Photovoltaic power generation unit flexible support control method and system

Abstract

The invention discloses a photovoltaic power generation unit flexible support control method and system, and relates to the technical field of photovoltaic power generation control, and the method comprises the steps: arranging a sensor for collecting environment data; constructing an optimization objective function, and constructing a data processing model by using an optimization algorithm; a control instruction is generated according to the data processing model, and adjustment of the flexible support of the photovoltaic power generation unit is executed; and detecting and correcting the feedback signal after adjustment. According to the method, multiple types of sensors are arranged to collect environment data, key parameters such as illumination intensity, wind speed and temperature are covered, and an optimal angle control model adjusted in real time is generated by constructing an optimized objective function and combining a genetic algorithm or a particle swarm optimization algorithm. Meanwhile, the servo motor drives the support to complete high-precision adjustment, and closed-loop feedback control is combined to correct adjustment errors in real time. The invention belongs to the technical field of photovoltaic power generation, and can significantly improve the operation efficiency and safety of a photovoltaic power generation system in a complex environment.

Description

Technical Field

[0001] The present invention relates to the technical field of photovoltaic power generation control, and in particular to a method and system for controlling a flexible support of a photovoltaic power generation unit. Background Art

[0002] Photovoltaic power generation has been widely used around the world as a clean and sustainable form of energy. With the rapid development of photovoltaic technology, the efficiency and stability of photovoltaic power generation systems have become the focus of research. Traditional photovoltaic power generation units usually adopt fixed bracket structures. Although the cost is low, their power generation efficiency and equipment safety face many challenges in areas with variable lighting conditions and strong winds. Fixed brackets cannot dynamically adjust the inclination of photovoltaic panels according to the angle of solar radiation, resulting in reduced light energy utilization. In addition, in windy weather, fixed brackets are susceptible to wind loads, which may cause equipment damage or even safety accidents. In order to cope with these problems, adjustable flexible brackets have gradually become a research hotspot, which can adapt to different environmental conditions by adjusting the angle of photovoltaic panels. The current flexible brackets mostly rely on preset mechanical or manual control, lack intelligent adjustment mechanisms, have weak adaptability to complex environments, and have hysteresis and error accumulation problems during the adjustment process.

[0003] Although the existing flexible bracket system has improved the efficiency of photovoltaic power generation to a certain extent, it still has shortcomings in terms of intelligence, real-time and environmental adaptability. First, the collection of environmental parameters in the existing technology is usually not comprehensive enough to fully reflect the operating environment of the photovoltaic unit. Secondly, during the bracket adjustment process, there is a lack of precise control models supported by optimization algorithms, resulting in insufficient accuracy and efficiency of angle adjustment. In addition, the feedback control mechanism is not perfect, and the adjustment deviation cannot be corrected in time, which may affect the long-term stable operation of the equipment. Therefore, how to construct an intelligent flexible bracket control method based on environmental data and combine it with optimization algorithms to achieve efficient and accurate bracket adjustment has become a technical problem that needs to be solved urgently. Summary of the invention

[0004] In view of the above-mentioned problems, the present invention is proposed.

[0005] Therefore, the technical problem solved by the present invention is: how to construct an intelligent flexible bracket control method based on environmental data with the existing technology, and combine it with the optimization algorithm to achieve efficient and accurate bracket adjustment.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: a photovoltaic power generation unit flexible support control method, comprising:

[0007] Deploy sensors to collect environmental data;

[0008] Construct optimization objective functions and use optimization algorithms to build data processing models;

[0009] Generate control instructions based on the data processing model to execute the adjustment of the flexible bracket of the photovoltaic power generation unit;

[0010] After adjustment, the feedback signal is detected and corrected.

[0011] As a preferred solution of the photovoltaic power generation unit flexible bracket control method of the present invention, wherein: the arrangement of sensors to collect environmental data includes arranging a light intensity sensor, a wind speed sensor, and a temperature sensor at the photovoltaic panel;

[0012] Simulate typical climatic conditions and collect data on sunny, cloudy and windy days to cover typical operating environments. Start the sensor power supply and confirm the connection status between the sensor and the main control system to ensure the normal operation of the data acquisition equipment.

[0013] As a preferred solution of the photovoltaic power generation unit flexible support control method of the present invention, wherein: the construction of the optimization objective function includes constructing the objective function and constraint conditions, and combining the actual power generation characteristic curve, the objective function formula is expressed as:

[0014] P=η·I·A·cos(θ)

[0015] Among them, the efficiency η of the photovoltaic panel changes with temperature, and the correction formula is expressed as:

[0016] η=η0·(1-k T (TT opt ))

[0017] Among them, η0 represents the initial efficiency, k T Represents the temperature coefficient, T opt Indicates the optimal temperature;

[0018] Define the maximum wind load. The wind load F must be less than the maximum load capacity F of the bracket. max , the formula is:

[0019] F=0.5·ρ·W 2 ·C d ·A≤F max

[0020] Where ρ represents the air density, C d is the drag coefficient; bracket angle range, the bracket angle limit is θ min ≤θ≤θ max .

[0021] As a preferred solution of the photovoltaic power generation unit flexible bracket control method of the present invention, wherein: the use of the optimization algorithm to construct the data processing model includes generating an initial population, each individual represents a possible bracket angle θ, the initial population size is set to N, and the θ value is randomly generated, the range [θ min ,θ max ]; define individual, individual is a real number θ i ;

[0022] Fitness function calculation, based on environmental data and current angle θ, calculates power generation and wind load constraints; if wind load F>F max , add the penalty to the fitness:

[0023] fitness=P-λ·(FF max )

[0024] Among them, λ represents the penalty coefficient;

[0025] Define boundary constraints if Then directly set the fitness to the minimum value;

[0026] Use the roulette wheel method to select individuals with high fitness to enter the next generation; the probability of each individual being selected is proportional to its fitness:

[0027]

[0028] Crossover strategy, randomly select two parent individuals θ parent 1, θ parent2 ; Generate offspring using uniform crossover:

[0029] θ child =w·θ parent1 +(1-w)·θ parent2

[0030] Where w∈[0,1] represents random weight; set the crossover probability to P c ;

[0031] Specify the mutation strategy and randomly select a descendant individual θ child , perform mutation according to Gaussian distribution:

[0032] θ′ child =θ child +Δθ

[0033] Among them, Δθ~N(0,σ 2 ), set the mutation probability P m ;

[0034] Iterate to the preset algebra G maxOr when the population fitness change is less than the threshold ∈, the iteration is terminated and the optimal control angle is output.

[0035] As a preferred solution of the photovoltaic power generation unit flexible support control method of the present invention, wherein: the control instruction generated according to the data processing model includes obtaining the optimal angle θ * , output the current optimal angle θ through the data processing module * ;

[0036] With environmental parameters, including current light intensity, current wind speed, and current temperature; generate motor control signals to adjust the optimal angle θ * Converted into the motor target rotation angle Δφ, the formula is expressed as:

[0037] Δφ=(θ * -θ current )·G

[0038] Where G is the angle-to-motor rotation scaling factor, θ current Indicates the actual inclination angle of the current bracket; calculates the motor driving time:

[0039]

[0040] Among them, ω m Indicates the angular velocity of the motor; format the control instruction and generate the instruction format. If Δφ>0, it is set to clockwise.

[0041] As a preferred solution of the photovoltaic power generation unit flexible support control method of the present invention, wherein: the execution of the photovoltaic power generation unit flexible support adjustment includes receiving a control signal, and the servo motor adjusts the support in a stepping manner, and the stepping signal control formula is expressed as:

[0042] ΔX=Δφ·R

[0043] Where R represents the support rotation radius; the support angle is adjusted, and the support structure model formula is expressed as:

[0044] L1sin(θ)=L2

[0045] Where ΔX represents the linear displacement of the driving rod, L1 represents the length of the support arm, and L2 represents the effective length of the driving rod. * , reverse calculation of servo motor steps:

[0046]

[0047] Among them, step_angle represents the angle of rotation of the single-step motor, and real-time angle adjustment is performed. After each step, the current angle θ is collected feedback Compare the target angle, if the error |θ feedback-θ * |>∈, ∈ is the tolerance, then repeat the adjustment process.

[0048] As a preferred solution of the photovoltaic power generation unit flexible bracket control method described in the present invention, wherein: the post-adjustment detection feedback signal and correction include using an angle sensor to detect the actual inclination angle θ of the bracket in real time feedback ;

[0049] The feedback signal detection formula is expressed as:

[0050] e(t)=θ * -θ feedback

[0051] Among them, e(t) represents the error signal; PID correction control is performed. If |e(t)|>∈, correction is performed through the PID controller; the motor control command is updated to drive the motor to further adjust the angle; to prevent oscillation, that is, anti-integral saturation, the integral limit is set, and the formula is expressed as:

[0052] ∫e(t)dt=max(min(∫e(t)dt,I max ),-I max )

[0053] Among them, I max Indicates the integral limit;

[0054] Repeat detection and correction, and continue the cycle of detection until |e(t)|≤∈.

[0055] As a preferred solution of the photovoltaic power generation unit flexible bracket control system described in the present invention, it includes: a data acquisition module, a light intensity sensor is installed at the top center of the photovoltaic panel to capture the vertical light intensity; a wind speed sensor is fixed on the side of the photovoltaic panel to capture the on-site wind speed; a temperature sensor is attached to the back of the photovoltaic panel to measure the panel temperature;

[0056] Data processing and optimization module, constructs optimization objective function and uses optimization algorithm to calculate the optimal bracket angle;

[0057] A control instruction generation and execution module generates control instructions and adjusts the bracket according to the optimal angle;

[0058] Feedback detection and correction module detects the adjustment results and performs error correction.

[0059] A computer device comprises a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement a step of a method for controlling a flexible support of a photovoltaic power generation unit.

[0060] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of a method for controlling a flexible support of a photovoltaic power generation unit.

[0061] Beneficial effects of the present invention: The photovoltaic power generation unit flexible bracket control method provided by the present invention provides a comprehensive solution to the deficiencies in data collection, optimization algorithms, control instruction generation and feedback correction in the prior art. The present invention collects environmental data by arranging multiple types of sensors, covering key parameters such as light intensity, wind speed and temperature, and generates an optimal angle control model for real-time adjustment by constructing an optimization objective function combined with a genetic algorithm or a particle swarm optimization algorithm. At the same time, high-precision adjustment is completed by driving the bracket through a servo motor, and combined with closed-loop feedback control, the adjustment error is corrected in real time. The present invention belongs to the field of photovoltaic power generation technology, and can significantly improve the operating efficiency and safety of photovoltaic power generation systems in complex environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0063] Figure 1 An overall flow chart of a photovoltaic power generation unit flexible support control method provided in the first embodiment of the present invention. DETAILED DESCRIPTION

[0064] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0065] Example 1, reference Figure 1 , as an embodiment of the present invention, provides a photovoltaic power generation unit flexible support control method, comprising:

[0066] S1: Arrange sensors to collect environmental data;

[0067] Furthermore, the arrangement of sensors to collect environmental data includes arranging a light intensity sensor, a wind speed sensor, and a temperature sensor at the photovoltaic panel.

[0068] Simulate typical climatic conditions and collect data on sunny, cloudy and windy days to cover typical operating environments. Start the sensor power supply and confirm the connection status between the sensor and the main control system to ensure the normal operation of the data acquisition equipment.

[0069] Light intensity sensor: installed next to the photovoltaic panel, at an angle parallel to the photovoltaic panel, to collect the actual light received by the photovoltaic panel.

[0070] Wind speed sensor: Installed on a tower 2 meters above the ground to avoid being blocked and reflected by the photovoltaic panels.

[0071] Temperature sensor: installed on the back of the photovoltaic panel to measure the backplane temperature.

[0072] At least one set of sensors should be installed on each photovoltaic array, and the sensors should be placed at the junction of slopes and flat land as much as possible.

[0073] It should be noted that the optimization of sensor positions reduces reflection, occlusion and angle errors. For example, the location design of the light intensity sensor directly avoids the power calculation error caused by angle deviation (usually the error can reach more than 5%). This improvement makes the collected data more suitable for real-time power optimization calculation.

[0074] S2: Construct an optimization objective function and use the optimization algorithm to build a data processing model.

[0075] Furthermore, the construction of the optimization objective function includes constructing an objective function and constraint conditions, and combining the actual power generation characteristic curve, the objective function formula is expressed as:

[0076] P=η·I·A·cos(θ)

[0077] Among them, the efficiency η of the photovoltaic panel changes with temperature, and the correction formula is expressed as:

[0078] η=η0·(1-k T (TT opt ))

[0079] Among them, η0 represents the initial efficiency, k T Represents the temperature coefficient, T opt Indicates the optimal temperature;

[0080] Define the maximum wind load. The wind load F must be less than the maximum load capacity F of the bracket. max , the formula is:

[0081] F=0.5·ρ·W 2 ·C d ·A≤F max

[0082] Where ρ represents the air density, C dIndicates the drag coefficient; bracket angle range, the bracket angle limit is θ min ≤θ≤θ max .

[0083] The use of the optimization algorithm to construct a data processing model includes generating an initial population, each individual representing a possible bracket angle θ, the initial population size is set to N, and θ values ​​are randomly generated in the range [θ max ,θ max ]; define individual, individual is a real number θ i ;

[0084] Fitness function calculation, based on environmental data and current angle θ, calculates power generation and wind load constraints; if wind load F>F max , add the penalty to the fitness:

[0085] fitness=P-λ·(FF max )

[0086] Among them, λ represents the penalty coefficient;

[0087] Define boundary constraints if Directly set the fitness to the minimum value;

[0088] Use the roulette wheel method to select individuals with high fitness to enter the next generation. The probability of each individual being selected is proportional to its fitness:

[0089]

[0090] Crossover strategy, randomly select two parent individuals θ parent 1, θ parent2 ; Generate offspring using uniform crossover:

[0091] θ child =w·θ parent1 +(1-w)·θ parent2

[0092] Where w∈[0,1] represents random weight; set the crossover probability to P c ;

[0093] Specify the mutation strategy and randomly select a descendant individual θ child , perform mutation according to a Gaussian distribution:

[0094] θ′ child =θ child +Δθ

[0095] Among them, Δθ~N(0,σ 2 ), set the mutation probability P m ;

[0096] Iterate to the preset algebra G max Or when the population fitness change is less than the threshold ∈, the iteration is terminated and the optimal control angle is output.

[0097] It should be noted that, combined with the actual characteristic curve of the photovoltaic panel, the objective function not only considers the power generation, but also introduces wind load restrictions through the penalty function, so that the optimization solution can meet the power generation needs and ensure structural safety. The complex physical scene is simplified into a mathematical model through constraints, making multi-objective optimization easier to achieve. Technical problem solving: Compared with the traditional single-objective optimization method, this objective function comprehensively considers efficiency and safety, and provides a scientific basis for the adjustment of the bracket angle in complex environments.

[0098] S3: Generate control instructions according to the data processing model to execute the adjustment of the flexible bracket of the photovoltaic power generation unit.

[0099] Get the optimal angle θ * , output the current optimal angle θ through the data processing module * .

[0100] With environmental parameters, including current light intensity, current wind speed, and current temperature; generate motor control signals to adjust the optimal angle θ * Converted into the motor target rotation angle Δφ, the formula is expressed as:

[0101] Δφ=(θ * -θ current )·G

[0102] Where G is the angle-to-motor rotation scaling factor, θ current Indicates the actual inclination angle of the current bracket; calculates the motor driving time:

[0103]

[0104] Among them, ω m Indicates the angular velocity of the motor; format the control instruction and generate the instruction format. If Δφ>0, it is set to clockwise.

[0105] The photovoltaic power generation unit flexible bracket adjustment includes receiving a control signal, and the servo motor adjusts the bracket in a stepping manner. The stepping signal control formula is expressed as:

[0106] ΔX=Δφ·R

[0107] Where R represents the support rotation radius; the support angle is adjusted, and the support structure model formula is expressed as:

[0108] L1sin(θ)=L2

[0109] Where ΔX represents the linear displacement of the driving rod, L1 represents the length of the support arm, and L2 represents the effective length of the driving rod. * , reverse calculation of servo motor steps:

[0110]

[0111] Among them, step_angle represents the angle of rotation of the single-step motor, and real-time angle adjustment is performed. After each step, the current angle θ is collected feedback Compare the target angle, if the error |θ feedback -θ * |>∈, ∈ is the tolerance, then repeat the adjustment process.

[0112] S4: After adjustment, detect the feedback signal and make corrections.

[0113] Furthermore, the post-adjustment detection feedback signal and correction includes using an angle sensor (such as a Hall effect angle sensor) to detect the actual inclination angle θ of the bracket in real time. feedback , feedback signal detection formula:

[0114] e(t)=θ * -θ feedback

[0115] Among them, e(t) is the error signal. Perform PID correction control. If |e(t)|>∈, correct it through the PID controller:

[0116]

[0117] Here, u(t) represents the adjustment signal (additional rotation angle).

[0118] Update the motor control command to drive the motor to further adjust the angle; prevent oscillation, that is, anti-integral saturation, and set the integral limit. The formula is expressed as:

[0119] ∫e(t)dt=max(min(∫e(t)dt,I max ),-I max )

[0120] Among them, I max Indicates the integral limit;

[0121] Repeat detection and correction, and continue the cycle detection until |e(t)|≤∈. .

[0122] Example 2: The following is an embodiment of the present invention, which provides a method for controlling a flexible bracket of a photovoltaic power generation unit. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0123] In order to verify the innovation and advantages of a photovoltaic power generation unit flexible bracket control method and system, an experiment was conducted under typical environmental conditions. The experimental site was selected at a photovoltaic power generation experimental base, where the sunlight radiation conditions were good and there were a variety of typical climatic conditions such as sunny days, cloudy days and strong winds. The experimental equipment included a light intensity sensor (measurement range: 0-1200W / m 2 , model: TSL2591), wind speed sensor (measuring range: 0-30m / s, model: FS300) and temperature sensor (measuring range: -20℃ to 80℃, model: DS18B20), all arranged near the surface of the photovoltaic panel. The system is also equipped with a servo motor (rated torque: 10N·m) and an angle sensor (resolution: 0.1°).

[0124] The test steps are as follows:

[0125] Sensor arrangement and calibration: The sensors are connected to the main control system through wireless modules to collect light intensity, wind speed and temperature data respectively. The sensors are calibrated before the experiment to ensure that the error is controlled within ±1%. Environmental data collection: All-weather environmental data are collected under sunny, cloudy and windy conditions. The data collection frequency is once every 10 seconds, and the total data collection time is 12 hours, covering different sunshine angles and wind speed changes.

[0126] Optimize the objective function and data processing, construct the optimization objective function, and optimize the optimal angle through the genetic algorithm, generate control instructions and perform adjustments: after the optimization algorithm outputs the optimal angle, the angle is converted into a motor drive instruction; the motor executes step control to complete the bracket adjustment.

[0127] Feedback detection and correction: Use an angle sensor to detect the adjusted bracket angle in real time, and correct the adjustment error through a PID controller.

[0128] Table 1 Experimental data table

[0129]

[0130] It can be seen from the experimental data that the photovoltaic power generation unit flexible bracket control method and system of the present invention show good environmental adaptability and power generation efficiency optimization effect under various climatic conditions.

[0131] Dynamic angle adjustment and improved power generation efficiency. Under sunny conditions, the system adjusts the bracket to the optimal angle (for example, 32°), and the actual adjusted angle is 31.8°, with an error of only 0.2°. This high-precision adjustment enables the power generation efficiency to reach 18.7%, which is significantly higher than the 12%-15% efficiency of fixed-angle brackets (industry average data). This shows that the system maximizes the utilization of light by optimizing the objective function and closed-loop control. Under strong wind conditions (wind speed 20m / s), the system automatically adjusts the bracket to a horizontal angle (0°), successfully reducing the wind load to 45.8N, which is far below the maximum load capacity of the bracket (50N). Compared with traditional systems, this system can respond in real time in strong wind environments, avoiding the risk of equipment damage and improving safety.

[0132] Embodiment 3, the following is an embodiment of the present invention, which provides a photovoltaic power generation unit flexible support control system, including:

[0133] The data acquisition module installs the light intensity sensor at the top center of the photovoltaic panel to capture the vertical light intensity; the wind speed sensor is fixed on the side of the photovoltaic panel to capture the on-site wind speed; the temperature sensor is attached to the back of the photovoltaic panel to measure the panel temperature.

[0134] The data processing and optimization module constructs the optimization objective function and uses the optimization algorithm to calculate the optimal bracket angle.

[0135] The control instruction generation and execution module generates control instructions and adjusts the bracket according to the optimal angle.

[0136] Feedback detection and correction module detects the adjustment results and performs error correction.

[0137] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0138] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0139] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering, or processing in another suitable manner as necessary, and then stored in a computer memory.

[0140] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc. It should be noted that the above embodiments are only used to illustrate the technical solution of the present invention and are not limited. Although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention, which should be included in the scope of the claims of the present invention.

[0141] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for controlling a flexible support of a photovoltaic power generation unit, characterized in that: include: Deploy sensors to collect environmental data; Construct optimization objective functions and use optimization algorithms to build data processing models; Generate control instructions based on the data processing model to execute the adjustment of the flexible bracket of the photovoltaic power generation unit; After adjustment, the feedback signal is detected and corrected.

2. The photovoltaic power generation unit flexible support control method according to claim 1, characterized in that: The arrangement of sensors to collect environmental data includes arranging a light intensity sensor, a wind speed sensor, and a temperature sensor at the photovoltaic panel; Simulate typical climatic conditions and collect data on sunny, cloudy and windy days to cover typical operating environments. Start the sensor power supply and confirm the connection status between the sensor and the main control system to ensure the normal operation of the data acquisition equipment.

3. The photovoltaic power generation unit flexible support control method according to claim 2, characterized in that: The construction of the optimization objective function includes constructing the objective function and constraint conditions, and combining the actual power generation characteristic curve. The objective function formula is expressed as: P=η·I·A·cos(θ) Among them, the efficiency of photovoltaic panels η varies with temperature, I represents the light intensity, A represents the effective area of ​​photovoltaic panels, and θ represents the inclination angle of the bracket; the correction formula is expressed as: η=η0·(1-k T (TT opt )) Among them, η0 represents the initial efficiency, k T Represents the temperature coefficient, T opt Indicates the optimal temperature; Define the maximum wind load. The wind load F must be less than the maximum load capacity F of the bracket. max , the formula is: F=0.5·ρ·W 2 ·C d ·A≤F max Where ρ represents the air density, C d is the drag coefficient, W represents the wind speed; the bracket angle range, the bracket angle limit is θ min ≤θ≤θ max .

4. The photovoltaic power generation unit flexible support control method according to claim 3, characterized in that: The use of the optimization algorithm to construct a data processing model includes generating an initial population, each individual representing a possible bracket angle θ, the initial population size is set to N, and θ values ​​are randomly generated in the range [θ min ,θ max ]; define individual, individual is a real number θ i ; Fitness function calculation, based on environmental data and current angle θ, calculates power generation and wind load constraints; if wind load F>F max , add the penalty to the fitness: fitness=P-λ·(F-F max ) Among them, λ represents the penalty coefficient; Define boundary constraints if Then directly set the fitness to the minimum value; Use the roulette wheel method to select individuals with high fitness to enter the next generation; the probability of each individual being selected is proportional to its fitness: Crossover strategy, randomly select two parent individuals θ parent 1, θ parent 2; Use uniform crossover to generate offspring: i child =w·θ parent1 +(1-w)·θ parent2 Where w∈[0,1] represents random weight; set the crossover probability to P c ; Specify the mutation strategy and randomly select a descendant individual θ child , perform mutation according to a Gaussian distribution: θ′ child =θ child +Δθ Among them, Δθ~N(0,σ 2 ), set the mutation probability P m ; Iterate to the preset algebra G max Or when the population fitness change is less than the threshold ∈, the iteration is terminated and the optimal control angle is output.

5. The photovoltaic power generation unit flexible support control method according to claim 4, characterized in that: The generating of control instructions according to the data processing model includes obtaining the optimal angle θ * , output the current optimal angle θ through the data processing module * ; With environmental parameters, including current light intensity, current wind speed, and current temperature; generate motor control signals to adjust the optimal angle θ * Converted into the motor target rotation angle Δφ, the formula is expressed as: Δφ=(θ * -θ current )·G Where G is the angle-to-motor rotation scaling factor, θ current Indicates the actual inclination angle of the current bracket; calculates the motor driving time: Among them, ω m Indicates the angular velocity of the motor; format the control instruction and generate the instruction format. If Δφ>0, it is set to clockwise.

6. The photovoltaic power generation unit flexible support control method according to claim 5, characterized in that: The photovoltaic power generation unit flexible bracket adjustment includes receiving a control signal, and the servo motor adjusts the bracket in a stepping manner. The stepping signal control formula is expressed as: ΔX=Δφ·R Where R represents the support rotation radius; the support angle is adjusted, and the support structure model formula is expressed as: L1sin(θ)=L2 Where ΔX represents the linear displacement of the driving rod, L1 represents the length of the support arm, and L2 represents the effective length of the driving rod. * , reverse calculation of servo motor steps: Among them, step_angle represents the angle of rotation of the single-step motor, and real-time angle adjustment is performed. After each step, the current angle θ is collected feedback Compare the target angle, if the error |θ feedback -θ * |>∈, ∈ is the tolerance, then repeat the adjustment process.

7. The photovoltaic power generation unit flexible support control method according to claim 6, characterized in that: The post-adjustment detection feedback signal and correction includes using an angle sensor to detect the actual inclination angle θ of the bracket in real time. feedback ; The feedback signal detection formula is expressed as: e(t)=θ * -θ feedback Wherein, e(t) represents the error signal; PID correction control is performed. If |e(t)|>∈, correction is performed through the PID controller; the motor control command is updated to drive the motor to further adjust the angle; to prevent oscillation, that is, anti-integral saturation, the integral limit is set, and the formula is expressed as: ∫e(t)dt=max(min(∫e(t)dt,I max ),-I max ) Among them, I max Indicates the integral limit; Repeat detection and correction, and continue the cycle of detection until |e(t)|≤∈.

8. A system using the photovoltaic power generation unit flexible support control method according to any one of claims 1 to 7, characterized in that: The data acquisition module installs the light intensity sensor at the center of the top of the photovoltaic panel to capture the vertical light intensity; the wind speed sensor is fixed on the side of the photovoltaic panel to capture the on-site wind speed; the temperature sensor is attached to the back of the photovoltaic panel to measure the panel temperature; Data processing and optimization module, constructs optimization objective function and uses optimization algorithm to calculate the optimal bracket angle; A control instruction generation and execution module generates control instructions and adjusts the bracket according to the optimal angle; Feedback detection and correction module detects the adjustment results and performs error correction.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the photovoltaic power generation unit flexible support control method described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the photovoltaic power generation unit flexible support control method described in any one of claims 1 to 7 are implemented.

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