Method for calibrating tower-type photo-thermal heliostat by unmanned aerial vehicle
By using drones equipped with RTK positioning and image analysis technology, combined with SIFT and Hough circle detection algorithms, all-weather high-precision heliostat calibration was achieved, solving the problems of limited calibration time and accuracy in traditional methods, and improving the operating efficiency and power generation of tower solar thermal power plants.
Patent Information
- Application Number
- CN202511186269.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-08-23
- Publication Date
- 2025-10-31
AI Technical Summary
Traditional heliostat calibration methods are limited by sunlight conditions, cannot be performed at night or on cloudy days, and are sensitive to environmental conditions, resulting in a limited calibration time window and insufficient accuracy.
By using a drone equipped with an RTK positioning module and a high-resolution gimbal camera, combined with SIFT feature matching and Hough circle detection algorithms, the offset of the center of the reflected light spot of the heliostat is calculated through image analysis, and the least squares method is used to fit multi-point deviation data to achieve high-precision calibration in all weather conditions.
It improves the efficiency and accuracy of heliostat calibration, enabling it to be performed at night or on cloudy days, significantly reducing human intervention and improving the power generation efficiency and economic benefits of tower solar thermal power plants.
Smart Images
Figure CN120868626A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of tower-type concentrated solar power generation technology, and in particular to a method for calibrating tower-type concentrated solar heliostats using a drone. Background Technology
[0002] Tower-type concentrated solar power (CSP) systems use heliostats to focus sunlight onto receivers on the collector tower, achieving efficient solar energy collection and conversion. In this process, the attitude accuracy of the heliostats has a decisive impact on their concentrating performance. However, in actual operation, due to factors such as installation errors, foundation settlement, wind loads, and temperature variations, the azimuth and elevation angles of the heliostats often deviate, causing the reflected light to deviate from the target and reducing the overall power generation efficiency of the system. Therefore, regular high-precision calibration of the heliostats is a crucial step in ensuring the efficient operation of CSP plants.
[0003] Currently, traditional heliostat calibration methods primarily rely on sunlight as a reference light source, adjusting the heliostat's attitude by analyzing the positional deviation of the reflected light spot. While this method can meet calibration requirements to some extent, it has significant limitations. First, traditional methods can only be performed on clear days with a suitable solar altitude angle, and cannot be implemented at night or on cloudy days. This not only limits the calibration time window but may also occupy daytime power generation time, directly impacting the power plant's economic efficiency. Second, because the sun's position changes constantly with time and geographical location, traditional methods require frequent adjustments to the reference coordinates during calibration, increasing operational complexity and uncertainty. Furthermore, traditional methods are sensitive to environmental conditions; for example, cloud cover or atmospheric turbulence can lead to significant errors in the measurement results, making it difficult to meet the requirements of high-precision calibration.
[0004] To overcome the above problems, there is an urgent need for a technical solution that can perform heliostat calibration under all-weather conditions to improve calibration efficiency and accuracy while avoiding interference with power generation time. Summary of the Invention
[0005] This invention provides a method for calibrating a tower-type photothermal heliostat using an unmanned aerial vehicle (UAV) to solve the problems mentioned in the background art.
[0006] To achieve the above objectives, the present invention adopts the following technical solution: A method for calibrating a tower-type solar thermal heliostat using a drone includes the following steps: S1. UAV Calibration and Data Acquisition Phase: First, configure the UAV equipment, specifically by integrating an RTK positioning module (planar positioning accuracy ±5cm and elevation positioning accuracy ±5cm) and a high-resolution gimbal camera (20 megapixels). Install high-brightness red or other color LED markers (wavelength range 620-750nm, nighttime visibility ≥500m) on the heliostat surface. Further, plan UAV hovering points, ensuring they cover the heliostat's normal direction adjustment range and are evenly distributed spherically. The angular distance between adjacent hovering points should be ≤10°. The UAV can be at the same or different altitudes, as long as the angle between the line connecting the UAV and the heliostat being measured is greater than 10 degrees. Specifically, the height of each hovering point needs to be set to more than five times the installation height of the heliostat to avoid the impact of field distortion on imaging accuracy. The higher the height, the higher the calculation accuracy. The deviation between the center of the heliostat and the image of the LED marker light in the mirror is obtained by taking a picture. The error tolerance range is ±3 pixels (generally, the drone position is not adjusted). At this time, the drone coordinates are the endpoint of the theoretical normal direction vector.
[0007] S2, Image Analysis and Deviation Calculation Stage: First, the SIFT feature matching algorithm in OpenCV or other algorithms are used to identify the corner points of the heliostat's frame, and a local coordinate system for the mirror surface is established based on these corner points. Further, the Hough circle detection algorithm or other algorithms are used to locate the center of the reflected light spot, and its pixel offset from the geometric center of the mirror surface is calculated. Specifically, the pixel offset is converted into an actual angle value by combining the camera focal length and pixel size. The deviation between the actual normal direction and the theoretical normal direction is calculated using the following formula:
[0008] In the above formula, and These represent the deviations of the azimuth and elevation angles, respectively. and These represent the theoretical azimuth and elevation angles, respectively. , , These represent the three-dimensional deviation components between the UAV coordinates and the endpoint of the theoretical normal direction vector. This represents the actual measured unit vector magnitude.
[0009] S3, Regression Analysis and Parameter Calibration Stage: Collect deviation data from multiple measurement points ( , And construct the least squares optimization problem: In the above formula, Represents the Jacobian matrix. Indicates the angle of inclination of the column. This represents the initial installation angle deviation. Further, the goodness of fit is evaluated through residual analysis, when R... 2 A value greater than 0.9 indicates that the fitting result meets the accuracy requirements. Specifically, the corrected parameters are input into the heliostat control system to achieve automatic compensation.
[0010] The technical solution of this invention has the following innovations: First, by using an UAV equipped with an RTK positioning module and a high-resolution gimbal camera, high-precision spatial positioning and imaging are achieved; second, by combining the SIFT feature matching algorithm and the Hough circle detection algorithm, the center position and pixel offset of the reflected light spot of the heliostat are accurately extracted and converted into angle values; third, by fitting the deviation data of multiple measurement points using the least squares method, the deviations of the column tilt angle and the initial installation angle are effectively separated, thereby achieving comprehensive calibration of the heliostat attitude deviation.
[0011] Compared with the prior art, the beneficial effects of the present invention are: This invention provides an efficient, high-precision, and flexible calibration method for tower-type photothermal heliostats by organically combining UAV aerial surveying and image analysis technology. It overcomes the limitation of traditional correction methods that rely on sunlight, significantly improves calibration efficiency and accuracy, and has important technical significance and broad application prospects.
[0012] The above description is merely an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention and to implement it according to the contents of the specification, the preferred embodiments of the present invention are described in detail below with reference to the accompanying drawings. Specific embodiments of the present invention are given in detail below with reference to the accompanying drawings. Attached Figure Description
[0013] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings: Figure 1 This is a schematic diagram of a method for calibrating a tower-type photothermal heliostat using a drone, as proposed in this invention. Detailed Implementation
[0014] The principles and features of the present invention are described below with reference to the accompanying drawings. The examples given are for illustrative purposes only and are not intended to limit the scope of the invention. The invention is described more specifically in the following paragraphs by way of example with reference to the accompanying drawings. It should be noted that the drawings are in a very simplified form and use non-precise proportions, and are only used to facilitate and clarify the illustration of the embodiments of the present invention. Detailed Implementation
[0015] This invention provides a method for calibrating a tower-type photothermal heliostat based on UAV aerial surveying and image analysis, combined with an appendix. Figure 1 The specific implementation methods are described in detail. For example... Figure 1 As shown, this method uses a UAV equipped with an RTK positioning module and a 20-megapixel gimbal camera to perform non-contact attitude detection of the heliostat under nighttime or cloudy conditions. This avoids the dependence on sunlight in traditional methods and significantly improves calibration efficiency and accuracy. In the implementation process, the UAV equipment must first be configured and calibrated. Then, data acquisition is performed according to the planned flight path. Finally, precise calibration of the heliostat's attitude is achieved through image analysis and deviation calculation.
[0016] During the drone equipment configuration phase, an RTK positioning module is first integrated onto the drone. This module has a planar positioning accuracy of ±5cm and an elevation positioning accuracy of ±5cm, ensuring accurate acquisition of the drone's hovering point coordinates. Simultaneously, a 20-megapixel gimbal camera is installed on the drone to capture the position of the center of the heliostat's reflected light spot and its geometric center. To facilitate subsequent image processing, high-brightness red LED markers with a wavelength range of 620-750nm and a nighttime visibility distance of ≥500m are installed on the heliostat surface. In actual operation, the drone's hovering point positions are pre-planned based on the actual layout of the heliostat field and the adjustment range of the heliostat's normal direction. These hovering points must cover the entire heliostat field and be evenly distributed spherically, with an angular spacing of ≤10° between adjacent hovering points. Furthermore, the height of each hovering point must be set to be at least five times higher than the heliostat's installation height to avoid the impact of field-of-view distortion on imaging accuracy. After the drone reaches the designated hovering point, its position is dynamically adjusted using a PID algorithm to ensure that the center of the heliostat's reflected light spot coincides with the LED marker light, with an error tolerance range of ±3 pixels. At this point, the drone's coordinates are the endpoint of the theoretical normal direction vector.
[0017] In the image analysis and deviation calculation stage, the SIFT feature matching algorithm in OpenCV or other algorithms are first used to identify the corner points of the heliostat's frame, and a local coordinate system for the mirror surface is established based on these corner points. Further, the Hough circle detection algorithm or other algorithms are used to locate the center of the reflected light spot, and its pixel offset from the geometric center of the mirror surface is calculated. Specifically, the pixel offset is converted into an actual angle value by combining the camera focal length and pixel size. Specifically, assuming the camera focal length is... The pixel size is and Then the horizontal and vertical angular offsets corresponding to the pixel offsets are respectively and ,in and These represent the horizontal and vertical components of the pixel offset, respectively. Using the above formula, the pixel offset can be converted into an actual angular offset value, and then the deviation between the endpoint of the actual normal direction vector and the endpoint of the theoretical normal direction vector can be calculated.
[0018] Furthermore, the deviation between the actual normal direction and the theoretical normal direction is calculated using the following formula:
[0019] In the above formula, and These represent the deviations of the azimuth and elevation angles, respectively. and These represent the theoretical azimuth and elevation angles, respectively. , , These represent the three-dimensional deviation components between the UAV coordinates and the endpoint of the theoretical normal direction vector. This represents the actual measured unit vector magnitude. Through the above steps, the deviation of the normal direction of the heliostat at the current hovering point can be accurately calculated.
[0020] During the regression analysis and parameter calibration phase, the UAV hovered over multiple points in the field of view, repeating the data acquisition and deviation calculation process described above to obtain deviation data from multiple measurement points. Assume a total of N measurement points were collected ( , The least squares optimization problem is then constructed as follows:
[0021] In the above formula, Represents the Jacobian matrix. Indicates the angle of inclination of the column. This represents the initial installation angle deviation. By solving the above optimization problem, the tilt angle of the heliostat's pillar and the initial installation angle deviation can be separated. Furthermore, the goodness of fit is evaluated through residual analysis. When R... 2 A value > 0.9 indicates that the fitting result meets the accuracy requirements. Specifically, the corrected parameters are input into the heliostat control system to achieve automatic compensation.
[0022] In practical applications, the technical solution of this invention reduces manual intervention and significantly improves calibration efficiency through autonomous drone flight and image processing algorithms. For example, in a tower-type solar thermal power plant, there are 500 heliostats in the mirror field. The traditional calibration time for each heliostat is about 30 minutes. After adopting the method of this invention, the calibration time for a single heliostat is shortened to less than 5 minutes, and the overall calibration efficiency is improved by 6 times. In addition, through a high-precision RTK positioning module and image analysis algorithm, the azimuth and elevation angle calibration accuracy reaches ±0.03°, which is far higher than the ±0.1° accuracy of the traditional method. At the same time, since the method of this invention can be carried out at night or in cloudy conditions, it avoids occupying the daytime power generation time of the heliostats, thereby increasing the overall power generation of the power plant. According to calculations, after adopting the method of this invention, the annual power generation of the power plant will increase by about 2% (adding the working time of 100 heliostats per day; if there are a total of 5100 heliostats, the increase is 100 divided by 5000, which equals 2%).
[0023] In summary, this invention organically combines UAV flight path planning, image acquisition and analysis, deviation calculation, and parameter calibration to form a complete heliostat attitude deviation detection and calibration process. This process can not only adapt to complex and ever-changing field environments but also be efficiently implemented in large-scale heliostat fields, providing strong technical support for the operation and maintenance of tower solar thermal power plants.
[0024] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Those skilled in the art can readily implement the present invention based on the accompanying drawings and the above description. However, any modifications, alterations, or variations made by those skilled in the art without departing from the scope of the present invention, utilizing the disclosed technical content, are equivalent embodiments of the present invention. Furthermore, any modifications, alterations, or variations made to the above embodiments based on the essential technology of the present invention are still within the protection scope of the present invention.
Claims
1. A method for calibrating a tower-type photothermal heliostat based on UAV aerial surveying and image analysis, characterized in that, Includes the following steps: S1. In the drone calibration and data acquisition stage, an RTK positioning module and a high-resolution gimbal camera are integrated on the drone. High-brightness red or other color LED markers that can be clearly distinguished from the background under low exposure are installed on the surface of the heliostat. The drone hovering point is planned and set at a height that is more than five times higher than the installation height of the heliostat. The higher the height, the higher the calculation accuracy. The deviation value between the center of the heliostat and the image of the LED marker in the mirror is obtained by taking pictures. S2. In the image analysis and deviation calculation stage, the SIFT feature matching algorithm or other algorithms are used to identify the corner points of the heliostat frame and establish the local coordinate system of the mirror. The Hough circle detection algorithm or other algorithms are used to locate the center of the reflected light spot and calculate its pixel offset from the geometric center of the mirror. The pixel offset is converted into the actual angle value by combining the camera focal length and pixel size. The deviation between the actual normal direction and the theoretical normal direction is calculated by formula. S3. In the regression analysis and parameter calibration stage, deviation data from multiple measurement points are collected and a least-squares optimization problem is constructed to separate the deviations of the column tilt angle and the initial installation angle, and the corrected parameters are input into the heliostat control system.
2. The tower-type photothermal heliostat calibration method as described in claim 1, characterized in that, The RTK positioning module has a planar positioning accuracy of ±5cm and an elevation positioning accuracy of ±5cm.
3. The tower-type photothermal heliostat calibration method as described in claim 2, characterized in that, The high-resolution gimbal camera has a resolution of 20 megapixels.
4. The tower-type photothermal heliostat calibration method as described in claim 3, characterized in that, The high-brightness red LED marker light has a wavelength range of 620-750nm and a nighttime visibility distance of ≥500m.
5. The tower-type photothermal heliostat calibration method as described in claim 4, characterized in that, The hovering points of the UAV are evenly distributed according to the position of the sphere or the projection onto the sphere, and the angular distance between adjacent hovering points is ≤10°. The UAV can be at the same height or at different heights, as long as the angle between the UAV and the line connecting the UAV and the heliostat being measured is greater than 10 degrees.
6. The tower-type photothermal heliostat calibration method as described in claim 5, characterized in that, The SIFT feature matching algorithm is used to identify the corner points of the heliostat frame and establish a local coordinate system for the mirror surface.
7. The tower-type photothermal heliostat calibration method as described in claim 6, characterized in that, The Hough circle detection algorithm locates the center of the reflected light spot and calculates its pixel offset from the geometric center of the mirror.
8. The tower-type photothermal heliostat calibration method as described in claim 7, characterized in that, In the least squares optimization problem, when the goodness of fit R... 2 A value greater than 0.9 indicates that the fitting result meets the accuracy requirements.