objective function value is less than the first predetermined threshold and the change in the control parameter is less than the second predetermined threshold after multiple consecutive iterations. Step S6.4: In each iteration, sensitivity analysis is used to evaluate the impact of control parameters on system performance and the optimization step size is dynamically adjusted.

[0018] Further, step S7 specifically includes: Step S7.1: Generate photovoltaic panel tilt angle adjustment command based on optimization results, and precisely control the photovoltaic panel angle through stepper motor. The adjustment range covers the first preset angle range, and the control accuracy meets the predetermined accuracy requirements. Step S7.2: Adjust the switching status of each string in the photovoltaic array by using a smart circuit breaker to change the wind-receiving area and wind load distribution of the photovoltaic array; Step S7.3: Adjust the pitch angle of the wind turbine, with the angle adjustment range covering the second preset angle range and the response time not exceeding the predetermined response time requirement; Step S7.4: Control the yaw system of the wind turbine to ensure that the wind turbine is always aligned with the prevailing wind direction and the yaw accuracy meets the predetermined yaw accuracy requirements; Step S7.5: Control the power generation load of the wind turbine through the converter and adjust the aerodynamic damping characteristics of the blades in real time; Step S7.6: Synchronously record the execution time and actual effect of all control actions for subsequent effect evaluation.

[0019] Furthermore, the effect evaluation mechanism in step S8 includes: Step S8.1: Monitor the dynamic response of the controlled system in real time using vibration and displacement sensors, including structural vibration acceleration and displacement amplitude; Step S8.2: Monitor the actual output power of the photovoltaic system and wind power system using power sensors, and calculate the degree of improvement in power generation efficiency; Step S8.3: Establish a multi-indicator evaluation system, including stability improvement rate, power generation efficiency improvement rate, and equipment stress reduction rate; Step S8.4: When the deviation between the evaluation index and the expected target exceeds the predetermined deviation threshold, the model parameter re-identification process is triggered; Step S8.5: Re-identify the parameters of the wind-resistant dynamic response model based on the parameter identification algorithm, including the coefficients of the mass matrix, damping matrix and stiffness matrix; Step S8.6: Update and optimize the control strategy, and send the new control parameters to the actuators.

[0020] Furthermore, it also includes step S9: Step S9.1: Based on historical wind speed data and meteorological forecast information, establish a time series wind speed prediction model to predict the wind speed change trend in a future predetermined period; Step S9.2: Based on the predicted wind speed, calculate the optimal preventive control strategy in advance, including pre-adjusting the tilt angle of the photovoltaic panels and the operating status of the wind turbine. Step S9.3: Compare the deviation between the actual wind speed and the predicted wind speed in real time. When the deviation exceeds the predetermined wind speed deviation threshold, activate the strategy correction mechanism. Step S9.4: Use the rolling optimization method to update the prediction model and control strategy at predetermined time intervals; Step S9.5: Establish an emergency response plan database and activate corresponding protective control strategies when extreme wind conditions are predicted.

[0021] The embodiments of the present invention have at least the following beneficial effects: 1. By constructing a monitoring network to collect photovoltaic, wind power and environmental meteorological data in real time and performing preprocessing, the system's operating status and environmental changes can be comprehensively and accurately grasped, providing reliable data support for subsequent collaborative optimization control. This effectively solves the problem of poor optimization control effect caused by inaccurate or incomplete data in existing technologies, and improves the system's adaptability to complex environments and wind resistance performance.

[0022] 2. A structural coupling relationship model and a wind-resistant dynamic response model for the photovoltaic-wind power system are established, taking into account the aerodynamic-structural coupling effect. This allows for in-depth analysis of the propagation characteristics and interactions of wind loads between the photovoltaic array and the wind turbine, thereby enabling accurate evaluation and optimization of the overall wind resistance performance of the system. This overcomes the problems of insufficient system stability and aggravated equipment fatigue damage caused by the lack of consideration for coupling effects in existing technologies, extends the service life of equipment, and improves the reliability and safety of the system.

[0023] 3. A multi-objective collaborative optimization function was designed and solved using a distributed optimization algorithm to generate a collaborative control strategy. This achieved collaborative optimization of the photovoltaic and wind power systems in terms of stability, power generation efficiency, and equipment lifespan. Furthermore, by adjusting the tilt angle of the photovoltaic panels, array layout, and wind turbine operating parameters in real time, it can quickly respond to wind speed changes, fully leveraging the complementary advantages of photovoltaic and wind power. This effectively solves the problems of insufficient coordination and inability of single-system wind resistance measures in existing technologies to simultaneously address multi-objective optimization, significantly improving the overall performance and power generation efficiency of the system. Attached Figure Description

[0024] The above and other objects, features, and advantages of exemplary embodiments of the present invention will become readily apparent from the following detailed description taken in conjunction with the accompanying drawings. Several embodiments of the invention are illustrated in the drawings by way of example and not limitation, wherein: Figure 1 This is a flowchart illustrating a photovoltaic-wind power synergistic wind resistance dynamic response optimization method provided in an embodiment of the present invention. Detailed Implementation

[0025] The principles and spirit of the invention will now be described with reference to several exemplary embodiments. It should be understood that these embodiments are provided merely to enable those skilled in the art to better understand and implement the invention, and are not intended to limit the scope of the invention in any way. Rather, these embodiments are provided to make the invention more thorough and complete, and to fully convey the scope of the invention to those skilled in the art.

[0026] Those skilled in the art will recognize that embodiments of the present invention can be implemented as a system, apparatus, device, method, or computer program product. Therefore, the present invention can be specifically implemented in the following forms: entirely hardware, entirely software (including firmware, resident software, microcode, etc.), or a combination of hardware and software.

[0027] It should be noted that the number of any elements in the accompanying drawings is for illustrative purposes only and not as a limitation, and any naming is for distinction only and has no limiting meaning.

[0028] The following is for reference. Figure 1 , Figure 1 This is a flowchart illustrating a photovoltaic-wind power synergistic wind resistance dynamic response optimization method according to an embodiment of the present invention. Figure 1 As shown, a photovoltaic-wind power collaborative wind resistance dynamic response optimization method includes: Step S1: Construct a monitoring network to collect real-time operating parameters of photovoltaic systems, wind power systems, and environmental meteorological data; Step S2: Preprocess the collected data, including data cleaning, outlier removal, and data standardization. Step S3: Based on the preprocessed data, establish a structural coupling relationship model of the photovoltaic-wind power system and analyze the propagation characteristics of wind load between the photovoltaic array and the wind turbine. Step S4: Construct a wind-resistant dynamic response model that considers the aerodynamic-structural coupling effect, wherein the model includes the dynamic characteristics of photovoltaic modules and wind turbines; Step S5: Design a multi-objective collaborative optimization function that simultaneously optimizes system stability, power generation efficiency, and equipment lifespan; Step S6: Solve the multi-objective collaborative optimization function using a distributed optimization algorithm to generate a collaborative control strategy for the photovoltaic system and the wind power system; Step S7: Implement a collaborative strategy to adjust the tilt angle of photovoltaic panels, the layout of photovoltaic arrays, and the operating parameters of wind turbines in real time; Step S8: Establish an effect evaluation mechanism to verify the control effect and dynamically update and optimize the model.

[0029] The main operating parameters of a photovoltaic system include the output power of the photovoltaic panels, temperature, and light intensity, which are directly related to the power generation efficiency of the photovoltaic system. The operating parameters of a wind power system include wind speed, wind direction, blade stress, and unit status, which are crucial for the safe operation and power generation efficiency of the wind power system. Environmental meteorological data such as air pressure, humidity, and temperature gradient will affect the operating environment of photovoltaic and wind power systems, thereby affecting the performance of the system.

[0030] Specifically, the acquisition of operating parameters for photovoltaic (PV) systems is primarily achieved by deploying illuminance sensors, temperature sensors, vibration acceleration sensors, and output power sensors within the PV field. These sensors can monitor parameters such as illuminance, temperature, vibration, and output power of the PV panels in real time. Wind power systems acquire operating parameters by deploying wind speed sensors, wind direction sensors, blade stress sensors, and turbine status sensors within the wind farm. These sensors can obtain real-time information on wind speed, wind direction, blade stress, and turbine operating status. The acquisition of environmental meteorological data requires the establishment of meteorological monitoring stations, which use specialized meteorological monitoring equipment to collect data such as air pressure, humidity, and temperature gradients. The deployment of these sensors and monitoring stations provides comprehensive and real-time data support for the system, enabling it to optimize control based on actual operating conditions and environmental changes.

[0031] Preferably, the construction of the monitoring network needs to consider the layout of sensors and the accuracy of data acquisition. The sensor layout should be rationally planned based on the specific terrain and equipment distribution of the photovoltaic and wind farm areas to ensure that the collected data comprehensively reflects the system's operating status. The data acquisition unit needs to have high-precision and high-frequency data acquisition capabilities to ensure data accuracy and real-time performance. Simultaneously, the data acquisition unit also needs to have data preprocessing functions, such as data cleaning, outlier removal, and data standardization, to improve data quality. These preprocessing steps can remove noise and outliers from the data, making the data more accurate and reliable, providing a better foundation for subsequent analysis and optimization.

[0032] In some embodiments, step S1 specifically includes: Step S1.1: Deploy light intensity sensors, temperature sensors, vibration acceleration sensors, and output power sensors in the photovoltaic field area; Step S1.2: Deploy wind speed sensors, wind direction sensors, blade stress sensors, and turbine status sensors in the wind farm area; Step S1.3: Set up a meteorological monitoring station to collect real-time meteorological data, including air pressure, humidity and temperature gradient; Step S1.4: The data from various sensors is collected and sent to the central processing system through the data acquisition unit.

[0033] It should be noted that solar irradiance sensors, temperature sensors, vibration acceleration sensors, and output power sensors are deployed in the photovoltaic (PV) field to monitor key operating parameters of the PV system in real time. Solar irradiance sensors measure the intensity of solar radiation received by the PV panels. Temperature sensors monitor the operating temperature of the PV panels, as temperature changes affect their output power. Vibration acceleration sensors detect the vibration of the PV panels, which is crucial for assessing their structural stability under wind loads. Output power sensors directly measure the power generation of the PV system, serving as a direct indicator of system performance. Similarly, wind speed sensors, wind direction sensors, blade stress sensors, and turbine status sensors are deployed in the wind farm area to monitor key operating parameters of the wind power system. Wind speed and wind direction sensors measure wind intensity and direction, providing fundamental data for wind power system operation.

[0034] Blade stress sensors are used to monitor the stress experienced by wind turbine blades during operation, which is crucial for assessing the structural safety and lifespan of the blades. Unit status sensors are used to monitor the overall operating status of the wind turbine, including parameters such as speed and torque, to ensure the normal operation of the unit.

[0035] Meteorological monitoring stations are set up to collect real-time meteorological data, including air pressure, humidity, and temperature gradients. This meteorological data is crucial for understanding the operating environment of photovoltaic and wind power systems, as changes in meteorological conditions directly affect the system's power generation efficiency and structural stability. Data acquisition units aggregate data from various sensors to a central processing system. This central processing system is the core of the entire monitoring network; it is responsible for receiving, storing, and initially processing data from various sensors, providing data support for subsequent analysis and optimized control.

[0036] Specifically, light intensity sensors are typically installed on or near the surface of photovoltaic (PV) panels to ensure accurate measurement of the solar radiation reaching them. Temperature sensors can be installed on or near the back of the PV panels to monitor their operating temperature. Vibration acceleration sensors are installed on the PV panel's support or the panel itself to detect vibration signals. Output power sensors are connected to the output of the PV system to directly measure the generated power. In wind farms, wind speed and direction sensors are typically installed on the top of the wind turbine tower or other suitable locations to measure wind intensity and direction. Blade stress sensors are installed inside or on the surface of the wind turbine blades to monitor stress distribution. Unit status sensors are installed on various key components of the wind turbine, such as the generator and gearbox, to monitor the unit's operating status. Meteorological monitoring stations typically include various meteorological instruments, such as barometers, hygrometers, and temperature sensors, to measure meteorological parameters such as air pressure, humidity, and temperature gradients. The data acquisition unit is a device that integrates multiple data interfaces and processing functions. It can receive analog or digital signals from various sensors and perform preliminary processing, such as data format conversion and data compression, before sending the processed data to the central processing system.

[0037] Preferably, the layout and installation location of the sensors need to be optimized based on the specific terrain, equipment distribution, and operating environment of the photovoltaic and wind farm areas. For example, in a photovoltaic farm area, the illuminance sensor should be installed in an area that avoids shading and reflection to ensure measurement accuracy. The temperature sensor should be installed in a location that represents the operating temperature of the photovoltaic panel, such as the center area on the back of the photovoltaic panel. The vibration acceleration sensor should be installed in a location that can effectively detect the vibration signal of the photovoltaic panel, such as by installing it on the supporting structure of the photovoltaic panel.

[0038] In wind farm areas, the installation height of wind speed and direction sensors should be consistent with the hub height of the wind turbine to ensure that the measured wind speed and direction accurately reflect the operating environment of the wind turbine. The installation location of blade stress sensors should cover the main stress areas of the blade, such as the root and middle of the blade. The installation location of unit status sensors should be able to comprehensively monitor key components of the wind turbine, such as the generator stator and rotor, and the input and output shafts of the gearbox. The location of the meteorological monitoring station should be representative of the meteorological conditions of the entire farm area, such as being installed in an open area to avoid the influence of surrounding buildings or terrain on the measurement results. The configuration of the data acquisition unit should consider the stability and reliability of data transmission, such as using wired or wireless communication to transmit data to the central processing system, and being equipped with data backup and fault alarm functions to ensure data integrity and normal system operation.

[0039] In some embodiments, establishing the structural coupling relationship model in step S3 includes: Step S3.1: Analyze the spatial relationship between the photovoltaic array and the wind turbine; Step S3.2: Establish a propagation path model for wind field disturbances in the photovoltaic-wind power hybrid system; Step S3.3: Determine the dynamic coupling coefficient between the photovoltaic support and the wind turbine tower.

[0040] It should be noted that the structural coupling model of the photovoltaic-wind power system is established to deeply analyze the interaction between the photovoltaic array and the wind turbine. The photovoltaic array and the wind turbine have a certain spatial positional relationship, which affects the propagation of wind loads between them. The structural coupling model helps us understand this propagation characteristic, thus providing a theoretical basis for optimizing system design and control strategies.

[0041] Specifically, spatial relationship refers to the relative positions of the photovoltaic array and wind turbine in geographic space, including their distance, angle, and layout. The wind field disturbance propagation path model describes how wind loads propagate in the photovoltaic-wind power hybrid system, which is crucial for assessing the system's overall wind resistance. The dynamic coupling coefficient quantifies the interaction strength between the photovoltaic support structure and the wind turbine tower, and is a key parameter for evaluating the system's structural stability.

[0042] Specifically, analyzing the spatial relationship between photovoltaic (PV) arrays and wind turbines requires considering factors such as the PV array's installation location, orientation, and tilt angle, as well as the wind turbine layout and spacing. These parameters directly affect the propagation path and intensity of wind loads between the two. For example, the tilt angle of the PV array affects its windward area, thus influencing the magnitude of the wind load; the spacing of the wind turbines affects the flow characteristics of the wind field. Establishing a wind field disturbance propagation path model requires considering meteorological parameters such as wind speed, wind direction, and turbulence intensity, as well as the geometry and layout of the PV array and wind turbines. These parameters collectively determine the propagation mode of wind loads within the system. Determining the dynamic coupling coefficient between the PV support and the wind turbine tower requires analyzing their vibration response and interaction under wind loads through experiments or numerical simulations. The dynamic coupling coefficient is typically a dimensionless parameter that reflects the vibration transmission efficiency between the PV support and the wind turbine tower.

[0043] Preferably, the process of establishing a structural coupling relationship model includes: The specific locations and layouts of the photovoltaic array and wind turbine units are determined through field measurements or GIS data. Computational Fluid Dynamics (CFD) software is used to simulate the flow characteristics of the wind field, analyzing the distribution of wind speed, direction, and turbulence intensity in the photovoltaic-wind power hybrid system. Based on the simulation results and actual measurement data, a wind field disturbance propagation path model is established. This model can be a mathematical model based on physical principles or a data-driven machine learning model. Through experiments or numerical simulations, the vibration responses of the photovoltaic support structure and wind turbine towers under different wind speeds and directions are measured, and the dynamic coupling coefficient is calculated.

[0044] In practice, different wind speeds and directions can be set to record parameters such as vibration acceleration and displacement of the photovoltaic support and wind turbine tower. Then, the specific values ​​of the dynamic coupling coefficients can be determined through methods such as correlation analysis or regression analysis. These steps ensure the accuracy and reliability of the structural coupling relationship model, providing a solid foundation for subsequent optimization of wind-resistant dynamic response.

[0045] In some embodiments, constructing the wind-resistant dynamic response model in step S4 includes: Step S4.1: Establish a mathematical model of wind load considering turbulence effects:

[0046] Where ρ is the air density. The drag coefficient, Let A be the mass coefficient, V(t) be the windward area, V(t) be the time-varying wind speed, and F(t) be the function of wind load over time. This is the derivative of wind speed with respect to time; Step S4.2: Construct the system dynamic equations:

[0047] Where M is the mass matrix, C is the damping matrix, K is the stiffness matrix, and X is the displacement vector. For acceleration vectors, For velocity vectors, For wind load, This refers to the coupling force of the system.

[0048] The wind-resistant dynamic response model comprises a wind load mathematical model and system dynamic equations. The wind load mathematical model describes the variation of wind load with time and space, while the system dynamic equations describe the motion state of the system under wind load. The combination of these two models provides theoretical support for optimizing control strategies.

[0049] Specifically, the parameters in the wind load mathematical model include air density, drag coefficient, mass coefficient, windward area, and time-varying wind speed. Air density refers to the mass of air per unit volume, typically 1.225 kg per cubic meter under standard atmospheric conditions.

[0050] The drag coefficient and mass coefficient are dimensionless parameters related to the shape and surface properties of an object; they can be obtained through experiments or theoretical calculations. Windward area refers to the projected area of ​​an object in the windward direction; for photovoltaic panels and wind turbine blades, this area varies with their tilt angle and layout. Time-varying wind speed refers to the change in wind speed over time, which can be measured in real time by meteorological monitoring stations.

[0051] The parameters in the system dynamics equations include the mass matrix, damping matrix, stiffness matrix, displacement vector, acceleration vector, and velocity vector. The mass matrix reflects the inertial characteristics of the system, the damping matrix describes the energy dissipation characteristics of the system, and the stiffness matrix represents the elastic characteristics of the system. Displacement, acceleration, and velocity vectors are physical quantities that describe the system's motion state; they can be obtained through sensor measurements or numerical simulations.

[0052] Preferably, the process of constructing a wind-resistant dynamic response model includes: The parameters in the wind load mathematical model are determined based on experimental data or theoretical calculations. These parameters can be obtained through wind tunnel experiments or computational fluid dynamics (CFD) simulations. When establishing the system dynamic equations, the mass matrix, damping matrix, and stiffness matrix need to be determined according to the specific structure and layout of the photovoltaic-wind power system. These matrices can be obtained using finite element analysis (FEA) software or through experimental modal analysis. Then, the wind load mathematical model and the system dynamic equations are combined to form a complete wind-resistant dynamic response model. In practical applications, the dynamic response of the system under different wind speed conditions, including parameters such as displacement, velocity, and acceleration, can be calculated by inputting real-time wind speed data. These calculations can be performed using numerical integration methods, such as the Runge-Kutta method. In this way, the dynamic response of the system can be monitored and predicted in real time, providing data support for optimizing control strategies.

[0053] In some embodiments, designing the multi-objective collaborative optimization function in step S5 includes: Step S5.1: Define the system stability objective function:

[0054] in, Let T be the system stability objective function, T be the integration time, and X be the displacement vector. Let Q be the velocity vector, Q be the displacement weight matrix, and R be the velocity weight matrix. This is the transpose of the displacement vector. This is the transpose of the velocity vector; Step S5.2: Define the objective function for power generation efficiency:

[0055] in, Let T be the objective function for power generation efficiency. The efficiency coefficient of a photovoltaic system. For the output power of the photovoltaic system, The efficiency coefficient of the wind power system. This refers to the output power of the wind power system. Step S5.3: Define the objective function for equipment lifespan:

[0056] in, Let T be the objective function for equipment lifespan, and T be the integration time. and t represents the fatigue damage index of photovoltaic modules and wind turbines, respectively, with t being the time variable.

[0057] It should be noted that the multi-objective collaborative optimization function is designed to simultaneously optimize multiple key performance indicators in a photovoltaic-wind power system, including system stability, power generation efficiency, and equipment lifespan. This multi-objective optimization method comprehensively considers various aspects of the system, ensuring that power generation efficiency is improved without sacrificing system stability and equipment lifespan. By defining different objective functions, these performance indicators can be quantified, and the optimal control strategy can be found through optimization algorithms.

[0058] Specifically, the system stability objective function assesses the vibration and displacement of the system under wind loads, ensuring stable operation under strong wind conditions. The power generation efficiency objective function measures the power output of the photovoltaic and wind power systems, improving overall system efficiency through optimized control strategies. The equipment lifespan objective function focuses on equipment fatigue damage, extending equipment lifespan by reducing stress and vibration. These objective functions are integrated into a single-objective optimization problem using a weighted sum method, where weight coefficients can be adjusted according to actual needs to balance the priorities of different objectives.

[0059] Preferably, the process of designing a multi-objective collaborative optimization function can be further refined into the following steps: First, when defining the system stability objective function, it is necessary to determine the weight matrices of displacement and velocity. These weight matrices can be adjusted according to the actual needs of the system to reflect the stability importance at different locations and directions. Second, when defining the power generation efficiency objective function, it is necessary to determine the efficiency coefficients of the photovoltaic and wind power systems. These coefficients can be obtained through experiments or historical data to reflect the power generation efficiency of different systems under different operating conditions. Finally, when defining the equipment lifespan objective function, it is necessary to determine the fatigue damage indices of photovoltaic modules and wind turbines. These indices can be obtained through experiments or numerical simulations to reflect the fatigue degree of the equipment under different operating conditions. Through these steps, a comprehensive multi-objective collaborative optimization function can be constructed, providing a theoretical basis for optimizing control strategies.

[0060] In some embodiments, the multi-objective collaborative optimization function is integrated into a single-objective optimization problem using a weighted sum method:

[0061] Where J is the integrated single-objective optimization function. For the weighting coefficients, satisfying , Let the system stability objective function be... Let the objective function be power generation efficiency. The objective function is the equipment lifespan.

[0062] It should be noted that the multi-objective collaborative optimization function is integrated into a single-objective optimization problem through a weighted sum method. This is to comprehensively consider multiple optimization objectives and, by assigning appropriate weights to different objectives, to balance the relationships between them during the optimization process, thereby finding a solution that achieves good results across multiple performance metrics. This method allows for trade-offs between system stability, power generation efficiency, and equipment lifespan to meet different needs in practical applications.

[0063] Specifically, the weighting coefficients in the weighted sum method These are parameters used to balance the importance of different objective functions. The value of the weighting coefficient determines the relative importance of each objective function in the overall optimization objective. For example, if system stability is more critical, then a weighting coefficient can be assigned to each objective function. A higher value; if power generation efficiency is the primary concern, then The value can be relatively high; if equipment lifespan is the focus of optimization, then... The value will be larger. The sum of these weight coefficients is 1, ensuring the normalization of the optimization process. Each objective function represents a key performance indicator of the system: the system stability objective function. Focus on the vibration and displacement of the system; objective function for power generation efficiency. Measuring the system's power output; equipment lifespan objective function Pay attention to equipment fatigue damage. By adjusting the weighting coefficients, these objectives can be prioritized according to actual needs.

[0064] In some embodiments, the distributed optimization algorithm used in step S6 includes: Step S6.1: Decompose the overall optimization problem into a photovoltaic subsystem optimization problem and a wind power system optimization problem. The photovoltaic subsystem optimization problem mainly optimizes the tilt angle and array layout of the photovoltaic panels, while the wind power system optimization problem mainly optimizes the pitch angle and power generation load of the wind turbine. Step S6.2: Design a coordination mechanism based on the consensus algorithm to achieve collaborative optimization of the two subsystems through alternating iterations, including exchanging boundary state information and control parameters in each optimization cycle; Step S6.3: Set convergence criteria. Stop the calculation when the change in the objective function value is less than the first predetermined threshold and the change in the control parameter is less than the second predetermined threshold after multiple consecutive iterations. Step S6.4: In each iteration, sensitivity analysis is used to evaluate the impact of control parameters on system performance and the optimization step size is dynamically adjusted.

[0065] Distributed optimization algorithms effectively handle optimization problems of large-scale complex systems by decomposing the overall optimization problem into multiple subproblems and coordinating among them. This approach is particularly suitable for photovoltaic-wind power synergistic systems because it can simultaneously optimize multiple control variables such as photovoltaic panel tilt angle, array layout, and wind turbine operating parameters, ensuring the stability and efficient operation of the system under complex wind farm conditions.

[0066] Specifically, the photovoltaic subsystem optimization problem in distributed optimization algorithms mainly involves optimizing the tilt angle of photovoltaic panels and the array layout. Optimizing the tilt angle aims to maximize the solar radiation received by the panels, thereby improving power generation efficiency; optimizing the array layout aims to reduce shading between panels and the impact of wind load. The wind turbine system optimization problem mainly involves optimizing the pitch angle and power generation load of the wind turbine. Optimizing the pitch angle aims to adjust the windward area of ​​the blades, thereby controlling the speed and power output of the wind turbine; optimizing the power generation load aims to ensure that the wind turbine operates efficiently under different wind speeds. The coordination mechanism is implemented through a consensus algorithm, which allows the two subsystems to exchange boundary state information and control parameters in each optimization cycle, thereby achieving collaborative optimization. The convergence criterion is used to determine when the optimization process stops; the optimization process ends when the changes in the objective function value and control parameters are less than a set threshold. Sensitivity analysis is used to evaluate the impact of control parameters on system performance, so as to dynamically adjust the optimization step size and improve optimization efficiency.

[0067] Preferably, the overall optimization problem is decomposed into two optimization problems: a photovoltaic (PV) subsystem and a wind power system. The input parameters for the PV subsystem optimization problem include the current tilt angle of the PV panels, array layout, irradiance, and temperature; the input parameters for the wind power system optimization problem include the current pitch angle of the wind turbine, power generation load, wind speed, and wind direction. Secondly, a coordination mechanism based on a consensus algorithm is designed to ensure that the two subsystems exchange boundary state information and control parameters in each optimization cycle. For example, the PV subsystem can provide the wind power system with the current PV panel tilt angle and array layout information, while the wind power system can provide the PV subsystem with the current wind speed and wind direction information. Then, a convergence criterion is set; for example, if the objective function value changes by less than 0.01% in multiple consecutive iterations, and the control parameter changes by less than 0.1 degrees (for angle parameters or 1 kW, for power parameters), the calculation stops. Finally, during each iteration, sensitivity analysis is used to evaluate the impact of the control parameters on system performance. For example, by changing the PV panel tilt angle by 1 degree, the change in system power generation efficiency is observed to determine the optimization step size. These steps enable efficient synergistic optimization of photovoltaic-wind power systems, thereby improving the overall performance of the system.

[0068] In some embodiments, step S7 specifically includes: Step S7.1: Generate photovoltaic panel tilt angle adjustment command based on optimization results, and precisely control the photovoltaic panel angle through stepper motor. The adjustment range covers the first preset angle range, and the control accuracy meets the predetermined accuracy requirements. Step S7.2: Adjust the switching status of each string in the photovoltaic array by using a smart circuit breaker to change the wind-receiving area and wind load distribution of the photovoltaic array; Step S7.3: Adjust the pitch angle of the wind turbine, with the angle adjustment range covering the second preset angle range and the response time not exceeding the predetermined response time requirement; Step S7.4: Control the yaw system of the wind turbine to ensure that the wind turbine is always aligned with the prevailing wind direction and the yaw accuracy meets the predetermined yaw accuracy requirements; Step S7.5: Control the power generation load of the wind turbine through the converter and adjust the aerodynamic damping characteristics of the blades in real time; Step S7.6: Synchronously record the execution time and actual effect of all control actions for subsequent effect evaluation.

[0069] The purpose of implementing a collaborative control strategy is to apply the control commands generated by the optimization algorithm to the actual photovoltaic-wind power system. By precisely adjusting the tilt angle of the photovoltaic panels, the layout of the photovoltaic array, and the operating parameters of the wind turbine, the system can achieve efficient operation and improved wind resistance. These control actions need to be carried out by specific actuators, such as stepper motors, smart circuit breakers, and converters, to ensure that the optimization strategy can be accurately implemented, thereby improving the overall performance and stability of the system.

[0070] The tilt angle adjustment command for photovoltaic panels is executed via a stepper motor. The stepper motor can precisely control the angle of the photovoltaic panels, and the adjustment range is typically set based on the installation angle of the photovoltaic panels and local solar radiation conditions, achieving a control accuracy of approximately 0.1 degrees. Intelligent circuit breakers are used to regulate the switching status of each string in the photovoltaic array, optimizing the operating efficiency of the photovoltaic system by changing the wind-receiving area and wind load distribution of the array. The pitch angle adjustment of the wind turbine is accomplished through a dedicated drive system. The adjustment range is set according to the design parameters of the wind turbine, and the response time is typically no more than 1 second to ensure that the wind turbine can quickly adapt to changes in wind speed.

[0071] The yaw system of the wind turbine is controlled by a yaw motor to keep the rotor aligned with the prevailing wind direction, achieving a yaw accuracy of approximately 0.5 degrees. The converter controls the wind turbine's power generation load, optimizing power generation efficiency by adjusting the aerodynamic damping characteristics of the blades. The execution time and actual effects of all control actions need to be recorded synchronously for subsequent performance evaluation and optimization.

[0072] Preferably, based on control commands generated by the optimization algorithm, the control system sends precise tilt adjustment commands to the stepper motor, which then adjusts the angle of the photovoltaic panel accordingly. The intelligent circuit breaker switches between strings in the photovoltaic array according to control commands, changing the array's wind-receiving area. The wind turbine's pitch angle adjustment system rapidly adjusts the pitch angle according to control commands to adapt to wind speed changes. The yaw system adjusts the wind turbine's direction via a yaw motor based on data from the wind direction sensor, ensuring the turbine is always aligned with the prevailing wind direction. The converter adjusts the wind turbine's power generation in real time according to optimized load commands.

[0073] In some embodiments, the effect evaluation mechanism in step S8 includes: Step S8.1: Monitor the dynamic response of the controlled system in real time using vibration and displacement sensors, including structural vibration acceleration and displacement amplitude; Step S8.2: Monitor the actual output power of the photovoltaic system and wind power system using power sensors, and calculate the degree of improvement in power generation efficiency; Step S8.3: Establish a multi-indicator evaluation system, including stability improvement rate, power generation efficiency improvement rate, and equipment stress reduction rate; Step S8.4: When the deviation between the evaluation index and the expected target exceeds the predetermined deviation threshold, the model parameter re-identification process is triggered; Step S8.5: Re-identify the parameters of the wind-resistant dynamic response model based on the parameter identification algorithm, including the coefficients of the mass matrix, damping matrix and stiffness matrix; Step S8.6: Update and optimize the control strategy, and send the new control parameters to the actuators.

[0074] The effectiveness of control strategies can be evaluated by monitoring the system's dynamic response and power generation efficiency in real time. If the evaluation indicators deviate significantly from the expected targets, the model parameters need to be re-identified to ensure that the optimized model accurately reflects the actual operating state of the system, thereby achieving continuous optimization.

[0075] Vibration and displacement sensors are used to monitor the system's dynamic response in real time after control, including structural vibration acceleration and displacement amplitude. The sensor output data reflects the system's stability and structural safety. Power sensors are used to monitor the actual output power of photovoltaic and wind power systems, assessing the impact of the control strategy on power generation efficiency by calculating the degree of improvement. A multi-index evaluation system includes indicators such as stability improvement rate, power generation efficiency improvement rate, and equipment stress reduction rate, obtained by comparing data before and after control. When the deviation of the evaluation index from the expected target exceeds a predetermined threshold, the model parameter re-identification process is triggered. The parameter identification algorithm is used to recalculate the parameters of the wind-resistant dynamic response model, such as the coefficients of the mass matrix, damping matrix, and stiffness matrix. Updating these parameters improves the model's accuracy and adaptability.

[0076] The implementation and effectiveness evaluation mechanism includes: installing vibration and displacement sensors at key locations, such as photovoltaic supports and wind turbine towers, to monitor the system's vibration and displacement in real time; collecting actual output power data of the photovoltaic and wind power systems using power sensors and calculating the degree of improvement in power generation efficiency; establishing a multi-index evaluation system and setting expected target values ​​for indicators such as stability improvement rate, power generation efficiency improvement rate, and equipment stress reduction rate; and triggering a model parameter re-identification process if the deviation between the actual evaluation indicators and the expected targets exceeds a set threshold, for example, if the stability improvement rate is less than 5%. Using parameter identification algorithms, such as least squares or Kalman filtering, the parameters of the wind-resistant dynamic response model are recalculated based on the actual monitoring data. Based on the updated model parameters, the control strategy is adjusted and optimized, and the new control parameters are sent to the actuators to achieve continuous optimization and improvement of the system.

[0077] In some embodiments, step S9 is also included: Step S9.1: Based on historical wind speed data and meteorological forecast information, establish a time series wind speed prediction model to predict the wind speed change trend in a future predetermined period; Step S9.2: Based on the predicted wind speed, calculate the optimal preventive control strategy in advance, including pre-adjusting the tilt angle of the photovoltaic panels and the operating status of the wind turbine. Step S9.3: Compare the deviation between the actual wind speed and the predicted wind speed in real time. When the deviation exceeds the predetermined wind speed deviation threshold, activate the strategy correction mechanism. Step S9.4: Use the rolling optimization method to update the prediction model and control strategy at predetermined time intervals; Step S9.5: Establish an emergency response plan database and activate corresponding protective control strategies when extreme wind conditions are predicted.

[0078] Specifically, a time-series wind speed prediction model is a forecasting tool based on historical data and weather forecast information, used to predict wind speed changes over a future period. Historical wind speed data refers to wind speed information recorded over a past period, reflecting seasonality, diurnal variation, and other characteristics of wind speed. Weather forecast information comes from professional meteorological service agencies, providing forecasts of wind speed, direction, and other weather conditions for the next few days or hours. By combining historical data and weather forecasts, a more accurate wind speed prediction model can be established. The optimal preventative control strategy involves adjusting the tilt angle of solar panels and the operating status of wind turbines in advance based on the predicted wind speed results to adapt to upcoming wind speed changes. The deviation between the actual and predicted wind speeds refers to the difference between the two; when this difference exceeds a predetermined threshold, it indicates that the prediction model needs adjustment or correction. The rolling optimization method is a method for dynamically updating the prediction model and control strategy. It periodically adjusts the prediction model and control strategy based on the latest data and information to maintain its accuracy and effectiveness. The emergency response plan library is a collection of protective control strategies for various extreme wind conditions. When extreme wind conditions are predicted, appropriate strategies can be selected from this library to protect the system.

[0079] Preferably, the process of implementing a time-series wind speed prediction model can be further refined into the following steps: First, collect and organize historical wind speed data, which can be obtained from meteorological stations or the system's monitoring equipment. Second, combine meteorological forecast information with statistical or machine learning methods to construct a time-series wind speed prediction model. For example, an ARIMA model or a neural network model can be used, with input parameters including historical wind speed data and meteorological forecast information. Then, based on the predicted wind speed results, calculate the optimal preventive control strategy in advance. This can be achieved through optimization algorithms, with input parameters including predicted wind speed, current tilt angle of the photovoltaic panels, and current operating status of the wind turbines. Next, compare the deviation between the actual wind speed and the predicted wind speed in real time. If the deviation exceeds a predetermined threshold, such as 10%, a strategy correction mechanism is activated. This can be achieved by adjusting the parameters of the prediction model or recalculating the control strategy. Finally, establish an emergency plan database. When extreme wind conditions are predicted, such as wind speed exceeding design limits, corresponding protective control strategies can be quickly activated, such as adjusting the tilt angle of the photovoltaic panels to a safe position or adjusting the wind turbines to a low-load operating state. Through these steps, the adaptability and safety of the system under complex wind field conditions can be improved.

[0080] This invention constructs a monitoring network to collect real-time photovoltaic, wind power, and environmental meteorological data, and performs preprocessing to comprehensively and accurately grasp the system's operating status and environmental changes, thereby improving the system's adaptability to complex environments and its wind resistance performance. It establishes a structural coupling relationship model and a wind resistance dynamic response model for the photovoltaic-wind power system, enabling precise evaluation and optimization of the system's overall wind resistance performance. This overcomes the problems of insufficient system stability and exacerbated equipment fatigue damage caused by the lack of consideration for coupling effects in existing technologies. Furthermore, it designs a multi-objective collaborative optimization function and uses a distributed optimization algorithm to solve it, generating a collaborative control strategy. This effectively solves the problems of insufficient coordination and inability of single-system wind resistance measures in existing technologies to simultaneously address multi-objective optimization, significantly improving the system's overall performance and power generation efficiency.

[0081] Furthermore, the storage medium in the embodiments of this application stores program instructions capable of implementing all the above methods. These program instructions can be stored in the storage medium in the form of a software product, including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks, or terminal devices such as computers, servers, mobile phones, and tablets.

[0082] The above description is merely an explanation of some preferred embodiments of the present invention and the technical principles employed. Those skilled in the art should understand that the scope of the invention as described in the embodiments of the present invention is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-described inventive concept. For example, technical solutions formed by substituting the above-described features with (but not limited to) technical features with similar functions disclosed in the embodiments of the present invention.

Claims

1. A method for optimizing the dynamic response of photovoltaic-wind power synergy against wind, characterized in that, Includes the following steps: Step S1: Construct a monitoring network to collect real-time operating parameters of photovoltaic systems, wind power systems, and environmental meteorological data; Step S2: Preprocess the collected data, including data cleaning, outlier removal, and data standardization. Step S3: Based on the preprocessed data, establish a structural coupling relationship model of the photovoltaic-wind power system and analyze the propagation characteristics of wind load between the photovoltaic array and the wind turbine. Step S4: Construct a wind-resistant dynamic response model that considers the aerodynamic-structural coupling effect, wherein the model includes the dynamic characteristics of photovoltaic modules and wind turbines; Step S5: Design a multi-objective collaborative optimization function that simultaneously optimizes system stability, power generation efficiency, and equipment lifespan; Step S6: Solve the multi-objective collaborative optimization function using a distributed optimization algorithm to generate a collaborative control strategy for the photovoltaic system and the wind power system; Step S7: Implement a collaborative strategy to adjust the tilt angle of photovoltaic panels, the layout of photovoltaic arrays, and the operating parameters of wind turbines in real time; Step S8: Establish an effect evaluation mechanism to verify the control effect and dynamically update and optimize the model.

2. The method as described in claim 1, characterized in that, Step S1 includes: Step S1.1: Deploy light intensity sensors, temperature sensors, vibration acceleration sensors, and output power sensors in the photovoltaic field area; Step S1.2: Deploy wind speed sensors, wind direction sensors, blade stress sensors, and turbine status sensors in the wind farm area; Step S1.3: Set up a meteorological monitoring station to collect real-time meteorological data, including air pressure, humidity and temperature gradient; Step S1.4: The data from various sensors is collected and sent to the central processing system through the data acquisition unit.

3. The method as described in claim 1, characterized in that, The step S3 of establishing the structural coupling relationship model includes: Step S3.1: Analyze the spatial relationship between the photovoltaic array and the wind turbine; Step S3.2: Establish a propagation path model for wind field disturbances in the photovoltaic-wind power hybrid system; Step S3.3: Determine the dynamic coupling coefficient between the photovoltaic support and the wind turbine tower.

4. The method as described in claim 1, characterized in that, The construction of the wind-resistant dynamic response model in step S4 includes: Step S4.1: Establish a mathematical model of wind load considering turbulence effects: Where ρ is the air density. The drag coefficient, Let A be the mass coefficient, V(t) be the windward area, V(t) be the time-varying wind speed, and F(t) be the function of wind load over time. This is the derivative of wind speed with respect to time; Step S4.2: Construct the system dynamic equations: Where M is the mass matrix, C is the damping matrix, K is the stiffness matrix, and X is the displacement vector. For acceleration vectors, For velocity vectors, For wind load, This refers to the coupling force of the system.

5. The method as described in claim 1, characterized in that, The design of the multi-objective collaborative optimization function in step S5 includes: Step S5.1: Define the system stability objective function: in, Let T be the system stability objective function, T be the integration time, and X be the displacement vector. Let Q be the velocity vector, Q be the displacement weight matrix, and R be the velocity weight matrix. This is the transpose of the displacement vector. This is the transpose of the velocity vector; Step S5.2: Define the objective function for power generation efficiency: in, Let T be the objective function for power generation efficiency. The efficiency coefficient of a photovoltaic system. For the output power of the photovoltaic system, The efficiency coefficient of the wind power system. This refers to the output power of the wind power system. Step S5.3: Define the objective function for equipment lifespan: in, Let T be the objective function for equipment lifespan, and T be the integration time. and t represents the fatigue damage index of photovoltaic modules and wind turbines, respectively, with t being the time variable.

6. The method as described in claim 5, characterized in that, The multi-objective collaborative optimization function is integrated into a single-objective optimization problem through a weighted sum method: Where J is the integrated single-objective optimization function. Let be the weighting coefficient, satisfying , Let the system stability objective function be... Let the objective function be power generation efficiency. The objective function is the equipment lifespan.

7. The method as described in claim 1, characterized in that, The distributed optimization algorithm used in step S6 includes: Step S6.1: Decompose the overall optimization problem into a photovoltaic subsystem optimization problem and a wind power system optimization problem. The photovoltaic subsystem optimization problem mainly optimizes the tilt angle and array layout of the photovoltaic panels, while the wind power system optimization problem mainly optimizes the pitch angle and power generation load of the wind turbine. Step S6.2: Design a coordination mechanism based on the consensus algorithm to achieve collaborative optimization of the two subsystems through alternating iterations, including exchanging boundary state information and control parameters in each optimization cycle; Step S6.3: Set convergence criteria. Stop the calculation when the change in the objective function value is less than the first predetermined threshold and the change in the control parameter is less than the second predetermined threshold after multiple consecutive iterations. Step S6.4: In each iteration, sensitivity analysis is used to evaluate the impact of control parameters on system performance and the optimization step size is dynamically adjusted.

8. The method as described in claim 1, characterized in that, Step S7 specifically includes: Step S7.1: Generate photovoltaic panel tilt angle adjustment command based on optimization results, and precisely control the photovoltaic panel angle through stepper motor. The adjustment range covers the first preset angle range, and the control accuracy meets the predetermined accuracy requirements. Step S7.2: Adjust the switching status of each string in the photovoltaic array by using a smart circuit breaker to change the wind-receiving area and wind load distribution of the photovoltaic array; Step S7.3: Adjust the pitch angle of the wind turbine, with the angle adjustment range covering the second preset angle range and the response time not exceeding the predetermined response time requirement; Step S7.4: Control the yaw system of the wind turbine to ensure that the wind turbine is always aligned with the prevailing wind direction and the yaw accuracy meets the predetermined yaw accuracy requirements; Step S7.5: Control the power generation load of the wind turbine through the converter and adjust the aerodynamic damping characteristics of the blades in real time; Step S7.6: Synchronously record the execution time and actual effect of all control actions for subsequent effect evaluation.

9. The method as described in claim 1, characterized in that, The effect evaluation mechanism in step S8 includes: Step S8.1: Monitor the dynamic response of the controlled system in real time using vibration and displacement sensors, including structural vibration acceleration and displacement amplitude; Step S8.2: Monitor the actual output power of the photovoltaic system and wind power system using power sensors, and calculate the degree of improvement in power generation efficiency; Step S8.3: Establish a multi-indicator evaluation system, including stability improvement rate, power generation efficiency improvement rate, and equipment stress reduction rate; Step S8.4: When the deviation between the evaluation index and the expected target exceeds the predetermined deviation threshold, the model parameter re-identification process is triggered; Step S8.5: Re-identify the parameters of the wind-resistant dynamic response model based on the parameter identification algorithm, including the coefficients of the mass matrix, damping matrix and stiffness matrix; Step S8.6: Update and optimize the control strategy, and send the new control parameters to the actuators.

10. The method as described in claim 1, characterized in that, It also includes step S9: Step S9.1: Based on historical wind speed data and meteorological forecast information, establish a time series wind speed prediction model to predict the wind speed change trend in a future predetermined period; Step S9.2: Based on the predicted wind speed, calculate the optimal preventive control strategy in advance, including pre-adjusting the tilt angle of the photovoltaic panels and the operating status of the wind turbine. Step S9.3: Compare the deviation between the actual wind speed and the predicted wind speed in real time. When the deviation exceeds the predetermined wind speed deviation threshold, activate the strategy correction mechanism. Step S9.4: Use the rolling optimization method to update the prediction model and control strategy at predetermined time intervals; Step S9.5: Establish an emergency response plan database and activate corresponding protective control strategies when extreme wind conditions are predicted.

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