A method for detecting axial angle deviation of photovoltaic tracking bracket

Abstract

The present invention discloses a method for detecting an axial angle deviation of a photovoltaic tracking bracket, which relates to the field of solar photovoltaic technology, and comprises the following steps: deploying a plurality of sensors, collecting photovoltaic tracking bracket data in real time, transmitting the data to a central processing unit, performing time synchronization, fusing the photovoltaic tracking bracket data, obtaining dynamic features and static features, performing deviation identification based on the dynamic features and the static features using a deviation identification function, calculating a deviation angle and a deviation direction based on the identified deviation using a deviation calculation function, comparing the deviation angle with an angle threshold, and controlling the photovoltaic tracking bracket to perform automatic adjustment in combination with the deviation direction, thereby realizing intelligent control of the photovoltaic tracking bracket, ensuring that the system can still maintain an optimal tracking state under complex environmental conditions, and significantly improving energy conversion efficiency and economy.

Description

Technical Field

[0001] The invention relates to the technical field of solar photovoltaics, and in particular to a method for detecting an axial angle deviation of a photovoltaic tracking bracket. Background Art

[0002] In the field of modern renewable energy, photovoltaic tracking systems have become a research hotspot. Early photovoltaic tracking technology mainly relied on a single sensor and performed poorly in complex environments such as cloud cover and sudden weather changes. Although photovoltaic tracking bracket technology has made significant progress in recent years, it still faces several challenges. On the one hand, the response speed and accuracy of traditional tracking systems when dealing with complex environmental factors still need to be improved. When encountering rapid weather changes, the system may not be able to adjust in time, resulting in energy loss. On the other hand, existing technologies are usually rough in detecting axial angle deviations and lack sensitivity to subtle changes in deviations, especially in low light or rainy weather. The system may reduce power generation efficiency due to failure to accurately align with the sun. In addition, most tracking systems rely on preset programs for angle adjustment strategies and lack the ability to make intelligent dynamic adjustments based on real-time environmental conditions. Summary of the invention

[0003] In view of the above existing problems, the present invention is proposed.

[0004] Therefore, the present invention provides a method for detecting the axial angle deviation of a photovoltaic tracking bracket, which solves the problem of inaccurate detection and slow response of the axial angle deviation of the photovoltaic tracking bracket.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0006] In a first aspect, an embodiment of the present invention provides a method for detecting an axial angle deviation of a photovoltaic tracking bracket, which includes deploying a variety of sensors to collect photovoltaic tracking bracket data in real time, transmitting the data to a central processing unit, and performing time synchronization;

[0007] The photovoltaic tracking bracket data is fused to obtain dynamic and static features;

[0008] Based on dynamic features and static features, deviation identification is performed using deviation identification functions;

[0009] Based on the identified deviation, the deviation angle and deviation direction are calculated using a deviation calculation function;

[0010] The deviation angle is compared with the angle threshold, and combined with the deviation direction, the photovoltaic tracking bracket is controlled to perform automatic adjustment.

[0011] As a preferred solution of the method for detecting the axial angle deviation of the photovoltaic tracking bracket of the present invention, the multiple sensors include GPS, magnetometer, gyroscope and temperature sensor;

[0012] The photovoltaic tracking bracket data includes geographical location, magnetic field strength, angular velocity and temperature.

[0013] As a preferred solution of the method for detecting the axial angle deviation of the photovoltaic tracking bracket of the present invention, the photovoltaic tracking bracket data is collected in real time and transmitted to the central processing unit for time synchronization, including the following steps:

[0014] Calibrate the time of the photovoltaic tracking bracket data to the same time source, collect data once per second, and use a timestamp to mark the precise time point of each data collection;

[0015] After data collection is completed, the data packets with time stamps are transmitted to the central processing unit through wireless networks and wired networks for time synchronization.

[0016] As a preferred solution of the method for detecting the axial angle deviation of the photovoltaic tracking bracket of the present invention, the photovoltaic tracking bracket data is fused, including the following steps:

[0017] The Kalman filter algorithm is used in combination with the composite data fusion function to fuse the photovoltaic tracking bracket data. The expression is:

[0018] ;

[0019] Among them, F(t) represents the value of data fusion, Z represents the normalization constant, represents the integral of the weighted historical data of the geographic location G(u) before time t, G(u) represents the geographic location data at time u, λ represents the time attenuation factor, α represents the weighting coefficient of the magnetic field intensity, β represents the weighting coefficient of the angular velocity, γ represents the weighting coefficient of the temperature, G(t) represents the geographic location at time t, M(t) represents the magnetic field intensity at time t, W(t) represents the angular velocity at time t, and T(t) represents the temperature data at time t.

[0020] As a preferred solution of the method for detecting the axial angle deviation of the photovoltaic tracking bracket of the present invention, the method comprises the following steps: obtaining dynamic features and static features based on the fused photovoltaic tracking bracket data:

[0021] The polynomial derivatives are used to extract dynamic features from the fused photovoltaic tracking bracket data. The expression is:

[0022] ;

[0023] Among them, D(t) represents the dynamic characteristics, N represents the highest order derivative, and w i represents the weight of the i-th order derivative, represents the i-th order derivative of the fused data F(t);

[0024] The exponential moving average method is used to smooth the fused photovoltaic tracking bracket data and extract static features. The expression is:

[0025] ;

[0026] Among them, S(t) represents the static characteristics, k represents the smoothing coefficient, exp(-kt) represents the exponential decay function, represents the integral from the infinite past to the current time t, exp(ku)F(u) represents the weighted value of F(u), the weight changes exponentially with time u, F(u) represents the value of composite data fusion F(t) at any time point u, u represents any value from a certain moment in the past until the current time point t, and du represents the integral differential element.

[0027] As a preferred solution of the method for detecting the axial angle deviation of the photovoltaic tracking bracket of the present invention, the deviation identification is performed based on the dynamic characteristics and the static characteristics using the deviation identification function, including the following steps:

[0028] The composite feature function is used to integrate the dynamic features and static features. The expression is:

[0029] ;

[0030] Among them, H(t) represents the value of the composite feature, Represents the nonlinear transformation of dynamic characteristics, represents a nonlinear transformation of static features, A positive real number parameter representing the degree of influence of adjusting static characteristics;

[0031] Based on the integrated feature values, the deviation identification threshold is set, and the deviation identification function is used to perform deviation identification;

[0032] When the integrated eigenvalue is greater than the deviation recognition threshold, it means that the deviation recognition function recognizes the deviation, and the photovoltaic tracking bracket is in an abnormal working state, and immediate measures must be taken to adjust it. When the integrated eigenvalue is less than or equal to the deviation recognition threshold, it means that the deviation recognition function does not recognize the deviation, and the photovoltaic tracking bracket operates stably.

[0033] As a preferred solution of the method for detecting the axial angle deviation of the photovoltaic tracking bracket of the present invention, the method comprises the following steps: calculating the deviation angle and the deviation direction based on the identified deviation:

[0034] The deviation angle calculation function is used to calculate the deviation angle of the identified deviation. The expression is:

[0035] ;

[0036] Where A(t) represents the deviation angle, ars represents the arc cosine function, P(t) represents the actual position vector of the photovoltaic tracking bracket at time t, and P d represents the target position vector of the photovoltaic tracking bracket, and R(t) represents the value of the deviation identification function;

[0037] The deviation direction calculation function is used to calculate the deviation direction of the identified deviation. The expression is:

[0038] ;

[0039] Where C(t) represents the deviation direction.

[0040] As a preferred solution of the method for detecting the axial angle deviation of the photovoltaic tracking bracket of the present invention, the deviation angle is compared with the angle threshold, and the photovoltaic tracking bracket is controlled to automatically adjust in combination with the deviation direction, including the following steps:

[0041] Based on the deviation angle at time t and combined with the historical behavior of the photovoltaic tracking bracket, a dynamic correction function is used to correct the deviation angle. The expression is:

[0042] ;

[0043] Among them, A d (t) represents the deviation angle value after the historical data trend and periodic pattern correction at time t, p represents the order of the autoregressive term, q represents the order of the moving average term, represents the autoregressive coefficient, where i is the order index of the autoregressive term, θ j denotes the moving average coefficient, where j is the order index of the moving average term, ε(tj) represents the difference from time t to j, and u represents the value of the deviation angle A(u) at the historical time point u;

[0044] According to the light intensity, temperature and the health of the photovoltaic tracking bracket, a dynamic adjustment function is used to dynamically adjust the angle threshold. The expression is:

[0045] ;

[0046] Among them, A th(t) represents the angle deviation threshold adjusted according to light intensity, temperature and health status of photovoltaic tracking bracket at time t, L(t) represents the light intensity at time t, G(t) represents the temperature at time t, Z(t) represents the health status of photovoltaic tracking bracket at time t, α, β and γ represent the coefficients of light intensity, temperature and health status on the angle threshold respectively;

[0047] Based on the corrected deviation angle and the adjusted angle threshold, a comparison function is used to determine whether the photovoltaic tracking bracket needs to be adjusted at time t. The expression is:

[0048] ;

[0049] Among them, C d (t) represents the comparison function between the deviation angle and the dynamic adjustment angle threshold. At time t, it is determined whether the deviation angle exceeds the threshold range. 2 It represents a preset small variance and measures uncertainty;

[0050] When the corrected deviation angle is less than the adjusted angle threshold, it means that the deviation angle is within the dynamically adjusted angle threshold range, and the photovoltaic tracking bracket is in normal working state and does not need to be adjusted;

[0051] When the corrected deviation angle is greater than or equal to the adjusted angle threshold, it means that the deviation angle exceeds the dynamically adjusted angle threshold, triggering the automatic adjustment mechanism, and using the motor to control the photovoltaic tracking bracket to adjust to the optimal tracking state.

[0052] In a second aspect, an embodiment of the present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program is executed by the processor, any step of the method for detecting the axial angle deviation of the photovoltaic tracking bracket as described in the first aspect of the present invention is implemented.

[0053] In a third aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, any step of the method for detecting the axial angle deviation of the photovoltaic tracking bracket as described in the first aspect of the present invention is implemented.

[0054] The beneficial effects of the present invention are as follows: by integrating GPS, magnetometer, gyroscope and temperature sensor, multi-dimensional data of the photovoltaic tracking bracket is collected and synchronized to the central processing unit in real time, ensuring the timeliness and consistency of the information, laying the foundation for precise control, using Kalman filtering and composite data fusion, combining dynamic features and static feature extraction, enhancing the system's perception and understanding of dynamic changes in the environment, and improving the sensitivity of deviation identification. With the help of deviation identification and calculation functions, the deviation angle and direction are accurately determined, which promotes the efficient operation of the automatic adjustment mechanism. Finally, the angle threshold is dynamically adjusted and combined with the deviation direction to realize intelligent control of the photovoltaic tracking bracket, ensuring that the system can still maintain the best tracking state under complex environmental conditions, and significantly improving energy conversion efficiency and economy. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0056] Figure 1 This is a flow chart of the method for detecting the axial angle deviation of the photovoltaic tracking bracket in Example 1.

[0057] Figure 2 This is a diagram of the calculation process for identifying deviations in Example 1. DETAILED DESCRIPTION

[0058] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0059] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0060] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0061] Example 1, reference Figure 1 and Figure 2, which is the first embodiment of the present invention, provides a method for detecting the axial angle deviation of a photovoltaic tracking bracket, comprising the following steps:

[0062] S1. Deploy a variety of sensors to collect photovoltaic tracking bracket data in real time.

[0063] S1.1. The multiple sensors include GPS, magnetometer, gyroscope, and temperature sensor;

[0064] The photovoltaic tracking bracket data includes geographical location, magnetic field strength, angular velocity and temperature.

[0065] Specifically, choose the highest point of the photovoltaic tracking bracket and install the GPS sensor vertically to ensure that the sensor can receive satellite signals from the sky without obstruction. The installation height should be at least 30 cm higher than the edge of the photovoltaic panel to avoid interference with the signal by the panel itself.

[0066] The magnetometer and gyroscope are installed at the geometric center of the photovoltaic tracking bracket. Before installation, the installation platform needs to be calibrated with a level ruler to ensure that the platform is completely level. A small, stable metal or composite material platform is installed at the center of the bracket as the installation base of the magnetometer and gyroscope. The platform should be designed with appropriate shock-absorbing measures, such as rubber pads or springs, to reduce the impact of external vibration on sensor readings;

[0067] The temperature sensor is installed in the shaded area of ​​the photovoltaic tracking bracket to avoid direct sunlight to ensure that the measured temperature can represent the actual ambient temperature around the bracket. It can also be installed on the surface of the bracket, but thermal paste is required to increase thermal contact. At the same time, a thin layer of insulation material is placed between the sensor and the bracket to prevent the temperature of the bracket itself from affecting the sensor reading.

[0068] S2. Collect photovoltaic tracking bracket data in real time and transmit it to the central processing unit for time synchronization.

[0069] S2.1. Calibrate the time of the photovoltaic tracking bracket data to the same time source, collect data once per second, and use a timestamp to mark the exact time point of each data collection;

[0070] After data collection is completed, the data packets with time stamps are transmitted to the central processing unit through wireless networks and wired networks for time synchronization;

[0071] Specifically, based on the timestamp data of the photovoltaic tracking bracket data read by the central processing unit, the weighted average method is used to perform time synchronization, and the expression is:

[0072] ;

[0073] in, represents the weighted average timestamp, Qi represents the quality factor associated with the i-th data collection, Ti represents the timestamp of the i-th data collection, and n represents the total number of sampling times.

[0074] Furthermore, for GPS sensors, the quality factor can be determined based on the number of satellites received and the quality of the signal. For magnetometers and gyroscopes, the quality factor depends on the stability of their readings. The quality factor of a temperature sensor may depend on its calibration results with a standard temperature source. Generally speaking, the quality factor is set in the range of [0,1], where 1 indicates the most reliable data.

[0075] Furthermore, each time a sensor collects data, the system clock generates a timestamp to accurately record the moment of data collection. All timestamps should refer to the same time source, usually GPS time, to ensure the temporal consistency of data from all sensors and assign a unique serial number to each data packet. The receiving end confirms the order and integrity of the data packet by checking the continuity of the serial number to avoid data packet disorder or loss.

[0076] S3. Fuse the photovoltaic tracking bracket data.

[0077] S3.1. Use the Kalman filter algorithm and the composite data fusion function to fuse the photovoltaic tracking bracket data. The expression is:

[0078] ;

[0079] Among them, F(t) represents the value of data fusion, Z represents the normalization constant, represents the integral of the weighted historical data of the geographic location G(u) before time t, G(u) represents the geographic location data at time u, λ represents the time attenuation factor, α represents the weighting coefficient of the magnetic field intensity, β represents the weighting coefficient of the angular velocity, γ represents the weighting coefficient of the temperature, G(t) represents the geographic location at time t, M(t) represents the magnetic field intensity at time t, W(t) represents the angular velocity at time t, and T(t) represents the temperature data at time t.

[0080] Specifically, the weighting coefficients α, β, and γ control the adaptive update of process noise, the adaptive update of observation noise, and the update rule of the covariance matrix, respectively. The specific values ​​need to be adjusted according to the experimental results.

[0081] S4. Based on the fused photovoltaic tracking bracket data, dynamic features and static features are obtained.

[0082] S4.1. Using the polynomial derivative, the dynamic features of the fused photovoltaic tracking bracket data are extracted. The expression is:

[0083] ;

[0084] Among them, D(t) represents the dynamic characteristics, N represents the highest order derivative, and w i represents the weight of the i-th order derivative, represents the i-th order derivative of the fused data F(t);

[0085] The exponential moving average method is used to smooth the fused photovoltaic tracking bracket data and extract static features. The expression is:

[0086] ;

[0087] Among them, S(t) represents the static characteristics, k represents the smoothing coefficient, exp(-kt) represents the exponential decay function, represents the integral from the infinite past to the current time t, exp(ku)F(u) represents the weighted value of F(u), the weight changes exponentially with time u, F(u) represents the value of composite data fusion F(t) at any time point u, u represents any value from a certain moment in the past until the current time point t, and du represents the integral differential element.

[0088] Specifically, considering the dynamic characteristics of the photovoltaic tracking bracket, N should usually be set to 2 or 3, corresponding to the velocity and acceleration or higher-order acceleration change rate, respectively. Secondly, the high-order derivative is very sensitive to noise, so when selecting N, it is necessary to balance the capture of dynamic characteristics and the need for noise suppression. For most photovoltaic tracking bracket applications, N=2 is initially set, that is, acceleration is calculated as a dynamic feature to capture the fast-changing dynamic response;

[0089] Based on the physical characteristics of the photovoltaic tracking bracket, such as response speed, inertia effect, etc., the importance of different order derivatives is adjusted, and the weight is adjusted according to the spectral characteristics of the signal. High-frequency components (high-order derivatives) may require smaller weights to reduce the impact of noise, while low-frequency components (low-order derivatives) may require larger weights to capture slowly changing dynamics. The initial setting can use equal weights w i =1 / N, fine-tuning is performed based on experimental results. For example, if it is found that the system does not respond sufficiently to rapid changes, the weight of acceleration (second-order derivative) can be appropriately increased;

[0090] For the setting of the smoothing coefficient k, if the data has obvious periodicity, k should be selected to be small enough to retain the periodic characteristics, but large enough to remove noise. If the data change rate is large, k should be selected to be large to avoid feature loss due to over-smoothing. The initial setting k=0.1 or 0.2 is usually sufficient to remove most of the noise while retaining the main trend of the signal. It can be adjusted according to the experimental results. If it is found that the smoothed signal is too smooth or not smooth enough, k can be reduced or increased respectively.

[0091] S5. Based on the dynamic features and static features, deviation identification is performed using a deviation identification function.

[0092] S5.1. Use composite feature function to integrate dynamic features and static features. The expression is:

[0093] ;

[0094] Among them, H(t) represents the value of the composite feature, Represents the nonlinear transformation of dynamic characteristics, represents a nonlinear transformation of static features, A positive real number parameter representing the degree of influence of adjusting static characteristics;

[0095] Based on the integrated feature values, the deviation identification threshold is set, and the deviation identification function is used to perform deviation identification;

[0096] When the integrated eigenvalue is greater than the deviation recognition threshold, it means that the deviation recognition function recognizes the deviation, and the photovoltaic tracking bracket is in an abnormal working state, and immediate measures must be taken to adjust it. When the integrated eigenvalue is less than or equal to the deviation recognition threshold, it means that the deviation recognition function does not recognize the deviation, and the photovoltaic tracking bracket operates stably.

[0097] Specifically, under typical environmental conditions, the normal tracking state and deviation state of the photovoltaic tracking bracket are determined through experiments, and the threshold range is preliminarily set according to the physical characteristics of the photovoltaic tracking bracket in combination with the experience of field experts;

[0098] When the light is strong, the tracking efficiency of the photovoltaic panel is higher. At this time, the threshold can be set slightly lower to detect deviations more sensitively. When the light is weak, the threshold can be set slightly higher to reduce false alarms. Temperature changes may affect the accuracy of the sensor. When the temperature is high or low, the threshold should be adjusted appropriately to compensate for the impact of temperature on the sensor readings. Regularly evaluate the health status of the photovoltaic tracking bracket, such as the accuracy of the sensor, the degree of mechanical wear of the bracket, etc., and dynamically adjust the threshold according to the health status to ensure that the system can effectively identify deviations under different health conditions.

[0099] S6. Calculate the deviation angle and deviation direction based on the identified deviation.

[0100] S6.1. Use the deviation angle calculation function to calculate the deviation angle for the identified deviation. The expression is:

[0101] ;

[0102] Where A(t) represents the deviation angle, ars represents the arc cosine function, P(t) represents the actual position vector of the photovoltaic tracking bracket at time t, and Pd represents the target position vector of the photovoltaic tracking bracket, and R(t) represents the value of the deviation identification function;

[0103] The deviation direction calculation function is used to calculate the deviation direction of the identified deviation. The expression is:

[0104] ;

[0105] Where C(t) represents the deviation direction.

[0106] Specifically, the actual position vector P(t) is usually used to establish a local coordinate system based on the installation position and orientation of the photovoltaic tracking bracket. The origin of this coordinate system can be selected at the geometric center of the bracket, with the X-axis pointing to the front of the photovoltaic panel, the Y-axis pointing to the side of the photovoltaic panel, and the Z-axis perpendicular to the surface of the photovoltaic panel. The local coordinate system is calibrated to ensure that the conversion relationship between it and the earth coordinate system (such as WGS84) is accurate.

[0107] Use GPS sensors to determine the geographic location of the photovoltaic tracking bracket, including latitude, longitude and altitude. Combined with magnetometer and gyroscope data, calculate the azimuth and tilt angle of the photovoltaic panel relative to the local coordinate system, convert the azimuth and tilt angle into vector representation in the local coordinate system, and obtain the actual position vector;

[0108] Target position vector P d The coordinate system should be defined in the same coordinate system to indicate the sun position that the photovoltaic panel should theoretically face. The astronomical algorithm or solar tracking model is used to calculate the sun's position according to the current time and geographic location, including the sun's azimuth and sun's altitude. The sun's azimuth and sun's altitude are converted into vector representations in the local coordinate system to obtain the target position vector.

[0109] Furthermore, check whether the calculated deviation angle and direction are reasonable and consistent with intuitive observation, and adjust the definition of the local coordinate system or the processing of sensor data to optimize the results.

[0110] S7, comparing the deviation angle with the angle threshold, and controlling the photovoltaic tracking bracket to automatically adjust in combination with the deviation direction.

[0111] S7.1. Based on the deviation angle at time t and combined with the historical behavior of the photovoltaic tracking bracket, a dynamic correction function is used to correct the deviation angle. The expression is:

[0112] ;

[0113] Among them, A d (t) represents the deviation angle value after the historical data trend and periodic pattern correction at time t, p represents the order of the autoregressive term, q represents the order of the moving average term, represents the autoregressive coefficient, where i is the order index of the autoregressive term, θ j denotes the moving average coefficient, where j is the order index of the moving average term, ε(tj) represents the difference from time t to j, and u represents the value of the deviation angle A(u) at the historical time point u;

[0114] According to the light intensity, temperature and the health of the photovoltaic tracking bracket, a dynamic adjustment function is used to dynamically adjust the angle threshold. The expression is:

[0115] ;

[0116] Among them, A th (t) represents the angle deviation threshold adjusted according to light intensity, temperature and health status of photovoltaic tracking bracket at time t, L(t) represents the light intensity at time t, G(t) represents the temperature at time t, Z(t) represents the health status of photovoltaic tracking bracket at time t, α, β and γ represent the coefficients of light intensity, temperature and health status on the angle threshold respectively;

[0117] Based on the corrected deviation angle and the adjusted angle threshold, a comparison function is used to determine whether the photovoltaic tracking bracket needs to be adjusted at time t. The expression is:

[0118] ;

[0119] Among them, C d (t) represents the comparison function between the deviation angle and the dynamic adjustment angle threshold. At time t, it is determined whether the deviation angle exceeds the threshold range. 2 It represents a preset small variance and measures uncertainty;

[0120] When the corrected deviation angle is less than the adjusted angle threshold, it means that the deviation angle is within the dynamically adjusted angle threshold range, and the photovoltaic tracking bracket is in normal working state and does not need to be adjusted;

[0121] When the corrected deviation angle is greater than or equal to the adjusted angle threshold, it means that the deviation angle exceeds the dynamically adjusted angle threshold, triggering the automatic adjustment mechanism, and using the motor to control the photovoltaic tracking bracket to adjust to the optimal tracking state.

[0122] Specifically, the order p of the autoregressive term and the order q of the moving average term are determined. The historical tracking data of the photovoltaic tracking bracket under different environmental conditions, including deviation angle, light intensity, temperature and health status, are collected. The deviation angle data is modeled using the autoregressive moving average model (ARMA model). The values ​​of p and q are preliminarily determined by analyzing the autocorrelation function (ACF) and partial autocorrelation function (PACF) graphs. The values ​​of p and q are further optimized by minimizing the residual sum of squares or information criteria (such as AIC, BIC), and the model that best fits the historical data is selected.

[0123] Using the selected order p and q, the optimization algorithm estimates and θ j The value of makes the error between the model's predicted deviation angle and the actual deviation angle minimum, and the prediction performance adjustment of the model is tested by retaining a part of the historical data as a validation set. and θ j Until the model performs best;

[0124] According to the dynamic characteristics of the system and experimental observations, the value of λ is initially set. If the system responds quickly, λ can be set larger. By observing the effect of the dynamic correction function, the value of λ is gradually adjusted until the optimal balance point is found, which neither relies too much on historical data nor ignores its influence;

[0125] Furthermore, the coefficients of the influence of light intensity, temperature and health status on the angle threshold are determined, a series of experiments are designed to change the light intensity, temperature and health status of the photovoltaic tracking bracket, the deviation angle and tracking efficiency are recorded, and the values ​​of α, β and γ are learned from the experimental data through multivariate linear regression analysis or machine learning methods to ensure that the angle threshold can be dynamically adjusted according to environmental changes to maintain the best tracking performance.

[0126] This embodiment also provides a computer device, which is suitable for the case of a method for detecting the axial angle deviation of a photovoltaic tracking bracket, comprising: a memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement the method for detecting the axial angle deviation of the photovoltaic tracking bracket proposed in the above embodiment.

[0127] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covered on the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0128] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, the method for detecting the axial angle deviation of the photovoltaic tracking bracket proposed in the above embodiment is implemented; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (Static Random Access Memory, referred to as SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, referred to as EEPROM), erasable programmable read-only memory (Erasable Programmable Read Only Memory, referred to as EPROM), programmable read-only memory (Programmable Red-Only Memory, referred to as PROM), read-only memory (Read-Only Memory, referred to as ROM), magnetic storage, flash memory, magnetic disk or optical disk.

[0129] In summary, by integrating GPS, magnetometer, gyroscope and temperature sensor, multi-dimensional data of photovoltaic tracking bracket is collected and synchronized to the central processing unit in real time, which ensures the timeliness and consistency of information and lays the foundation for precise control. Kalman filtering and composite data fusion are used, combined with dynamic feature and static feature extraction to enhance the system's perception and understanding of dynamic changes in the environment and improve the sensitivity of deviation identification. With the help of deviation identification and calculation functions, the deviation angle and direction are accurately determined, which promotes the efficient operation of the automatic adjustment mechanism. Finally, the angle threshold is dynamically adjusted and combined with the deviation direction to realize intelligent control of photovoltaic tracking bracket, ensuring that the system can still maintain the best tracking state under complex environmental conditions, significantly improving energy conversion efficiency and economy. Example 2

[0130] Referring to Table 1, which is the second embodiment of the present invention, in order to further verify the advancement of the present invention, experimental simulation data of a method for detecting the axial angle deviation of a photovoltaic tracking bracket are provided.

[0131] A photovoltaic power station located in a desert area in China was selected as the experimental site. The power station was equipped with two sets of photovoltaic tracking brackets, namely traditional fixed-tilt photovoltaic panels and the intelligent photovoltaic tracking system described in the present invention. A comparative test was carried out for 30 consecutive days, covering different weather conditions such as sunny, cloudy, and rainy, to comprehensively evaluate the performance of the system.

[0132] First, the intelligent photovoltaic tracking system is equipped with GPS, magnetometer, gyroscope and temperature sensor to collect the geographic location, magnetic field strength, angular velocity and temperature data of the photovoltaic tracking bracket in real time, and transmit it to the central processing unit through the wireless network once per second for time synchronization;

[0133] Secondly, in the data fusion stage, the Kalman filter algorithm and the composite data fusion function are used to combine dynamic and static feature extraction to enhance the system's ability to perceive environmental changes;

[0134] Next, the intelligent photovoltaic tracking system detects the axial angle deviation through the deviation identification function and calculates the deviation angle and deviation direction using the deviation calculation function;

[0135] Finally, the system compares the dynamically adjusted angle threshold with the deviation angle, and intelligently controls the photovoltaic tracking bracket to automatically adjust according to the deviation direction to ensure that the photovoltaic panel is always aligned with the sun and improve the energy conversion efficiency, as shown in Table 1:

[0136] Table 1 Experimental record table

[0137]

[0138] As can be seen from the table above, compared with traditional fixed tilt photovoltaic panels, the axial deviation angle of the intelligent photovoltaic tracking system is significantly reduced under different light intensity and temperature conditions, from 5.3 to 0.5°, which shows that the intelligent system can more accurately adjust the direction of the photovoltaic panel to adapt to changes in the position of the sun. In terms of tracking efficiency, the intelligent system performed better than the traditional system under all test conditions, up to 93.4%, which is about 11.3% higher than the traditional system. In addition, the tracking response time of the intelligent system is also significantly shortened, from 120 seconds to 25 seconds, reflecting the system's ability to quickly adapt to environmental changes.

[0139] Through comparative analysis, the intelligent photovoltaic tracking system achieves high-precision detection and rapid response to axial angle deviation through multi-sensor fusion and complex algorithms, significantly reduces the deviation angle and improves tracking accuracy. The tracking efficiency of the intelligent system far exceeds that of the traditional system. Even in low light or changing weather conditions, it can maintain a high energy conversion efficiency, proving its adaptability and stability in complex environments. The intelligent system can quickly adjust the direction of the photovoltaic panel, reduce tracking delays, ensure that the photovoltaic panel is always in the best light reception state, and further improve energy utilization.

[0140] In summary, the intelligent photovoltaic tracking system, through advanced data acquisition, fusion and deviation detection technologies combined with a dynamic adjustment mechanism, not only solves the tracking accuracy and response speed problems of traditional systems in complex environments, but also greatly improves the energy conversion efficiency of the photovoltaic system, demonstrating its significant innovation and practical value in the field of photovoltaic tracking technology.

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

Claims

1. A method for detecting axial angle deviation of a photovoltaic tracking bracket. Features: include, Deploy a variety of sensors to collect photovoltaic tracking bracket data in real time and transmit it to the central processing unit for time synchronization; The photovoltaic tracking bracket data is fused to obtain dynamic and static features; Based on dynamic features and static features, deviation identification is performed using deviation identification functions; Based on the identified deviation, the deviation angle and deviation direction are calculated using a deviation calculation function; Compare the deviation angle with the angle threshold, and combine the deviation direction to control the photovoltaic tracking bracket to automatically adjust; The multiple sensors include GPS, magnetometer, gyroscope, and temperature sensor; The photovoltaic tracking bracket data includes geographical location, magnetic field strength, angular velocity and temperature; Real-time collection of photovoltaic tracking bracket data, transmission to the central processing unit, and time synchronization include the following steps: Calibrate the time of the photovoltaic tracking bracket data to the same time source, collect data once per second, and use a timestamp to mark the precise time point of each data collection; After data collection is completed, the data packets with time stamps are transmitted to the central processing unit through wireless networks and wired networks for time synchronization; The fusion of photovoltaic tracking bracket data includes the following steps: The Kalman filter algorithm is used in combination with the composite data fusion function to fuse the photovoltaic tracking bracket data. The expression is: ； Among them, F(t) represents the value of data fusion, Z represents the normalization constant, represents the integral of the weighted historical data of the geographic location G(u) before time t, G(u) represents the geographic location data at time u, λ represents the time attenuation factor, α represents the weighting coefficient of the magnetic field intensity, β represents the weighting coefficient of the angular velocity, γ represents the weighting coefficient of the temperature, G(t) represents the geographic location at time t, M(t) represents the magnetic field intensity at time t, W(t) represents the angular velocity at time t, and T(t) represents the temperature data at time t; Based on the fused photovoltaic tracking bracket data, dynamic features and static features are obtained, including the following steps: The polynomial derivatives are used to extract dynamic features from the fused photovoltaic tracking bracket data. The expression is: ； Among them, D(t) represents the dynamic characteristics, N represents the highest order derivative, and w i represents the weight of the i-th order derivative, represents the i-th order derivative of the fused data F(t); The exponential moving average method is used to smooth the fused photovoltaic tracking bracket data and extract static features. The expression is: ； Among them, S(t) represents the static characteristics, k represents the smoothing coefficient, exp(-kt) represents the exponential decay function, represents the integral from the infinite past to the current time t, exp(ku)F(u) represents the weighted value of F(u), the weight changes exponentially with time u, F(u) represents the value of composite data fusion F(t) at any time point u, u represents any value from a certain moment in the past until the current time point t, and du represents the integral differential element.

2. The method for detecting the axial angle deviation of the photovoltaic tracking bracket according to claim 1, Features: Based on dynamic features and static features, deviation identification is performed using a deviation identification function, including the following steps: The composite feature function is used to integrate the dynamic features and static features. The expression is: ； Among them, H(t) represents the value of the composite feature, Represents the nonlinear transformation of dynamic characteristics, represents a nonlinear transformation of static features, A positive real number parameter representing the degree of influence of adjusting static characteristics; Based on the integrated feature values, the deviation identification threshold is set, and the deviation identification function is used to perform deviation identification; When the integrated eigenvalue is greater than the deviation recognition threshold, it means that the deviation recognition function recognizes the deviation, and the photovoltaic tracking bracket is in an abnormal working state, and immediate measures must be taken to adjust it. When the integrated eigenvalue is less than or equal to the deviation recognition threshold, it means that the deviation recognition function does not recognize the deviation, and the photovoltaic tracking bracket operates stably.

3. The method for detecting the axial angle deviation of the photovoltaic tracking bracket according to claim 2, Features: Based on the identified deviation, the deviation angle and deviation direction are calculated, including the following steps: The deviation angle calculation function is used to calculate the deviation angle of the identified deviation. The expression is: ； Where A(t) represents the deviation angle, ars represents the arc cosine function, P(t) represents the actual position vector of the photovoltaic tracking bracket at time t, and P d represents the target position vector of the photovoltaic tracking bracket, and R(t) represents the value of the deviation identification function; The deviation direction calculation function is used to calculate the deviation direction of the identified deviation. The expression is: ； Where C(t) represents the deviation direction.

4. The method for detecting the axial angle deviation of the photovoltaic tracking bracket according to claim 3, Features: The deviation angle is compared with the angle threshold, and combined with the deviation direction, the photovoltaic tracking bracket is controlled to automatically adjust, including the following steps: Based on the deviation angle at time t and combined with the historical behavior of the photovoltaic tracking bracket, a dynamic correction function is used to correct the deviation angle. The expression is: ； Among them, A d (t) represents the deviation angle value after the historical data trend and periodic pattern correction at time t, p represents the order of the autoregressive term, q represents the order of the moving average term, represents the autoregressive coefficient, where i is the order index of the autoregressive term, θ j denotes the moving average coefficient, where j is the order index of the moving average term, ε(tj) represents the difference from time t to j, and u represents the value of the deviation angle A(u) at the historical time point u; According to the light intensity, temperature and the health of the photovoltaic tracking bracket, a dynamic adjustment function is used to dynamically adjust the angle threshold. The expression is: ； Among them, A th (t) represents the angle deviation threshold adjusted according to light intensity, temperature and health status of photovoltaic tracking bracket at time t, L(t) represents the light intensity at time t, G(t) represents the temperature at time t, Z(t) represents the health status of photovoltaic tracking bracket at time t, α, β and γ represent the coefficients of light intensity, temperature and health status on the angle threshold respectively; Based on the corrected deviation angle and the adjusted angle threshold, a comparison function is used to determine whether the photovoltaic tracking bracket needs to be adjusted at time t. The expression is: ； Among them, C d (t) represents the comparison function between the deviation angle and the dynamic adjustment angle threshold. At time t, it is determined whether the deviation angle exceeds the threshold range. 2 It represents a preset small variance and measures uncertainty; When the corrected deviation angle is less than the adjusted angle threshold, it means that the deviation angle is within the dynamically adjusted angle threshold range, and the photovoltaic tracking bracket is in normal working state and does not need to be adjusted; When the corrected deviation angle is greater than or equal to the adjusted angle threshold, it means that the deviation angle exceeds the dynamically adjusted angle threshold, triggering the automatic adjustment mechanism, and using the motor to control the photovoltaic tracking bracket to adjust to the optimal tracking state.

5. A computer device comprising a memory and a processor, wherein the memory stores a computer program, Features: When the processor executes the computer program, the steps of the method for detecting the axial angle deviation of the photovoltaic tracking bracket according to any one of claims 1 to 4 are implemented.

6. A computer-readable storage medium having a computer program stored thereon, Features: When the computer program is executed by a processor, the steps of the method for detecting the axial angle deviation of the photovoltaic tracking bracket according to any one of claims 1 to 4 are implemented.

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