A cooperative control method and system suitable for high-altitude wind-solar complementary power generation system

Abstract

The application belongs to the technical field of renewable energy development, and particularly relates to a collaborative control method and system suitable for a high-altitude wind-solar complementary power generation system. The method collects environmental and equipment parameters at a high frequency, constructs a wind power and photovoltaic control model, and predicts wind and light resources based on historical and real-time data. A power generation model is established based on a multi-objective optimization function and system constraints to generate an initial regulation scheme and realize wind-solar-storage collaborative operation. Through real-time collection of operation data, calculation of deviations and analysis of reasons, the system executes immediate control correction or generates a next period strategy correction instruction, thereby completing closed-loop optimization control. The application improves the power supply reliability in high-altitude areas.

Description

Technical Field

[0001] This application belongs to the field of renewable energy development technology, and in particular relates to a collaborative control method and system suitable for high-altitude wind-solar hybrid power generation systems. Background Technology

[0002] In the field of renewable energy development, wind-solar hybrid power generation has become an important direction for energy supply in high-altitude areas because it can alleviate the intermittency problem of single energy sources. In particular, in areas such as Tibet where wind and solar resources are abundant but the power grid is weak, the demand for its application continues to grow.

[0003] Current technologies have yielded preliminary solutions for high-altitude environments: In wind power, customized low-temperature and lightning-resistant turbines, combined with digital twin and AI fault warning technologies, have optimized operational stability under low air density conditions, extending the mean time between failures (MTBF) for some projects; in photovoltaics, UV-resistant photovoltaic modules, intelligent tracking, and IV diagnostic technologies, along with modular design to reduce maintenance complexity, are employed to adapt to environments with high radiation and significant temperature differences. Furthermore, the industry has recognized the seasonal complementarity of wind and solar power, with some large-scale power plants achieving basic power supply by separately configuring wind and solar systems.

[0004] However, existing technologies still have limitations. Wind power and photovoltaic power are mostly controlled independently, lacking a deep collaborative scheduling mechanism based on real-time environmental parameters at high altitudes, thus failing to fully unleash the potential of wind-solar complementarity. Furthermore, they are not adaptable to distributed, small-capacity scenarios. Existing solutions mostly focus on large power plants, making it difficult to meet the low-cost and high-reliability requirements of distributed sites at high altitudes, and their integration with existing site monitoring systems is also low. Summary of the Invention

[0005] To address the aforementioned issues, this application provides a collaborative control method and system applicable to high-altitude wind-solar hybrid power generation systems. By constructing a high-altitude-adaptive wind-solar prediction and multi-objective optimization model, combined with a real-time closed-loop correction mechanism, collaborative control of wind, solar, and energy storage is achieved, improving power supply reliability, equipment lifespan, and system energy efficiency in high-altitude areas.

[0006] Firstly, this application provides a collaborative control method applicable to high-altitude wind-solar hybrid power generation systems, the method comprising: High-frequency acquisition of core environmental parameters at high altitudes and basic parameters of wind, solar and energy storage equipment, and verification of the initial state of each execution unit; Construct control models for wind power systems and photovoltaic systems; Wind power resource forecasting and photovoltaic resource forecasting are based on historical data, real-time sensing data and meteorological linkage data. A wind-solar hybrid power generation model is constructed with a multi-objective function as the core and combined with constraints. The wind power resource forecast results and the photovoltaic resource forecast results are input into the wind-solar hybrid power generation model to obtain the initial control scheme; Based on the initial control scheme, the wind turbine generator set, photovoltaic power generation unit and energy storage device are controlled to operate in a coordinated manner. Real-time data collection of actual system operation is performed and compared with the target state in the initial control scheme. The operation deviation is calculated based on the comparison results. The type, magnitude, and cause of the operational deviations are analyzed. Based on the analysis results, real-time control corrections are executed or strategy correction instructions for the next scheduling cycle are generated to modify the initial control scheme and achieve closed-loop optimized operation of the wind-solar hybrid power generation system.

[0007] Furthermore, Constructing a control model for a wind power system specifically includes: Based on wind speed and generator operating status, the control process of wind turbine generator set is divided into continuous start-up state, underpower state and rated power state, and corresponding pitch angle control strategy and generator slip rate adjustment strategy are adopted in different states to construct variable pitch collaborative control module. The generator slip rate is dynamically adjusted by regulating the equivalent resistance of the rotor circuit in order to convert transient wind energy into wind turbine kinetic energy for storage or release, and to integrate the rotor current control module. Before and after grid connection, different speed controllers are used to operate the system. Based on the wind speed fluctuation frequency, the high-frequency component is allocated to the rotor current control module for rapid power balancing, and the low-frequency component is allocated to the variable pitch collaborative control module for pitch angle adjustment.

[0008] Furthermore, Constructing a photovoltaic system control model specifically includes: A physical equivalent circuit model of a solar cell is established, and a dynamic temperature correction algorithm is introduced into the equivalent circuit model. Construct an inverter efficiency model that matches the load characteristics.

[0009] Furthermore, Wind power resource forecasting and photovoltaic resource forecasting are based on historical data, real-time sensing data, and meteorological linkage data, specifically including: Predicted wind speed and predicted air pressure are obtained based on historical data, real-time sensing data, and meteorological linkage data. By introducing a pressure correction coefficient associated with the predicted air pressure, the predicted wind speed is corrected to obtain an effective wind speed for high-altitude wind energy calculation. Based on the effective wind speed and wind turbine power characteristic curve, the predicted wind power is calculated. Predicted light intensity, predicted ultraviolet radiation intensity, and predicted air pressure are obtained based on historical data, real-time sensing data, and meteorological linkage data. A radiation correction factor associated with the predicted ultraviolet radiation intensity and a heat dissipation correction factor associated with the predicted air pressure are introduced. The predicted photovoltaic power is calculated based on the photovoltaic power under standard test conditions, the predicted irradiance, the radiation correction factor, and the heat dissipation correction factor, combined with the temperature characteristics of the photovoltaic module.

[0010] Furthermore, A wind-solar hybrid power generation model is constructed based on a multi-objective function and combined with constraints, specifically including: The multi-objective function is a weighted sum function that includes maximizing total power generation, minimizing the frequency of energy storage charging and discharging, and minimizing the duration of equipment over-temperature. The constraints include at least the real-time power balance constraint of the system, the upper and lower limits of the output power of wind, solar and energy storage equipment, the upper and lower limits of the state of charge of energy storage equipment, and the upper limit of the safe operating temperature of key equipment.

[0011] Furthermore, The initial control plan was obtained, specifically including: An optimization algorithm based on the multi-objective function and constraints is used to calculate the time-series data of the predicted wind power and predicted photovoltaic power within the scheduling cycle; The solution yields a time-series allocation scheme for the optimal output power of photovoltaic power, the optimal output power of wind power, and the charging and discharging power of energy storage, which optimizes the multi-objective function while meeting load requirements and equipment safety. This scheme serves as the initial control scheme.

[0012] Furthermore, The operational deviation is calculated based on the comparison results, specifically including: The actual output power, actual equipment temperature, and actual state of charge of energy storage are compared with the corresponding target values ​​in the initial control scheme, and the power deviation, temperature deviation, and state of charge deviation are calculated. The power deviation, temperature deviation, and state of charge deviation are compared with their respective preset deviation threshold ranges, and the degree of deviation is determined based on the comparison results.

[0013] Furthermore, The analysis includes the type, magnitude, and causes of the operational deviations, specifically including: The power deviation, temperature deviation, and state of charge deviation are correlated and analyzed with the actual environmental data and equipment operating status data collected simultaneously. Based on the correlation analysis results, the causes of the operational deviations were determined.

[0014] Furthermore, Based on the analysis results, immediate control corrections are executed or policy correction instructions for the next scheduling cycle are generated, specifically including: If the analysis determines that the operational deviation is a serious fault deviation that seriously affects power supply safety or equipment safety, then the immediate control correction process is triggered, and adjustment instructions are immediately sent to the actuators of the corresponding wind turbine generator, photovoltaic power generation unit or energy storage device. The adjustment instructions include reducing output power, starting auxiliary heat dissipation or switching to a safe shutdown state. If the analysis determines that the operational deviation is a non-urgent resource prediction deviation or performance fluctuation deviation, the strategy correction process is triggered. The current deviation data and the corresponding correction suggestions are input as feedback information into the next process of wind and solar resource prediction and wind and solar complementary power generation model construction, which is used to generate the optimized control strategy for the next scheduling cycle.

[0015] Secondly, based on the same inventive concept, this application provides a collaborative control system suitable for high-altitude wind-solar hybrid power generation systems, the system comprising: The data acquisition module is used to collect high-frequency core environmental parameters at high altitudes and basic parameters of wind, solar and energy storage equipment, and to verify the initial state of each execution unit. The initial construction module is used to build the control model of the wind power system and the control model of the photovoltaic system; The prediction module is used to predict wind power resources and photovoltaic resources based on historical data, real-time sensing data and meteorological linkage data. The model building module constructs a wind-solar hybrid power generation model by using a multi-objective function as the core and combining it with constraints. The control module is used to input the wind power resource prediction results and the photovoltaic resource prediction results into the wind-solar hybrid power generation model to obtain the initial control scheme; The operation module is used to control the coordinated operation of the wind turbine generator set, photovoltaic power generation unit and energy storage device based on the initial control scheme; The calculation module is used to collect the actual operating data of the system in real time, compare it with the target state in the initial control scheme, and calculate the operating deviation. The optimization module is used to perform real-time control correction or generate strategy correction instructions for the next scheduling cycle based on the type, magnitude and cause of the operating deviation, so as to correct the initial control scheme and realize the closed-loop optimized operation of the wind-solar hybrid power generation system.

[0016] Compared with the prior art, this application has the following advantages: 1. This application achieves coordinated control of wind, solar and reservoir depth by constructing a high-altitude-adaptive wind and solar prediction and multi-objective optimization model and combining it with a real-time closed-loop correction mechanism. Compared to traditional independent control schemes, this method achieves significant breakthroughs in the following aspects: First, it innovatively introduces air pressure correction coefficients and radiation correction coefficients to address the characteristics of high-altitude environments. Through multi-dimensional data fusion, it improves the accuracy of wind and solar resource prediction, effectively solving the power prediction deviation problem in low-pressure, high-radiation scenarios. Second, it adopts a dual-mode regulation mechanism of variable pitch coordinated control and rotor current control to dynamically optimize wind energy capture efficiency under different operating conditions such as startup, underpower, and rated power. Combined with a photovoltaic model based on physical equivalent circuits, this improves the overall power generation efficiency of the system. Third, by constructing a three-objective weighted function encompassing maximizing power generation, minimizing energy storage losses, and controlling equipment temperature rise, and combining composite constraints of power balance, equipment constraints, and temperature constraints, it achieves globally optimal power allocation within the scheduling cycle. Finally, the established deviation classification correction mechanism can intelligently identify fault deviations and fluctuation deviations, ensuring rapid response in emergency situations and continuously optimizing model parameters through strategy iteration. This forms a closed-loop optimization system of "prediction-regulation-correction," providing a highly reliable and low-maintenance solution for distributed renewable energy power supply.

[0017] 2. This application solves the control deviation problem caused by unclear initial state of equipment in complex high-altitude environments by high-frequency acquisition of environmental and equipment parameters and verification of the initial state of the execution unit, thereby improving the stability of system startup and operation.

[0018] 3. This application achieves fine-grained regulation of wind power by dividing the wind turbine generator into multiple operating states and matching them with differentiated control strategies, combined with modular speed control before and after grid connection, thus solving the problem of unstable power output under wind speed fluctuations.

[0019] 4. This application improves the accuracy of photovoltaic power calculation under high-altitude, low-temperature, and low-pressure environments and reduces model errors by introducing a dynamic temperature correction algorithm and a load-matching inverter efficiency model into the photovoltaic system control model. By introducing a pressure correction coefficient to optimize wind speed prediction and dual correction coefficients for radiation and heat dissipation to optimize photovoltaic power prediction, the impact of special environmental parameters at high altitudes on the accuracy of wind and solar resource prediction is addressed, thus improving the reliability of power generation prediction.

[0020] Other features and advantages of this application will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the application. The objectives and other advantages of this application may be realized and obtained by means of the structures pointed out in the description, claims and drawings. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 A flowchart illustrating a collaborative control method for a high-altitude wind-solar hybrid power generation system according to an embodiment of this application is shown. Figure 2 A control block diagram of a conventional variable pitch wind turbine generator set according to an embodiment of this application is shown. Figure 3 A distribution diagram of a novel variable pitch control system according to an embodiment of this application is shown; Figure 4 A flowchart illustrating the operation of a speed controller A according to an embodiment of this application is shown; Figure 5 A flowchart illustrating the operation of a speed controller B according to an embodiment of this application is shown; Figure 6 A block diagram of a power control system according to an embodiment of this application is shown; Figure 7 A model diagram of a pitch angle control input quantity according to an embodiment of this application is shown; Figure 8 A reference model diagram of pitch angle control power according to an embodiment of this application is shown; Figure 9 A proportional-integral control model diagram of a pitch angle control according to an embodiment of this application is shown; Figure 10 A model diagram of a pitch angle adjustment limiting mechanism according to an embodiment of this application is shown; Figure 11 An equivalent circuit diagram of a solar cell according to an embodiment of this application is shown. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0024] Figure 1A flowchart illustrating a collaborative control method for a high-altitude wind-solar hybrid power generation system according to an embodiment of this application is shown, as follows: Figure 1 As shown in the embodiment of this application, the collaborative control method applicable to high-altitude wind-solar hybrid power generation systems includes, S1 collects high-frequency core environmental parameters and basic parameters of wind, solar and energy storage equipment at high altitudes, and verifies the initial state of each execution unit. In this embodiment of the application, the high-frequency acquisition of core environmental parameters at high altitude includes: atmospheric pressure (P), ambient temperature (T), real-time wind speed (v_actual), light intensity (G_actual), and ultraviolet radiation intensity (UV_actual), with a sampling frequency of ≤1s; The basic parameters of the wind, solar and energy storage equipment include: Basic parameters of wind turbine generator sets: rated power of the turbine, starting wind speed, and rated wind speed; Basic parameters of a photovoltaic power generation unit: standard power of the photovoltaic module under standard test conditions, photovoltaic temperature coefficient; Basic parameters of energy storage equipment: rated capacity and rated voltage of energy storage battery, and the state of charge (SOC) operating range set to adapt to high-altitude and low-temperature environments, where the lower limit of SOC_min is ≥20% and the upper limit of SOC_max is 80%; The initial state verification of each execution unit includes: verifying that the wind turbine pitch control mechanism, rotor current control (RCC) unit, photovoltaic inverter and energy storage converter are fault-free, and confirming that the grounding and lightning protection facilities are normal.

[0025] S2, construct the control model of the wind power system and the control model of the photovoltaic system; In this embodiment of the application, the basic automatic movement control requirements of the wind turbine generator set include: Start-up and grid connection control: When the average wind speed within 10 minutes is within the system's operating range, the wind turbine starts up → soft cut-in state → the unit is connected to the grid; Low wind and reverse power grid disconnection: The unit is in standby wind state → the average wind speed over 10 minutes is less than the disconnection wind speed → disconnection from the grid → wind speed rises again → the fan rotates → grid connection. Normal fault disconnection and shutdown: Parameter exceeding limit, abnormal status → normal shutdown → brake → soft disconnection → mechanical brake → computer automatically recovers; Emergency fault disconnection shutdown: Emergency fault (runaway, overspeed, load loss, etc.) → Emergency shutdown → Yaw control (90 degrees) → Disconnection → Mechanical brake; Safety chain action shutdown: Electrical control system soft protection control failure → hard shutdown → shutdown; High wind disconnection control: When the average wind speed over 10 minutes is greater than 25 m / s → overspeed, overload → disconnection and shutdown → pneumatic brake → yaw control (90 degrees) → disconnection after power reduction → mechanical brake → safe shutdown → after the wind speed returns to the working wind speed zone → automatic wind countermeasure resumes → after the speed increases → automatic grid connection. Wind control: When the unit is in the operating wind zone, the yaw adjustment angle is determined based on the sensitivity of the nacelle. 90-degree deflection for wind control: When the unit is operating at high wind speeds or overspeed, → reduce the power of the wind turbine generator set → shut down safely. → When the 10-minute average wind speed is greater than 25 m / s or exceeds the overspeed limit, → the wind turbine generator set will deflect 90 degrees → pneumatic braking → disconnect from the grid → shutdown; Power regulation: When the unit is running on the grid above the rated wind speed → stall type unit → the generator power will not exceed 15% of the rated power → overload → disconnect from the grid and shut down; Soft cut-in control: soft cut-in, soft disconnection from the grid → limiting the conduction angle → controlling the soft cut-in current at the generator terminal to 1.5 times the rated current → controlling the generator terminal voltage.

[0026] In this embodiment of the application, step S2 specifically includes: S21. Based on wind speed and generator operating status, the control process of the wind turbine generator set is divided into continuous start-up state, underpower state and rated power state. Corresponding pitch angle control strategies and generator slip rate adjustment strategies are adopted in different states to construct a variable pitch collaborative control module. S22 dynamically adjusts the generator slip by adjusting the equivalent resistance of the rotor circuit to convert transient wind energy into wind turbine kinetic energy for storage or release, and integrates a rotor current control module. S23 is operated by different speed controllers before and after grid connection. Based on the wind speed fluctuation frequency, the high-frequency component is allocated to the rotor current control module for rapid power balancing, and the low-frequency component is allocated to the variable pitch collaborative control module for pitch angle adjustment.

[0027] In this embodiment of the application, the control model of the wind power system further includes: Configure multi-level safety protection modules to implement electrical protection and grounding protection, and predefine graded shutdown logic for high wind, overspeed and overload faults to ensure equipment safety under extreme operating conditions.

[0028] In the embodiments of this application, the variable pitch wind turbine generator set can be divided into three operating states according to the role played by the variable pitch system: the starting state (speed control), underpower control (no control), and rated power state (power control).

[0029] 1. Start-up status: When the blades of a variable pitch wind turbine are stationary, the pitch angle is 90°, and the airflow does not generate torque on the blades. When the wind speed reaches the starting wind speed, the blades rotate towards 0° until the airflow creates a certain angle of attack on the blades, and the wind turbine starts. Before the generator is connected to the grid, the pitch setpoint of the variable pitch system is controlled by the generator's speed signal. The speed controller provides a speed reference value according to a certain speed rise slope, and the variable pitch system adjusts the blade pitch angle according to the given speed reference value to control the speed.

[0030] 2. Underpowered state: Underpowered operation refers to the low-power state of a generator after it is connected to the grid, where the generator operates below its rated power due to wind speeds below the rated wind speed. To improve the aerodynamic characteristics of the wind turbine at low wind speeds, Optitip technology is used. This technology adjusts the generator's slip rate according to the wind speed to keep it operating at the optimal tip speed ratio, thereby optimizing power output.

[0031] 3. Rated power condition: When the wind speed reaches or exceeds the rated wind speed, the wind turbine generator enters the rated power state. In the traditional variable pitch control method, speed control is switched to power control, and the variable pitch system begins to control according to the generator's power signal. The power feedback signal is compared with the rated power. When the power exceeds the rated power, the blade pitch rotates by an angle in the direction of decreasing windward area, and vice versa.

[0032] Figure 2 A control block diagram of a conventional variable pitch wind turbine generator set according to an embodiment of this application is shown. See also: Figure 2 The working process involves the system measuring the generator's output power and the wind turbine / generator's rotational speed in real time. The power controller and speed controller calculate the optimal blade pitch angle command based on the difference between the measured and target values. The pitch control system receives the command and drives the pitch-changing mechanism to adjust the blade pitch angle. Changes in the blade angle directly affect the amount of energy captured by the wind turbine. The captured mechanical energy is accelerated by a speed increaser to drive the generator, causing the generator's output power and the system's rotational speed to change accordingly. These changes are then measured again and fed back to the controller, forming a continuous, dynamic closed-loop regulation process to cope with constantly changing wind speeds and load demands, achieving efficient, stable, and safe power generation. However, due to the limited response speed of the pitch-changing system, controlling output power by changing the pitch is not ideal for rapidly changing wind speeds.

[0033] In this embodiment, before the generator is connected to the grid, the generator speed is directly controlled by speed controller A based on the generator speed feedback signal and a given signal; after the generator is connected to the grid, speed controller B and power controller take effect. The main task of the power controller is to provide a corresponding power curve based on the generator speed, adjust the generator slip, and determine the speed setpoint of speed controller B.

[0034] The given reference value for the pitch is provided by the controller based on the operating status of the wind turbine generator. As shown in the figure, it is provided by speed controller A before the wind turbine generator is connected to the grid; and by speed controller B after the wind turbine generator is connected to the grid.

[0035] Figure 3 A distribution diagram of a novel variable pitch control system according to an embodiment of this application is shown. See also: Figure 3 As shown, it includes: (1) Pitch angle adjustment loop (upper part): Speed ​​controller A receives the "speed setpoint" (target speed) and the actual speed feedback ("×" in the figure is the speed feedback signal) and calculates the speed deviation; The deviation signal is input to the "pitch angle controller", which outputs control commands to the "pitch mechanism" to adjust the blade pitch angle of the wind turbine (for example, rotating from 90° to 0°). Changes in pitch angle directly alter the wind-catching capacity of the wind turbine, thereby adjusting the turbine speed and achieving "closed-loop speed control" (corresponding to the "start-up state" and "rated power state" adjustment of the wind power system).

[0036] (2) Speed-power coordinated loop (lower part): The "wind speed" signal is input to "speed controller B", and the speed reference value is calculated in combination with the "speed setpoint". It is compared with the "generator speed" feedback signal to optimize speed control; The "maximum power input" (such as the power corresponding to the optimal tip speed ratio at low wind speed) is input to the "power controller" and combined with the "current input" (generator current feedback) to achieve closed-loop power control—ensuring that the output is either "optimal power" (underpowered state) or "rated power" (above rated wind speed) at different wind speeds.

[0037] (3) Execution and output: After the pitch mechanism adjusts the angle of the wind turbine blades, the wind energy captured by the wind turbine is increased in speed by the "speed increaser" to drive the "generator" to generate electricity; The generator output (terminals A and B in the diagram) is directly connected to the power grid to complete the power grid connection.

[0038] In this embodiment, speed controller A is activated when the wind turbine generator enters standby mode or restarts from standby mode. Figure 4A flowchart of the operation of a speed controller A according to an embodiment of this application is shown in Figure 4. During this process, the rotational speed increases at a certain rate of change by controlling the pitch angle. The controller is also used for control at synchronous speed (1500 rpm at 50 Hz). When the generator speed is within ±10 rpm of the synchronous speed... The generator will connect to the power grid within 1 second.

[0039] The controller includes conventional PD and PI controllers, followed by a nonlinearization stage for the pitch angle. Through nonlinearization, the gain decreases as the pitch angle increases, thereby compensating for the nonlinearity caused by rotor aerodynamics. This is because, when the power is constant, the ratio of torque to pitch angle increases as the pitch angle increases.

[0040] When a wind turbine generator transitions from standby to operation, the pitch control system first rapidly rotates the blade pitch angle to 45°, allowing the rotor to reach synchronous speed while idling. As the speed increases from 0 to 1500... At this time, the pitch angle is linearly reduced from 45° to 5°. This process not only gives the rotor a high starting torque, but also enables rapid starting when the wind speed increases rapidly.

[0041] After the generator is connected to the power grid, speed control system B is activated. Figure 5 A flowchart of the operation of a speed controller B according to an embodiment of this application is shown, as follows: Figure 5 As shown, speed controller B is controlled by both generator speed and wind speed. Before reaching the rated value, the speed setpoint increases proportionally with the power setpoint. The rated speed setpoint is 1569 r / min, corresponding to a generator slip of 4%. If the wind speed and power output remain below the rated values, the generator slip will decrease to 2%, and the pitch control will adjust to the optimal state based on the wind speed to optimize the tip speed ratio.

[0042] If the wind speed exceeds the rated value, the generator speed will track the corresponding speed setpoint by changing the pitch. The power output will remain stably at the rated value. As shown in the diagram, a low-pass filter is installed at the wind speed signal input, and pitch control does not affect transient wind speeds.

[0043] In the embodiments of this application, Figure 6 A block diagram of a power control system according to an embodiment of this application is shown, such as... Figure 6 As shown, it consists of two control loops. The outer loop generates a power reference curve by measuring the rotational speed. The inner loop is a power servo loop that controls the generator slip through a rotor current controller (RCC) to ensure that the generator power tracks the power setpoint. If the power is lower than the rated power, this control loop will change the slip, and thus the blade pitch angle, to enable the wind turbine to obtain maximum power.

[0044] In this embodiment, the rotor current controller consists of a fast digital PI controller and an equivalent rheostat. It changes the generator slip by altering the rotor circuit and resistance based on a given current value. At rated power, the generator slip can range from 1% to 10% (1515 to 1650). As the power changes, the average rotor resistance changes from 0 to 100%. When the power changes, i.e., the rotor current changes, the PI regulator quickly adjusts the rotor resistance to make the rotor current track the given value. If the current given value from the main controller is constant, it will keep the rotor current constant, thus keeping the power output constant.

[0045] The relationship between rotor resistance and generator slip can be explained using the formula for electromagnetic torque. The electromagnetic torque of the generator is:

[0046] In the formula: —Number of pole pairs of the motor; —Number of stator phases of the motor; —Stator angular frequency, i.e., the grid angular frequency; —Stator rated phase voltage; —Slippage rate; —The resistance of the stator windings; --Loop reactance of stator windings; —Rotor phase resistance referred to the stator side; --The leakage reactance per phase of the rotor converted to the stator side.

[0047] In the formula, as long as Unchanged, electromagnetic torque The wind speed can remain constant, and the generator's power output can remain constant. When the wind speed increases, the rotational speed of the wind turbine and generator increases, which is the generator's slip. By increasing the pitch, the output power can be kept constant simply by changing the rotor resistance of the generator. The RCC control unit effectively reduces the operating frequency and amplitude of the pitch control mechanism, keeping the generator's output power balanced. This enables the variable pitch wind turbine to output its rated power above the rated wind speed, effectively reducing the adverse effects of wind turbines on the power grid caused by wind speed changes.

[0048] In this embodiment, the blade pitch angle of the variable pitch wind turbine is 90° when the blades are stationary. When the wind speed reaches the starting wind speed, the blades rotate towards 0° until the airflow creates a certain angle of attack on the blades, at which point the wind turbine starts. When the wind speed reaches or exceeds the rated wind speed, the wind turbine enters the rated power state, switching from speed control to power control. The variable pitch system begins to control the turbine based on the generator's power signal. The power feedback signal is compared with the rated power. If the power exceeds the rated power, the blade pitch rotates by an angle in the direction of decreasing frontal area; conversely, if the power is less than the rated power, it rotates by an angle in the direction of increasing frontal area.

[0049] Figure 7 A model diagram of a pitch angle control input quantity according to an embodiment of this application is shown. See also: Figure 7 As shown, the pitch angle control input model selects the active power feedback value or setpoint of the asynchronous generator as the input of the pitch angle control power based on the control selector.

[0050] Figure 8 A reference model diagram of pitch angle control power according to an embodiment of this application is shown. See also: Figure 8 As shown, the reference value (Pref) model for pitch angle control power uses the generator's rated power as the reference value for the power input of the control system. The actual value is compared with it, and the magnitude of the obtained value can be used to determine whether the power output is stable. Thus, power can be adjusted by changing the pitch angle.

[0051] Figure 9 A proportional-integral control model diagram of a pitch angle control according to an embodiment of this application is shown. See also: Figure 9 As shown, the proportional-integral (PI) stage model for pitch angle control converts the power comparison difference into an angle parameter using a proportional-integral controller.

[0052] In this embodiment, the filter modifies the waveform output by the proportional-integral converter to prevent harmonic components from adversely affecting the system.

[0053] Figure 10 A model diagram of a pitch angle adjustment limiting mechanism according to an embodiment of this application is shown. See also: Figure 10 As shown, the pitch angle adjustment limiting circuit model compares the output feedback value of the pitch angle with the input value after filtering, and then integrates the input differential limiting and differentiator circuits to output the final value. This value is then compared with the given pitch angle value, input to the pitch angle limiting circuit, and outputs the pitch angle.

[0054] In this embodiment of the application, step S2 further includes: S24, Establish a physical equivalent circuit model of the solar cell, and introduce a temperature dynamic correction algorithm into the equivalent circuit model; S25, Construct an inverter efficiency model that matches the load characteristics.

[0055] In the embodiments of this application, Figure 11 An equivalent circuit diagram of a solar cell according to an embodiment of this application is shown, such as... Figure 11 As shown, it consists of an equivalent current source, a diode, and an equivalent series resistor. Equivalent parallel resistance composition.

[0056] From the equivalent circuit, we can conclude that:

[0057] in: —The current flowing through the load; —Photogenerated current that is directly proportional to the intensity of sunlight; —The current flowing through the diode; —Leakage current of solar photovoltaic cells.

[0058] and

[0059]

[0060] In the above formula —Reverse saturation current; ; K-Boltzmann constant ; T - Absolute temperature ; A-PN junction ideality factor; A photovoltaic cell parallel resistor A photovoltaic cell connected in series with a resistor.

[0061] From the above formula, we can derive

[0062] in .

[0063] The parameters of the above equation, obtained from the measured photovoltaic cell voltage and current characteristic data through least squares fitting, are shown in Table 1 below: Table 1. Solar cell model parameters:

[0064] because With light intensity It increases proportionally, and 25℃ is taken as the zero point of temperature. The change will be +0.1% / K as the temperature increases. Therefore:

[0065] In this application embodiment, the inverter mathematical model Hybrid power generation systems place special demands on inverter efficiency (especially under light load conditions), requiring high conversion efficiency. The inverter's input current must meet the requirements of the AC load, as expressed by the following formula:

[0066] In the formula: —This is the input current of the inverter; —This represents the current of the AC load; —This represents the AC terminal voltage; — represents the DC terminal voltage; —Inverter conversion efficiency; at the same time

[0067] In the formula: This refers to the input power of the inverter; This refers to the inverter's output power.

[0068] S3 predicts wind power resources and photovoltaic resources based on historical data, real-time sensing data and meteorological linkage data; In this embodiment of the application, step S3 specifically includes: S31, based on historical data, real-time sensing data and meteorological linkage data, obtains predicted wind speed and predicted air pressure; S32, Introduce a pressure correction coefficient associated with the predicted air pressure to correct the predicted wind speed and obtain an effective wind speed for high-altitude wind energy calculation; S33, Based on the effective wind speed and wind turbine power characteristic curve, calculate and predict the wind power; S34, based on historical data, real-time sensing data and meteorological linkage data, obtains predicted light intensity, predicted ultraviolet radiation intensity and predicted air pressure; S35, introduce a radiation correction factor associated with the predicted ultraviolet radiation intensity and a heat dissipation correction factor associated with the predicted air pressure; S36. Based on the photovoltaic power under standard test conditions, the predicted illuminance, the radiation correction coefficient, and the heat dissipation correction coefficient, and in combination with the temperature characteristics of the photovoltaic module, the predicted photovoltaic power is calculated.

[0069] In this embodiment of the application, wind power resource forecasting includes: Considering the "attenuation effect" of low air pressure at high altitudes on wind speed (low air pressure leads to reduced air resistance, resulting in an actual effective wind speed lower than meteorological observations), a pressure correction coefficient is introduced. (in To predict air pressure (kPa), correct the predicted wind speed:

[0070] Then, combining the wind turbine power curve (which needs to be calibrated in advance according to the characteristics of high-altitude blades), the predicted wind power is calculated:

[0071] in: To set the starting wind speed, such as 2.5 m / s; Rated wind speed, such as 12 m / s The rated power of the wind power is expressed in kW.

[0072] In this embodiment of the application, photovoltaic resource prediction includes: Considering the "gain effect" of strong ultraviolet radiation at high altitudes on photovoltaic modules (strong radiation can increase the concentration of photogenerated carriers in the module) and the "impact of low air pressure on heat dissipation efficiency", a radiation correction coefficient is introduced. (in: To predict ultraviolet radiation intensity (W / m²) and heat dissipation correction factor Correcting the predicted photovoltaic power:

[0073] in: Photovoltaic power, in kW, under standard test conditions; This represents the photovoltaic temperature coefficient, such as -0.004 / ℃. To predict the temperature of photovoltaic modules (°C, calculated by linking ambient temperature and light intensity), To predict light intensity.

[0074] This sub-model can reduce the prediction error of high-altitude wind and solar resources to ≤10%, providing accurate input for subsequent power allocation.

[0075] S4, with a multi-objective function as its core, and combined with constraints, constructs a wind-solar hybrid power generation model; In this embodiment of the application, step S4 specifically includes: The multi-objective function is a weighted sum function that includes maximizing total power generation, minimizing the frequency of energy storage charging and discharging, and minimizing the duration of equipment over-temperature. The constraints include at least the real-time power balance constraint of the system, the upper and lower limits of the output power of wind, solar and energy storage equipment, the upper and lower limits of the state of charge of energy storage equipment, and the upper limit of the safe operating temperature of key equipment.

[0076] In this embodiment, the objective function (the weights can be adjusted according to actual needs) The multi-objective function is transformed into a single-objective function using a weighted summation method:

[0077] in: , , For the weighting coefficients, satisfying + + =1, which can be determined using the analytic hierarchy process (AHP), such as =0.5, =0.3, =0.2 Objective 1: Maximize the total power generation of the system (The integration interval is the scheduling period, such as 24 hours) Objective 2: Minimize the frequency of energy storage charging and discharging. =Statistical scheduling period The number of times the energy changes from positive to negative or from negative to positive is limited to avoid frequent charging and discharging, which can lead to a decrease in the lifespan of the energy storage device. Objective 3: Minimize the duration of device over-temperature.

[0078] =The duration during which the temperature of photovoltaic modules is greater than 45℃ or the temperature of wind turbine gearbox is greater than 85℃ within the statistical scheduling cycle.

[0079] In this embodiment of the application, the constraints (adapting to high-altitude scenarios) include: 1. Power balance constraint: (meets the load power supply requirements in real time)

[0080] 2. Equipment power constraints: (1) Photovoltaics:

[0081] in: Set the minimum output power for photovoltaics, such as 0kW, to avoid frequent start-stop cycles.

[0082] (2) Wind power:

[0083] in: This represents the minimum output power of the wind power, such as 0kW, which is 0 when the wind speed is below the starting speed.

[0084] (3) Energy storage:

[0085] in: The maximum charge and discharge power of the energy storage is determined by the energy storage capacity and the charge and discharge rate.

[0086] Energy storage SOC constraints:

[0087] In high-altitude, low-temperature environments, The limit should be increased to 20% to prevent battery freezing. Set the charge to 80% to avoid overcharging.

[0088]

[0089] in: For energy storage voltage, Energy storage capacity, Ah.

[0090] 3. Equipment temperature constraints: (Based on the high-altitude equipment tolerance limit setting).

[0091] S5, input the wind power resource prediction results and the photovoltaic resource prediction results into the wind-solar hybrid power generation model to obtain the initial control scheme; In this embodiment of the application, step S5 specifically includes: S51, using an optimization algorithm based on the multi-objective function and constraints, calculate the time-series data of the predicted wind power and predicted photovoltaic power within the scheduling cycle; S52, solve for the timing allocation scheme of the photovoltaic optimal output power, wind power optimal output power and energy storage charging and discharging power that achieves the optimization of the multi-objective function under the premise of meeting load requirements and equipment safety, and use it as the initial control scheme.

[0092] S6, based on the initial control scheme, control the wind turbine generator set, photovoltaic power generation unit and energy storage device to operate in coordination; S7. Collect the actual operating data of the system in real time and compare it with the target state in the initial control scheme to calculate the operating deviation; In this embodiment of the application, step S7 specifically includes: S71, compare the actual output power, actual equipment temperature, and actual state of charge of energy storage with the corresponding target values ​​in the initial control scheme, and calculate the power deviation, temperature deviation, and state of charge deviation. S72, compare the power deviation, temperature deviation and state of charge deviation with their respective preset deviation threshold ranges, and determine the degree of deviation based on the comparison results.

[0093] In this embodiment, sensors deployed at key nodes of the system collect the following actual operating data at high frequency (sampling frequency ≤ 1s, much higher than the scheduling cycle, ensuring the capture of instantaneous fluctuations): Power data: Actual output power of photovoltaic power P_pv_actual (kW), actual output power of wind power P_wind_actual (kW), actual charging and discharging power of energy storage P_bat_actual (kW), actual output power of the system P_out_actual (kW), and actual load power P_load_actual (kW). Equipment status data: Photovoltaic module actual temperature T_pv_actual (°C), wind turbine gearbox actual temperature T_gear_actual (°C), energy storage actual SOC value SOC_actual (%), energy storage actual temperature T_bat_actual (°C); Actual environmental data: actual wind speed v_actual (m / s), actual light intensity G_actual (W / m²), and actual atmospheric pressure P_actual (kPa) (used to verify the accuracy of the predicted data).

[0094] In this embodiment, the collected actual data is compared with the "target value," the deviation value is calculated, and the deviation is categorized according to its source to provide a basis for subsequent corrections. The "target value" includes two categories: The first layer of predictions includes values ​​such as v_pred (predicted wind speed) and G_pred (predicted light intensity), with deviations such as Δv = v_actual (actual wind speed) - v_pred and ΔG = G_actual (actual light intensity) - G_pred. The optimal values ​​for the second layer include P_pv_opt (optimal photovoltaic power), P_wind_opt (optimal wind power), and SOC_target (target SOC for energy storage), with deviations such as ΔP_pv = P_pv_actual - P_pv_opt and ΔSOC = SOC_actual - SOC_target. Based on the magnitude and impact of the deviation, the deviations are divided into three categories, as shown in Table 2 below: Table 2 Deviation Classification Table

[0095] S8. Based on the type, magnitude, and cause of the operational deviation, execute immediate control correction or generate a strategy correction instruction for the next scheduling cycle to correct the initial control scheme and achieve closed-loop optimized operation of the wind-solar hybrid power generation system.

[0096] In this embodiment of the application, step S8 specifically includes: S81, perform correlation analysis on the power deviation, temperature deviation and state of charge deviation with the simultaneously collected actual environmental data and equipment operating status data; S82, Based on the correlation analysis results, the cause of the operational deviation is obtained.

[0097] In this embodiment, for moderate and severe deviations, this layer analyzes the causes in conjunction with the characteristics of the high-altitude environment to avoid blindly adjusting strategies. Common causes of deviations and their analysis logic are as follows: Example 1: =-0.8kW (actual output is far below the optimal value) If G_actual=400W / m² (far lower than G_pred=600W / m²) is detected at the same time, and there is no equipment fault signal, then the cause of the deviation is determined to be "inaccurate solar power prediction", and the correction direction is "adjust the photovoltaic power expectation and increase the energy storage discharge to supplement the power". If G_actual = 600W / m² (consistent with G_pred), but T_pv_actual = 48℃ (overheating), then the deviation is determined to be caused by "the photovoltaic module overheating leading to a decrease in efficiency", and the correction direction is "reduce the photovoltaic output power and start the heat dissipation equipment". Example 2: ΔSOC = -8% (Actual SOC is much lower than target SOC) If P_pv_actual + P_wind_actual = 4.2kW ​​and P_load_actual = 5.0kW (total wind and solar power is insufficient), the cause is determined to be "insufficient wind and solar power generation", and the correction direction is "increase the priority of wind power and limit non-critical loads". If P_pv_actual + P_wind_actual = 5.5kW and P_load_actual = 5.0kW (total wind and solar power is sufficient), then the cause is determined to be "energy storage charging and discharging fault", and the correction direction is "suspend energy storage charging and trigger fault alarm".

[0098] In this application embodiment, based on the correlation analysis results, the causes of the operational deviation are classified as follows: wind and solar resource prediction deviation caused by the mismatch between predicted environmental data and actual environmental data, equipment performance degradation deviation caused by the influence of high-altitude extreme environment, power supply gap deviation caused by insufficient total wind and solar power generation, or fault deviation caused by abnormality of energy storage equipment or power generation equipment itself.

[0099] In this embodiment of the application, step S8 further includes: If the analysis determines that the operational deviation is a serious fault deviation that seriously affects power supply safety or equipment safety, then the immediate control correction process is triggered, and adjustment instructions are immediately sent to the actuators of the corresponding wind turbine generator, photovoltaic power generation unit or energy storage device. The adjustment instructions include reducing output power, starting auxiliary heat dissipation or switching to a safe shutdown state. If the analysis determines that the operational deviation is a non-urgent resource prediction deviation or performance fluctuation deviation, the strategy correction process is triggered. The current deviation data and the corresponding correction suggestions are input as feedback information into the next process of wind and solar resource prediction and wind and solar complementary power generation model construction, which is used to generate the optimized control strategy for the next scheduling cycle.

[0100] In this embodiment, based on the type and cause of the deviation, this layer employs two methods: "instant correction" and "periodic correction" to ensure response speed and strategy consistency. 1. Instant Correction (for severe deviations, response time ≤ 100ms): If the deviation affects equipment safety or power supply stability (e.g., T_gear_actual = 85℃, P_out_actual is 10% lower than P_load_actual), this layer will skip the scheduling cycle and directly send correction commands to the execution unit (e.g., photovoltaic inverter, wind turbine controller, energy storage converter). (1) Equipment protection correction: For example, when T_gear_actual=85℃, immediately send the instruction "wind power output power drops to 0.7×P_wind_opt" and start the gearbox cooling system at the same time; (2) Power supply guarantee correction: If P_out_actual=4.0kW<P_load_actual=5.0kW, immediately send the instruction "Increase energy storage discharge power by 1.0kW" to ensure P_out_actual=5.0kW.

[0101] 2. Periodic Correction (for minor / moderate deviations, synchronized with the scheduling cycle): If the deviation does not affect core security, this layer will feed back the deviation data and correction suggestions to the first and second layers, and the correction will be reflected in the strategy of the next scheduling cycle. (1) Feedback to the first layer (resource prediction layer): If ΔG=G_actual - G_pred=-200W / m² (the solar irradiance prediction is too high), it is recommended that G_pred be reduced by 15% in the next round (to avoid insufficient photovoltaic power again). (2) Feedback to the second layer (power allocation layer): If ΔSOC=-5% (actual SOC is lower than the target), it is recommended to increase the priority of wind power in the next round (e.g., increase P_wind_opt from 1.8kW to 2.2kW), while reducing unnecessary photovoltaic output (e.g., from 3.6kW to 3.2kW) and increasing energy storage charging power.

[0102] Based on the above method, this application also provides a collaborative control system for high-altitude wind-solar hybrid power generation systems, corresponding to the above method, the system comprising: The data acquisition module is used to collect high-frequency core environmental parameters at high altitudes and basic parameters of wind, solar and energy storage equipment, and to verify the initial state of each execution unit. The initial construction module is used to build the control model of the wind power system and the control model of the photovoltaic system; The prediction module is used to predict wind power resources and photovoltaic resources based on historical data, real-time sensing data and meteorological linkage data. The model building module constructs a wind-solar hybrid power generation model by using a multi-objective function as the core and combining it with constraints. The control module is used to input the wind power resource prediction results and the photovoltaic resource prediction results into the wind-solar hybrid power generation model to obtain the initial control scheme; The operation module is used to control the coordinated operation of the wind turbine generator set, photovoltaic power generation unit and energy storage device based on the initial control scheme; The calculation module is used to collect the actual operating data of the system in real time, compare it with the target state in the initial control scheme, and calculate the operating deviation. The optimization module is used to perform real-time control correction or generate strategy correction instructions for the next scheduling cycle based on the type, magnitude and cause of the operating deviation, so as to correct the initial control scheme and realize the closed-loop optimized operation of the wind-solar hybrid power generation system.

[0103] Based on the same inventive concept disclosed above, this application also provides an electronic device. The electronic device of this application includes at least one processor and at least one memory electrically connected to the processor. The memory is electrically connected to the processor, wherein the memory stores instructions executable by the at least one processor, which, when executed by the at least one processor, enables the at least one processor to perform the method described above.

[0104] It should be noted that the electrical connections between the above-mentioned units do not necessarily represent the connections between lines. Indirect connections are applicable to the embodiments of this application as long as they achieve the purpose of this application.

[0105] Based on the same inventive concept, this application also provides a computer storage medium storing a computer program, which, when executed by a processor, implements the steps of the above method.

[0106] Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A collaborative control method for high-altitude wind-solar hybrid power generation systems, characterized in that, The method includes, High-frequency acquisition of core environmental parameters at high altitudes and basic parameters of wind, solar and energy storage equipment, and verification of the initial state of each execution unit; Construct control models for wind power systems and photovoltaic systems; Wind power resource forecasting and photovoltaic resource forecasting are based on historical data, real-time sensing data and meteorological linkage data. A wind-solar hybrid power generation model is constructed with a multi-objective function as the core and combined with constraints. The wind power resource forecast results and the photovoltaic resource forecast results are input into the wind-solar hybrid power generation model to obtain the initial control scheme; Based on the initial control scheme, the wind turbine generator set, photovoltaic power generation unit and energy storage device are controlled to operate in a coordinated manner. Real-time data collection of actual system operation is performed and compared with the target state in the initial control scheme. The operation deviation is calculated based on the comparison results. The type, magnitude, and cause of the operational deviations are analyzed. Based on the analysis results, real-time control corrections are executed or strategy correction instructions for the next scheduling cycle are generated to modify the initial control scheme and achieve closed-loop optimized operation of the wind-solar hybrid power generation system.

2. The method according to claim 1, characterized in that, Constructing a control model for a wind power system specifically includes: Based on wind speed and generator operating status, the control process of wind turbine generator set is divided into continuous start-up state, underpower state and rated power state, and corresponding pitch angle control strategy and generator slip rate adjustment strategy are adopted in different states to construct variable pitch collaborative control module. The generator slip rate is dynamically adjusted by regulating the equivalent resistance of the rotor circuit in order to convert transient wind energy into wind turbine kinetic energy for storage or release, and to integrate the rotor current control module. Before and after grid connection, different speed controllers are used to operate the system. Based on the wind speed fluctuation frequency, the high-frequency component is allocated to the rotor current control module for rapid power balancing, and the low-frequency component is allocated to the variable pitch collaborative control module for pitch angle adjustment.

3. The method according to claim 1, characterized in that, Constructing a photovoltaic system control model specifically includes: A physical equivalent circuit model of a solar cell is established, and a dynamic temperature correction algorithm is introduced into the equivalent circuit model. Construct an inverter efficiency model that matches the load characteristics.

4. The method according to claim 1, characterized in that, Wind power resource forecasting and photovoltaic resource forecasting are based on historical data, real-time sensing data, and meteorological linkage data, specifically including: Predicted wind speed and predicted air pressure are obtained based on historical data, real-time sensing data, and meteorological linkage data. By introducing a pressure correction coefficient associated with the predicted air pressure, the predicted wind speed is corrected to obtain an effective wind speed for high-altitude wind energy calculation. Based on the effective wind speed and wind turbine power characteristic curve, the predicted wind power is calculated. Predicted light intensity, predicted ultraviolet radiation intensity, and predicted air pressure are obtained based on historical data, real-time sensing data, and meteorological linkage data. A radiation correction factor associated with the predicted ultraviolet radiation intensity and a heat dissipation correction factor associated with the predicted air pressure are introduced. The predicted photovoltaic power is calculated based on the photovoltaic power under standard test conditions, the predicted irradiance, the radiation correction factor, and the heat dissipation correction factor, combined with the temperature characteristics of the photovoltaic module.

5. The method according to claim 1, characterized in that, A wind-solar hybrid power generation model is constructed based on a multi-objective function and combined with constraints, specifically including: The multi-objective function is a weighted sum function that includes maximizing total power generation, minimizing the frequency of energy storage charging and discharging, and minimizing the duration of equipment over-temperature. The constraints include at least the real-time power balance constraint of the system, the upper and lower limits of the output power of wind, solar and energy storage equipment, the upper and lower limits of the state of charge of energy storage equipment, and the upper limit of the safe operating temperature of key equipment.

6. The method according to claim 1, characterized in that, The initial control plan was obtained, specifically including: An optimization algorithm based on the multi-objective function and constraints is used to calculate the time-series data of the predicted wind power and predicted photovoltaic power within the scheduling cycle; The solution yields a time-series allocation scheme for the optimal output power of photovoltaic power, the optimal output power of wind power, and the charging and discharging power of energy storage, which optimizes the multi-objective function while meeting load requirements and equipment safety. This scheme serves as the initial control scheme.

7. The method according to claim 1, characterized in that, The operational deviation is calculated based on the comparison results, specifically including: The actual output power, actual equipment temperature, and actual state of charge of energy storage are compared with the corresponding target values ​​in the initial control scheme, and the power deviation, temperature deviation, and state of charge deviation are calculated. The power deviation, temperature deviation, and state of charge deviation are compared with their respective preset deviation threshold ranges, and the degree of deviation is determined based on the comparison results.

8. The method according to claim 7, characterized in that, The analysis includes the type, magnitude, and causes of the operational deviations, specifically including: The power deviation, temperature deviation, and state of charge deviation are correlated and analyzed with the actual environmental data and equipment operating status data collected simultaneously. Based on the correlation analysis results, the causes of the operational deviations were determined.

9. The method according to claim 1, characterized in that, Based on the analysis results, immediate control corrections are executed or policy correction instructions for the next scheduling cycle are generated, specifically including: If the analysis determines that the operational deviation is a serious fault deviation that seriously affects power supply safety or equipment safety, then the immediate control correction process is triggered, and adjustment instructions are immediately sent to the actuators of the corresponding wind turbine generator, photovoltaic power generation unit or energy storage device. The adjustment instructions include reducing output power, starting auxiliary heat dissipation or switching to a safe shutdown state. If the analysis determines that the operational deviation is a non-urgent resource prediction deviation or performance fluctuation deviation, the strategy correction process is triggered. The current deviation data and the corresponding correction suggestions are input as feedback information into the next process of wind and solar resource prediction and wind and solar complementary power generation model construction, which is used to generate the optimized control strategy for the next scheduling cycle.

10. A collaborative control system suitable for high-altitude wind-solar hybrid power generation systems, characterized in that, The system includes: The data acquisition module is used to collect high-frequency core environmental parameters at high altitudes and basic parameters of wind, solar and energy storage equipment, and to verify the initial state of each execution unit. The initial construction module is used to build the control model of the wind power system and the control model of the photovoltaic system; The prediction module is used to predict wind power resources and photovoltaic resources based on historical data, real-time sensing data and meteorological linkage data. The model building module constructs a wind-solar hybrid power generation model by using a multi-objective function as the core and combining it with constraints. The control module is used to input the wind power resource prediction results and the photovoltaic resource prediction results into the wind-solar hybrid power generation model to obtain the initial control scheme; The operation module is used to control the coordinated operation of the wind turbine generator set, photovoltaic power generation unit and energy storage device based on the initial control scheme; The calculation module is used to collect the actual operating data of the system in real time, compare it with the target state in the initial control scheme, and calculate the operating deviation. The optimization module is used to perform real-time control correction or generate strategy correction instructions for the next scheduling cycle based on the type, magnitude and cause of the operating deviation, so as to correct the initial control scheme and realize the closed-loop optimized operation of the wind-solar hybrid power generation system.

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