A stress monitoring method for a flexible photovoltaic support system

Abstract

This application relates to the technical field of stress monitoring, and specifically relates to a stress monitoring method for a flexible photovoltaic support system. The method includes: obtaining stress data, cable force data, vibration data, as well as wind speed and wind direction data of the photovoltaic support system; obtaining the wind load influence coefficient at each point at each moment, obtaining the vibration response coefficient at each point in each interval, and then calculating the consistency coefficient at each point; obtaining the first fluctuation coefficient of the single-row support structure according to the fluctuation form of the consistency coefficient of the single-row support structure; obtaining the second fluctuation coefficient of the single-row support structure according to the cable force data of the single-row support structure; combining the stress data to construct an abnormal value of the stress state of the single-row support structure; and monitoring the stress state of the flexible photovoltaic support system according to the abnormal value of the stress state. This application can improve the stress monitoring accuracy of the flexible photovoltaic support system and accurately monitor the stress stability of the flexible photovoltaic support system.

Description

Technical Field

[0001] This application relates to the technical field of stress monitoring, and specifically relates to a stress monitoring method for a flexible photovoltaic support system. Background Art

[0002] Affected by geological changes, climate, oxidation, corrosion, and aging factors, under the action of long-term static and dynamic loads, flexible photovoltaic support structures are prone to damage. Through stress monitoring, the stress state of the support structure can be grasped in real time, and potential structural safety hazards can be discovered in time.

[0003] During the stress monitoring of the support structure, it is easily interfered by environmental factors. Temperature changes will directly affect the thermal expansion and contraction of materials, and the changes in wind speed and direction have a more significant impact on the stress of the flexible photovoltaic support system. Strong winds will cause the structure to vibrate and produce dynamic responses, resulting in stress fluctuations, and may even cause changes in stress concentration areas. Moreover, the photovoltaic panels during the power generation process will automatically change angles with the direction of sunlight irradiation, making it easier to be interfered by wind loads during the stress monitoring process. Conventional methods do not fully consider the influence of wind loads and the changes in the angles of photovoltaic panels during the monitoring process, resulting in the difficulty of the photovoltaic support system to discover potential abnormal risks, and the reliability of the stress monitoring method is relatively low. Summary of the Invention

[0004] In order to solve the above technical problems, this application provides a stress monitoring method for a flexible photovoltaic support system to solve the existing problems.

[0005] The stress monitoring method for a flexible photovoltaic support system of this application adopts the following technical solutions:

[0006] An embodiment of this application provides a stress monitoring method for a flexible photovoltaic support system, and this method includes the following steps:

[0007] S1: Obtain the stress data, cable force data, vibration data of each point of each row of support structures in the photovoltaic support system at each moment, and the wind speed and wind direction data of the area where the photovoltaic support system is located;

[0008] S2: Calculate the wind load influence coefficients of each photovoltaic panel at each moment: Obtain the unit normal vectors of the planes where each photovoltaic panel is located in the direction of its back at each moment, take the modulus of each unit normal vector in the vertical direction as the inclination of the corresponding photovoltaic panel at each moment, calculate the absolute value of the cosine value of the angle formed between the unit normal vector of each photovoltaic panel at each moment and the wind direction at each moment, obtain the ratio of the angle formed between the unit normal vector of each photovoltaic panel at each moment and the wind direction at each moment to 180 degrees, take the sum of the absolute value and the ratio as the wind direction influence coefficient of each photovoltaic panel at each moment, and record the ratio of the product of the wind direction influence coefficient of each photovoltaic panel at each moment and the wind speed to the inclination as the wind load influence coefficient of each photovoltaic panel at each moment;

[0009] S3: Divide the vibration data of each point into multiple intervals; Based on the difference situation and the average level of the vibration data of each point in each interval, obtain the vibration response coefficients of each point in each interval; Combine the correlation between the wind load influence coefficient and the vibration response coefficient of each point in each interval to construct the consistency coefficient of each point;

[0010] S4: According to the degree of dispersion and the degree of drastic change of the consistency coefficients of all points in the single-row support structure, obtain the first fluctuation coefficient of the single-row support structure; According to the degree of dispersion and the degree of drastic change of the cable force data of all points in the single-row support structure, obtain the second fluctuation coefficient of the single-row support structure; Analyze the degree of fluctuation of the stress data of all points in the single-row support structure, and combine the mean values of the first fluctuation coefficient and the second fluctuation coefficient to obtain the stress state abnormal value of the single-row support structure;

[0011] S5: Combine the stress state abnormal values of each row of support structures to monitor the stress states of each row of support structures.

[0012] Preferably, the determination method for dividing into multiple intervals is as follows:

[0013] Take the vibration data of each point as the input of the segmentation algorithm respectively, and the output is the positions of all segmentation points in the vibration data;

[0014] If the time interval between two adjacent segmentation points is less than or equal to the preset value, then take the ceiling result of the median value of the corresponding moments of the two adjacent segmentation points as the new segmentation point to replace the original two adjacent segmentation points, and perform a merging operation on the segmentation points;

[0015] Divide the vibration data of each point into multiple intervals according to the segmentation points after the merging operation.

[0016] Preferably, the vibration response coefficient of each point in each interval is the product of the entropy and the mean value of all vibration data of each point in each interval.

[0017] Preferably, the method for determining the consistency coefficient of each point is as follows: The mean values of the wind load influence coefficients and the vibration response coefficients of each point in all intervals are arranged in ascending order of time to obtain the wind load influence sequence and the vibration response sequence of each point; the Pearson correlation coefficient between the wind load influence sequence and the vibration response sequence of each point is used as the consistency coefficient of each point.

[0018] Preferably, the method for determining the first fluctuation coefficient of the single-row support structure is as follows:

[0019] , where is the first fluctuation coefficient of the j-th row of support structures, is the sum of the shortest distances between each data in the consistency sequence of the j-th row of support structures and the fitting curve of the consistency sequence of the j-th row of support structures; is the sum of the absolute values of the slopes of the fitting curve of the consistency sequence of the j-th row of support structures at each point, where the consistency sequence of the j-th row of support structures is obtained by arranging the consistency coefficients of each point in the j-th row of support structures in the order of the positions of each point.

[0020] Preferably, the method for determining the second fluctuation coefficient of the single-row support structure is as follows:

[0021] The mean values of all cable force data of each point are calculated to obtain the average cable force data of each point, and the sequence composed of the average cable force data of all points of each row of support structures is denoted as the cable force mean sequence;

[0022] The expression of the second fluctuation coefficient of the j-th row of support structures is: , where is the second fluctuation coefficient of the j-th row of support structures, is the sum of the shortest distances between each data in the cable force mean sequence of the j-th row of support structures and the fitting curve of the cable force mean sequence, is the sum of the absolute values of the slopes of the fitting curve of the cable force mean sequence of the j-th row of support structures at each point.

[0023] Preferably, the calculation method of the stress state outlier of the single-row support structure is the sum of the standard deviation of the stress data of all points in the single-row support structure and the mean values of the first fluctuation coefficient and the second fluctuation coefficient.

[0024] Preferably, the monitoring of the stress states of each row of support structures further includes: If the stress state outlier is greater than or equal to the outlier threshold, the stress state of the corresponding support structure is abnormal.

[0025] The present application has at least the following beneficial effects:

[0026] By deeply analyzing the inclination degree of the photovoltaic panel and the relationship between the photovoltaic panel and the wind direction, this application calculates the wind load influence coefficient, so that the stress monitoring results are corrected by the wind load, improving the accuracy of stress monitoring. Further considering the correlation degree between the wind load influence and the vibration data of the support and the complexity of the data fluctuation patterns of each row of support structures, the stress state abnormal values of each row of support structures are calculated. Instead of evaluating the stress state of a single point, the stress state of each row of support structures is evaluated, making the evaluation results more comprehensive. Compared with the conventional method of directly comparing real-time stress data, its advantage lies in being able to accurately evaluate the potential influence of the wind load on the support system and obtain the comprehensive stress monitoring results of each row of support structures in the photovoltaic support system, which helps to improve the reliability of stress monitoring. Description of the Drawings

[0027] To more clearly illustrate the technical solutions and advantages in the embodiments of the present application or the prior art, the drawings required for use in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0028] Figure 1 It is a flowchart of the steps of a method for stress monitoring of a flexible photovoltaic support system provided by the present application;

[0029] Figure 2 It is a flowchart for constructing the stress state abnormal value provided by the present application. Detailed Embodiments

[0030] To further elaborate on the technical means and effects adopted by the present application to achieve the intended invention purpose, the following combines the drawings and preferred embodiments to detail the specific implementation manner, structure, features, and effects of a method for stress monitoring of a flexible photovoltaic support system proposed according to the present application. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0031] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which this application belongs.

[0032] The following specifically describes the specific solution of a method for stress monitoring of a flexible photovoltaic support system provided by the present application with reference to the drawings.

[0033] A stress monitoring method for a flexible photovoltaic support system provided by an embodiment of the present application. Specifically, the following stress monitoring method for a flexible photovoltaic support system is provided. Please refer to Figure 1 , the method includes the following steps:

[0034] Embodiment:

[0035] S1: Obtain the stress data, cable force data, vibration data of each point of each row of support structures in the photovoltaic support system, as well as the wind speed and wind direction data of the area where the photovoltaic support system is located.

[0036] During the stress monitoring of the flexible photovoltaic support system, it is crucial to monitor the stress, cable force of the monitoring support, and the vibration data of the support. The support is the foundation of the support system, directly bearing the weight load of the upper structure. Real-time monitoring of the support stress can timely detect potential structural problems. The cable maintains the balance of the structure through pre-tension, and the change of the cable force may affect the overall stability. The vibration characteristics of the support directly affect the dynamic stability of the structure, especially under the action of wind load. And the entire support structure is composed of multiple supports, steel cables, and fixtures connected. When subjected to stress changes, the stress states of different points affect each other. Therefore, this application collects data at multiple points in the entire support structure, and the specific points can be selected according to the type and actual situation of the flexible photovoltaic support system. Among them, strain gauges are installed at each point to collect the stress data of the support, cable force gauges are installed to collect the cable force data of the cable, and accelerometers are installed to collect the vibration data of the support. To evaluate the influence of wind load on the stress of the photovoltaic support system, the wind speed and wind direction data of the area where the photovoltaic support system is located are collected by an anemometer. The acquisition frequency of each item of data is set to 10Hz.

[0037] So far, the stress data, cable force data, vibration data, as well as the wind speed and wind direction data at each point and each moment are obtained. Through the 5G wireless transmission method, the data collected by each sensor is sent to the monitoring platform for data analysis at the monitoring platform end.

[0038] S2: Analyze the inclination of the surface of each photovoltaic panel to obtain the inclination of each photovoltaic panel at each moment, and combine the wind speed at each moment and the relationship between the position of each photovoltaic panel and the wind direction at each moment to obtain the wind load influence coefficient at each point at each moment.

[0039] After the monitoring platform receives the data collected by the sensor, it performs real-time analysis and processing on the data. By analyzing the deep features between different types of data, the stress state of the photovoltaic support system can be evaluated.

[0040] In a flexible photovoltaic support system, wind load is an important factor affecting its stress state. Since photovoltaic modules usually have a large area, a light mass, and a low stiffness, they are highly sensitive to wind load. Under the action of strong wind, the support structure is prone to deformation and vibration. In addition, during the power generation process of photovoltaic modules, their placement postures are dynamically adjusted according to the sunlight irradiation angle, and at the same time, the wind direction may also change. The magnitude of the wind speed and the angle between the photovoltaic module and the wind direction will both have a significant impact on the wind load, thereby resulting in different stress states.

[0041] Furthermore, the variation characteristics between the angle of the photovoltaic panel and the wind direction are analyzed as follows. It should be noted that each point corresponds to a photovoltaic panel. In this application, the wind direction data collected by the anemometer is along the horizontal direction. When the angle between the photovoltaic panel and the horizontal plane is larger, the area of the photovoltaic panel affected by the wind is larger. In addition, the wind may also blow towards the photovoltaic panel at different angles. If the wind direction is perpendicular to the photovoltaic panel, the acting force of the wind load is stronger at this time, while when the wind direction is parallel to the photovoltaic panel, the stress area is smaller and the acting force of the wind load is weaker. Moreover, in the windward and leeward states, the magnitudes of the wind loads are also different. Affected by the complex structure and rough surface of the back of the photovoltaic panel, the wind load in the leeward state is usually larger than that in the windward state. Therefore, the influence characteristics of the wind load on the stress are obtained by combining the inclination angle of the photovoltaic panel itself with the horizontal plane direction and the angle between the wind direction and the photovoltaic panel.

[0042] In this application, the position state of the photovoltaic panel at each moment is obtained through the adjustment device of the photovoltaic support system. Taking any photovoltaic panel at the i-th moment as an example for detailed description. To consider the difference in wind load between the windward and leeward sides of the photovoltaic panel, first, the unit normal vector of the plane where the photovoltaic panel is located along its back direction is obtained, where the back of the photovoltaic panel is the side facing away from the sun. Given that the influence of the wind load on the photovoltaic panel is mainly reflected in these two key factors: its own inclination angle and the relationship between the position of the photovoltaic panel and the wind direction. The modulus of the obtained unit normal vector in the vertical direction is taken as the inclination degree of the photovoltaic panel, which is the inclination degree of the photovoltaic panel at the i-th moment, The larger it is, the closer the normal vector of the photovoltaic panel plane is to the vertical direction at the \(i\)-th moment, the closer the inclination direction of the photovoltaic panel is to the horizontal direction, and the smaller the degree of influence by the wind load. When the unit normal vector is close to parallel to the wind direction, the photovoltaic panel is in the attitude of facing or backing the wind direction. The closer the angle formed between the unit normal vector and the wind direction is to 0 degrees or 180 degrees, the greater the influence of the wind force it receives, and the greater the influence of the wind load in the leeward state; when the unit normal vector is perpendicular to the wind direction, the photovoltaic panel is in the attitude of side-facing the wind direction. The closer the angle formed between the unit normal vector and the wind direction is to 90 degrees, the smaller the influence of the wind force it receives. Thus, first calculate the absolute value of the cosine value corresponding to the angle formed between the unit normal vector of each photovoltaic panel at each moment and the wind direction at each moment, and then calculate the ratio of the angle formed between the unit normal vector of each photovoltaic panel at each moment and the wind direction at each moment to 180 degrees. The larger the ratio, the closer the photovoltaic panel is to the leeward state, and the smaller the ratio, the closer the photovoltaic panel is to the windward state. Denote the sum of the absolute value and the ratio as the wind direction influence coefficient of each photovoltaic panel at each moment. represents the wind direction influence coefficient of the photovoltaic panel at the \(i\)-th moment, and the obtained The larger it is, the greater the degree of influence of the photovoltaic panel by the wind direction.

[0043] In addition, the greater the wind speed, the higher the wind load on the photovoltaic panel. Furthermore, the formula for calculating the wind load influence coefficient at the \(i\)-th moment is: , where in the formula, is the wind load influence coefficient of the photovoltaic panel at the \(i\)-th moment, is the wind speed data at the \(i\)-th moment, is the wind direction influence coefficient of the photovoltaic panel at the \(i\)-th moment, is the inclination of the photovoltaic panel at the \(i\)-th moment. The obtained The larger it is, the greater the influence of the photovoltaic panel by the wind load at that moment.

[0044] S3: Divide the vibration data of each point into multiple intervals; based on the difference situation of the vibration data of each point in each interval and the average level of the vibration data, obtain the vibration response coefficient of each point in each interval; combine the correlation relationship between the wind load influence coefficient and the vibration response coefficient of each point in each interval to construct the consistency coefficient of each point.

[0045] Furthermore, the cable forces of the stay cables and the vibration characteristics of the supports directly affect the dynamic stability of the photovoltaic support structure, especially under wind loads. Since the flexible photovoltaic support system is supported by prestressed flexible cables, its vibration amplitude is larger than that of rigid supports. The mutual influence between various parts is also more significant. Drag structures such as stabilizing cables and ground anchors are locally arranged, so its vibration condition is more complex than that of rigid supports. In addition, the action of wind loads has certain pulsating characteristics, which means that the action intensity of the wind on the support structure is not constant, but shows irregular periodic changes, thus making the vibration data of the support have corresponding irregular periodic changes. When the stability of the support structure is worse, the vibration degree of the support will change greatly with the increase of wind loads; when the stability of the support structure is better, the vibration degree of the support will change less with the increase of wind loads. Therefore, when the stability state of the support structure is worse, the correlation characteristics between the corresponding wind loads and the vibration characteristics of the support are stronger.

[0046] Therefore, further analyze the irregular periodic changes of the vibration data caused by the pulsating characteristics of wind loads. Since the stress state of the photovoltaic support system responds quickly to the wind, the duration of each stress monitoring period should not be too long. Preferably, in this embodiment, the time length of each stress monitoring period is set to 20 seconds. There are high-frequency small-range fluctuations up and down in the vibration data of the support. When subjected to the action of strong wind, the amplitude of the corresponding vibration data will increase significantly and rapidly. The action of the wind may last for a short and unstable period of time. When the action of the strong wind ends, the amplitude of the corresponding vibration data will decrease significantly and rapidly, and this process occurs intermittently. The increase and decrease amplitude of the vibration data is usually greater than its fluctuation amplitude. Taking a certain period as an example, the Bernaola Galvan (BG) segmentation algorithm is used in this application to divide the period of the vibration data. The output of this algorithm is the positions of all segmentation points in the vibration data. Since there are still fluctuations in the vibration data of the support during the rapid increase and decrease process, there may be multiple segmentation points in a local range. These segmentation points that are relatively close to each other will interfere with the period division. Further merge the multiple segmentation points in the local range. Specifically, if the difference between the corresponding time values of two adjacent segmentation points is less than or equal to m, preferably, m is set to 9 in this embodiment, then the upward rounding result of the median of the corresponding times of these two segmentation points is used as the new segmentation point. Thus, the vibration data is divided into multiple intervals according to the segmentation points after the merging operation.

[0047] Under the influence of high-intensity wind forces, the vibration of the support is relatively intense, and the jitter of the vibration data in the corresponding interval is more unstable. Thus, the vibration characteristics in each interval are obtained. Calculate the product of the Shannon entropy and the mean value of the corresponding vibration data in each interval as the vibration response coefficient corresponding to each interval. The larger the obtained vibration response coefficient, the greater the vibration degree of the support in that interval. The wind load influence coefficient is calculated for each moment in the interval, and the mean value of all wind load influence coefficients in each interval is calculated respectively. The mean values of the wind load influence coefficients and the vibration response coefficients are sorted in ascending order of time, and the wind load influence sequence and the vibration response sequence can be obtained. Based on the correlation characteristics between the wind load influence and the vibration degree of the support, calculate the Pearson correlation coefficient between the above two sequences as the consistency coefficient of the wind load and the vibration characteristics of the support. The larger the obtained consistency coefficient, the worse the stability degree of the support at that point in the corresponding time period. Thus, the consistency coefficient corresponding to each point in the photovoltaic support system is obtained.

[0048] S4: Obtain the first fluctuation coefficient of the single-row support structure according to the dispersion degree and the degree of violent change of the consistency coefficients of all points in the single-row support structure; obtain the second fluctuation coefficient of the single-row support structure according to the dispersion degree and the degree of violent change of the cable force data of all points in the single-row support structure; analyze the fluctuation degree of the stress data of all points in the single-row support structure, and combine the mean values of the first fluctuation coefficient and the second fluctuation coefficient to obtain the stress state outlier of the single-row support structure.

[0049] The flexible photovoltaic support system adopts an array design, and each photovoltaic panel unit in each row is connected by a cable. Due to its large span, the single-row support structure is prone to bending vibration with wave characteristics. The placement angle of the photovoltaic panels during power generation is dynamically changing, and the wind direction may also change. The wind speed and the angle between the photovoltaic panels and the wind direction will affect the wind load, thereby causing stress effects of different degrees. The data sequence of the single-row support structure may show different fluctuation patterns. Among them, if the fluctuation pattern shows a relatively smooth overall fluctuation, it indicates that the support structure is more stable and the stress distribution is more balanced; if the fluctuation pattern shows multiple complex waveforms, it indicates that the distortion degree of the support structure is greater, the stress distribution is more uneven, and it is more likely to have problems such as corrosion aging and loosening of the connectors. Through analyzing the data fluctuation pattern of the single-row support structure, this application can comprehensively evaluate the stress of the support system.

[0050] Taking the support structure in the j-th row as an example, for the points with greater undulations, the stability of the support is worse, and the corresponding consistency coefficient is larger. Therefore, the undulation pattern of the single-row support structure can be reflected by the consistency coefficient at consecutive points. The consistency coefficients of each point in the single-row support structure are arranged in the order of the positions of each point to obtain the corresponding consistency sequence. It should be noted that in this embodiment, the position order is to sequentially sort the positions of each point from one end of the support structure to the other end, and the specific starting end point is not particularly required by the implementer. To analyze the complexity of the undulation, polynomial fitting technology is used to fit the consistency sequence to obtain the corresponding fitting curve, and then the absolute value of the slope of the fitting curve at each point is obtained, and the shortest distance between each data in the consistency sequence and the fitting curve is calculated respectively. Furthermore, the first undulation coefficient of the j-th row support structure is obtained , and the specific formula is: , where is the first undulation coefficient of the j-th row support structure, which is used to characterize the unevenness of the stability of each point in the single-row support structure, is the sum of the shortest distances between each data in the consistency sequence of the j-th row support structure and the fitting curve, and this value reflects the degree of dispersion of the vibration waveform of the single-row support structure, where is the sum of the absolute values of the slopes of the fitting curve at each point, and this value reflects the degree of severity of the structural changes at each point. The higher the obtained undulation pattern complexity coefficient, the more unstable the single-row flexible photovoltaic support is.

[0051] Generally, the greater the undulation of a point, the greater the cable force it receives. To some extent, the cable force data at each point has similar undulation characteristics to the consistency coefficient. The average cable force data of each point is obtained by averaging all the cable force data of each point within a stress monitoring period, and the sequence composed of the average cable force data from one end to the other end of the single-row support structure is denoted as the cable force mean sequence. The cable force mean sequence is used as the input of the polynomial fitting technology to obtain the corresponding fitting curve. Based on the cable force mean sequence of the j-th row support structure, the corresponding second undulation coefficient can be obtained, and the specific formula is: , where is the second undulation coefficient of the j-th row support structure, which is used to characterize the unevenness of the cable force received by each point in the single-row support structure, is the sum of the shortest distances between each data in the cable force mean sequence of the j-th row support structure and the fitting curve corresponding to the cable force mean sequence, is the sum of the absolute values of the slopes of the fitting curve of the cable force mean sequence of the j-th row support structure at each point.

[0052] As the force-bearing fulcrum of the support and cable, the vibration of the support and the cable force of the cable will cause stress changes in the support. Therefore, the stress state is evaluated by combining the changes in stress data and the magnitudes of the complexity coefficients of various fluctuation patterns. The sum of the standard deviation of the stress data at all points in the single-row support structure and the means of the first and second fluctuation coefficients is used as the stress state outlier value of the single-row support structure. The flowchart for constructing the stress state outlier value is as shown in Figure 2 shown. The obtained stress state outlier value reflects the instability degree of the stress of the corresponding single-row support structure.

[0053] S5: Monitor the stress states of each row of support structures by combining the stress state outlier values of each row of support structures.

[0054] Based on the stress state outlier value, in this embodiment, the softmax function is used to normalize the obtained stress state outlier value, and the outlier threshold is set to 0.7. Then, the magnitude of the normalization result and the outlier threshold are judged. If the stress state outlier value of the j-th row of support structures is greater than or equal to the outlier threshold, it indicates that the stress state of the corresponding support structure is abnormal, and the stability degree of this row of support structures in the array is poor, and a warning message needs to be sent to the staff; if the stress state outlier value of the j-th row of support structures is less than the outlier threshold, it indicates that this row of support structures in the array is operating stably.

[0055] It should be noted that: the above sequence of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. And the above specific embodiments of this specification have been described. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0056] Each embodiment in this specification is described in a progressive manner. The same or similar parts between each embodiment can be referred to each other, and the key points of each embodiment are the differences from other embodiments.

[0057] The above-described embodiments are only used to illustrate the technical solutions of the present application and are not intended to limit them; modifying the technical solutions recorded in the foregoing embodiments or equivalently replacing some of the technical features does not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of each embodiment of the present application, and all should be included in the protection scope of the present application.

Claims

1. A stress monitoring method for a flexible photovoltaic support system, characterized in that: The method comprises the following steps: S1: Obtain stress data, cable force data, vibration data of each point of each row of support structures in the photovoltaic support system at each time, as well as wind speed and wind direction data in the area where the photovoltaic support system is located; S2: Calculate the wind load influence coefficient of each photovoltaic panel at each moment: obtain the unit normal vector of the plane where each photovoltaic panel is located along the back direction at each moment, take the modulus of each unit normal vector in the vertical direction as the inclination of each photovoltaic panel at each moment, calculate the absolute value of the cosine value corresponding to the angle between the unit normal vector of each photovoltaic panel at each moment and the wind direction at each moment, obtain the ratio of the angle between the unit normal vector of each photovoltaic panel at each moment and the wind direction at each moment to 180 degrees, take the sum of the absolute value and the ratio as the wind direction influence coefficient of each photovoltaic panel at each moment, and take the ratio of the product of the wind direction influence coefficient of each photovoltaic panel at each moment and the wind speed to the inclination as the wind load influence coefficient of each photovoltaic panel at each moment; S3: Divide the vibration data of each point into multiple intervals; multiply the entropy and the mean of all vibration data of each point in each interval as the vibration response coefficient of each point in each interval; arrange the mean of the wind load influence coefficient and the vibration response coefficient of each point in all intervals in ascending time order to obtain the wind load influence sequence and vibration response sequence of each point; take the Pearson correlation coefficient between the wind load influence sequence and the vibration response sequence of each point as the consistency coefficient of each point; S4: According to the degree of dispersion and the degree of variation of the consistency coefficient of all points in the single-row support structure, obtain the first fluctuation coefficient of the single-row support structure; according to the degree of dispersion and the degree of variation of the cable force data of all points in the single-row support structure, obtain the second fluctuation coefficient of the single-row support structure; analyze the degree of fluctuation of the stress data of all points in the single-row support structure, and obtain the stress state abnormal value of the single-row support structure by combining the mean of the first fluctuation coefficient and the second fluctuation coefficient; S5: Monitor the stress state of each row of supporting structures in combination with the abnormal values ​​of the stress state of each row of supporting structures.

2. A stress monitoring method for a flexible photovoltaic support system as claimed in claim 1, characterized in that: The method for determining the division into multiple intervals is: The vibration data of each point is used as the input of the segmentation algorithm, and the output is the positions of all segmentation points in the vibration data; If the time interval between two adjacent segmentation points is less than or equal to a preset value, the result of rounding up the median of the corresponding moments of the two adjacent segmentation points is used as a new segmentation point to replace the original two adjacent segmentation points, and the segmentation points are merged; The vibration data of each point is divided into multiple intervals according to the segmentation points after the merging operation.

3. A stress monitoring method for a flexible photovoltaic support system as claimed in claim 1, characterized in that: The method for determining the first fluctuation coefficient of the single-row support structure is: , where is the first wave coefficient of the j-th row support structure, is the cumulative sum of the shortest distances between each data in the consensus sequence of the j-th row support structure and the fitting curve of the consensus sequence of the j-th row support structure; It is the cumulative sum of the absolute values ​​of the slopes of the fitting curve of the j-th row of support structures at each point, wherein the consistency sequence of the j-th row of support structures is obtained by arranging the consistency coefficients of each point in the j-th row of support structures in the order of their positions.

4. A stress monitoring method for a flexible photovoltaic support system as claimed in claim 1, characterized in that: The method for determining the second fluctuation coefficient of the single-row support structure is: The average of all the cable force data at each point is calculated to obtain the average cable force data at each point, and the sequence composed of the average cable force data at all points of each row of supporting structures is recorded as the cable force average sequence; The expression of the second wave coefficient of the j-th row support structure is: , where is the second wave coefficient of the j-th row support structure, is the cumulative sum of the shortest distances between each data in the cable force mean sequence of the j-th row of support structures and the fitting curve of the cable force mean sequence, It is the cumulative sum of the absolute values ​​of the slopes of the fitting curve of the cable force mean sequence of the j-th row of support structures at each point.

5. A stress monitoring method for a flexible photovoltaic support system as claimed in claim 1, characterized in that: The calculation method of the stress state abnormal value of the single-row support structure is the sum of the standard deviation of the stress data of all points in the single-row support structure and the mean of the first fluctuation coefficient and the second fluctuation coefficient.

6. A stress monitoring method for a flexible photovoltaic support system as claimed in claim 1, characterized in that: The monitoring of the stress state of each row of support structures further includes: if the stress state abnormal value is greater than or equal to the abnormal threshold, then the stress state of the corresponding support structure is abnormal.

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