Photovoltaic car shed assembly inclination angle adjusting method and system facing multiple climate zones

Abstract

The invention provides a multi-climate zone-oriented photovoltaic car shed assembly inclination angle adjusting method and system, and the method comprises the steps: obtaining a low-altitude topographic cumulus real-time distribution image and sky brightness data through an all-sky imager, and combining the speed and direction of valley wind collected by an anemorumbometer, generating a meteorological feature data set including a cloud body edge, a shadow boundary and a moving path; extracting a shadow coverage state of the high-altitude shed from the shadow evolution time sequence, if the high-altitude shed is not in shadow coverage, determining that a solar panel of the high-altitude shed maintains a target direct light receiving inclination angle, and generating an inclination angle maintaining instruction; the inclination angle adjusting instruction and the inclination angle maintaining instruction are distributed to solar panel control units of low-altitude and high-altitude carports, servo motors are driven to adjust panel angles, and inclination angle optimization is completed; and evaluating the data set according to the optimization effect, calculating the deviation between the photoelectric conversion efficiency and the preset expected scattered light capture efficiency, and updating the terrain constraint parameters according to the deviation.

Description

Technical Field

[0001] The present invention relates to the field of information technology, and in particular to a method and system for adjusting the inclination angle of a photovoltaic carport assembly for multiple climate zones. Background Art

[0002] The efficient utilization of solar energy in distributed mountain photovoltaic carport networks is crucial to the development of new energy. The key lies in optimizing the tilt angle of solar panels to adapt to complex terrain and dynamic meteorological conditions, thereby maximizing solar energy capture. Research in this area is directly related to the economic and sustainable development of renewable energy and is crucial for achieving carbon neutrality goals. However, existing photovoltaic tilt optimization methods are mostly based on flat terrain and static meteorological models, making them inefficient in addressing topographically driven localized climate events in mountainous environments, such as orographic cumulus clouds caused by afternoon valley winds. These methods often ignore the dynamic evolution of cloud shadows and topographic constraints, resulting in low solar energy capture efficiency in complex environments. The key challenge lies in accurately identifying and predicting the short-term shadow evolution of orographic cumulus clouds and dynamically adjusting the panel tilt angle based on localized shading conditions. Specifically, orographic cumulus clouds have highly reflective bright edges and sharp shadow boundaries, and their movement paths are constrained by mountainous terrain, making it difficult for traditional sensor networks to capture their dynamic characteristics in real time. Furthermore, panel tilt adjustment must simultaneously account for the differentiated capture requirements of non-uniform scattered light in shaded areas and direct light in unshaded areas. However, existing technologies lack refined control strategies for these complex scenarios. Therefore, how to accurately identify the short-term predictable shadow evolution characteristics of orographic cumulus clouds through sensor networks, and dynamically adjust the inclination angle of solar panels in distributed photovoltaic carport networks accordingly, so as to preferentially capture scattered light around and below the cloud body when it is partially shaded, while maintaining the optimal direct light receiving posture in the unshaded area, has become a key issue in improving the energy efficiency of mountain photovoltaic systems. Summary of the Invention

[0003] The present invention provides a method for adjusting the inclination angle of a photovoltaic carport assembly for multiple climate zones, which mainly includes:

[0004] The all-sky imager is used to obtain real-time distribution images of low-altitude orographic cumulus clouds and sky brightness data. Combined with the valley wind speed and direction collected by the anemometer, a meteorological feature dataset containing cloud edges, shadow boundaries, and movement paths is generated.

[0005] The coordinates of the edge of orographic cumulus clouds, shadow boundaries, and movement velocity vectors were extracted from the dataset. Combined with the regional three-dimensional elevation and slope aspect data acquired by remote sensing, a terrain constraint model was established to identify areas where the cloud movement path overlapped with the hillside terrain. Dynamic shadow simulation and spatiotemporal distribution analysis were performed on the region to obtain a shadow evolution time series.

[0006] Based on the shadow evolution time series, determine whether the low-altitude carport is covered by shadows. If so, obtain the sky brightness distribution within the target range, extract the scattering intensity and direction of the cloud gap light, calculate the target gentle tilt angle of the carport solar panel, and generate tilt adjustment instructions;

[0007] The shadow coverage status of the high-altitude carport is extracted from the shadow evolution time series. If it is not in shadow coverage, the solar panels of the high-altitude carport are determined to maintain the target direct light receiving tilt angle, and a tilt maintenance instruction is generated;

[0008] Distribute tilt adjustment and tilt maintenance commands to the solar panel control units at low and high altitude carports, driving the servo motors to adjust the panel angles and achieve tilt optimization.

[0009] Collect the adjusted photovoltaic conversion efficiency data of the solar panels in real time, combine it with the updated cloud distribution data, and judge whether the tilt angle optimization matches the shadow evolution characteristics of the current terrain cumulus clouds based on the photovoltaic conversion efficiency data and the updated cloud distribution data to generate an optimization effect evaluation dataset;

[0010] Based on the optimization effect evaluation data set, the deviation between the photoelectric conversion efficiency and the preset expected scattered light capture efficiency is calculated, and the terrain constraint parameters are updated according to the deviation.

[0011] The present invention provides a photovoltaic carport assembly tilt adjustment system for multiple climate zones, which mainly includes:

[0012] The meteorological data acquisition module is used to use the all-sky imager to obtain real-time distribution images of low-altitude orographic cumulus clouds and sky brightness data. Combined with the valley wind speed and direction collected by the anemometer, it generates a meteorological feature dataset that includes cloud edges, shadow boundaries, and movement paths.

[0013] The terrain constraint modeling module is used to extract the coordinates of the edge of the orographic cumulus cloud, the shadow boundary, and the movement velocity vector from the data set. It combines the regional three-dimensional elevation and slope aspect data obtained by remote sensing to establish a terrain constraint model, identify the area where the cloud movement path overlaps with the hillside terrain, and perform shadow dynamic simulation and spatiotemporal distribution analysis on the area to obtain a shadow evolution time series.

[0014] The shadow evolution analysis module is used to determine whether the low-altitude carport is covered by shadows based on the shadow evolution time series. If so, it obtains the sky brightness distribution within the target range, extracts the scattering intensity and direction of the cloud gap light, calculates the target gentle tilt angle of the carport solar panel, and generates tilt adjustment instructions;

[0015] The high-altitude tilt angle decision module is used to extract the shadow coverage status of the high-altitude carport from the shadow evolution time series. If it is not in shadow coverage, it determines whether the solar panels of the high-altitude carport maintain the target direct light receiving tilt angle and generates a tilt angle maintenance instruction;

[0016] The command execution control module is used to distribute the tilt adjustment command and the tilt maintenance command to the solar panel control units of the low-altitude and high-altitude carports, driving the servo motor to adjust the panel angle and complete the tilt optimization;

[0017] The optimization evaluation feedback module is used to collect the photovoltaic conversion efficiency data of the adjusted solar panels in real time, combine it with the updated cloud distribution data, and judge whether the tilt angle optimization matches the shadow evolution characteristics of the current terrain cumulus clouds based on the photovoltaic conversion efficiency data and the updated cloud distribution data, and generate an optimization effect evaluation data set;

[0018] The optimization evaluation feedback module is used to evaluate the data set based on the optimization effect, calculate the deviation between the photoelectric conversion efficiency and the preset expected scattered light capture efficiency, and update the terrain constraint parameters according to the deviation.

[0019] The technical solution provided by the embodiment of the present invention may have the following beneficial effects:

[0020] The present invention discloses a method for adjusting the tilt angle of photovoltaic carport components for multiple climate zones. The method uses an all-sky imager and anemometer to obtain cumulus cloud distribution and movement data on low-altitude terrain, and establishes a terrain constraint model in combination with regional three-dimensional terrain information to achieve shadow dynamic simulation and spatiotemporal distribution analysis. Based on the shadow evolution time series, the present invention determines the shadow coverage status of low-altitude and high-altitude carports, generates panel tilt adjustment or maintenance instructions respectively, and drives the servo motor to execute. By collecting the adjusted photoelectric conversion efficiency data in real time and combining it with the cloud distribution update information, the present invention evaluates the tilt optimization effect and updates the terrain constraint model parameters accordingly. The method can achieve dynamic optimization of the solar panel tilt angle and improve the photoelectric conversion efficiency based on the cloud shadow change characteristics under complex terrain conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 The present invention is a flow chart of a method for adjusting the inclination angle of a photovoltaic carport assembly for multiple climate zones.

[0022] Figure 2 The diagram is a structural diagram of a photovoltaic carport assembly tilt adjustment system for multiple climate zones according to the present invention. DETAILED DESCRIPTION

[0023] To help those skilled in the art better understand the technical solutions in this specification, the following will provide a clear and complete description of the technical solutions in the embodiments of this specification, in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of this specification, not all of them. All other embodiments derived by those skilled in the art based on the embodiments in this specification without creative effort shall fall within the scope of protection of this specification.

[0024] like Figure 1-2 In this embodiment, a method for adjusting the inclination angle of a photovoltaic carport assembly for multiple climate zones may specifically include:

[0025] S101. Use the all-sky imager to obtain real-time distribution images of low-altitude orographic cumulus clouds and sky brightness data. Combined with the valley wind speed and direction collected by the anemometer, generate a meteorological feature dataset that includes cloud edges, shadow boundaries, and movement paths.

[0026] Cloud edge contours are extracted from real-time images acquired by the all-sky imager. The Canny edge detection algorithm is used to identify the boundary between the cloud and the sky. Gradient amplitudes are calculated by calculating the grayscale differences between adjacent pixels. Edge points are identified when the gradient amplitude exceeds a preset threshold, resulting in a cloud edge coordinate sequence. The cloud's geometric center is calculated from this cloud edge coordinate sequence as the centroid position. Based on the solar azimuth and elevation data recorded by the all-sky imager, the cloud shadow position on the terrain surface is calculated using geometric projection. The shadow boundary is corrected using terrain elevation data to obtain a set of shadow boundary coordinates. Based on the continuous time series of cloud centroid positions and valley wind data collected by an anemometer, the cloud velocity is calculated by dividing the difference between adjacent centroid positions by the time interval. The actual cloud motion direction is corrected using the valley wind velocity vector to generate a cloud trajectory. The cloud edge coordinate sequence, shadow boundary coordinate set, and cloud trajectory are integrated with the corresponding sky brightness and wind speed and direction data to construct a record containing timestamps, spatial coordinates, and meteorological parameters, resulting in a real-time distribution image of low-altitude orographic cumulus clouds and a dataset of meteorological characteristics.

[0027] For example, in low-altitude meteorological monitoring, all-sky imagers (ALLSIs) are crucial observation devices capable of capturing a complete hemispherical image of the sky. Using a fisheye lens, these devices capture a 180-degree view of the sky, automatically taking photos every few seconds to form a continuous record of cloud distribution. As cumulus clouds pass over valley terrain, their morphology and movement characteristics change due to the terrain, directly impacting the amount of solar radiation received by the ground.

[0028] Specifically, the Canny edge detection algorithm has unique advantages when it comes to identifying cloud edges. The algorithm first applies a Gaussian filter to the original image to eliminate image noise. It then calculates the grayscale change rate of each pixel in the horizontal and vertical directions. When the grayscale value of a pixel differs from that of its neighbors by a certain amount, it is determined to be at the edge of a cloud. In practical applications, the grayscale values ​​of pixels in clear sky areas are typically above 200, while those in cloud areas are generally below 100. This significant grayscale difference enables edge detection to accurately identify cloud contours.

[0029] It's important to note that calculating the cloud's centroid is crucial for tracking its movement. By taking a weighted average of the coordinates of all pixels within the identified cloud's edge, the cloud's geometric center can be determined. By continuously acquiring the centroid position at multiple moments, the cloud's movement speed and direction can be calculated. Valley winds in valley terrain typically flow from the valley floor to the hillsides during the day, reaching speeds of several meters per second. This local circulation significantly influences the movement of cumulus clouds.

[0030] In one possible implementation, the determination of the shadow boundary requires a comprehensive consideration of the sun's position and the terrain. The solar azimuth indicates the horizontal angle of the sun relative to due north, while the altitude indicates the angle between the sun and the horizon. These two parameters can be used to determine the direction of incidence of sunlight. When light is blocked by clouds, a shadow is formed on the ground. Due to the elevation changes in valley terrain, the shadow projection position needs to be corrected based on the terrain data. For example, when a cloud is located above a hillside, its shadow may be cast on the opposite hillside rather than simply on the horizontal ground.

[0031] Ideally, the construction of a meteorological characteristic dataset requires the spatiotemporal alignment of multi-source data. Each data record contains the acquisition time, the cloud edge coordinate point set, the corresponding shadow coverage, wind speed and direction values, and the sky brightness distribution. This comprehensive dataset can reflect the dynamic changes in cumulus clouds under specific terrain conditions, providing basic data support for subsequent solar radiation forecasts and photovoltaic power generation estimation. By analyzing cloud movement patterns and shadow change patterns, changes in lighting conditions at a specific location can be predicted in advance, thereby optimizing energy scheduling strategies.

[0032] S102. Extract the coordinates of the edge of orographic cumulus clouds, shadow boundaries, and movement velocity vectors from the dataset. Combined with the regional three-dimensional elevation and slope aspect data obtained by remote sensing, establish a terrain constraint model, identify the area where the cloud movement path overlaps with the hillside terrain, perform shadow dynamic simulation and spatiotemporal distribution analysis on the area, and obtain a shadow evolution time series.

[0033] The dataset extracts the coordinate sequence of the orographic cumulus cloud edge, shadow boundary, and movement velocity vector. Combined with the regional three-dimensional elevation data obtained by remote sensing, a terrain constraint grid containing elevation, slope, and aspect attributes is constructed as the terrain constraint model. The overlap between the cloud movement path and the hillside terrain is determined by determining whether the elevation of the cloud edge projection point is lower than the elevation value of the corresponding terrain grid. Based on the slope and aspect attributes of each grid within the overlapping area and the solar azimuth and altitude parameters extracted from the dataset, the angle between the sun's ray vector and the terrain normal is calculated as the incident angle. When the incident angle is less than 90 degrees minus the slope value, the grid is considered to be in shadow, resulting in the shadow distribution grid at the current moment. Using the shadow distribution grid and the cloud movement velocity vector, a time-stepping method is used to calculate the cloud position at the next moment. The incident angle calculation and shadow determination process is repeated to obtain a sequence of shadow distribution grids at consecutive moments, forming a shadow evolution time series that reflects the spatial position of shadows over time.

[0034] For example, terrain-constrained grids, as a spatial data structure, play a key role in analyzing cloud shadows in mountainous areas. This grid discretizes the continuous terrain surface into regular square cells, each storing the elevation, slope, and aspect information at that location. As clouds move over a valley, their shadows interact in complex ways with the terrain at varying elevations. A grid representation can accurately capture these spatial relationships.

[0035] Specifically, determining the cloud edge projection point involves spatial geometry calculations. Assume the cloud base is 2,500 meters high, while the valley terrain elevation varies between 1,800 and 2,200 meters. When a point on the cloud edge is projected onto the ground, if the terrain elevation corresponding to that point is 2,100 meters and the cloud base is 2,500 meters, then that point is indeed within the cloud projection range. Conversely, if the terrain elevation reaches 2,600 meters, exceeding the cloud base, then that point will not be obscured by the cloud, thus forming the boundary of the overlapping area between the cloud and terrain.

[0036] It should be noted that the calculation of the angle between the sun's rays and the terrain normal vector directly determines the conditions for the generation of shadows. The terrain normal vector is a vector perpendicular to the ground surface, and its direction is determined by both the slope and the aspect.

[0037] For example, the normal vector of a south slope points south and tilts upward at an angle equal to the slope value. When the sun is in the southeast at an altitude of 45 degrees, sunlight shines diagonally downward from the southeast. In this case, the angle between the sun's ray vector and the terrain's normal vector must be calculated. If the angle is greater than 90 degrees, the sun's rays cannot directly reach the terrain surface, resulting in shadows.

[0038] In one possible implementation, the application of a time-stepping method makes dynamic simulation possible. Based on the cloud's velocity vector, the cloud's position at the next moment can be predicted.

[0039] For example, if a cloud moves northeast at 5 meters per second, after 10 seconds, the center of the cloud will have moved 50 meters northeast. This change in position causes a corresponding shift in the cloud's shadow area, which in turn alters the distribution of the ground shadow. The undulations of the hillside can cause the shadow boundary to assume an irregular shape, with the eastern slope potentially entering the shadow earlier, while the western slope is later.

[0040] The generation of shadow evolution time series should ideally consider the choice of temporal resolution. Shorter time intervals can capture rapidly changing shadow details, but they increase computational effort. By recording the complete transition of each grid cell from shadowless to shadowed and back to shadowless, the duration of shadows in that area can be analyzed. This spatiotemporal distribution information is valuable for assessing the power generation potential of mountain photovoltaic power plants. It can identify areas frequently covered by cloud shadows and those less affected, thereby optimizing the layout design of photovoltaic panels.

[0041] S103. Determine whether the low-altitude carport is covered by shadows based on the shadow evolution time series. If so, obtain the sky brightness distribution within the target range, extract the scattering intensity and direction of the cloud gap light, calculate the target gentle inclination angle of the carport solar panel, and generate an inclination adjustment instruction.

[0042] Based on the spatial coordinates and time stamps in the shadow evolution time series, the shadow state value of the low-altitude carport location is extracted. If the shadow state value at the current carport coordinate is blocked, the carport is determined to be shadowed, and this moment is recorded as the shadow coverage start time. Starting from the shadow coverage start time, the full-sky imager collects a hemispherical sky image above the carport. The area at the cloud edge where the pixel brightness value exceeds a preset threshold is identified as the crepuscular light location. The azimuth and elevation angle corresponding to the center point of this area are extracted as the crepuscular light direction. Based on the crepuscular light direction, the angle between this direction and the current normal direction of the carport solar panel is calculated. The estimated received irradiance of the panel is obtained by multiplying the cosine of this angle by the average brightness of the crepuscular light area. The panel tilt angle is adjusted to maximize the received irradiance estimate, and the corresponding tilt angle is determined as the target flat tilt angle. The difference between the target flat tilt angle and the current tilt angle of the solar panel is compared to generate a tilt adjustment instruction containing the tilt adjustment amount and adjustment direction.

[0043] For example, a shadow evolution time series, as a spatiotemporal data structure, records the changing shadow state at each spatial location at different moments in time. As cumulus clouds move over mountainous areas, their shadows shift over time across the surface, forming a dynamic shadow distribution pattern. For solar carports in low-altitude areas, this shadow coverage directly affects photovoltaic power generation efficiency, making accurate assessment of shadow coverage a key prerequisite for optimizing power generation.

[0044] In one possible implementation, the shadow status value is typically represented as a binary value, where 1 indicates shadow coverage and 0 indicates no shadow. When the status value at the carport location changes from 0 to 1, it marks the start of shadow coverage. Recording this moment is crucial, not only indicating the carport's entry into the shadow area but also providing a time reference for subsequent lighting condition analysis.

[0045] It's important to note that the phenomenon of crepuscular rays holds special significance in meteorological optics. When thick cumulus clouds block the sun, bright beams of light appear at the edges or gaps between the clouds. These are known as crepuscular rays. A full-sky imager (WSI) uses a fisheye lens to capture images of the entire hemisphere of the sky, where crepuscular rays appear as clusters of high-brightness pixels. By setting a brightness threshold, such as identifying areas with a grayscale value exceeding 200 as potential crepuscular rays, crepuscular rays can be effectively distinguished from the normal sky background.

[0046] Specifically, extracting the direction of crepuscular rays involves converting image coordinates into celestial coordinates. Each pixel in a full-sky image corresponds to a direction in the sky. Using the projection relationship of a fisheye lens, pixel coordinates can be converted into azimuth and elevation. Azimuth represents the horizontal angle relative to true north, while elevation represents the vertical angle relative to the horizon. Once a crepuscular light region is identified, the azimuth and elevation corresponding to the region's geometric center are calculated to obtain the primary incident direction of the crepuscular rays.

[0047] In one embodiment, the direction of the normal of a solar panel determines its efficiency in receiving light. The panel normal is a direction vector perpendicular to the panel surface, pointing outward. When the incident light direction aligns with the normal direction, the reception efficiency is highest; when the two are perpendicular, the reception efficiency is zero. The cosine value of the angle precisely reflects this relationship: a larger cosine value indicates that the light is closer to vertical incidence, and the reception efficiency is higher. By multiplying the cosine value of the angle by the brightness of the cloud gap light, the amount of irradiance received by the panel at the current angle can be estimated.

[0048] Optimally, the goal of tilt adjustment is to maximize the exposure of the panels to crevasse light. By trialing different tilt angles, the estimated irradiance at each angle is calculated, and the angle that maximizes the estimated value is selected as the target tilt. This optimization process accounts for the peculiarities of crevasse light, which primarily originate from specific directions rather than being evenly distributed. Therefore, dynamic adjustment of the panel orientation is required to adapt to changing lighting conditions. The generated tilt adjustment instructions contain the specific adjustment amount and direction, enabling the carport PV system to effectively utilize scattered light energy even in shaded conditions.

[0049] S104: extracting the shadow coverage state of the high-altitude carport from the shadow evolution time series; if it is not in shadow coverage, determining whether the solar panel of the high-altitude carport maintains a target direct light receiving inclination angle, and generating an inclination maintenance instruction.

[0050] The shadow state value corresponding to the spatial coordinates of the high-altitude carport is queried from the shadow evolution time series, where a state value of 0 indicates no shadow coverage and a state value of 1 indicates shadow coverage. If the state value of the coordinate at the current moment is 0, the high-altitude carport is determined to be not in a shadow coverage state. Based on the determination that it is not in a shadow coverage state, the solar azimuth and altitude parameters at the current moment are extracted from the meteorological data set, and the three directional components of the direct sunlight vector are calculated. The horizontal east component is the product of the cosine of the altitude angle and the sine of the azimuth angle, the horizontal north component is the product of the cosine of the altitude angle and the cosine of the azimuth angle, and the vertical component is the sine of the altitude angle. Based on the three directional components of the direct sunlight vector, the optimal tilt angle for the current solar panel to receive direct sunlight is determined. This tilt angle aligns the panel normal direction with the direction of the direct sunlight. This tilt angle is set as the target direct light reception tilt angle, and a tilt maintenance instruction containing this target tilt angle value is generated.

[0051] For example, in a photovoltaic power generation system in a mountainous area, high-altitude carports are less likely to be affected by cloud shadows due to their higher elevation. The shadow evolution time series records the changes in lighting conditions at each spatial location using a binary state. This concise data structure facilitates quick query and judgment. When the system detects a state value of 0 at the location of a high-altitude carport, it indicates that the area is receiving sufficient direct sunlight. At this time, maintaining the optimal light reception angle becomes the key to improving power generation efficiency.

[0052] Specifically, solar azimuth and altitude are two fundamental parameters that describe the sun's position. The azimuth angle, with due north as 0 degrees, increases clockwise, indicating the sun's direction above the horizon; the altitude angle indicates the sun's elevation relative to the horizon. Both parameters vary continuously over time, with the altitude reaching its maximum at noon, while the azimuth angle approaches 90 degrees at sunrise and 270 degrees at sunset. Meteorological datasets typically include hourly records of these parameters, providing fundamental data for the dynamic control of photovoltaic systems.

[0053] It should be noted that calculating the direct sunlight vector involves converting spherical coordinates to rectangular coordinates. In three-dimensional space, any direction can be expressed using three components: easting, northing, and vertical. When the sun's altitude is 60 degrees and its azimuth is 150 degrees, the cosine of the altitude is approximately 0.5, and the sine is approximately 0.866. The sine of an azimuth of 150 degrees is approximately 0.5, and the cosine is approximately -0.866. Therefore, the horizontal easting component is 0.5 multiplied by 0.5, which equals 0.25. The horizontal northing component is 0.5 multiplied by -0.866, which equals -0.433. The vertical component is the sine of the altitude, 0.866. Together, these three components define the precise direction of the sunlight.

[0054] In one possible implementation, the optimal tilt angle of a solar panel requires considering the alignment of the panel normal with sunlight. The panel normal is perpendicular to the panel surface and points outward. When this direction aligns perfectly with the direction of direct sunlight, the light strikes the panel surface perpendicularly, achieving maximum light energy conversion efficiency. By adjusting the panel's tilt angle and azimuth, the panel normal can be aligned with the sun.

[0055] Optimally, the generation of tilt-angle maintenance commands reflects the system's intelligent control characteristics. Unlike low-altitude carports that need to adjust their tilt angle to capture scattered light in shaded conditions, high-altitude carports should maintain an optimal angle to track direct sunlight when unobstructed. This differentiated control strategy fully considers the lighting characteristics of different altitudes, enabling the entire mountain photovoltaic system to optimize the power generation efficiency of each carport based on actual lighting conditions, maximizing energy collection.

[0056] S105: Distribute the tilt adjustment instruction and the tilt maintenance instruction to the solar panel control units of the low-altitude and high-altitude carports to drive the servo motors to adjust the panel angles and complete the tilt optimization.

[0057] A tilt adjustment command containing the target tilt angle value is distributed to the solar panel control unit of the low-altitude carport, while a tilt maintenance command containing the target tilt angle value is distributed to the solar panel control unit of the high-altitude carport. The command data packets are transmitted via a serial communication interface, and the control unit returns a receipt confirmation signal. Based on the target tilt angle value in the confirmation signal, the solar panel control unit reads the current tilt angle value fed back by the encoder and calculates the difference between the two as the adjustment angle. When the absolute value of the adjustment angle exceeds the angle threshold, the direction of rotation is determined based on the positive or negative sign of the difference, and a pulse control signal containing a rotation direction identifier and the adjustment angle value is generated. After receiving the pulse control signal, the servo motor rotates in the specified direction and angle, driving the solar panel through a mechanical transmission device. The encoder collects the panel tilt angle value in real time and feeds it back to the control unit. When the absolute difference between the target tilt angle value and the feedback tilt angle value falls below the angle accuracy threshold, the control unit stops outputting the pulse control signal, completing the tilt optimization.

[0058] For example, in the actual operation of photovoltaic power generation systems in mountainous areas, the command distribution mechanism plays a critical role in connecting the upper and lower levels. Based on the target tilt angle values ​​calculated in advance, the system needs to accurately transmit these control parameters to the control terminals of carports distributed at different altitudes. Carports at low altitudes receive dynamic adjustment commands to adapt to changes in cloud cover; carports at high altitudes receive maintenance commands to maintain the optimal angle for receiving direct sunlight.

[0059] Specifically, the serial communication interface, a standard communication method in industrial control, implements data transmission via the RS485 or RS232 protocol. Each command data packet contains a start bit, device address, function code, target inclination angle value, checksum, and end bit. Once the control unit receives the complete data packet, it verifies the checksum to confirm the correctness of the data transmission and then returns a response signal containing the device address and a confirmation flag. This handshake mechanism ensures reliable command transmission even in the complex electromagnetic environment of mountainous areas.

[0060] It should be noted that the encoder plays an important role in position feedback in the entire control loop. The absolute encoder can directly output the actual inclination value of the panel with a resolution of up to 0.1 degrees. When the control unit reads that the current inclination angle is 45 degrees and the target inclination angle is 60 degrees, it calculates that an adjustment of 15 degrees is required. The positive or negative sign of this difference determines the direction of rotation: a positive value indicates an upward adjustment, and a negative value indicates a downward adjustment. The system will only initiate the adjustment action when the adjustment angle exceeds the set dead zone range, such as 0.5 degrees, to avoid mechanical wear caused by frequent small adjustments.

[0061] In one possible implementation, the generation of the pulse control signal follows the stepper motor control principle. Each pulse corresponds to a fixed angle of rotation of the motor. The pulse frequency determines the rotation speed, and the number of pulses determines the rotation angle.

[0062] For example, if the servo motor has a step angle of 1.8 degrees and a reduction ratio of 100:1, approximately 833 pulses are required to achieve a 15-degree adjustment of the panel. The control unit adjusts the pulse frequency to achieve acceleration and deceleration control, using lower frequencies at the beginning and end stages and higher frequencies in the middle stages, forming a trapezoidal speed curve.

[0063] Preferably, the mechanical transmission device adopts a worm gear structure with self-locking characteristics, which can keep the panel angle unchanged in the event of a power outage. The real-time feedback mechanism ensures adjustment accuracy through closed-loop control. The encoder collects the panel inclination angle at a fixed period and uploads it to the control unit, which calculates the deviation between the actual inclination angle and the target inclination angle. When the deviation gradually decreases and enters the accuracy threshold range, such as ±0.2 degrees, the control unit stops outputting the pulse signal, the servo motor brakes, and the inclination optimization process is completed. This precise angle control enables the photovoltaic panel to always maintain the optimal light reception angle, significantly improving power generation efficiency.

[0064] S106. Collect the adjusted photovoltaic conversion efficiency data of the solar panel in real time, combine it with the updated cloud distribution data, and determine whether the tilt angle optimization matches the shadow evolution characteristics of the current terrain cumulus clouds based on the photovoltaic conversion efficiency data and the updated cloud distribution data, and generate an optimization effect evaluation data set.

[0065] The photovoltaic current and voltage sensors collect the adjusted output current and voltage of the solar panel in real time. The product of current and voltage is calculated to obtain the real-time output power. Simultaneously, the irradiance sensor obtains the solar radiation intensity received by the panel surface. The output power is divided by the product of the radiation intensity and the panel area to obtain the current photovoltaic conversion efficiency data. Based on the current photovoltaic conversion efficiency data, the cloud edge coordinate update data for the corresponding time is obtained from the all-sky imager. The ratio of the number of pixels covered by the cloud to the total number of pixels in the sky image is calculated as the shadow area fraction. The cloud movement speed is calculated by dividing the difference in the cloud geometric center coordinates between adjacent moments by the time interval. The shadow area fraction and cloud movement speed are then output. Based on the shadow area fraction and cloud movement speed, the time span from the cloud first blocking the solar panel to the cloud completely leaving the panel is tracked. This time span is the shadow duration. If the photovoltaic conversion efficiency remains above the preset minimum efficiency threshold during this period, the current tilt setting is determined to match the shadow evolution characteristics of orographic cumulus clouds. The matching judgment results are associated with the corresponding photoelectric conversion efficiency data, shadow area ratio, cloud movement speed value and shadow duration, and the timestamp of the collection time and the solar panel number are added to construct record entries containing multi-dimensional evaluation parameters, which are summarized to form an optimization effect evaluation data set.

[0066] For example, photoelectric conversion efficiency, a core indicator for evaluating the performance of solar power generation systems, reflects the ability of photovoltaic panels to convert received solar energy into electrical energy. In actual measurements, current sensors use the Hall effect principle to detect the current flowing through the wire, while voltage sensors use a voltage divider circuit to obtain the voltage value across the panel. When the panel outputs 10 amperes of current and 50 volts of voltage, the output power is 500 watts. Irradiance sensors, typically using thermopiles or photodiodes, can accurately measure the solar radiation power received per unit area.

[0067] Specifically, calculating photoelectric conversion efficiency requires considering the ratio of actual output to theoretical input. Assuming the irradiance sensor measures 800 watts per square meter of solar radiation and the panel area is 2 square meters, the theoretical input power is 1600 watts. Dividing the output power of 500 watts by the input power of 1600 watts yields a conversion efficiency of 31.25%. This efficiency value fluctuates with lighting conditions, temperature, and panel angle, necessitating real-time monitoring to assess optimization effectiveness.

[0068] It should be noted that the dynamic changes in cloud distribution directly affect the intensity of light received on the ground. The all-sky imager captures a sky image every few seconds and identifies cloud outlines through image processing. Calculating the percentage of shadow area involves pixel counting. If a sky image contains a total of 1 million pixels, and 200,000 of them are identified as cloud-covered areas, the shadow area percentage is 20%. Calculating cloud movement speed requires tracking the positional changes of the cloud's geometric center in successive frames, and then dividing the displacement by the time interval to obtain the velocity value.

[0069] In one possible implementation, shadow duration statistics are crucial for evaluating tilt optimization effectiveness. The system uses timestamps to record the moment a cloud begins to obscure the panel and the moment it completely leaves the panel. The difference between the two timestamps represents the shadow duration.

[0070] For example, a cumulus cloud began to block the panel at 10:15:30 AM and completely left at 10:18:45 AM, with the shadow lasting for 3 minutes and 15 seconds. During this period, if the panel can still maintain a conversion efficiency of more than 15% through tilt adjustment, it means that the tilt optimization strategy has effectively adapted to the cloud shadow changes.

[0071] Optimally, the construction of an optimization effect evaluation dataset requires integrating information from multiple dimensions. Each record contains not only instantaneous data at a specific moment, but also correlates the changing trends from previous moments to next. Solar panel numbers are used to distinguish devices at different locations, facilitating subsequent analysis of optimization effects at different altitudes. By accumulating this evaluation data over time, the system can identify the optimal tilt adjustment strategy for specific weather patterns, forming an empirical knowledge base that further enhances the adaptability and power generation efficiency of mountain photovoltaic power generation systems in complex meteorological conditions.

[0072] S107 , calculating the deviation between the photoelectric conversion efficiency and the preset expected scattered light capture efficiency based on the optimization effect evaluation data set, and updating the terrain constraint parameters based on the deviation.

[0073] The measured photoelectric conversion efficiency values ​​at each moment were extracted from the optimization effect evaluation dataset and compared item by item with the target conversion efficiency values ​​under pre-set diffuse light conditions. The efficiency difference at each moment was calculated and the root mean square deviation (RMS) of the efficiency was obtained by summing the squares of the differences across all moments and dividing by the total number of records. The RMS deviation of the efficiency was used to evaluate the effect of terrain constraints. If the deviation exceeded a preset threshold, records from the evaluation dataset for periods of time with covered shadows were filtered. The corresponding measured irradiance values ​​were compared with the baseline irradiance values ​​under unobstructed conditions. The ratio of the measured value to the baseline value was calculated as the terrain shading coefficient. Based on the terrain shading coefficient and the cloud brightness distribution recorded by the all-sky imager, the cloud transmittance was calculated using the Lambert-Beer law. The negative logarithm of the transmittance is proportional to the cloud thickness, and the cloud optical thickness was derived from this inverse equation. The wind field influence factor was also calculated by calculating the ratio of the wind speed difference between adjacent moments to the time interval. The gradient descent method is used to adjust the parameter values ​​in the terrain constraint model according to the efficiency deviation. The terrain obscuration coefficient, cloud optical thickness value and wind field influencing factor are used as new model parameter inputs to update the terrain constraint model parameter configuration.

[0074] For example, the RMS efficiency deviation (RMSED) is a key indicator for evaluating system performance, reflecting the overall difference between actual operating results and expected targets. In photovoltaic power generation systems, the target conversion efficiency under diffuse light conditions is typically pre-set based on historical data and theoretical calculations.

[0075] For example, under conditions of complete shadow coverage, if it is expected that the conversion efficiency through cloud gap light and sky scattered light can still reach 15%, and the actual measured value fluctuates between 12% and 18%, the square of the difference at each moment is accumulated and then squared to obtain the root mean square value reflecting the overall degree of deviation.

[0076] Specifically, the calculation of the terrain obstruction coefficient requires establishing a comparative benchmark. The baseline irradiance value under unobstructed conditions is typically taken from measurements taken at a high-altitude, unshaded area during the same time period, or from theoretically calculated clear-sky irradiance. When a carport at a low altitude is shadowed by a mountain, the measured irradiance may be only 30% of the baseline value. This ratio of 0.3 is the terrain obstruction coefficient. This coefficient directly reflects the extent to which terrain factors affect lighting conditions and serves as a critical reference for subsequent optimization and control.

[0077] It should be noted that the Lambert-Beer law, in atmospheric optics, describes the attenuation of light as it passes through a medium. When sunlight passes through clouds, the attenuation of light intensity is exponentially related to cloud thickness. Cloud transmittance represents the ratio of the intensity of light after passing through the cloud to the intensity of the incident light, and its negative logarithm is proportional to the optical thickness of the cloud. By measuring the brightness difference between the upper and lower surfaces of a cloud, the transmittance can be calculated, and the optical thickness of the cloud can be inferred. Thicker cumulus clouds can have an optical thickness of over 20, while thin clouds may only have an optical thickness of 2 to 5.

[0078] In one possible implementation, the wind field influencing factor accounts for the effect of local circulation in valley terrain on cloud motion. Valley winds flow from the valley floor to the hillside during the day, and the rate of change in their velocity reflects the instability of the wind field. For example, if two consecutive wind speed measurements are 3 meters per second and 5 meters per second, respectively, with a 30-second interval, the wind speed change rate is 0.067 meters per square second. This factor affects the accuracy of cloud motion predictions, and thus influences the formulation of tilt adjustment strategies.

[0079] Preferably, the gradient descent method, as a classic optimization algorithm, plays an important role in the parameter update process. This method determines the direction and amplitude of parameter adjustment by calculating the partial derivatives of the objective function with respect to each parameter. In the terrain constraint model, the objective function can be defined as the sum of the squares of the deviations between the predicted efficiency and the measured efficiency. By iteratively adjusting the terrain shading coefficient, cloud optical thickness and wind field influencing factor, the deviation is gradually reduced. In each iteration, the parameter update amount is proportional to the product of the partial derivative and the learning rate. The choice of learning rate needs to balance the convergence speed and stability. After multiple iterations, the model parameters are gradually optimized, enabling the system to more accurately predict and respond to changes in illumination under complex terrain conditions.

[0080] The present invention provides a photovoltaic carport assembly tilt adjustment system for multiple climate zones, which mainly includes:

[0081] The meteorological data acquisition module is used to use the all-sky imager to obtain real-time distribution images of low-altitude orographic cumulus clouds and sky brightness data. Combined with the valley wind speed and direction collected by the anemometer, it generates a meteorological feature dataset that includes cloud edges, shadow boundaries, and movement paths.

[0082] The terrain constraint modeling module is used to extract the coordinates of the edge of the orographic cumulus cloud, the shadow boundary, and the movement velocity vector from the data set. It combines the regional three-dimensional elevation and slope aspect data obtained by remote sensing to establish a terrain constraint model, identify the area where the cloud movement path overlaps with the hillside terrain, and perform shadow dynamic simulation and spatiotemporal distribution analysis on the area to obtain a shadow evolution time series.

[0083] The shadow evolution analysis module is used to determine whether the low-altitude carport is covered by shadows based on the shadow evolution time series. If so, it obtains the sky brightness distribution within the target range, extracts the scattering intensity and direction of the cloud gap light, calculates the target gentle tilt angle of the carport solar panel, and generates tilt adjustment instructions;

[0084] The high-altitude tilt angle decision module is used to extract the shadow coverage status of the high-altitude carport from the shadow evolution time series. If it is not in shadow coverage, it determines whether the solar panels of the high-altitude carport maintain the target direct light receiving tilt angle and generates a tilt angle maintenance instruction;

[0085] The command execution control module is used to distribute the tilt adjustment command and the tilt maintenance command to the solar panel control units of the low-altitude and high-altitude carports, driving the servo motor to adjust the panel angle and complete the tilt optimization;

[0086] The optimization evaluation feedback module is used to collect the photovoltaic conversion efficiency data of the adjusted solar panels in real time, combine it with the updated cloud distribution data, and judge whether the tilt angle optimization matches the shadow evolution characteristics of the current terrain cumulus clouds based on the photovoltaic conversion efficiency data and the updated cloud distribution data, and generate an optimization effect evaluation data set;

[0087] The optimization evaluation feedback module is used to evaluate the data set based on the optimization effect, calculate the deviation between the photoelectric conversion efficiency and the preset expected scattered light capture efficiency, and update the terrain constraint parameters according to the deviation.

[0088] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for adjusting the inclination angle of a photovoltaic carport assembly for multiple climate zones, characterized in that: The method comprises: obtaining a low-altitude terrain cumulus cloud distribution image and sky brightness data through an all-sky imager, combining the valley wind speed and direction collected by an anemometer, and generating a meteorological feature data set; extracting the terrain cumulus cloud edge coordinates, shadow boundary, and moving speed vector from the meteorological feature data set, combining the regional three-dimensional elevation and slope aspect data obtained by remote sensing, building a terrain constraint model, identifying the overlapping area between the cloud movement path and the hillside terrain, and generating a shadow evolution time series; judging whether the low-altitude carport is in a shadow coverage state according to the shadow evolution time series, and generating a low-altitude carport according to the judgment result. The invention relates to a method for generating a tilt angle adjustment instruction for the solar panel of the carport at a high altitude; extracting the shadow coverage state of the high-altitude carport from the shadow evolution time series, and generating a tilt angle maintenance instruction for the solar panel of the high-altitude carport; distributing the tilt angle adjustment instruction and the tilt angle maintenance instruction to the solar panel control units of the low-altitude carport and the high-altitude carport, and driving the solar panel to adjust to the target tilt angle; collecting the photoelectric conversion efficiency data of the adjusted solar panel, combining it with the cloud distribution update data of the all-sky imager, and generating an optimization effect evaluation data set; updating the parameter configuration of the terrain constraint model according to the optimization effect evaluation data set.

2. The method for adjusting the inclination angle of a photovoltaic carport assembly for multiple climate zones according to claim 1, characterized in that: The method obtains low-altitude terrain cumulus cloud distribution images and sky brightness data using an all-sky imager, and combines them with valley wind speed and direction collected by an anemometer to generate a meteorological feature dataset. The method includes: extracting cloud edge contours from the distribution images obtained by the all-sky imager to generate a cloud edge coordinate sequence; calculating the shadow position of the cloud on the terrain surface based on the cloud edge coordinate sequence and solar azimuth and altitude data, correcting the shadow boundary based on terrain elevation data, and generating a shadow boundary coordinate set; calculating the cloud movement speed and direction based on a continuous time series of cloud edge coordinate sequences and the valley wind speed and direction collected by the anemometer to generate a cloud movement path trajectory; and integrating the cloud edge coordinate sequence, the shadow boundary coordinate set, the cloud movement path trajectory, and the sky brightness data to construct the meteorological feature dataset.

3. The method for adjusting the inclination angle of a photovoltaic carport assembly for multiple climate zones according to claim 2, characterized in that: The method extracts the coordinates of the edge of the orographic cumulus cloud, the shadow boundary, and the moving speed vector from the meteorological characteristic data set, combines the regional three-dimensional elevation and slope and aspect data obtained by remote sensing, constructs a terrain constraint model, identifies the overlapping area between the cloud movement path and the hillside terrain, and generates a shadow evolution time series. The method includes: extracting the cloud edge coordinate sequence, the shadow boundary coordinate set, and the cloud movement path trajectory from the meteorological characteristic data set, and combining the regional three-dimensional elevation and slope and aspect data obtained by remote sensing to construct a terrain constraint grid containing elevation and slope attributes; identifying the overlapping area between the cloud movement path and the hillside terrain by comparing the elevation of the projection point of the cloud edge coordinate sequence with the elevation of the terrain constraint grid; calculating the shadow distribution grid according to the slope and aspect attributes of the overlapping area and the solar azimuth altitude angle parameter; and generating the shadow evolution time series at consecutive moments based on the shadow distribution grid and the moving speed vector.

4. The method for adjusting the inclination angle of a photovoltaic carport assembly for multiple climate zones according to claim 1, characterized in that: The method of determining whether the low-altitude carport is in a shadow-covered state based on the shadow evolution time series, and generating a tilt adjustment instruction for the solar panel of the low-altitude carport based on the determination result, includes: extracting the shadow state value of the low-altitude carport coordinates from the shadow evolution time series to determine whether it is in an obstructed state; collecting a sky image using the full-sky imager to extract the brightness value and direction of the crepuscular light area; calculating the target tilt angle of the solar panel based on the brightness value and direction of the crepuscular light area, and generating an instruction including a tilt adjustment amount.

5. The method for adjusting the inclination angle of a photovoltaic carport assembly for multiple climate zones according to claim 1, characterized in that: The extracting the shadow coverage state of the high-altitude carport from the shadow evolution time series and generating an inclination maintenance instruction for the solar panel of the high-altitude carport includes: extracting the shadow state value of the high-altitude carport coordinates from the shadow evolution time series to determine whether it is in a shadow-free state; based on the determination result that it is not in a shadow coverage state, extracting solar azimuth and altitude angle parameters from the meteorological characteristic data set to calculate the direct sunlight vector; and determining the target inclination angle of the solar panel based on the direct sunlight vector, and generating an instruction including the target inclination angle.

6. The method for adjusting the inclination angle of a photovoltaic carport assembly for multiple climate zones according to claim 1, characterized in that: The method of distributing the tilt adjustment instruction and the tilt maintaining instruction to the solar panel control units of the low-altitude carport and the high-altitude carport to drive the solar panels to adjust to the target tilt angle includes: distributing the tilt adjustment instruction containing the target tilt angle value to the solar panel control unit of the low-altitude carport, and distributing the tilt maintaining instruction containing the target tilt angle value to the solar panel control unit of the high-altitude carport, the control unit returning a reception confirmation signal; calculating the difference between the target tilt angle in the reception confirmation signal and the current tilt angle, and generating a pulse control signal; and the servo motor adjusting the solar panel to the target tilt angle according to the pulse control signal.

7. The method for adjusting the inclination angle of a photovoltaic carport assembly for multiple climate zones according to claim 1, characterized in that: The collected photoelectric conversion efficiency data of the adjusted solar panel is combined with the updated cloud distribution data from the all-sky imager to generate an optimization effect evaluation data set, including: collecting the output power and irradiation intensity of the adjusted solar panel through a sensor to calculate the photoelectric conversion efficiency; obtaining updated cloud edge coordinate data from the all-sky imager to calculate the shadow area ratio and movement speed; and generating an optimization effect evaluation data set including a timestamp based on the photoelectric conversion efficiency, shadow area ratio, and movement speed.

8. The method for adjusting the inclination angle of a photovoltaic carport assembly for multiple climate zones according to claim 1, characterized in that: The updating of the parameter configuration of the terrain constraint model according to the optimization effect evaluation data set includes: extracting the photoelectric conversion efficiency value from the optimization effect evaluation data set and calculating the deviation from the expected efficiency; calculating the terrain shielding coefficient and the cloud optical thickness according to the deviation and the cloud brightness distribution; calculating the wind field influence factor according to the wind speed data; and updating the terrain constraint model parameters according to the terrain shielding coefficient, the cloud optical thickness and the wind field influence factor.

9. A photovoltaic carport module tilt adjustment system for multiple climate zones, characterized in that: The system comprises: The meteorological data acquisition module is used to use the all-sky imager to obtain real-time distribution images of low-altitude orographic cumulus clouds and sky brightness data. Combined with the valley wind speed and direction collected by the anemometer, it generates a meteorological feature dataset that includes cloud edges, shadow boundaries, and movement paths. The terrain constraint modeling module is used to extract the coordinates of the edge of the orographic cumulus cloud, the shadow boundary, and the movement velocity vector from the data set. It combines the regional three-dimensional elevation and slope aspect data obtained by remote sensing to establish a terrain constraint model, identify the area where the cloud movement path overlaps with the hillside terrain, and perform shadow dynamic simulation and spatiotemporal distribution analysis on the area to obtain a shadow evolution time series. The shadow evolution analysis module is used to determine whether the low-altitude carport is covered by shadows based on the shadow evolution time series. If so, it obtains the sky brightness distribution within the target range, extracts the scattering intensity and direction of the cloud gap light, calculates the target gentle tilt angle of the carport solar panel, and generates tilt adjustment instructions; The high-altitude tilt angle decision module is used to extract the shadow coverage status of the high-altitude carport from the shadow evolution time series. If it is not in shadow coverage, it determines whether the solar panels of the high-altitude carport maintain the target direct light receiving tilt angle and generates a tilt angle maintenance instruction; The command execution control module is used to distribute the tilt adjustment command and the tilt maintenance command to the solar panel control units of the low-altitude and high-altitude carports, driving the servo motor to adjust the panel angle and complete the tilt optimization; The optimization evaluation feedback module is used to collect the photovoltaic conversion efficiency data of the adjusted solar panels in real time, combine it with the updated cloud distribution data, and judge whether the tilt angle optimization matches the shadow evolution characteristics of the current terrain cumulus clouds based on the photovoltaic conversion efficiency data and the updated cloud distribution data, and generate an optimization effect evaluation data set; The optimization evaluation feedback module is used to evaluate the data set based on the optimization effect, calculate the deviation between the photoelectric conversion efficiency and the preset expected scattered light capture efficiency, and update the terrain constraint parameters according to the deviation.

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