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CN118882580B - A method for detecting axial angle deviation of photovoltaic tracking bracket - Google Patents

A method for detecting axial angle deviation of photovoltaic tracking bracket Download PDF

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Publication number
CN118882580B
CN118882580B CN202411047076.XA CN202411047076A CN118882580B CN 118882580 B CN118882580 B CN 118882580B CN 202411047076 A CN202411047076 A CN 202411047076A CN 118882580 B CN118882580 B CN 118882580B
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deviation
photovoltaic tracking
angle
tracking bracket
time
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CN118882580A (en
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王中华
沈福官
龚赟
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Suzhou Yimi New Energy Technology Co ltd
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Suzhou Yimi New Energy Technology Co ltd
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
    • G01B21/00—Measuring arrangements or details thereof, where the measuring technique is not covered by the other groups of this subclass, unspecified or not relevant
    • G01B21/22—Measuring arrangements or details thereof, where the measuring technique is not covered by the other groups of this subclass, unspecified or not relevant for measuring angles or tapers; for testing the alignment of axes
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E10/00—Energy generation through renewable energy sources
    • Y02E10/50—Photovoltaic [PV] energy

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  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Photovoltaic Devices (AREA)

Abstract

The invention discloses a method for detecting axial angle deviation of a photovoltaic tracking bracket, which relates to the technical field of solar energy photovoltaics and comprises the steps of deploying a plurality of sensors, collecting photovoltaic tracking bracket data in real time, transmitting the data to a central processing unit, synchronizing time, fusing the photovoltaic tracking bracket data to obtain dynamic characteristics and static characteristics, utilizing a deviation recognition function to recognize the deviation based on the dynamic characteristics and the static characteristics, utilizing a deviation calculation function to calculate a deviation angle and a deviation direction based on the recognized deviation, comparing the deviation angle with an angle threshold value, combining the deviation direction, controlling the photovoltaic tracking bracket to automatically adjust, realizing intelligent control of the photovoltaic tracking bracket, ensuring that a system can still maintain an optimal tracking state under complex environment conditions, and obviously improving energy conversion efficiency and economy.

Description

Method for detecting axial angle deviation of photovoltaic tracking bracket
Technical Field
The invention relates to the technical field of solar energy photovoltaics, in particular to a method for detecting axial angle deviation of a photovoltaic tracking bracket.
Background
In the field of modern renewable energy sources, photovoltaic tracking systems become research hotspots, early photovoltaic tracking technologies mainly rely on a single sensor, perform poorly in complex environments such as cloud cover and weather mutation, although the photovoltaic tracking bracket technology has made remarkable progress in recent years, a plurality of challenges remain, on one hand, the response speed and accuracy of the traditional tracking systems in processing complex environmental factors still need to be improved, when encountering rapid weather changes, the systems may not be adjusted in time, resulting in energy loss, on the other hand, the detection of axial angle deviation in the prior art is generally rough, the sensitivity to the small change of deviation is lack, especially in low-light or overcast weather, the system may reduce the power generation efficiency due to the fact that the system cannot be accurately aligned with the sun, and furthermore, most tracking systems rely on preset programs in angle adjustment strategies, and lack the ability to perform intelligent dynamic adjustment according to real-time environmental conditions.
Disclosure of Invention
The present invention has been made in view of the above-described problems occurring in the prior art.
Therefore, the invention provides a method for detecting the axial angle deviation of the photovoltaic tracking bracket, which solves the problems of inaccurate detection and slow response of the axial angle deviation of the photovoltaic tracking bracket.
In order to solve the technical problems, the invention provides the following technical scheme:
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 plurality of sensors, collecting data of the photovoltaic tracking bracket in real time, and transmitting the data to a central processing unit for time synchronization;
fusing the photovoltaic tracking bracket data to obtain dynamic characteristics and static characteristics;
Based on the dynamic characteristics and the static characteristics, performing deviation recognition by using a deviation recognition function;
Calculating a deviation angle and a deviation direction using a deviation calculation function based on the identified deviation;
And comparing the deviation angle with an angle threshold value, and controlling the photovoltaic tracking bracket to automatically adjust by combining the deviation direction.
As a preferable scheme of the method for detecting the axial angle deviation of the photovoltaic tracking bracket, the invention comprises the following steps that the various sensors comprise a GPS, a magnetometer, a gyroscope and a temperature sensor;
The photovoltaic tracking stent data includes geographic location, magnetic field strength, angular velocity, and temperature.
The invention relates to a method for detecting axial angle deviation of a photovoltaic tracking bracket, which comprises the following steps of collecting data of the photovoltaic tracking bracket in real time, transmitting the data to a central processing unit, and performing time synchronization:
Calibrating the time of the photovoltaic tracking bracket data to the same time source, acquiring data once per second, and marking the accurate time point of each data acquisition by using a time stamp;
After the data acquisition is completed, the data packet with the time stamp is transmitted to the central processing unit through the wireless network and the wired network for time synchronization.
As a preferable scheme of the method for detecting the axial angle deviation of the photovoltaic tracking bracket, the method for detecting the axial angle deviation of the photovoltaic tracking bracket comprises the following steps of:
And adopting a Kalman filtering algorithm, combining a composite data fusion function to fuse the photovoltaic tracking bracket data, wherein the expression is as follows:
;
Wherein F (t) represents the value of the data fusion, Z represents the normalization constant, The integral of the weighted history data before time T is represented for the geographic position G (u), G (u) for the geographic position data at time u, λ for the time decay factor, α for the weighting factor of the magnetic field strength, β for the weighting factor of the angular velocity, γ for the weighting factor of the temperature, G (T) for the geographic position at time T, M (T) for the magnetic field strength at time T, W (T) for the angular velocity at time T, and T (T) for the temperature data at time T.
The method for detecting the axial angle deviation of the photovoltaic tracking bracket is used as a preferable scheme, wherein dynamic characteristics and static characteristics are obtained based on fused photovoltaic tracking bracket data, and comprises the following steps:
and extracting dynamic characteristics of the fused photovoltaic tracking bracket data by utilizing polynomial order derivatives, wherein the expression is as follows:
;
wherein D (t) represents a dynamic feature, N represents the highest derivative, w i represents the weight of the ith derivative, Representing the ith derivative of the fusion data F (t);
And smoothing the fused photovoltaic tracking bracket data by using an exponential moving average method, extracting static characteristics, wherein the expression is as follows:
;
Wherein S (t) represents a static feature, k represents a smoothing coefficient, exp (-kt) represents an exponential decay function, Representing integration from infinity past to current time t, exp (ku) F (u) represents weighting the value of F (u) exponentially with time u, F (u) represents the value of the composite data fusion F (t) at any point in time u, u represents any value from some point in time past until current point in time t, du represents an integral infinitesimal.
The invention relates to a method for detecting axial angle deviation of a photovoltaic tracking bracket, which is a preferable scheme, wherein the method is based on dynamic characteristics and static characteristics and utilizes a deviation recognition function to carry out deviation recognition and comprises the following steps:
And integrating dynamic characteristics and static characteristics by adopting a composite characteristic function, wherein the expression is as follows:
;
Wherein H (t) represents the value of the composite feature, A non-linear transformation representing the dynamic characteristics,A non-linear transformation representing a static feature,A positive real parameter representing the degree of influence of the adjusting static feature;
Setting a deviation recognition threshold based on the integrated characteristic values, and performing deviation recognition by using a deviation recognition function;
When the integrated characteristic value is larger than the deviation recognition threshold, the deviation recognition function recognizes the deviation, the photovoltaic tracking bracket has an abnormal working state and needs to be immediately adjusted by taking measures, and when the integrated characteristic value is smaller than or equal to the deviation recognition threshold, the deviation recognition function does not recognize the deviation, and the photovoltaic tracking bracket is stable in operation.
The method for detecting the axial angle deviation of the photovoltaic tracking bracket is characterized by comprising the following steps of:
and calculating the deviation angle of the identified deviation by adopting a deviation angle calculation function, wherein the expression is as follows:
;
Wherein A (t) represents a deviation angle, ars represents an inverse cosine function, P (t) represents an actual position vector of the photovoltaic tracking bracket at time t, P d represents a target position vector of the photovoltaic tracking bracket, and R (t) represents a value of a deviation recognition function;
and calculating the deviation direction of the identified deviation by adopting a deviation direction calculation function, wherein the expression is as follows:
;
Wherein C (t) represents the direction of deviation.
The invention relates to a method for detecting the axial angle deviation of a photovoltaic tracking bracket, which comprises the following steps of comparing the deviation angle with an angle threshold value, and controlling the photovoltaic tracking bracket to automatically adjust by combining the deviation direction:
based on the deviation angle of the time t, and by combining the historical behavior of the photovoltaic tracking bracket, a dynamic correction function is adopted to correct the deviation angle, and the expression is as follows:
;
Wherein A d (t) represents the deviation angle value after the historical data trend and the periodic pattern correction at time t, p represents the order of the autoregressive term, q represents the order of the moving average term, Representing an autoregressive coefficient, wherein i is an order index of an autoregressive term, θ j denotes a moving average coefficient, wherein j is an order index of a moving average term, ε (t-j) represents a difference between times t and j, and u represents a value of a deviation angle A (u) at a history time point u;
According to the illumination intensity, the temperature and the health condition of the photovoltaic tracking bracket, a dynamic adjustment function is adopted to dynamically adjust an angle threshold, and the expression is as follows:
;
Wherein A th (t) represents an angle deviation threshold value adjusted according to the illumination intensity, the temperature and the health condition of the photovoltaic tracking bracket at time t, L (t) represents the illumination intensity at time t, G (t) represents the temperature at time t, Z (t) represents the health condition of the photovoltaic tracking bracket at time t, and alpha, beta and gamma represent coefficients of the influence of the illumination intensity, the temperature and the health condition on the angle threshold value respectively;
based on the corrected deviation angle and the adjusted angle threshold, a comparison function is adopted to judge whether the photovoltaic tracking bracket needs to be adjusted at time t, and the expression is as follows:
;
Wherein, C d (t) represents a comparison function of the deviation angle and a dynamic adjustment angle threshold value, whether the deviation angle exceeds the threshold value range is judged at time t, sigma 2 represents a preset small variance, and uncertainty is measured;
When the corrected deviation angle is smaller than the adjusted angle threshold, the deviation angle is within the dynamically adjusted angle threshold range, and the photovoltaic tracking bracket is in a normal working state without adjustment;
when the corrected deviation angle is larger than or equal to the adjusted angle threshold, the deviation angle exceeds the dynamically adjusted angle threshold, an automatic adjustment mechanism is triggered, and the photovoltaic tracking bracket is controlled by a motor to be adjusted to an optimal tracking state.
In a second aspect, an embodiment of the present invention provides a computer device, including a memory and a processor, where the memory stores a computer program, where the computer program when executed by the processor implements any step of the method for detecting an axial angle deviation of a photovoltaic tracking bracket according to the first aspect of the present invention.
In a third aspect, an embodiment of the present invention provides a computer readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements any step of the method for detecting an axial angle deviation of a photovoltaic tracking stand according to the first aspect of the present invention.
The intelligent control system has the beneficial effects that the GPS, the magnetometer, the gyroscope and the temperature sensor are integrated, the multidimensional data of the photovoltaic tracking bracket are collected and synchronized to the central processing unit in real time, the timeliness and consistency of information are ensured, an accurate control foundation is laid, the Kalman filtering and the composite data fusion are utilized, the perception and understanding of the system on the dynamic change of the environment are enhanced, the acuity of deviation recognition is improved, the deviation angle and the direction are accurately measured by means of the deviation recognition and calculation function, the efficient operation of an automatic adjustment mechanism is promoted, finally, the intelligent control of the photovoltaic tracking bracket is realized by dynamically adjusting the angle threshold and combining the deviation direction, the optimal tracking state of the system can be still maintained under the complex environment condition, and the energy conversion efficiency and the economical efficiency are remarkably improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart of a method for detecting axial angle deviation of a photovoltaic tracking stand in example 1.
Fig. 2 is a diagram showing the calculation process of the deviation identified in example 1.
Detailed Description
In order that the above-recited objects, features and advantages of the present invention will become more readily apparent, a more particular description of the invention will be rendered by reference to specific embodiments thereof which are illustrated in the appended drawings.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways other than those described herein, and persons skilled in the art will readily appreciate that the present invention is not limited to the specific embodiments disclosed below.
Further, reference herein to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic can be included in at least one implementation of the invention. The appearances of the phrase "in one embodiment" in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments.
Embodiment 1, referring to fig. 1 and 2, is a first embodiment of the present invention, and this embodiment provides a method for detecting axial angle deviation of a photovoltaic tracking bracket, which includes the following steps:
S1, deploying various sensors, and collecting photovoltaic tracking bracket data in real time.
S1.1, the various sensors comprise a GPS, a magnetometer, a gyroscope and a temperature sensor;
The photovoltaic tracking stent data includes geographic location, magnetic field strength, angular velocity, and temperature.
Specifically, the highest point of the photovoltaic tracking bracket is selected, the GPS sensor is vertically installed, the sensor can receive satellite signals from the sky without shielding, the installation height is at least 30 cm higher than the edge of the photovoltaic panel, and the interference of the panel on the signals is avoided;
The magnetometer and the gyroscope are arranged at the geometric center position of the photovoltaic tracking bracket, a leveling ruler is required to be used for leveling the mounting platform before the mounting, the platform is ensured to be completely level, a small and stable metal or composite material platform is arranged at the center position of the bracket and used as a mounting base of the magnetometer and the gyroscope, and the platform is designed with proper damping measures, such as a rubber pad or a spring, so that the influence of external vibration on the reading of the sensor is reduced;
The temperature sensor is arranged in a shielding area of the photovoltaic tracking bracket, direct sunlight irradiation is avoided, measured temperature can represent real ambient temperature around the bracket, the temperature sensor can be arranged on the surface of the bracket, but thermal contact is increased by using thermal conductive paste, and meanwhile, a layer of thin heat insulation material is arranged between the sensor and the bracket, so that the temperature of the bracket is prevented from affecting the reading of the sensor.
S2, collecting photovoltaic tracking bracket data in real time, and transmitting the photovoltaic tracking bracket data to a central processing unit for time synchronization.
S2.1, calibrating the time of the photovoltaic tracking bracket data to the same time source, acquiring data once per second, and marking the accurate time point of each data acquisition by using a time stamp;
After the data acquisition is completed, transmitting the data packet with the time stamp to a central processing unit through a wireless network and a wired network for time synchronization;
specifically, based on the timestamp data of the photovoltaic tracking bracket data read by the central processing unit, a weighted average method is adopted to perform time synchronization, and the expression is as follows:
;
Wherein, Representing a weighted average timestamp, qi represents a quality factor associated with the ith data acquisition, ti represents a timestamp of the ith data acquisition, and n represents the total number of samples.
Further, for a GPS sensor, the quality factor may be determined based on the number of satellites received and the signal quality, for magnetometers and gyroscopes, the quality factor may depend on the stability of their readings, the quality factor of the temperature sensor may depend on its calibration results with a standard temperature source, and in general, the quality factor is set to a range between [0,1], 1 representing the most reliable data;
Furthermore, when the sensor collects data once, the system clock can generate a time stamp, the time of the data collection is accurately recorded, all time stamps should refer to the same time source, usually GPS time, the consistency of the data of all the sensors in time is ensured, each data packet is assigned with a unique serial number, and the receiving end confirms the sequence and the integrity of the data packets by checking the continuity of the serial numbers, so that the data packets are prevented from being disordered or lost.
And S3, fusing the photovoltaic tracking bracket data.
S3.1, combining the data of the photovoltaic tracking bracket by adopting a Kalman filtering algorithm and combining a composite data fusion function, wherein the expression is as follows:
;
Wherein F (t) represents the value of the data fusion, Z represents the normalization constant, The integral of the weighted history data before time T is represented for the geographic position G (u), G (u) for the geographic position data at time u, λ for the time decay factor, α for the weighting factor of the magnetic field strength, β for the weighting factor of the angular velocity, γ for the weighting factor of the temperature, G (T) for the geographic position at time T, M (T) for the magnetic field strength at time T, W (T) for the angular velocity at time T, and T (T) for the temperature data at time T.
Specifically, the weighting coefficients α, β and γ control the adaptive update of the process noise, the adaptive update of the observation noise and the update rule of the covariance matrix, respectively, and specific numerical values need to be adjusted according to the experimental results.
And S4, obtaining dynamic characteristics and static characteristics based on the fused photovoltaic tracking bracket data.
S4.1, extracting dynamic characteristics of the fused photovoltaic tracking bracket data by utilizing polynomial order derivatives, wherein the expression is as follows:
;
wherein D (t) represents a dynamic feature, N represents the highest derivative, w i represents the weight of the ith derivative, Representing the ith derivative of the fusion data F (t);
And smoothing the fused photovoltaic tracking bracket data by using an exponential moving average method, extracting static characteristics, wherein the expression is as follows:
;
Wherein S (t) represents a static feature, k represents a smoothing coefficient, exp (-kt) represents an exponential decay function, Representing integration from infinity past to current time t, exp (ku) F (u) represents weighting the value of F (u) exponentially with time u, F (u) represents the value of the composite data fusion F (t) at any point in time u, u represents any value from some point in time past until current point in time t, du represents an integral infinitesimal.
Specifically, considering the dynamic characteristics of the photovoltaic tracking stent, N should be generally set to 2 or 3, corresponding to the speed and the acceleration or higher order of the acceleration change rate, respectively, and the second higher derivative is very sensitive to noise, so the need to balance the capturing of dynamic characteristics and noise suppression is required when selecting N, for most photovoltaic tracking stent applications, n=2 is initially set, i.e. the acceleration is calculated as a dynamic characteristic to capture the rapidly changing dynamic response;
Adjusting the importance of different order derivatives based on the physical characteristics of the photovoltaic tracking stent, such as response speed, inertial effect, etc., adjusting the weights according to the spectral characteristics of the signal, the high frequency component (higher order derivative) may need less weight to reduce noise effects, while the low frequency component (lower order derivative) may need more weight to capture slowly varying dynamics, the initial setting may be fine-tuned with equal weight w i =1/N according to experimental results, for example, if the system is found to be insufficiently responsive to rapid changes, the weight of the acceleration (second derivative) may be increased appropriately;
For the setting of the smoothing coefficient k, if the data has a significant periodicity, k should be chosen small enough to preserve the periodic characteristics, but large enough to remove noise, if the rate of change of the data is large, k should be chosen large to avoid excessive smoothing resulting in loss of characteristics, and the initial setting k=0.1 or 0.2, which is usually sufficient to remove most of the noise while preserving the main trend of the signal, is adjusted according to experimental results, if the smoothed signal is found to be too smooth or not smooth enough, k can be reduced or increased, respectively.
S5, based on the dynamic characteristics and the static characteristics, utilizing a deviation recognition function to recognize the deviation.
S5.1, adopting a composite characteristic function to integrate dynamic characteristics and static characteristics, wherein the expression is as follows:
;
Wherein H (t) represents the value of the composite feature, A non-linear transformation representing the dynamic characteristics,A non-linear transformation representing a static feature,A positive real parameter representing the degree of influence of the adjusting static feature;
Setting a deviation recognition threshold based on the integrated characteristic values, and performing deviation recognition by using a deviation recognition function;
When the integrated characteristic value is larger than the deviation recognition threshold, the deviation recognition function recognizes the deviation, the photovoltaic tracking bracket has an abnormal working state and needs to be immediately adjusted by taking measures, and when the integrated characteristic value is smaller than or equal to the deviation recognition threshold, the deviation recognition function does not recognize the deviation, and the photovoltaic tracking bracket is stable in operation.
Specifically, under typical environmental conditions, determining the normal tracking state and the deviation state of the photovoltaic tracking bracket through experiments, and preliminarily setting a threshold range according to the physical characteristics of the photovoltaic tracking bracket by combining the experience of field experts;
When the illumination is strong, the tracking efficiency of the photovoltaic panel is high, the threshold value can be set to be slightly low so as to more sensitively detect the deviation, when the illumination is weak, the threshold value can be set to be slightly high so as to reduce false alarm, the temperature change possibly affects the accuracy of the sensor, and when the temperature is high or low, the threshold value is properly adjusted so as to compensate the influence of the temperature on the reading of the sensor, the health condition of the photovoltaic tracking bracket, such as the accuracy of the sensor, the mechanical abrasion degree of the bracket and the like, is periodically evaluated, and the threshold value is dynamically adjusted according to the health condition so as to ensure that the system can effectively identify the deviation under different health conditions.
S6, calculating a deviation angle and a deviation direction based on the identified deviation.
S6.1, calculating a deviation angle by adopting a deviation angle calculation function, wherein the expression is as follows:
;
Wherein A (t) represents a deviation angle, ars represents an inverse cosine function, P (t) represents an actual position vector of the photovoltaic tracking bracket at time t, P d represents a target position vector of the photovoltaic tracking bracket, and R (t) represents a value of a deviation recognition function;
and calculating the deviation direction of the identified deviation by adopting a deviation direction calculation function, wherein the expression is as follows:
;
Wherein C (t) represents the direction of deviation.
Specifically, the actual position vector P (t) is usually based on the installation position and orientation of the photovoltaic tracking bracket, and a local coordinate system is established, wherein the origin of the coordinate system can be selected to be in the geometric center of the bracket, the X-axis points to the front surface of the photovoltaic panel, the Y-axis points to the side surface of the photovoltaic panel, the Z-axis is perpendicular to the surface of the photovoltaic panel, and the local coordinate system is calibrated to ensure that the conversion relationship between the local coordinate system and the global coordinate system (such as WGS 84) is accurate;
Determining the geographic position of the photovoltaic tracking bracket by using a GPS sensor, wherein the geographic position comprises latitude, longitude and altitude, calculating the azimuth angle and the inclination angle of the photovoltaic panel relative to a local coordinate system by combining magnetometer and gyroscope data, and converting the azimuth angle and the inclination angle into vector representation in the local coordinate system to obtain an actual position vector;
The coordinate system of the target position vector P d should be defined in the same coordinate system to represent the solar position that the photovoltaic panel should theoretically face, and an astronomical algorithm or a solar tracking model is used to calculate the position of the sun according to the current time and the geographic position, including the solar azimuth angle and the solar altitude, and the solar azimuth angle and the solar altitude are converted into vector representations in the local coordinate system to obtain the target position vector;
further, it is checked whether the calculated deviation angle and direction are reasonable, and whether it coincides with the visual observation, and the definition of the local coordinate system or the processing of the sensor data is adjusted to optimize the result.
And S7, comparing the deviation angle with an angle threshold value, and controlling the photovoltaic tracking bracket to automatically adjust by combining the deviation direction.
S7.1, correcting the deviation angle by adopting a dynamic correction function based on the deviation angle of time t and combining the historical behavior of the photovoltaic tracking bracket, wherein the expression is as follows:
;
Wherein A d (t) represents the deviation angle value after the historical data trend and the periodic pattern correction at time t, p represents the order of the autoregressive term, q represents the order of the moving average term, Representing an autoregressive coefficient, wherein i is an order index of an autoregressive term, θ j denotes a moving average coefficient, wherein j is an order index of a moving average term, ε (t-j) represents a difference between times t and j, and u represents a value of a deviation angle A (u) at a history time point u;
According to the illumination intensity, the temperature and the health condition of the photovoltaic tracking bracket, a dynamic adjustment function is adopted to dynamically adjust an angle threshold, and the expression is as follows:
;
Wherein A th (t) represents an angle deviation threshold value adjusted according to the illumination intensity, the temperature and the health condition of the photovoltaic tracking bracket at time t, L (t) represents the illumination intensity at time t, G (t) represents the temperature at time t, Z (t) represents the health condition of the photovoltaic tracking bracket at time t, and alpha, beta and gamma represent coefficients of the influence of the illumination intensity, the temperature and the health condition on the angle threshold value respectively;
based on the corrected deviation angle and the adjusted angle threshold, a comparison function is adopted to judge whether the photovoltaic tracking bracket needs to be adjusted at time t, and the expression is as follows:
;
Wherein, C d (t) represents a comparison function of the deviation angle and a dynamic adjustment angle threshold value, whether the deviation angle exceeds the threshold value range is judged at time t, sigma 2 represents a preset small variance, and uncertainty is measured;
When the corrected deviation angle is smaller than the adjusted angle threshold, the deviation angle is within the dynamically adjusted angle threshold range, and the photovoltaic tracking bracket is in a normal working state without adjustment;
when the corrected deviation angle is larger than or equal to the adjusted angle threshold, the deviation angle exceeds the dynamically adjusted angle threshold, an automatic adjustment mechanism is triggered, and the photovoltaic tracking bracket is controlled by a motor to be adjusted to an optimal tracking state.
Specifically, determining the order p of the autoregressive term and the order q of the moving average term, modeling the deviation angle data by utilizing an autoregressive moving average model (ARMA model) by collecting historical tracking data of the photovoltaic tracking bracket under different environmental conditions, such as deviation angle, illumination intensity, temperature, health condition and the like, preliminarily determining the values of p and q by analyzing an autocorrelation function (ACF) and a partial autocorrelation function (PACF) graph, further optimizing p and q by using a minimum residual square sum or information criterion (such as AIC and BIC), and selecting a model which can be best fit to the historical data;
Estimation by optimization algorithm using selected orders p and q And values of θ j, so that the error between the predicted deviation angle and the actual deviation angle of the model is minimized, and the prediction performance adjustment of the model is checked by retaining a part of the history data as a verification setAnd θ j until the model performs optimally;
According to the dynamic characteristics of the system and experimental observation, initially setting the value of lambda, if the system response is faster, lambda can be set larger, gradually adjusting the value of lambda by observing the effect of the dynamic correction function until an optimal balance point is found, and not excessively depending on historical data nor neglecting the influence of the historical data;
Further, the factors of the influence of the illumination intensity, the temperature and the health condition on the angle threshold value are determined, a series of experiments are designed, the illumination intensity, the temperature and the health condition of the photovoltaic tracking bracket are changed, the deviation angle and the tracking efficiency are recorded, the values of alpha, beta and gamma are learned from experimental data through a multiple linear regression analysis or a machine learning method, and the angle threshold value is ensured to be dynamically adjusted according to the environmental change so as to maintain the optimal tracking performance.
The embodiment also provides computer equipment, which is suitable for the condition of the method for detecting the axial angle deviation of the photovoltaic tracking bracket and comprises a memory and a processor, wherein the memory is used for storing computer executable instructions, and the processor is used for executing the computer executable instructions to realize the method for detecting the axial angle deviation of the photovoltaic tracking bracket, which is provided by the embodiment.
The computer device may be a terminal comprising a processor, a memory, a communication interface, a display screen and input means connected by a system bus. Wherein the processor of the computer device is configured 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 computer programs in the non-volatile storage media. The communication interface of the computer device is used for carrying out wired or wireless communication with an external terminal, and the wireless mode can be realized through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, can also be keys, a track ball or a touch pad arranged on the shell of the computer equipment, and can also be an external keyboard, a touch pad or a mouse and the like.
The present embodiment also provides a storage medium, on which a computer program is stored, which when executed by a processor implements the method for detecting the axial angle deviation of the photovoltaic tracking stand as set forth in the above embodiment, where the storage medium may be implemented by any type of volatile or non-volatile storage device or combination thereof, such as a static random access Memory (Static Random Access Memory, SRAM for short), an electrically erasable Programmable Read-Only Memory (ELECTRICALLY ERASABLE PROGRAMMABLE READ-Only Memory, EEPROM for short), an erasable Programmable Read-Only Memory (Erasable Programmable Read Only Memory, EPROM for short), a Programmable Read-Only Memory (PROM for short), a Read-Only Memory (ROM for short), a magnetic Memory, a flash Memory, a magnetic disk, or an optical disk.
In summary, through integrating GPS, magnetometer, gyroscope and temperature sensor, gather and synchronous photovoltaic tracking support's multidimensional data to central processing unit in real time, guaranteed timeliness and uniformity of information, establish accurate control basis, utilize Kalman filtering and compound data fusion, combined dynamic feature and static feature extraction, the perception and the understanding of system to the environment dynamic change have been strengthened, the acuity of deviation discernment is promoted, with the help of deviation discernment and calculation function, accurate survey deviation angle and direction, the high-efficient operation of automatic adjustment mechanism has been promoted, finally, dynamic adjustment angle threshold value combines the deviation direction, intelligent control of photovoltaic tracking support has been realized, ensure that the system still can maintain best tracking state under the complex environmental condition, show improvement energy conversion efficiency and economic nature.
Example 2
Referring to table 1, for the second embodiment of the present invention, experimental simulation data of a method for detecting axial angle deviation of a photovoltaic tracking bracket are provided for further verifying the advancement of the present invention.
A photovoltaic power station in a desert area in China is selected as an experimental field, the power station is provided with a photovoltaic panel which is installed in a traditional fixed inclined mode and two groups of photovoltaic tracking supports of the intelligent photovoltaic tracking system, continuous 30-day comparison tests are carried out, different weather conditions such as sunny, cloudy, overcast and rainy are covered, and the performance of the system is comprehensively evaluated.
Firstly, an intelligent photovoltaic tracking system is configured with a GPS, a magnetometer, a gyroscope and a temperature sensor, the geographic position, the magnetic field intensity, the angular velocity and the temperature data of a photovoltaic tracking bracket are collected in real time, and the frequency of once per second is transmitted to a central processing unit through a wireless network to carry out time synchronization;
Secondly, a Kalman filtering algorithm and a composite data fusion function are adopted in a data fusion stage, and dynamic characteristics and static characteristics are combined to extract so as to enhance the perception capability of the system on environmental changes;
then, the intelligent photovoltaic tracking system detects axial angle deviation through a deviation recognition function, and calculates a deviation angle and a deviation direction by using a deviation calculation function;
finally, the system compares the angle threshold value with the deviation angle according to dynamic adjustment, and combines the deviation direction, the intelligent control photovoltaic tracking support carries out automatic adjustment, ensures that the photovoltaic panel is always aligned with the sun, improves the energy conversion efficiency, and is specifically shown in table 1:
Table 1 table of experimental records
As can be seen from the above table, the intelligent photovoltaic tracking system has significantly reduced axial deviation angle from 5.3 to 0.5 ° under different illumination intensity and temperature conditions compared to conventional fixed tilt mounted photovoltaic panels, indicating that the intelligent system can more accurately adjust the photovoltaic panel orientation to accommodate changes in solar position. In terms of tracking efficiency, the intelligent system shows performance superior to that of the traditional system under all test conditions, which can reach 93.4% at the highest, and is about 11.3% higher than that of the traditional system. In addition, the tracking response time of the intelligent system is obviously shortened from 120 seconds to 25 seconds, and the rapid adaptation capability of the system to environmental changes is embodied.
Through comparative analysis, the intelligent photovoltaic tracking system realizes high-precision detection and quick response of axial angle deviation through multi-sensor fusion and complex algorithm, deviation angle is obviously reduced, tracking precision is improved, tracking efficiency of the intelligent system is far higher than that of a traditional system, high energy conversion efficiency can be maintained even under low illumination or changed weather conditions, adaptability and stability of the intelligent system in complex environment are proved, the intelligent system can quickly adjust the direction of a photovoltaic panel, tracking delay is reduced, the photovoltaic panel is ensured to be always in an optimal illumination receiving state, and energy utilization rate is further improved.
In summary, the intelligent photovoltaic tracking system combines a dynamic adjustment mechanism through advanced data acquisition, fusion and deviation detection technologies, so that the problems of tracking precision and response speed of the traditional system in a complex environment are solved, the energy conversion efficiency of the photovoltaic system is greatly improved, and the remarkable innovation and practical value of the intelligent photovoltaic tracking system in the technical field of photovoltaic tracking are shown.
It should be noted that the above embodiments are only for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present invention may be modified or substituted without departing from the spirit and scope of the technical solution of the present invention, which is intended to be covered in the scope of the claims of the present invention.

Claims (6)

1.一种光伏跟踪支架轴向角度偏差的检测方法，其特征在于：包括，1. A method for detecting axial angle deviation of a photovoltaic tracking bracket, characterized by: comprising: 部署多种传感器，实时采集光伏跟踪支架数据，传输至中央处理单元，进行时间同步；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; 对光伏跟踪支架数据进行融合，得到动态特征和静态特征；The photovoltaic tracking bracket data is fused to obtain dynamic and static features; 基于动态特征和静态特征，利用偏差识别函数，进行偏差识别；Based on dynamic features and static features, deviation identification is performed using deviation identification functions; 基于识别出的偏差，利用偏差计算函数，计算偏差角度和偏差方向；Based on the identified deviation, the deviation angle and deviation direction are calculated using a deviation calculation function; 将偏差角度与角度阈值进行比较，并结合偏差方向，控制光伏跟踪支架进行自动调整；Compare the deviation angle with the angle threshold, and combine the deviation direction to control the photovoltaic tracking bracket to automatically adjust; 所述多种传感器包括GPS 、磁力计、陀螺仪、温度传感器；The multiple sensors include GPS, magnetometer, gyroscope, and temperature sensor; 所述光伏跟踪支架数据包括地理位置、磁场强度、角速度和温度；The photovoltaic tracking bracket data includes geographical location, magnetic field strength, angular velocity and temperature; 实时采集光伏跟踪支架数据，传输至中央处理单元，进行时间同步，包括如下步骤：Real-time collection of photovoltaic tracking bracket data, transmission to the central processing unit, and time synchronization include the following steps: 将光伏跟踪支架数据的时间校准至同一时间源，每秒采集一次数据，使用时间戳标记每次数据采集的精确时间点；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; 在数据采集完成后，将带有时间戳的数据包通过无线网络、有线网络传输至中央处理单元，进行时间同步；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; 对光伏跟踪支架数据进行融合，包括如下步骤：The fusion of photovoltaic tracking bracket data includes the following steps: 采用卡尔曼滤波算法，结合复合数据融合函数，对光伏跟踪支架数据进行融合，表达式为：The Kalman filter algorithm is used in combination with the composite data fusion function to fuse the photovoltaic tracking bracket data. The expression is: ； ; 其中，F（t）表示数据融合的值，Z表示归一化常数，表示地理位置G（u）在时间t之前的加权历史数据的积分，G（u）表示在时间u的地理位置数据，λ表示时间衰减因子，α表示磁场强度的加权系数，β表示角速度的加权系数，γ表示温度的加权系数，G（t）表示在时间t的地理位置，M（t）表示在时间t的磁场强度，W（t）表示时间t的角速度，T（t）表示在时间t的温度数据；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; 基于融合后的光伏跟踪支架数据，得到动态特征和静态特征，包括如下步骤：Based on the fused photovoltaic tracking bracket data, dynamic features and static features are obtained, including the following steps: 利用多项式阶导数，对融合后的光伏跟踪支架数据，进行提取动态特征，表达式为：The polynomial derivatives are used to extract dynamic features from the fused photovoltaic tracking bracket data. The expression is: ； ; 其中，D（t）表示动态特征，N表示最高阶导数，wi表示第i阶导数的权重，表示融合数据F（t）的第i阶导数；Among them, D(t) represents the dynamic characteristics, N represents the highest order derivative, wi represents the weight of the i-th order derivative, represents the i-th order derivative of the fused data F(t); 使用指数移动平均方法，对融合后的光伏跟踪支架数据进行平滑处理，提取静态特征，表达式为：The exponential moving average method is used to smooth the fused photovoltaic tracking bracket data and extract static features. The expression is: ； ; 其中，S（t）表示静态特征，k表示平滑系数，exp（-kt）表示指数衰减函数，表示从无穷远的过去到当前时间t的积分，exp（ku）F（u）表示对F（u）的值进行加权，权重随时间u呈指数变化，F（u）表示复合数据融合F（t）在任意时间点u的值，u表示从过去的某个时刻直到当前时间点t的任何值，du表示积分微元。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.如权利要求1所述的光伏跟踪支架轴向角度偏差的检测方法，其特征在于：基于动态特征和静态特征，利用偏差识别函数，进行偏差识别，包括如下步骤：2. The method for detecting the axial angle deviation of the photovoltaic tracking bracket according to claim 1 is characterized in that: based on dynamic characteristics and static characteristics, using a deviation identification function, deviation identification is performed, comprising the following steps: 采用复合特征函数，对动态特征和静态特征进行整合，表达式为：The composite feature function is used to integrate the dynamic features and static features. The expression is: ； ; 其中，H（t）表示复合特征的值，表示动态特征的非线性变换，表示静态特征的非线性变换，表示调节静态特征影响程度的正实数参数；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; 基于整合后的特征值，设定偏差识别阈值，采用偏差识别函数进行偏差识别；Based on the integrated feature values, the deviation identification threshold is set, and the deviation identification function is used to perform deviation identification; 当整合后的特征值大于偏差识别阈值时，表示偏差识别函数识别到偏差，光伏跟踪支架存在异常工作状态，需立即采取措施进行调整，当整合后的特征值小于等于偏差识别阈值时，则表示偏差识别函数未识别到偏差，光伏跟踪支架运行稳定。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.如权利要求2所述的光伏跟踪支架轴向角度偏差的检测方法，其特征在于：基于识别出的偏差，计算偏差角度和偏差方向，包括如下步骤：3. The method for detecting the axial angle deviation of the photovoltaic tracking bracket according to claim 2, characterized in that: based on the identified deviation, calculating the deviation angle and the deviation direction, comprises the following steps: 采用偏差角度计算函数，对识别出的偏差，进行计算偏差角度，表达式为：The deviation angle calculation function is used to calculate the deviation angle of the identified deviation. The expression is: ； ; 其中，A（t）表示偏差角度，ars表示反余弦函数，P（t）表示光伏跟踪支架在时间t的实际位置向量，Pd表示光伏跟踪支架的目标位置向量，R（t）表示偏差识别函数的值；Wherein, 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, Pd represents the target position vector of the photovoltaic tracking bracket, and R(t) represents the value of the deviation identification function; 采用偏差方向计算函数，对识别出的偏差，进行计算偏差方向，表达式为：The deviation direction calculation function is used to calculate the deviation direction of the identified deviation. The expression is: ； ; 其中，C（t）表示偏差方向。Where C(t) represents the deviation direction. 4.如权利要求3所述的光伏跟踪支架轴向角度偏差的检测方法，其特征在于：将偏差角度与角度阈值进行比较，并结合偏差方向，控制光伏跟踪支架进行自动调整，包括如下步骤：4. The method for detecting the axial angle deviation of the photovoltaic tracking bracket according to claim 3 is characterized in that 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, comprising the following steps: 基于时间t的偏差角度，并结合光伏跟踪支架的历史行为，采用动态修正函数，对偏差角度进行修正，表达式为：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: ； ; 其中，Ad（t）表示在时间t经过历史数据趋势和周期性模式修正后的偏差角度值，p表示自回归项的阶数，q表示移动平均项的阶数，表示自回归系数，其中i是自回归项的阶数索引，θj标示移动平均系数，其中j是移动平均项的阶数索引，ε(t-j)表示在时间t至j的差异，u表示偏差角度A（u）在历史时间点u的值；Where A d (t) represents the deviation angle value after correction of historical data trend and periodic pattern at time t, p represents the order of autoregressive term, q represents the order of moving average term, represents the autoregressive coefficient, where i is the order index of the autoregressive term, θj represents 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; 根据光照强度、温度和光伏跟踪支架的健康情况，采用动态调整函数，对角度阈值进行动态调整，表达式为：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: ； ; 其中，Ath（t）表示在时间t根据光照强度、温度和光伏跟踪支架健康状况调整后的角度偏差阈值，L（t）表示在时间t的光照强度，G（t）表示在时间t的温度，Z（t）表示在时间t的光伏跟踪支架健康状况，α、β和γ表示分别是光照强度、温度和健康状况对角度阈值影响的系数；Wherein, Ath (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 angle threshold respectively; 基于修正后的偏差角度和调整后的角度阈值，采用比较函数，判断在时间t是否需要调整光伏跟踪支架，表达式为：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: ； ; 其中，Cd（t）表示偏差角度与动态调整角度阈值的比较函数，在时间t 判断偏差角度是否超出阈值范围，σ2表示预设的小方差，衡量不确定性；Wherein, C d (t) represents the comparison function between the deviation angle and the dynamically adjusted angle threshold, judging whether the deviation angle exceeds the threshold range at time t, and σ 2 represents the preset small variance, measuring uncertainty; 当修正后的偏差角度小于调整后的角度阈值，表示偏差角度在动态调整的角度阈值范围内，光伏跟踪支架处于正常工作状态无需调整；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; 当修正后的偏差角度大于等于调整后的角度阈值，表示偏差角度超出动态调整的角度阈值，触发自动调整机制，利用电机控制光伏跟踪支架调整到最优跟踪状态。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. 5.一种计算机设备，包括存储器和处理器，所述存储器存储有计算机程序，其特征在于：所述处理器执行所述计算机程序时实现权利要求1~4任一所述的光伏跟踪支架轴向角度偏差的检测方法的步骤。5. A computer device, comprising a memory and a processor, wherein the memory stores a computer program, wherein the processor implements the steps of the method for detecting the axial angle deviation of a photovoltaic tracking bracket as described in any one of claims 1 to 4 when executing the computer program. 6.一种计算机可读存储介质，其上存储有计算机程序，其特征在于：所述计算机程序被处理器执行时实现权利要求1~4任一所述的光伏跟踪支架轴向角度偏差的检测方法的步骤。6. A computer-readable storage medium having a computer program stored thereon, characterized in that: when the computer program is executed by a processor, the steps of the method for detecting the axial angle deviation of a photovoltaic tracking bracket according to any one of claims 1 to 4 are implemented.
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