Wind resistance control method and system for slope photovoltaic support

Abstract

The invention discloses a wind resistance control method and system for a slope photovoltaic support, and relates to the technical field of photovoltaic support structure engineering, and the method comprises the steps: collecting three-dimensional topographic data and wind speed gradient data; inputting the three-dimensional topographic data set into the topographic transfer function model, and calculating a resonance risk level in combination with the wind speed gradient data; extracting resonance frequency based on the resonance risk level, and calculating the curvature value of the target flow guide wing in real time by adopting the resonance frequency; when the resonance risk level reaches a risk threshold value, calculating a safety dip angle based on an actual curvature value, and controlling an electric push rod to adjust the dip angle of the slope photovoltaic bracket to the safety dip angle; the slope photovoltaic support displacement is recorded on the basis that the slope photovoltaic support inclination angle is adjusted to the safe inclination angle, the displacement attenuation rate is calculated in combination with the displacement before slope photovoltaic support adjustment, and a structure safety report is generated; the structure response attenuation characteristic is dynamically evaluated through the displacement sensor, and the structure stability and operation safety of the slope photovoltaic support in the strong wind environment are enhanced.

Description

Technical Field

[0001] This invention relates to the field of photovoltaic support structure technology, and in particular to a method and system for controlling the wind resistance of photovoltaic supports on slopes. Background Technology

[0002] With the rapid development of renewable energy technologies, photovoltaic (PV) power generation systems are increasingly being used in complex terrain conditions, especially in mountainous and hilly slope areas, where the scale of PV power plant construction continues to expand. However, these areas typically have significant topographic relief and complex local meteorological conditions, resulting in highly non-uniform and dynamically time-varying wind field distribution. Wind load, as a key environmental excitation factor affecting the structural safety of PV support structures on slopes, is influenced not only by the atmospheric boundary layer wind speed gradient but also by the superimposed effects of multiple mechanisms, including vortex shedding caused by topographic disturbances, wind shadow effects, and aeroelastic coupling. In recent years, computational fluid dynamics-based numerical simulation technology for wind fields has been widely applied to the wind load assessment of PV supports, enabling the prediction of local wind environments by constructing three-dimensional wind field models.

[0003] While existing technologies have made some progress in wind field modeling and structural response monitoring, they still suffer from response lag and limited control mechanisms in addressing the risk of dynamic wind-induced resonance caused by complex terrain. Specifically, traditional CFD simulations are mostly based on steady-state or quasi-steady-state assumptions, making it difficult to reflect the transient characteristics of wind speed gradient changes with terrain in real time, resulting in a lack of timeliness in the prediction of resonance risk. At the same time, existing guide structures are mostly passive devices with fixed geometries, unable to dynamically adjust their aerodynamic shape according to real-time wind field changes, thus limiting their adaptability in variable wind environments. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide a method and system for wind resistance control of photovoltaic supports on slopes, which solves the problems of lag in dynamic wind-induced resonance response and single control mechanism.

[0005] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is: a method for wind resistance control of photovoltaic supports on slopes, comprising the following steps: Collect 3D terrain data and wind speed gradient data; The three-dimensional terrain dataset is input into the terrain transfer function model, and combined with wind speed gradient data, the resonance risk level is calculated. The resonance frequency is extracted based on the resonance risk level, and the target guide vane curvature value is calculated in real time using the resonance frequency. The target airfoil curvature value is converted into a driving current command, and the actual airfoil curvature value is adjusted to the target airfoil curvature value through the shape memory alloy driving circuit to generate the actual curvature value; When the resonance risk level reaches the risk threshold, the safe tilt angle is calculated based on the actual curvature value, and the electric push rod is controlled to adjust the tilt angle of the slope photovoltaic support to the safe tilt angle. Based on the adjustment of the slope photovoltaic support tilt angle to a safe tilt angle, the displacement of the slope photovoltaic support is recorded, and the displacement attenuation rate is calculated by combining the displacement of the slope photovoltaic support before adjustment, and a structural safety report is generated.

[0006] Preferably, the specific steps for collecting the three-dimensional terrain data and wind speed gradient data are as follows: Record the three-dimensional coordinates, echo intensity, and timestamp of the laser reflection point to form the original laser point cloud dataset; The original laser point cloud dataset was corrected using a GNSS reference station, the elevation deviation compensation was calculated, the slope vector was extracted, three-dimensional terrain data was generated, and wind speed gradient data was collected using an ultrasonic anemometer.

[0007] Preferably, the specific steps for inputting the three-dimensional terrain dataset into the terrain transfer function model and calculating the resonance risk level in conjunction with wind speed gradient data are as follows: Discretize the terrain transfer function model, divide the three-dimensional terrain dataset of the target slope into spatial grids, obtain the unit cell grid resolution, and establish a corrected terrain spatial grid. Based on the corrected terrain spatial grid and wind speed gradient data, the wind pressure spectral density is calculated to obtain the natural frequency and corresponding mode shape of the photovoltaic support. The resonance risk level is determined based on the wind pressure spectral density and the natural frequency and corresponding mode shape of the photovoltaic support.

[0008] Preferably, the step of extracting the resonance frequency based on the resonance risk level and using the resonance frequency to calculate the target guide vane curvature value in real time includes the following specific steps: The resonance risk level is mapped to the resonance frequency using an RFTF function; The effective wind speed component perpendicular to the guide vane is calculated based on wind speed gradient data using the voltage projection formula. Calculate the minimum aerodynamic damping ratio required to suppress resonance based on the resonant frequency and effective wind speed components; The minimum aerodynamic damping ratio is input into the wind tunnel relationship model, and the curvature value of the target guide vane is generated by inversion curvature calculation.

[0009] Preferably, the conversion of the target airfoil curvature value into a drive current command refers to using the physical properties of the shape memory alloy and combining it with a temperature sensor for dynamic compensation of the shape memory alloy, converting the target airfoil curvature value into a corresponding drive current intensity value, and thus obtaining a drive current command.

[0010] Preferably, the step of adjusting the actual airfoil curvature value to the target airfoil curvature value through the shape memory alloy driving circuit to generate the actual curvature value involves the following specific steps: The drive current command is input into the H-bridge circuit, the PWM duty cycle is adjusted in real time by the PID controller, and the current is output to the shape memory alloy through the MOSFET bridge arm to trigger the phase change contraction of the guide vane, thereby adjusting the actual guide vane curvature value to the target guide vane curvature value and obtaining the center point of the guide vane. The center point of the guide vane is scanned by a laser displacement meter to output the actual displacement, and the real-time curvature value is calculated using the curvature formula.

[0011] Preferably, when the resonance risk level reaches the risk threshold, the safe tilt angle is calculated based on the actual curvature value, and the electric push rod is controlled to adjust the tilt angle of the photovoltaic support to the safe tilt angle. The specific steps are as follows: When the resonance risk level exceeds the preset risk threshold, the actual curvature value is extracted and combined with the structural dynamic response parameters corresponding to the current resonance risk level. The critical stiffness attenuation is obtained through structural stability analysis, and the safe tilt angle is output. Input the safety tilt angle into the kinematic conversion center of the push rod, calculate the target extension and retraction of the electric push rod based on the geometric relationship of the support rotation mechanism and the measured tilt angle of the photovoltaic support on the current slope, and mark the displacement direction symbol; The actual tilt angle is collected to deviate from the target value. When the actual tilt angle deviates from the target value and exceeds the allowable deviation, the gain adaptive adjustment is triggered until the operation of adjusting the tilt angle of the photovoltaic support to a safe tilt angle is completed.

[0012] Preferably, the process of recording the displacement of the slope photovoltaic support based on the adjustment of the slope photovoltaic support tilt angle to a safe tilt angle, and calculating the displacement attenuation rate by combining the displacement of the slope photovoltaic support before adjustment, involves the following specific steps: Displacement sensors are installed at the base of the photovoltaic support on the slope to collect vibration displacement data of the photovoltaic support on the slope. By collecting vibration displacement data, the average vibration displacement data of the slope photovoltaic support before adjustment was calculated; The displacement attenuation rate is generated by calculating the displacement difference from the average value of the vibration displacement data.

[0013] Preferably, the structural safety report includes displacement attenuation rate and resonance risk level.

[0014] A control system for implementing a method such as wind resistance control for slope photovoltaic supports includes: The acquisition module is used to acquire 3D terrain data and wind speed gradient data; The risk level module is used to input the 3D terrain dataset into the terrain transfer function model and, in combination with wind speed gradient data, calculate the resonance risk level. The guide vane curvature module is used to retrieve the target guide vane curvature value corresponding to the resonance risk level from the computational fluid dynamics database based on the resonance risk level. The drive module is used to convert the target airfoil curvature value into a drive current command, and adjust the airfoil curvature value to the target airfoil curvature value through the shape memory alloy drive circuit to generate the actual curvature value; The control module is used to calculate the safe tilt angle based on the actual curvature value when the resonance risk level reaches the risk threshold, and control the electric push rod to adjust the tilt angle of the slope photovoltaic support to the safe tilt angle; The reporting module is used to record the displacement of the photovoltaic support on the slope based on the adjustment of the slope photovoltaic support tilt angle to a safe tilt angle, calculate the displacement attenuation rate by combining the displacement of the slope photovoltaic support before adjustment, and generate a structural safety report.

[0015] This invention provides a method and system for wind-resistant control of photovoltaic supports on slopes. By integrating UAV lidar and GNSS correction technology to acquire high-precision three-dimensional terrain data and combining it with an ultrasonic anemometer to collect wind speed gradients, it achieves accurate perception of terrain-wind field coupling information in complex slope environments, providing a reliable data foundation for wind-resistant control. Furthermore, it predicts resonance risk by constructing a terrain transfer function model and innovatively introduces a mechanism for calculating the curvature value of the target guide vane based on the resonance frequency and effective wind speed components. Combined with a shape memory alloy drive system, it realizes real-time adaptive adjustment of the guide vane curvature, effectively suppressing the formation and development of wind-induced resonance. At the same time, under high-risk conditions, it uses an electric actuator to adjust the tilt angle of the photovoltaic support to a safe angle and uses a displacement sensor to dynamically evaluate the structural response attenuation characteristics, forming a closed-loop control system of "perception-prediction-control-verification". Attached Figure Description

[0016] The present invention will be further described below with reference to the accompanying drawings and embodiments: Figure 1 This is a flowchart of a method for controlling the wind resistance of photovoltaic supports on slopes.

[0017] Figure 2 This is a schematic diagram of the wind-resistant control system for a photovoltaic support structure on a slope.

[0018] Figure 3 A flowchart for calculating the curvature value of the target airfoil.

[0019] Figure 4 A flowchart for generating a structural safety report. Detailed Implementation

[0020] like Figure 1-4 As shown, a method for wind resistance control of photovoltaic supports on slopes includes the following steps: S1: Collect 3D terrain data and wind speed gradient data.

[0021] S1.1: Using a vertical take-off fixed-wing UAV equipped with a lidar to scan the slope, the three-dimensional coordinates, echo intensity, and timestamp of the laser reflection points are recorded to form an original laser point cloud dataset.

[0022] Specifically, the vertical take-off and landing fixed-wing UAV is equipped with a lidar to perform slope scanning tasks, and the flight altitude, speed, flight path direction and flight path spacing are set; The lidar records the three-dimensional coordinates, echo intensity, and timestamp of each reflection point by emitting a laser beam. The three-dimensional coordinates are obtained by combining the laser ranging value with the real-time GNSS position of the VTOL fixed-wing UAV and the azimuth / pitch angle of the laser beam. The echo intensity is obtained in decibels based on the power ratio of the reflected signal to the transmitted signal. The timestamp uses Coordinated Universal Time (UTC) synchronized with the VTOL fixed-wing UAV's GNSS. All reflection point data is stored in real time as temporary files in LAS 1.4 format and merged to generate the original laser point cloud dataset after the flight ends.

[0023] S1.2: Correct the original laser point cloud dataset using a GNSS reference station, calculate the elevation deviation compensation, extract the slope vector, generate three-dimensional terrain data, and collect wind speed gradient data using an ultrasonic anemometer.

[0024] Specifically, three Trimble-R12-GNSS reference stations are deployed around the slope. The original laser point cloud dataset is imported into RiPROCESS software, and the elevation deviation compensation is calculated for each reference station. All laser points within a 10-meter radius of the reference station are identified, and the difference between the known elevation of the reference station and the average elevation of the point cloud is obtained (e.g., the elevation of reference station 1 is 100.000 meters, the average elevation of the point cloud is 99.980 meters, and the difference is +0.020 meters). The arithmetic mean of the elevation deviation values ​​of the three reference stations is calculated to obtain the elevation deviation compensation (e.g., +0.018 meters). The elevation values ​​of all laser points are superimposed with the elevation deviation compensation to generate a corrected point cloud.

[0025] Slope vector extraction based on corrected point cloud: Elevation surface data is constructed in Global-Mapper software, and the east-west slope component and north-south slope component are calculated grid by grid. The slope angle and aspect angle are synthesized, and the elevation surface data, slope angle and aspect angle are merged to generate three-dimensional terrain data (including three attributes: elevation value, slope angle and aspect angle). At the same time, an ultrasonic anemometer is deployed on the slope to record the three-dimensional wind speed component at a sampling frequency of 20 Hz to generate wind speed gradient data.

[0026] S2: Input the 3D terrain dataset into the terrain transfer function model, combine it with wind speed gradient data, and calculate the resonance risk level.

[0027] S2.1: Discretize the terrain transfer function model, divide the target slope 3D terrain dataset into spatial grids, obtain the unit cell grid resolution, and establish a corrected terrain spatial grid.

[0028] Specifically, the steps include: discretizing the terrain transfer function model and applying it to the target slope's 3D terrain dataset; performing spatial mesh generation: setting the unit cell mesh resolution based on the photovoltaic support foundation size; the mesh range covering the boundary of the slope's 3D terrain dataset and extending outwards, for example, a 20-meter buffer zone; dividing the 3D terrain dataset: using a quadrilateral structured mesh for flat areas and a triangular unstructured mesh for steep slope areas; assigning elevation, slope angle, and aspect angle to each node; if the total number of meshes is greater than 800,000 units, a simplification algorithm is activated: adjacent meshes with slope changes of <5° are merged; the new node elevation is averaged; the slope / aspect angle is weighted by the unit area; the merged size is ≤2 times the reference resolution and the curvature error is ≤0.1; generating a corrected terrain spatial mesh containing node coordinates, elevation values, slope angle, and aspect angle.

[0029] It should be noted that the terrain transfer function model is a deterministic spatial discretization tool that does not require training or data fitting.

[0030] S2.2: Based on the corrected terrain spatial grid and wind speed gradient data, calculate the wind pressure spectral density and obtain the natural frequency and corresponding mode shape of the photovoltaic support.

[0031] Specifically, based on the corrected topographic spatial grid and wind speed gradient data collected by the ultrasonic anemometer, a three-dimensional flow field is established on the numerical computing platform. The corrected topographic spatial grid is used as the topographic boundary condition, and the wind speed gradient data is used as the initial field condition. The Reynolds-averaged Navier-Stokes equations are solved to obtain the pulsating wind pressure field. Monitoring points (e.g., 100) are selected in the photovoltaic array area (e.g., the center point of the support foundation), and the pulsating wind pressure time history data is extracted. The wind pressure spectral density distribution is generated by fast Fourier transform. The geometric dimensions and material properties of the photovoltaic support are collected, and the stiffness matrix and mass matrix containing all beam elements of the support are constructed. The generalized characteristic equation is solved using the Block Lanczos method to obtain the natural frequency of the photovoltaic support (2.1Hz for the first order in the example) and the corresponding mode shape of the photovoltaic support.

[0032] It should be noted that the expression for calculating the natural frequency of the photovoltaic support and the corresponding mode shape is as follows: ; in It is the stiffness matrix. p is the first The inherent frequency of the photovoltaic bracket, It is the first The mode shape corresponding to the first natural frequency of the photovoltaic support. It is the quality matrix.

[0033] S2.3: The resonance risk level is classified according to the wind pressure spectral density and the natural frequency and corresponding mode shape of the photovoltaic support.

[0034] Specifically, based on the wind pressure spectral density distribution and the natural frequencies and corresponding mode displacement cloud maps of the photovoltaic support, resonance risk level classification is performed: For the first three natural frequencies of the photovoltaic support (e.g., 1.2 Hz, 2.8 Hz, 4.5 Hz), an example value ±0.2 Hz risk bandwidth (e.g., 2.6-3.0 Hz bandwidth for 2.8 Hz) is set. The average wind pressure spectral density within the risk bandwidth is calculated, and its ratio to the average wind pressure spectral density across the entire frequency band is calculated (e.g., ratio = 2.50). For frequency points with a ratio ≥ 150% (e.g., 2.8 Hz), the spatial distribution of frequency wind pressure and the corresponding mode displacement cloud map (e.g., second-order torsional mode) are extracted. The spatial correlation coefficient between nodal wind pressure and displacement is calculated, and its expression is: ; in, Represents the spatial correlation coefficient. It is a node The wind pressure value, It is a node The modal displacement values, It is the average wind pressure across all nodes. It is the average displacement of all nodal modes. It is the total number of nodes participating in the calculation. It is a node index variable.

[0035] Based on the calculated spatial correlation coefficient between nodal wind pressure and displacement, risk levels are classified according to a five-level classification rule: for example, a ratio < 50% is Level I (low risk); 50% ≤ ratio < 150% is Level II (attention level); and 150% ≤ ratio < 300% and... <0.4 indicates Level III (early warning level), and 150% ≤ ratio < 300% and A ratio ≥0.4 is classified as Level IV (High Risk), and a ratio ≥300% is classified as Level V (Emergency) (e.g., a ratio = 250% and...). =0.55 is judged as Level IV risk); output resonance risk level.

[0036] S3: Extract the resonance frequency based on the resonance risk level, and use the resonance frequency and the real-time calculation of the target guide vane curvature value.

[0037] S3.1: Map the resonance risk level to the resonance frequency using the RFTF function.

[0038] Specifically, the steps include the following: inputting the resonance risk level into the risk-frequency conversion function (RFTF function). The risk-frequency conversion function is based on the frequency ranges corresponding to the five risk levels. For example, Level I risk corresponds to 0.5-1.0 Hz, Level II to 1.0-2.0 Hz, Level III to 2.0-3.5 Hz, Level IV to 3.5-5.0 Hz, and Level V to 5.0-10.0 Hz. The median frequency of the corresponding range is automatically selected as the resonance frequency for each risk level (for example, the median frequency of 2.75 Hz is taken for the Level III risk range of 2.0-3.5 Hz).

[0039] S3.2: Calculate the effective wind speed component perpendicular to the guide vane using the voltage projection formula based on wind speed gradient data. This includes the following steps: Based on wind speed gradient data acquired by an ultrasonic anemometer and the real-time azimuth angle of the guide vane, wind speed components are obtained through the time difference of ultrasonic wave propagation in three-dimensional space. These components include eastward, northward, and vertical wind speed components. The azimuth angle of the guide vane is acquired in real-time using a rotary encoder to obtain the wind-guide vane normal angle. The effective wind speed component perpendicular to the guide vane is calculated using the voltage projection formula, and its expression is as follows: ; in, It is the effective wind speed component. It is the eastward wind speed component. Northward wind speed component It is the angle between the wind direction and the normal to the deflector. It is the vertical wind speed component. It is the vertical correction factor.

[0040] S3.3: Calculate the minimum aerodynamic damping ratio required to suppress resonance based on the resonant frequency and effective wind speed components. This includes the following steps: Based on the resonant frequency and effective wind speed components, the minimum aerodynamic damping ratio required to suppress resonance is calculated using the Strocha number, and the expression is: ; in, This represents the minimum aerodynamic damping ratio. Storoha number; The formula for calculating the Storoja number is: ; in, It is the resonant frequency. Characteristic width of the guide vane This represents the effective wind speed component.

[0041] It should be noted that the characteristic width of the guide vane is obtained by measuring the maximum projected width after the guide vane is manufactured. For example, if the guide vane is a rectangular plate, then the characteristic width is the width of the plate (the dimension perpendicular to the direction of the incoming flow); if it is curved, then the width is taken in the reference state (such as the straight state).

[0042] S3.4: Input the minimum aerodynamic damping ratio into the wind tunnel relationship model, and generate the target guide vane curvature value by inverting curvature calculation.

[0043] Specifically, the steps include: the minimum aerodynamic damping ratio is the energy threshold for resonance suppression, calculated from the Strocha number using a deterministic formula, serving as the core of guide vane deformation control; the minimum aerodynamic damping ratio is input into the wind tunnel relation model (the wind tunnel relation model is a computational tool for structured storage and interpolation of physical experimental data, containing historical wind tunnel experimental datasets, recording the mapping relationship between guide vane curvature and aerodynamic damping ratio under different wind speeds), and the target curvature inversion calculation is performed: in the wind tunnel relation model, a subset of data matching the current effective wind speed component (e.g., 2.80 m / s) is selected (wind speed range 2.5-3.0 m / s), and the experimental data point closest to the minimum aerodynamic damping ratio (0.092) is retrieved (e.g., a curvature of 0.14 radians per meter corresponds to a damping ratio of 0.090, and a curvature of 0.16 radians per meter corresponds to 0.095), to obtain the target guide vane curvature value.

[0044] It should be noted that: the wind tunnel relationship model was trained by traversing the wind speed range and guide vane curvature range of the target working condition, and the steady-state aerodynamic damping ratio corresponding to each set of parameters was measured and recorded to form the original experimental dataset; the data was cleaned (outliers were removed based on physical constraints and the 3σ criterion), and the wind speed and curvature parameters were normalized to the [0,1] interval; based directly on the normalized data, a continuous mapping surface of wind speed-curvature-damping ratio was constructed through a bivariate interpolation algorithm; the interpolation accuracy was verified, and the surface was discretized into a high-density lookup table and embedded into the wind tunnel relationship model to obtain the trained wind tunnel relationship model.

[0045] S4: Convert the target airfoil curvature value into a driving current command, and adjust the actual airfoil curvature value to the target airfoil curvature value through the shape memory alloy driving circuit to generate the actual curvature value.

[0046] S4.1: Converting the target airfoil curvature value into a drive current command refers to using the physical properties of the shape memory alloy, combined with dynamic compensation of the shape memory alloy via a temperature sensor, to convert the target airfoil curvature value into a corresponding drive current intensity value, thus obtaining the drive current command. Specifically, this includes the following steps: Based on the target airfoil curvature value and the physical properties of the shape memory alloy (the phase transition behavior of the shape memory alloy under temperature changes), a temperature sensor measures the current temperature value of the shape memory alloy in real time, captures temperature change factors during operation, and performs dynamic compensation for the shape memory alloy: utilizing the sensitivity of the shape memory alloy's physical properties to temperature changes, the inherent physical properties of the shape memory alloy are collected, combined with the difference in real-time temperature sensor data, scaled proportionally, and a compensation current is generated; the compensation current is used to offset the shape deviation of the shape memory alloy caused by temperature fluctuations, and the compensated target airfoil curvature value is obtained. Calculate the corresponding driving current intensity value: Based on the curvature value of the target guide vane after compensation, the driving current intensity value is obtained by reverse calculation through the linear relationship between current intensity and heat generation in the physical properties of shape memory alloy (for example, the value is 5 degrees Celsius temperature rise per ampere of current), and is directly used as the driving current command.

[0047] S4.2: The drive current command is input to the H-bridge circuit. The PWM duty cycle is adjusted in real time by the PID controller, and the current is output to the shape memory alloy through the MOSFET bridge arm to trigger the phase change contraction of the guide vane. This adjusts the actual guide vane curvature value to the target guide vane curvature value, thus obtaining the center point of the guide vane. Specifically, the steps are as follows: The drive current command is input to the H-bridge circuit, which adjusts the pulse width modulation duty cycle in real time via a proportional-integral-derivative controller (e.g., a target current of 3.08 amps corresponds to a 62% duty cycle). The difference between the drive current command (e.g., a 3.08V voltage signal, corresponding to 3.08A) and the actual current feedback value of the shape memory alloy is used as the current error value. The current error value is applied to a proportional control algorithm, and the product of the current error value and the proportional gain in the applied proportional control algorithm is used as the duty cycle value. This value is then used to drive the PID controller to adjust the duty cycle. The pulse width modulation signal period is divided according to the duty cycle value (fixed at 1 millisecond period), and the high-level duration and low-level duration are calculated. The timer controls the pin level transition according to the high / low level duration: the timer starts at a high level, pulls the level low when the timer reaches the example value of 620.8 microseconds, and resets and restarts the high level after 1 millisecond period, generating the pulse width modulation signal waveform. Combining the field-effect transistor (FET) of metal-oxide-semiconductor (MOS) systems, the transistor bridge arm outputs current to the shape memory alloy based on the pulse width modulation (PWM) signal, triggering a phase transition and contraction of the shape memory alloy (e.g., a current of 3.08 amperes drives a 0.148 radian per meter change in the curvature of the guide vane). The actual curvature value of the guide vane is collected in real time by a fiber optic curvature sensor. If the deviation between the actual curvature value and the target curvature value of the guide vane exceeds 0.02 radians per meter (based on aerodynamic performance sensitivity settings), the proportional-integral-derivative (PID) controller dynamically adjusts the PWM duty cycle (e.g., increasing the duty cycle to 65%). Three reflective markers are installed on the surface of the guide vane. A laser tracker emits a nano-infrared laser beam to track the markers in real time. The three-dimensional coordinates of the markers are obtained through interferometric ranging and an angle encoder. The geometric centroid is calculated based on the three coordinates of the markers. After temperature drift compensation and atmospheric refraction correction, the centroid coordinates are output to calibrate the center point of the guide vane in real time.

[0048] S4.3: Use a laser displacement meter to scan the center point of the guide vane, output the actual displacement, and calculate the real-time curvature value using the curvature formula.

[0049] Specifically, the process includes the following steps: the laser displacement meter emits a laser beam perpendicular to the surface of the center point of the guide vane, scans and measures the instantaneous vertical distance between the center point of the guide vane and the fixed base, and obtains the actual displacement value; the actual displacement value contains a discrete point sequence, and the real-time curvature value is calculated using the curvature formula.

[0050] S5: When the resonance risk level reaches the risk threshold, calculate the safe tilt angle based on the actual curvature value, and control the electric push rod to adjust the tilt angle of the photovoltaic bracket to the safe tilt angle.

[0051] S5.1: When the resonance risk level exceeds the preset risk threshold, extract the actual curvature value and combine it with the structural dynamic response parameters corresponding to the current risk level. Obtain the critical stiffness attenuation through structural stability analysis and output the safe tilt angle.

[0052] Specifically, the steps include: extracting the actual curvature value of the guide vane (provided by a real-time curvature measurement device); retrieving the structural dynamic response parameter values ​​corresponding to the current resonance risk level (achieved through matching a pre-stored table, for example, resonance risk level III corresponds to a critical wind speed of 15 m / s and a turbulence intensity of 18%); performing structural stability analysis and applying the Lagrange equation to calculate structural stability; iteratively solving the displacement response under vibration load excitation; recording the stiffness attenuation ratio when the displacement amplitude exceeds 150% of the initial displacement (the example value is the critical judgment condition), and obtaining the critical stiffness attenuation value; and directly querying the safe tilt angle value corresponding to the critical stiffness attenuation value based on the tilt angle-stiffness attenuation mapping table (sourced from wind tunnel vibration table test data).

[0053] It should be noted that the risk threshold is determined by collecting vibration displacement data of the guide vane under different working conditions through wind tunnel vibration table tests, and recording the critical point of displacement amplitude increase by measuring the actual displacement with a laser displacement meter. For example, the guide vane will experience resonance failure when the displacement amplitude increase reaches 150%. Based on the failure case analysis, a numerical classification standard for resonance risk level is extracted. For example, levels I to IV correspond to displacement amplitude increases of 50%, 100%, 150%, and 200%, respectively. The preset risk threshold is then obtained.

[0054] S5.2: Input the safe tilt angle into the kinematic conversion center of the push rod. Based on the geometric relationship of the support rotation mechanism and the measured tilt angle of the photovoltaic support on the current slope, calculate the target extension / retraction of the electric push rod, and mark the displacement direction. Collect the actual tilt angle deviation from the target value. When the actual tilt angle deviates from the target value and exceeds the allowable deviation, trigger adaptive gain adjustment until the photovoltaic support tilt angle is adjusted to the safe tilt angle. Specifically, this includes the following steps: Input the safe tilt angle into the kinematic conversion center of the push rod. Combined with the geometric relationship of the support rotation mechanism and the current measured tilt angle of the photovoltaic support on the slope (e.g., 3.8 degrees), calculate the target extension / retraction amount of the electric push rod: calculate the difference between the safe tilt angle and the measured tilt angle based on the spatial vector method, and calculate the extension / retraction amount by combining the fixed value of the rotation radius; mark "+" (push rod extension) when the obtained safe tilt angle is greater than the measured tilt angle, and mark "-" (push rod retraction) otherwise. Example: 5.2 degrees > 3.8 degrees is marked "+0.049 meters"; after the electric push rod executes the extension / retraction command, the tilt angle sensor monitors the actual tilt angle in real time at a sampling frequency of 50 Hz; if the deviation between the actual tilt angle and the target tilt angle exceeds the allowable value (e.g., ±0.5 degrees), the proportional-integral controller is triggered to adjust the dynamic extension / retraction speed until the safe tilt angle is reached; the expression for calculating the target extension / retraction amount of the electric push rod is: ; in, It is the change in the extension and retraction of the electric actuator target. It is the radius of rotation. It is the safe tilt angle. It is the measured tilt angle.

[0055] S6: Based on the adjustment of the slope photovoltaic support tilt angle to a safe tilt angle, record the displacement of the slope photovoltaic support, combine the displacement of the slope photovoltaic support before adjustment to calculate the displacement attenuation rate, and generate a structural safety report.

[0056] S6.1: Install displacement sensors at the base of the photovoltaic support on the slope to collect vibration displacement data of the photovoltaic support on the slope.

[0057] Specifically, the steps include: installing a linear variable differential transformer displacement sensor on the flange at the root of the photovoltaic support on the slope; fixing the displacement sensor to the connection between the root of the photovoltaic support and the foundation with bolts and performing horizontal calibration, for example, with an error ≤ 0.1 degrees; and continuously collecting vibration displacement data of the photovoltaic support on the slope at a sampling frequency of 200 Hz.

[0058] S6.2: Calculate the average vibration displacement data of the slope photovoltaic support before adjustment by collecting vibration displacement data. This specifically includes the following steps: Based on the vibration displacement data collected by the linear variable differential transformer displacement sensor installed at the root flange of the photovoltaic support on the slope; before the electric push rod tilt angle adjustment command is issued, for example within 5 seconds, all displacement sampling points are extracted (1000 points corresponding to a sampling frequency of 200 Hz), and the arithmetic mean of the three-axis displacement data in the X, Y and Z directions is calculated respectively; the peak value of each axial displacement (for example, the peak value in the X direction is 22.1 mm) is identified simultaneously, and the average value of the vibration displacement data before adjustment is output.

[0059] S6.3: Perform displacement difference calculation on the average value of vibration displacement data to generate displacement attenuation rate.

[0060] Specifically, the process includes the following steps: Based on the average vibration displacement before adjustment (including time interval, coordinates, and average triaxial displacement) and the adjusted linear variable differential transformer displacement sensor, 10 minutes of vibration displacement data are collected (200 Hz sampling frequency corresponds to 12,000 points). Displacement attenuation rate calculation is performed: the average displacement of each axis after adjustment is calculated (e.g., the average displacement in the X direction is 8.35 mm); the average triaxial displacement value contained in the vibration displacement data is extracted as the vibration energy benchmark; the average displacement of each axis after adjustment is obtained based on the data collected by the adjusted linear variable differential transformer displacement sensor; displacement difference calculation is performed to obtain the absolute difference between the average displacement value before adjustment and the average displacement value after adjustment for each axis; and the displacement attenuation rate is generated based on the average triaxial displacement and the average triaxial displacement after adjustment using a proportional formula.

[0061] S6.4: The structural safety report includes the displacement attenuation rate and resonance risk level.

[0062] The system integrates the displacement attenuation rate calculation results (including coordinates and triaxial displacement attenuation rates) with the resonance risk level distribution data (including coordinates and risk levels). Based on the coordinate points, spatial matching is performed: when the coordinate deviation is ≤0.001 meters and the timestamp difference is ≤1 millisecond, the system extracts the X-direction displacement attenuation rate (e.g., 45.5%), Y-direction displacement attenuation rate (e.g., 54.3%), and Z-direction displacement attenuation rate (e.g., 55.8%) from the displacement attenuation rate data and the risk level (e.g., Level IV) from the resonance risk level data, and integrates the output to generate a structural safety report.

[0063] This embodiment also provides a wind-resistant control system for a slope photovoltaic support, including: a data acquisition module, a risk level module, a guide vane curvature module, a drive module, a control module, and a reporting module; The acquisition module is used to acquire 3D terrain data and wind speed gradient data; The risk level module is used to input the 3D terrain dataset into the terrain transfer function model and, in combination with wind speed gradient data, calculate the resonance risk level. The guide vane curvature module is used to retrieve the target guide vane curvature value corresponding to the resonance risk level from the computational fluid dynamics database based on the resonance risk level. The drive module is used to convert the target airfoil curvature value into a drive current command, and adjust the airfoil curvature to the target airfoil curvature value through the shape memory alloy drive circuit to generate the actual curvature value; The control module is used to calculate the safe tilt angle based on the actual curvature value when the resonance risk level reaches the risk threshold, and control the electric push rod to adjust the tilt angle of the slope photovoltaic support to the safe tilt angle; The reporting module is used to record the displacement of the photovoltaic support on the slope based on the adjustment of the slope photovoltaic support tilt angle to a safe tilt angle, calculate the displacement attenuation rate by combining the displacement of the slope photovoltaic support before adjustment, and generate a structural safety report.

[0064] This embodiment also provides a computer device applicable to the wind resistance control method for slope photovoltaic supports, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to realize the wind resistance control method for slope photovoltaic supports as proposed in the above embodiment. The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0065] This embodiment also provides a storage medium storing a computer program, which, when executed by a processor, implements the wind resistance control method for slope photovoltaic supports as proposed in the above embodiments. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0066] In summary, this invention achieves precise perception of terrain-wind field coupling information in complex slope environments by integrating UAV lidar and GNSS correction technology to acquire high-precision three-dimensional terrain data and combining it with ultrasonic anemometers to collect wind speed gradients, providing a reliable data foundation for wind resistance control. Furthermore, it predicts resonance risk by constructing a terrain transfer function model and innovatively introduces a mechanism for calculating the curvature value of the target guide vane based on the resonance frequency and effective wind speed components. Combined with a shape memory alloy drive system, it realizes real-time adaptive adjustment of the guide vane curvature, effectively suppressing the formation and development of wind-induced resonance. At the same time, under high-risk conditions, it uses a linkage electric actuator to adjust the tilt angle of the photovoltaic support to a safe angle and dynamically evaluates the structural response attenuation characteristics through displacement sensors, enhancing the structural stability and operational safety of the slope photovoltaic support in strong wind environments.

[0067] The above embodiments are merely preferred technical solutions of the present invention and should not be considered as limitations on the present invention. The scope of protection of the present invention should be limited to the technical solutions described in the claims, including equivalent substitutions of the technical features described in the claims. That is, equivalent substitutions and improvements within this scope are also within the scope of protection of the present invention.

Claims

1. A method for wind resistance control of photovoltaic supports on slopes, characterized in that, Includes the following steps: Collect 3D terrain data and wind speed gradient data; The three-dimensional terrain dataset is input into the terrain transfer function model, and combined with wind speed gradient data, the resonance risk level is calculated. The resonance frequency is extracted based on the resonance risk level, and the target guide vane curvature value is calculated in real time using the resonance frequency. The target airfoil curvature value is converted into a driving current command, and the actual airfoil curvature value is adjusted to the target airfoil curvature value through the shape memory alloy driving circuit to generate the actual curvature value; When the resonance risk level reaches the risk threshold, the safe tilt angle is calculated based on the actual curvature value, and the electric push rod is controlled to adjust the tilt angle of the slope photovoltaic support to the safe tilt angle. Based on the adjustment of the slope photovoltaic support tilt angle to a safe tilt angle, the displacement of the slope photovoltaic support is recorded, and the displacement attenuation rate is calculated by combining the displacement of the slope photovoltaic support before adjustment, and a structural safety report is generated.

2. The method for wind resistance control of a photovoltaic support structure on a slope according to claim 1, characterized in that, The specific steps for collecting 3D terrain data and wind speed gradient data are as follows: Record the three-dimensional coordinates, echo intensity, and timestamp of the laser reflection point to form the original laser point cloud dataset; The original laser point cloud dataset was corrected using a GNSS reference station, the elevation deviation compensation was calculated, the slope vector was extracted, three-dimensional terrain data was generated, and wind speed gradient data was collected using an ultrasonic anemometer.

3. The method for wind resistance control of a photovoltaic support structure on a slope according to claim 1, characterized in that, The specific steps for inputting the 3D terrain dataset into the terrain transfer function model and combining it with wind speed gradient data to calculate the resonance risk level are as follows. Discretize the terrain transfer function model, divide the three-dimensional terrain dataset of the target slope into spatial grids, obtain the unit cell grid resolution, and establish a corrected terrain spatial grid. Based on the corrected terrain spatial grid and wind speed gradient data, the wind pressure spectral density is calculated to obtain the natural frequency and corresponding mode shape of the photovoltaic support. The resonance risk level is determined based on the wind pressure spectral density and the natural frequency and corresponding mode shape of the photovoltaic support.

4. The method for wind resistance control of a photovoltaic support structure on a slope according to claim 1, characterized in that, The process of extracting the resonance frequency based on the resonance risk level and using the resonance frequency in conjunction with the real-time calculation of the target guide vane curvature value involves the following steps: The resonance risk level is mapped to the resonance frequency using an RFTF function; The effective wind speed component perpendicular to the guide vane is calculated based on wind speed gradient data using the voltage projection formula. Calculate the minimum aerodynamic damping ratio required to suppress resonance based on the resonant frequency and effective wind speed components; The minimum aerodynamic damping ratio is input into the wind tunnel relationship model, and the curvature value of the target guide vane is generated by inversion curvature calculation.

5. The method for wind resistance control of a photovoltaic support structure on a slope according to claim 1, characterized in that, The process of converting the target airfoil curvature value into a drive current command refers to using the physical properties of the shape memory alloy and combining it with a temperature sensor for dynamic compensation of the shape memory alloy to convert the target airfoil curvature value into a corresponding drive current intensity value, thereby obtaining the drive current command.

6. The method for wind resistance control of a photovoltaic support structure on a slope according to claim 1, characterized in that, The process of adjusting the actual airfoil curvature value to the target airfoil curvature value using a shape memory alloy driving circuit to generate the actual curvature value involves the following steps: The drive current command is input into the H-bridge circuit, the PWM duty cycle is adjusted in real time by the PID controller, and the current is output to the shape memory alloy through the MOSFET bridge arm to trigger the phase change contraction of the guide vane, thereby adjusting the actual guide vane curvature value to the target guide vane curvature value and obtaining the center point of the guide vane. The center point of the guide vane is scanned by a laser displacement meter to output the actual displacement, and the real-time curvature value is calculated using the curvature formula.

7. The method for wind resistance control of a photovoltaic support structure on a slope according to claim 1, characterized in that, When the resonance risk level reaches the risk threshold, a safe tilt angle is calculated based on the actual curvature value, and the electric push rod is controlled to adjust the tilt angle of the photovoltaic support to a safe tilt angle. The specific steps are as follows: When the resonance risk level exceeds the preset risk threshold, the actual curvature value is extracted and combined with the structural dynamic response parameters corresponding to the current resonance risk level. The critical stiffness attenuation is obtained through structural stability analysis, and the safe tilt angle is output. Input the safety tilt angle into the kinematic conversion center of the push rod, calculate the target extension and retraction of the electric push rod based on the geometric relationship of the support rotation mechanism and the measured tilt angle of the photovoltaic support on the current slope, and mark the displacement direction symbol; The actual tilt angle is collected to deviate from the target value. When the actual tilt angle deviates from the target value and exceeds the allowable deviation, the gain adaptive adjustment is triggered until the operation of adjusting the tilt angle of the photovoltaic support to a safe tilt angle is completed.

8. The method for wind resistance control of a photovoltaic support structure on a slope according to claim 1, characterized in that, The process involves adjusting the slope photovoltaic support tilt angle to a safe tilt angle, recording the displacement of the slope photovoltaic support, and calculating the displacement attenuation rate by combining this with the displacement of the slope photovoltaic support before adjustment. The specific steps are as follows: Displacement sensors are installed at the base of the photovoltaic support on the slope to collect vibration displacement data of the photovoltaic support on the slope. By collecting vibration displacement data, the average vibration displacement data of the slope photovoltaic support before adjustment was calculated; The displacement attenuation rate is generated by calculating the displacement difference from the average value of the vibration displacement data.

9. The method for wind resistance control of a photovoltaic support structure on a slope according to claim 1, characterized in that, The structural safety report includes displacement attenuation rate and resonance risk level.

10. A control system for implementing the wind resistance control method for slope photovoltaic supports as described in any one of claims 1-9, characterized in that, include: The acquisition module is used to acquire 3D terrain data and wind speed gradient data; The risk level module is used to input the 3D terrain dataset into the terrain transfer function model and, in combination with wind speed gradient data, calculate the resonance risk level. The guide vane curvature module is used to retrieve the target guide vane curvature value corresponding to the resonance risk level from the computational fluid dynamics database based on the resonance risk level. The drive module is used to convert the target airfoil curvature value into a drive current command, and adjust the airfoil curvature value to the target airfoil curvature value through the shape memory alloy drive circuit to generate the actual curvature value; The control module is used to calculate the safe tilt angle based on the actual curvature value when the resonance risk level reaches the risk threshold, and control the electric push rod to adjust the tilt angle of the slope photovoltaic support to the safe tilt angle; The reporting module is used to record the displacement of the photovoltaic support on the slope based on the adjustment of the slope photovoltaic support tilt angle to a safe tilt angle, calculate the displacement attenuation rate by combining the displacement of the slope photovoltaic support before adjustment, and generate a structural safety report.

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