Active vibration suppression and deformation regulation and control system and method for flexible photovoltaic support

Abstract

The invention provides an active vibration suppression and deformation regulation and control system and method for a flexible photovoltaic support, and the method comprises the steps: deploying an aerovane, a miniature accelerometer, a cable force sensor and a GNSS displacement monitoring module at a key span section of the flexible support, and synchronously collecting the dynamic response characteristics and wind environment data of a structure; performing digital filtering processing on the acquired multi-source heterogeneous signals, extracting effective signals related to low-frequency vibration of the structure, and identifying current dominant vibration frequency and vibration mode through numerical integration and spectral analysis; on the basis of the recognized vibration characteristics, a self-adaptive fuzzy PID control algorithm is adopted to carry out online setting on control parameters, and a real-time control force instruction matched with the dynamic response of the structure is generated; the control force instruction is converted into a PWM signal for driving a linear motor, a movable mass block is driven to move through a ball screw, and active vibration suppression force in the opposite direction is applied to a support structure. Structural displacement and dynamic stress can be remarkably reduced, system safety is improved, and the service life is prolonged.

Description

Technical Field

[0001] This invention belongs to the technical field of active vibration suppression system for flexible photovoltaic supports in mountainous areas, specifically relating to an active vibration suppression and deformation control system and method for flexible photovoltaic supports. Background Technology

[0002] As a core component of photovoltaic power plant structural design, the dynamic stability of photovoltaic (PV) support structures directly impacts the safety and economy of the power plant. In mountainous PV fields, flexible support structures are widely used due to their advantages such as large spans, high clearance, strong terrain adaptability, and high land utilization. However, under the complex and variable wind fields of mountainous areas, they are prone to significant wind-induced vibrations, leading to microcracks in PV modules, structural connection fatigue, and even failure. Existing solutions mainly rely on passive control methods such as increasing structural stiffness, installing passive dampers, and mechanical fastening. Increasing structural stiffness involves increasing the cross-section of steel cables, increasing the number of stabilizing cables, or using thicker support rods to improve overall stiffness. This is the most direct but least economical method. Installing passive dampers involves installing passive friction dampers or viscous dampers between cable segments to dissipate energy through relative motion. However, their parameters are fixed and cannot adapt to the random variations of complex wind loads in mountainous environments, resulting in a sharp drop in vibration suppression effect when deviating from the design conditions. Mechanical fastening: Photovoltaic modules are firmly fixed to the cable net using a large number of rigid connectors and fasteners. However, this partially sacrifices the adaptability of the flexible support structure and may directly transfer dynamic loads to the fragile cells, increasing the risk of microcracks. Existing technologies are insufficient to meet the dual requirements of structural safety and module integrity for mountain photovoltaic power plants, becoming a key technological bottleneck restricting their large-scale application. Summary of the Invention

[0003] The present invention aims to at least partially solve one of the technical problems in the related art.

[0004] Therefore, the first objective of this invention is to provide an active control system and method with excellent vibration damping effect and strong adaptability.

[0005] The second objective of this invention is to propose an optimized design device for flexible photovoltaic support structures in mountainous environments.

[0006] The third objective of this invention is to provide a computer device.

[0007] A fourth objective of this invention is to provide a non-transitory computer-readable storage medium.

[0008] To achieve the above objectives, a first aspect of the present invention proposes a method for active vibration suppression and deformation control of flexible photovoltaic supports for mountainous environments, comprising: S1, deploy wind direction and speed meters, miniature accelerometers, cable force sensors and GNSS displacement monitoring modules in key spans of the flexible support to simultaneously collect structural dynamic response characteristics and wind environment data; S2 performs digital filtering on the collected multi-source heterogeneous signals to extract the effective signals related to the low-frequency vibration of the structure, and identifies the current dominant vibration frequency and mode shape through numerical integration and spectrum analysis. S3, based on the identified vibration characteristics, adopts an adaptive fuzzy PID control algorithm to tune the control parameters online and generate real-time control force commands that match the dynamic response of the structure; S4, the control force command is converted into a PWM signal to drive the linear motor, which drives the movable mass block to move through the ball screw, and applies an active damping force in the opposite direction to the support structure.

[0009] In one embodiment of the present invention, S2 includes: S21 uses a fourth-order Butterworth low-pass digital filter to process the physical quantity signal, and its difference equation is: y[n]= ×x[ni]- ×y[nj], Where x[n] is the current input, y[n] is the current output, and ai and bj are the filter coefficients pre-calculated based on the cutoff frequency; S22, the filtered acceleration signal is integrated twice using the trapezoidal integral method to obtain velocity and displacement, and GNSS absolute displacement data is used to correct the integral drift.

[0010] In one embodiment of the present invention, S3 includes: S31, map the error and the rate of change of error to a fuzzy set, use the triangular membership function to fuzzify it, define the universe of discourse of the fuzzy set as [-L,L], and divide it into 7 fuzzy subsets; S32 performs inference based on a preset fuzzy rule base, activates each rule using AND operation, and finally obtains the precise PID parameter increment by defuzzifying using the centroid method.

[0011] In one embodiment of the present invention, S4 includes: S41, the duty cycle is calculated using the formula DutyCycle(t)=K*Fcmd(t) / Fmax+0.5, where K is the gain coefficient, Fmax is the maximum thrust of the linear motor, and DutyCycle is limited to between 10% and 90%. S42, the controller's internal timer generates a triangular carrier wave at a fixed frequency of 20kHz, compares it with DutyCycle(t), and sends a pulse width modulation signal to the motor driver through the high-speed digital output port.

[0012] In one embodiment of the present invention, the method further includes: S5 starts an independent monitoring thread to record sensor data, control commands and algorithm internal states in real time. It analyzes the matching degree between vibration characteristics and control force through preset thresholds, generates a system performance evaluation report, and dynamically updates the boundary values ​​of control parameters in the fuzzy rule base based on the evaluation results.

[0013] To achieve the above objectives, a second aspect of the present invention provides an active vibration damping and deformation control device for flexible photovoltaic supports in mountainous environments, comprising: The distributed sensor network deployment module is used to deploy wind direction and speed meters, miniature accelerometers, cable force sensors and GNSS displacement monitoring modules in the key spans of the flexible support, and to simultaneously collect structural dynamic response characteristics and wind environment data. The multi-source signal processing module is used to perform digital filtering on the acquired multi-source heterogeneous signals, extract the effective signals related to the low-frequency vibration of the structure, and identify the current dominant vibration frequency and mode shape through numerical integration and spectrum analysis. The adaptive fuzzy PID control module is used to tune the control parameters online based on the identified vibration characteristics and the adaptive fuzzy PID control algorithm to generate real-time control force commands that match the dynamic response of the structure. The PWM signal generation and driving module is used to convert the control force command into a PWM signal to drive the linear motor, which in turn drives the movable mass block to move through the ball screw, applying an active damping force in the opposite direction to the support structure.

[0014] The present invention provides a method and apparatus for active vibration suppression and deformation control of flexible photovoltaic supports in mountainous environments, which can bring the following beneficial effects: 1. Significantly improved vibration suppression effect: Through real-time closed-loop control of "sensing-decision-execution", a reverse force can be applied to specific vibration modes, which can reduce the wind-induced displacement of flexible supports under medium wind speeds by 40% to 60%, effectively protecting photovoltaic modules.

[0015] 2. Strong adaptability: Adopting an adaptive fuzzy PID algorithm, the system can automatically adjust the control strategy according to real-time wind conditions, and maintain excellent vibration suppression performance under different wind speeds and directions, making it especially suitable for complex working conditions with variable wind direction and speed in mountainous areas.

[0016] 3. High economic efficiency: Compared with simply increasing the amount of materials used, this system achieves the same level of safety through intelligent control strategies, reducing initial material costs, and at the same time reducing long-term maintenance costs by extending the structural fatigue life.

[0017] 4. High system intelligence and reliability: It integrates an independent monitoring thread, which can diagnose the system status in real time and record the running data, providing support for performance evaluation and algorithm iteration optimization, and ensuring long-term stable operation.

[0018] To achieve the above objectives, a third aspect of this application provides a computer device comprising a processor and a memory; wherein the processor runs a program corresponding to the executable program code stored in the memory, for implementing an active vibration suppression and deformation control method for a flexible photovoltaic support in a mountainous environment as described in the first aspect embodiment.

[0019] To achieve the above objectives, the fourth aspect of this application proposes a non-transitory computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements an active vibration suppression and deformation control method for a flexible photovoltaic support in a mountainous environment as described in the first aspect embodiment.

[0020] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0021] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1 This is a flowchart of an active vibration damping and deformation control method for a flexible photovoltaic support in a mountainous environment according to an embodiment of the present invention; Figure 2 This is an architectural diagram of an active vibration damping and deformation control method for a flexible photovoltaic support in a mountainous environment according to an embodiment of the present invention. Figure 3 This is a schematic diagram illustrating the core working principle and data closed loop according to an embodiment of the present invention; Figure 4 This is a structural diagram of a flexible photovoltaic support active vibration damping and deformation control device for mountainous environments according to an embodiment of the present invention; Figure 5 It is a computer device according to an embodiment of the present invention. Detailed Implementation

[0022] To enable those skilled in the art to better understand the present invention, a specific embodiment is described below.

[0023] In a mountain photovoltaic power station, the system of this invention was deployed at the 1 / 4 and 1 / 2 spans of a flexible support unit with a span of 45 meters. The sensing nodes include XYZ triaxial accelerometers (range ±5g) and a GNSS displacement monitoring module; the controller uses an industrial computer based on an ARM Cortex-A series chip, with a control cycle set to 10ms; the actuator is a linear motor with a rated thrust of 500N. Field tests showed that in a mountain wind field with an average wind speed of 12m / s and a turbulence intensity of approximately 0.2, the system successfully reduced the peak value of the first-order lateral vibration displacement of the main beam of the support by approximately 52%, and the controller power consumption was less than 150W, verifying the effectiveness and engineering applicability of the invention.

[0024] It should be noted that, unless otherwise specified, the embodiments and features described in the present invention can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0025] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0026] The following description, with reference to the accompanying drawings, describes an active vibration damping and deformation control system and method for flexible photovoltaic supports in mountainous environments, according to an embodiment of the present invention.

[0027] Figure 1 This is a flowchart of an active vibration damping and deformation control system and method for flexible photovoltaic supports in mountainous environments according to an embodiment of the present invention, as shown below. Figure 1 As shown, it includes: S1, deploy wind direction and speed meters, miniature accelerometers, cable force sensors and GNSS displacement monitoring modules in key spans of the flexible support to simultaneously collect structural dynamic response characteristics and wind environment data; S2 performs digital filtering on the collected multi-source heterogeneous signals to extract the effective signals related to the low-frequency vibration of the structure, and identifies the current dominant vibration frequency and mode shape through numerical integration and spectrum analysis. S3, based on the identified vibration characteristics, adopts an adaptive fuzzy PID control algorithm to tune the control parameters online and generate real-time control force commands that match the dynamic response of the structure; S4, the control force command is converted into a PWM signal to drive the linear motor, which drives the movable mass block to move through the ball screw, and applies an active damping force in the opposite direction to the support structure.

[0028] The present invention discloses an active vibration suppression and deformation control system and method for flexible photovoltaic supports in mountainous environments, which can realize real-time adaptive vibration suppression and deformation control of flexible photovoltaic supports in mountainous areas under wind load, significantly reducing structural displacement and dynamic stress, and improving system safety and service life.

[0029] The following description, in conjunction with the accompanying drawings, details an active vibration damping and deformation control system and method for flexible photovoltaic supports in mountainous environments according to an embodiment of the present invention.

[0030] The core of this invention lies in providing an active control system integrating perception, decision-making, and execution. Through real-time monitoring and feedback, it dynamically applies inhibitory forces to counteract large vibrational energy with a small energy input. Figure 2 As shown.

[0031] Part 1: Distributed Sensor Networks (Perception Layer).

[0032] Several sensing nodes are arranged on the load-bearing main cables and stabilizing cables of the key spans of the flexible support. Each node includes: 1. Wind direction and speed meter: Collects real-time wind environment data acting on this span.

[0033] 2. Miniature accelerometer: measures the vibration acceleration of cable structures.

[0034] 3. Cable tension sensor: monitors changes in tension within the cable.

[0035] 4. GNSS displacement monitoring module: measures the horizontal displacement of the top of the support column in real time.

[0036] Its role is to constitute the "nerve endings" of the system, to perceive the dynamic response state of the structure and the input of environmental loads in a comprehensive and real-time manner, and to provide a data basis for control decisions.

[0037] Part Two: Active Controller (Decision-Making Layer)

[0038] This is an industrial control computer embedded in the field, whose core runs an adaptive fuzzy PID control algorithm.

[0039] Detailed Workflow: This controller is an industrial control computer based on a real-time operating system (such as VxWorks or embedded Linux). Its core workflow is a strict, periodic closed-loop control cycle, which executes the following steps in each control cycle (e.g., Δt = 10 milliseconds): Step 1: Data Synchronization and Acquisition The controller, through its multi-channel synchronous analog-to-digital converter (ADC), simultaneously reads the voltage signals from all sensors at the beginning of each control cycle. This includes all accelerometers (measuring vibration accelerations ax, ay, az), cable force sensors (measuring tension F), and the GNSS module (receiving displacement data δx, δy via serial port). The aim is to ensure that the timestamps of all data within the same cycle are strictly aligned, providing a consistent spatiotemporal reference for subsequent analysis.

[0040] Step 2: Signal Conditioning and Digitization The acquired raw voltage signal V raw The following formula can be used to convert it into an engineering physical quantity: Acceleration: a = (V raw -V offset )*S a Among them, V offset S is the zero-drift voltage of the sensor. a This is the sensitivity coefficient (e.g., 100mV / g).

[0041] Cable force: F = (V raw -V bridge) *K f Among them, V bridge K is the bridge balancing voltage. f These are calibration coefficients.

[0042] The goal is to restore electrical signals to numerical values ​​with actual physical meaning.

[0043] Step 3: Digital Filtering and Noise Reduction A fourth-order Butterworth low-pass digital filter is applied to the converted physical quantity signal. Its difference equation is as follows: y[n]=a0*x[n]+a1*x[n-1]+…+a4*x[n-4]-b1*y[n-1]-…-b4*y[n-4] Where x[n] is the current input, y[n] is the current output, and a0...a4,b1...b4 are coefficients pre-calculated based on the cutoff frequency (e.g., 2Hz, higher than the main vibration frequency of the support).

[0044] The aim is to filter out high-frequency electromagnetic noise and irrelevant mechanical noise, while retaining the effective low-frequency signals related to structural vibration.

[0045] Step 4: Feature Extraction and State Recognition (1) Perform a numerical integration on the filtered acceleration signal (using the trapezoidal integration method) to obtain the velocity; then perform another integration to obtain the displacement relative to the equilibrium position. At the same time, the GNSS displacement data is used as a low-frequency absolute displacement reference to correct the integration drift.

[0046] (2) Apply Fast Fourier Transform to the acceleration signal of the current period and several previous periods (such as a 1-second data window) to calculate the power spectral density.

[0047] (3) Identify the first 2-3 peaks with the largest amplitude in the power spectrum. The corresponding frequencies are the current dominant vibration frequencies f1 and f2. By comparing the phase and amplitude relationship of different sensor positions at the same frequency, determine the main vibration mode (such as first-order lateral bending or torsion).

[0048] The goal is to transform time-domain signals into frequency-domain and state characteristics that can be guided and controlled.

[0049] Step 5: Control Algorithm Decision (Adaptive Fuzzy PID): (1) Input: The key point displacement error e(t) calculated in this cycle = target displacement (usually 0) - measured displacement, and the error change rate ec(t) = (e(t) - e(t-1)) / Δt. Output: The required control force F cmd (t).

[0050] (2) Detailed algorithm flow: a. Fuzzification: Mapping the precise inputs e(t) and ec(t) to a fuzzy set. The universe of discourse is defined as [-L, L], and divided into 7 fuzzy subsets: {Negative Large (NB), Negative Medium (NM), Negative Small (NS), Zero (ZO), Positive Small (PS), Positive Medium (PM), Positive Large (PB)}. A triangular membership function is used. For example, for the error e, the membership degree μPS(e) belonging to "Positive Small (PS)" is calculated as follows: μPS(e)=max(0,min((e-ThNS) / (ThPS-ThNS),(ThPM-e) / (ThPM-ThPS))) Among them, ThNS, ThPS, and ThPM are the core boundary values ​​of the subsets "negative small", "positive small", and "positive middle".

[0051] b. Fuzzy Reasoning: Decisions are made based on a pre-defined fuzzy rule base. The rule format is: "If e is A and ec is B, then ΔKp is C, ΔKi is D, and ΔKd is E." For example: "If e is PS (positive small) and ec is NB (negative large), then ΔKp is PM (positive middle), ΔKi is NS (negative small), and ΔKd is PS (positive small)." Each rule is activated using an AND operation (minimum value).

[0052] c. Defuzzification: Using the centroid method, the fuzzy output of the PID parameter increments (ΔKp, ΔKi, ΔKd) obtained through reasoning is converted into precise values.

[0053] For example, for ΔKp: ΔKp=(Σ(μi * wi)) / Σ(μi) Where μi is the activation of the i-th rule, and wi is the clarity value corresponding to the output subset of the rule (e.g., PM=+3).

[0054] d. Parameter self-tuning and force calculation: Adjust PID parameters online based on fuzzy inference results. Kp(t)=Kp0+ΔKp;Ki(t)=Ki0+ΔKi;Kd(t)=Kd0+ΔKd Where Kp0, Ki0, and Kd0 are the initial setpoints. Finally, the current control force is calculated: Fcmd(t)=Kp(t)*e(t)+Ki(t)*Σe(t)*Δt+Kd(t)*ec(t).

[0055] Step 6: Control command generation and output: The calculated continuous control force Fcmd(t) is converted into a PWM (Pulse Width Modulation) signal to drive the linear motor.

[0056] Calculate the duty cycle: DutyCycle(t) = K * Fcmd(t) / F max +0.5. Where K is the gain coefficient, F max This represents the maximum thrust of the linear motor. Limit the DutyCycle between 10% and 90% to allow for a safety margin.

[0057] PWM generation: The controller’s internal timer generates a triangular carrier wave at a fixed frequency (e.g., 20kHz) and compares it with DutyCycle(t) to generate a digital signal with the corresponding pulse width, which is then sent to the motor driver through the high-speed digital output (DO) port.

[0058] The goal is to convert digital control commands into physical signals that can directly drive the actuators.

[0059] Step 7: Looping and Monitoring: After completing all calculations and outputs for the current cycle, the controller enters sleep mode, awaiting the interrupt trigger of the next cycle to begin a new loop. Simultaneously, a separate monitoring thread records all sensor data, control commands, and the internal state of the algorithm in real time for fault diagnosis and performance optimization. The aim is to ensure the real-time performance and systematic nature of the control.

[0060] like Figure 3 The core working principle and data closed loop of the system of this invention are shown below, and the process is explained as follows: 1. Process Start Point (Sensing Layer): Sensor arrays deployed at key locations on the support structure continuously monitor the physical state of the structure (vibration, cable force, displacement). These analog signals are synchronously acquired and converted into digital signals, ensuring data consistency over time and laying the foundation for precise control.

[0061] 2. Core data processing path (decision-making level): (1) Signal conditioning: converting the original digital signal into a physical quantity with engineering significance (such as m / s², N).

[0062] (2) Digital filtering: High-frequency noise is filtered out by a fourth-order Butterworth low-pass filter, and effective signal components related to low-frequency vibration of the structure are extracted.

[0063] (3) Feature extraction: Integrate the clean signal (to obtain displacement / velocity) and perform Fast Fourier Transform (FFT) spectrum analysis to identify the current dominant vibration frequency and mode shape, and calculate the displacement error e and error change rate ec used for control.

[0064] (4) Intelligent decision-making (adaptive fuzzy PID): This is the "brain" of the controller. It first fuzzifies the precise e and ec, converting them into language descriptions such as "positive large" and "negative small"; then it infers based on the preset expert fuzzy rule base to decide how to adjust the PID parameters; then it obtains the precise parameter increment through defuzzification; finally, it tunes the optimal PID parameters online and calculates the real-time control force Fcmd required to counteract vibration.

[0065] 3. Action Execution Path (Execution Layer): The controller converts the calculated control force command Fcmd into a PWM (Pulse Width Modulation) signal that can drive the motor. Its duty cycle is proportional to the magnitude of the required force. This signal drives a linear motor, which in turn drives a movable mass block to move precisely through a ball screw, thereby applying an active vibration damping force to the support in the opposite direction and with controllable magnitude.

[0066] 4. Closed-loop feedback and monitoring: After the vibration damping force is applied to the flexible support structure, it changes its vibration state. This new state is then detected by the sensor array, forming a closed-loop feedback. Simultaneously, all data is sent to the monitoring terminal for system performance evaluation and fault diagnosis, ensuring long-term reliable operation.

[0067] The embodiments of the present invention also have the following implementation schemes: A tuned mass damper combined with an electric variable stiffness device is used.

[0068] The linear motor actuator is replaced with a tuned mass damper, but the position or support stiffness of its mass block can be adjusted by a small servo motor. By changing its frequency characteristics, it is always aligned with the main vibration frequency of the structure, achieving a hybrid control approach of "passive as the foundation and active fine-tuning". This solution consumes less energy, but its response is slightly slower when suppressing sudden strong winds.

[0069] Vibration suppression scheme based on tension regulation.

[0070] Instead of applying a horizontal force directly to the top of the column, an electrically operated tensioner is connected in series with the stabilizing cable in the span. By finely adjusting the tension of the stabilizing cable in real time, the stiffness and dynamic characteristics of the entire cable net system are altered, thereby avoiding resonance or suppressing specific vibration modes. This approach is more gentle, but it places extremely high demands on the precision and durability of the actuator.

[0071] To achieve the above embodiments, such as Figure 4 As shown, this embodiment also provides a flexible photovoltaic support optimization design device 10 for mountainous environments. The device 10 includes a distributed sensor network layout module 100, a multi-source signal processing module 200, an adaptive fuzzy PID control module 300, and a PWM signal generation and driving module 400.

[0072] The distributed sensor network deployment module 100 is used to deploy wind direction and speed meters, miniature accelerometers, cable force sensors and GNSS displacement monitoring modules in the key spans of the flexible support, and to simultaneously collect structural dynamic response characteristics and wind environment data. The multi-source signal processing module 200 is used to perform digital filtering on the acquired multi-source heterogeneous signals, extract the effective signals related to the low-frequency vibration of the structure, and identify the current dominant vibration frequency and mode shape through numerical integration and spectrum analysis. The adaptive fuzzy PID control module 300 is used to tune the control parameters online based on the identified vibration characteristics using an adaptive fuzzy PID control algorithm, and generate real-time control force commands that match the dynamic response of the structure. The PWM signal generation and driving module 400 is used to convert the control force command into a PWM signal to drive the linear motor, which drives the movable mass block to move through the ball screw, and applies an active damping force in the opposite direction to the support structure.

[0073] Furthermore, the aforementioned multi-source signal processing module 200 is also used for: A fourth-order Butterworth low-pass digital filter is used to process the physical quantity signal. Its difference equation is as follows: y[n]= ×x[ni]- ×y[nj], Where x[n] is the current input, y[n] is the current output, ..., ai,bj are the filter coefficients pre-calculated based on the cutoff frequency; The filtered acceleration signal was integrated twice using the trapezoidal integral method to obtain velocity and displacement, and the integral drift was corrected using GNSS absolute displacement data.

[0074] Furthermore, the aforementioned adaptive fuzzy PID control module 300 is also used for: The error and the rate of change of error are mapped to a fuzzy set, and the fuzzification is performed using a triangular membership function. The universe of discourse of the fuzzy set is defined as [-L,L], and it is divided into 7 fuzzy subsets. Reasoning is performed based on a preset fuzzy rule base, and each rule is activated by AND operation. Finally, the precise PID parameter increment is obtained by defuzzification using the centroid method.

[0075] Furthermore, the aforementioned PWM signal generation and driving module 400 is also used for: The duty cycle is calculated using the formula DutyCycle(t)=K*Fcmd(t) / Fmax+0.5, where K is the gain coefficient, Fmax is the maximum thrust of the linear motor, and DutyCycle is limited to between 10% and 90%. The controller's internal timer generates a triangular carrier wave at a fixed frequency of 20kHz and compares it with DutyCycle(t). The pulse width modulation signal is then sent to the motor driver through the high-speed digital output port.

[0076] Furthermore, device 10 also includes: The monitoring thread module starts an independent monitoring thread to record sensor data, control commands and algorithm internal states in real time. It analyzes the matching degree between vibration characteristics and control force through preset thresholds, generates a system performance evaluation report, and dynamically updates the boundary values ​​of control parameters in the fuzzy rule base based on the evaluation results.

[0077] An embodiment of the present invention provides an optimized design device for flexible photovoltaic supports in mountainous environments, which can realize real-time adaptive vibration suppression and deformation control of flexible photovoltaic supports in mountainous areas under wind loads, significantly reducing structural displacement and dynamic stress, and improving system safety and service life.

[0078] To implement the methods of the above embodiments, the present invention also provides a computer device, such as... Figure 5 As shown, the computer device 600 includes a memory 601 and a processor 602; wherein, the processor 602 reads executable program code stored in the memory 601 to run a program corresponding to the executable program code, so as to implement the various steps of the method described above.

[0079] To implement the above embodiments, this application also proposes a non-transitory computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the method described in the foregoing embodiments.

[0080] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0081] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this invention, "a plurality of" means at least two, such as two, three, etc., unless otherwise explicitly specified.

Claims

1. A method for active vibration suppression and deformation control of a flexible photovoltaic support, characterized in that, include: S1, deploy wind direction and speed meters, miniature accelerometers, cable force sensors and GNSS displacement monitoring modules in key spans of the flexible support to simultaneously collect structural dynamic response characteristics and wind environment data; S2 performs digital filtering on the collected multi-source heterogeneous signals to extract the effective signals related to the low-frequency vibration of the structure, and identifies the current dominant vibration frequency and mode shape through numerical integration and spectrum analysis. S3, based on the identified vibration characteristics, adopts an adaptive fuzzy PID control algorithm to tune the control parameters online and generate real-time control force commands that match the dynamic response of the structure; S4, the control force command is converted into a PWM signal to drive the linear motor, which drives the movable mass block to move through the ball screw, and applies an active damping force in the opposite direction to the support structure.

2. The method as described in claim 1, characterized in that, The S2 includes: S21 uses a fourth-order Butterworth low-pass digital filter to process the physical quantity signal, and its difference equation is: y[n]= ×x[n-i]- ×y[n-j], Where x[n] is the current input, y[n] is the current output, and ai and bj are the filter coefficients pre-calculated based on the cutoff frequency; S22, the filtered acceleration signal is integrated twice using the trapezoidal integral method to obtain velocity and displacement, and GNSS absolute displacement data is used to correct the integral drift.

3. The method as described in claim 1, characterized in that, The S3 includes: S31, map the error and the rate of change of error to a fuzzy set, use the triangular membership function to fuzzify it, define the universe of discourse of the fuzzy set as [-L,L], and divide it into 7 fuzzy subsets; S32 performs inference based on a preset fuzzy rule base, activates each rule using AND operation, and finally obtains the precise PID parameter increment by defuzzifying using the centroid method.

4. The method as described in claim 1, characterized in that, The S4 further includes: S41, using the formula DutyCycle(t)=K*F cmd (t) / F max +0.5 is used to calculate the duty cycle, where K is the gain coefficient and F... max DutyCycle limits the maximum thrust of the linear motor to between 10% and 90%. S42, the controller's internal timer generates a triangular carrier wave at a fixed frequency of 20kHz, compares it with DutyCycle(t), and sends a pulse width modulation signal to the motor driver through the high-speed digital output port.

5. The method as described in claim 1, characterized in that, The method further includes: S5 starts an independent monitoring thread to record sensor data, control commands and algorithm internal states in real time. It analyzes the matching degree between vibration characteristics and control force through preset thresholds, generates a system performance evaluation report, and dynamically updates the boundary values ​​of control parameters in the fuzzy rule base based on the evaluation results.

6. A flexible photovoltaic support active vibration damping and deformation control device, characterized in that, include: The distributed sensor network deployment module is used to deploy wind direction and speed meters, miniature accelerometers, cable force sensors and GNSS displacement monitoring modules in the key spans of the flexible support, and to simultaneously collect structural dynamic response characteristics and wind environment data. The multi-source signal processing module is used to perform digital filtering on the acquired multi-source heterogeneous signals, extract the effective signals related to the low-frequency vibration of the structure, and identify the current dominant vibration frequency and mode shape through numerical integration and spectrum analysis. The adaptive fuzzy PID control module is used to tune the control parameters online based on the identified vibration characteristics and the adaptive fuzzy PID control algorithm to generate real-time control force commands that match the dynamic response of the structure. The PWM signal generation and driving module is used to convert the control force command into a PWM signal to drive the linear motor, which in turn drives the movable mass block to move through the ball screw, applying an active damping force in the opposite direction to the support structure.

7. The apparatus as claimed in claim 6, characterized in that, The multi-source signal processing module is also used for: A fourth-order Butterworth low-pass digital filter is used to process the physical quantity signal. Its difference equation is as follows: y[n]= ×x[n-i]- ×y[n-j], Where x[n] is the current input, y[n] is the current output, and ai and bj are the filter coefficients pre-calculated based on the cutoff frequency; The filtered acceleration signal was integrated twice using the trapezoidal integral method to obtain velocity and displacement, and the integral drift was corrected using GNSS absolute displacement data.

8. The apparatus as claimed in claim 6, characterized in that, The adaptive fuzzy PID control module is also used for: The error and the rate of change of error are mapped to a fuzzy set, and the fuzzification is performed using a triangular membership function. The universe of discourse of the fuzzy set is defined as [-L,L], and it is divided into 7 fuzzy subsets. Reasoning is performed based on a preset fuzzy rule base, and each rule is activated by AND operation. Finally, the precise PID parameter increment is obtained by defuzzification using the centroid method.

9. A computer device, characterized in that, Including processor and memory; The processor reads the executable program code stored in the memory to run the program corresponding to the executable program code, so as to implement the active vibration suppression and deformation control method of flexible photovoltaic support as described in any one of claims 1-5.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements a method for active vibration suppression and deformation control of a flexible photovoltaic support as described in any one of claims 1-5.

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