A distributed photovoltaic power generation equipment intelligent monitoring system

Abstract

The application belongs to the technical field of photovoltaic power generation equipment fault monitoring, and discloses a kind of distributed photovoltaic power generation equipment intelligent monitoring system, including light intensity equipment exception monitoring module, control equipment exception monitoring module, drive equipment exception monitoring module, tracking system exception analysis module, support structure data acquisition module, support structure exception analysis module and fault cause pointing identification module.The application analyzes the abnormal conditions of tracking system and support structure, and then comprehensively evaluates the health status of each automatic tracking photovoltaic power generation device.This analysis method takes into account the tracking system and the support structure, covering the key aspects of device operation and support, which greatly improves the accuracy of evaluation, accurately grasps the real situation of the equipment, discovers potential problems in time, formulates more targeted maintenance strategies, effectively ensures the stable operation of photovoltaic power generation devices, improves power generation efficiency and reduces equipment failure risk.

Description

Technical Field

[0001] This invention belongs to the field of photovoltaic power generation equipment fault monitoring technology, and relates to an intelligent monitoring system for distributed photovoltaic power generation equipment. Background Technology

[0002] An automatic tracking photovoltaic (PV) power generation device is a power generation system that automatically adjusts the angle of its photovoltaic panels to ensure they are always aligned with the sun, maximizing sunlight reception. It mainly consists of PV panels, a support structure, and a tracking system. The support structure is fundamental, providing support for angle adjustment and adapting to different installation environments. The tracking system is the core component, significantly improving power generation efficiency by adjusting the PV panel angle in real time to maximize sunlight utilization. Therefore, research on intelligent fault monitoring of PV power generation equipment based on automatic tracking PV power generation devices is of great significance.

[0003] In the prior art, there are also relevant solutions for photovoltaic power generation equipment fault monitoring technology. For example, the Chinese invention patent application with publication number CN118074624A, which discloses a method and system for monitoring the fault of a photovoltaic tracking bracket, includes: the tracker collects the initial running signal of the drive motor in real time and sends it to the data acquisition and monitoring control system; at the same time, the tracker generates and sends a first analysis running signal in the first time window, and the system generates a second analysis running signal in the second time window with a larger time span, and finally judges whether the photovoltaic bracket is operating abnormally based on these two types of signals.

[0004] Another Chinese invention patent application with publication number CN118783876A discloses a controllable photovoltaic bracket and installation method, which includes: an optimization module determining the orientation of the photovoltaic panel based on the light threshold; when it is not optimal, the light module determines the turning direction angle; the control module instructs the motor to adjust; and the energy collection module monitors the energy collection value in real time to realize self-detection of the photovoltaic panel during use.

[0005] While the two solutions mentioned above offer some solutions for fault monitoring technology of photovoltaic power generation equipment, they still have certain limitations: On the one hand, some existing technical solutions only target the single-sided signal acquisition of the drive system or control system, lacking a comprehensive analysis of the overall performance of the tracking system and the health status of the support structure, resulting in incomplete fault monitoring, omission of potential hidden dangers, and easy to cause misdiagnosis or missed diagnosis, failure of maintenance strategies, and decline in power generation efficiency.

[0006] On the other hand, existing technical solutions do not establish a benchmark for the ideal tracking direction in the same region, making it impossible to achieve accurate anomaly identification by comparing actual light intensity data. This results in low efficiency in troubleshooting, delayed discovery of minor anomalies, and difficulty in carrying out overall monitoring and differentiated management of devices within the region, thus weakening the stability and efficiency of system operation. Summary of the Invention

[0007] In view of this, in order to solve the problems mentioned in the background technology, an intelligent monitoring system for distributed photovoltaic power generation equipment is proposed.

[0008] The objective of this invention can be achieved through the following technical solution: A distributed photovoltaic power generation equipment intelligent monitoring system, comprising: a light intensity equipment anomaly monitoring module, used to obtain the ideal tracking direction of each automatic tracking photovoltaic power generation device using light intensity monitoring equipment, and then analyze the corresponding reference ideal direction angle and reference ideal pitch angle and jointly evaluate the corresponding light intensity equipment anomaly.

[0009] The control equipment anomaly monitoring module is used to collect the azimuth and pitch angles of each automatic tracking photovoltaic power generation device using position sensors and tilt sensors, obtain control equipment adjustment data, and evaluate the corresponding control equipment anomalies based on the control equipment adjustment data.

[0010] The drive equipment abnormality monitoring module is used to obtain the temperature of the drive motor in real time using a temperature sensor, and at the same time obtain the rotation ratio of each automatic tracking photovoltaic power generation device to evaluate the abnormal conditions of the corresponding drive equipment.

[0011] The tracking system anomaly analysis module is used to assess the anomaly situation of the tracking system based on the corresponding anomalies of the light intensity device, control device, and drive device.

[0012] The support structure data acquisition module is used to acquire images of the support structure using a high-definition camera and obtain support structure data for each automatic tracking photovoltaic power generation device.

[0013] The stent structure anomaly analysis module is used to analyze the corresponding stent structure anomalies based on the stent structure data.

[0014] The fault cause identification module is used to identify the specific abnormality.

[0015] Compared with the prior art, the beneficial effects of the present invention are as follows: (1) The present invention analyzes the abnormality of the tracking system and the abnormality of the support structure, and then comprehensively evaluates the health status of each automatic tracking photovoltaic power generation device. This analysis method takes into account both the tracking system and the support structure, and covers the key aspects of device operation and support. This greatly improves the accuracy of the evaluation, can accurately grasp the real condition of the equipment, discover potential problems in time, and formulate more targeted maintenance strategies to effectively ensure the stable operation of the photovoltaic power generation device, improve power generation efficiency, and reduce the risk of equipment failure.

[0016] (2) This invention analyzes the abnormalities of light intensity equipment, control equipment, and drive equipment, and then analyzes the abnormalities of the tracking system of each automatic tracking photovoltaic power generation device. This analysis method can accurately locate the fault and clarify whether the problem lies in the light energy reception, command control, or power transmission links, avoiding blind troubleshooting. The analysis is comprehensive, covering key parts such as energy source, coordination control, and power transmission, ensuring no omissions. Moreover, this analysis facilitates targeted maintenance, allowing for the development of customized maintenance plans based on the abnormalities of different equipment, improving maintenance efficiency, reducing costs, effectively extending the service life of equipment, and ensuring the stable operation of photovoltaic power generation devices.

[0017] (3) When monitoring the abnormality of light intensity equipment, this invention analyzes the abnormality of light intensity equipment of each automatic tracking photovoltaic power generation device based on the ideal tracking direction corresponding to each automatic tracking photovoltaic power generation device. This analysis method can quickly locate the problematic device, improve the troubleshooting efficiency, and avoid ineffective inspection of normal equipment. At the same time, based on the ideal tracking direction, the actual light intensity data can be accurately compared, and subtle deviations of the light intensity equipment can be keenly detected, thereby accurately judging the type and degree of equipment failure, providing strong support for targeted maintenance, and ensuring the efficient and stable operation of photovoltaic power generation devices. Attached Figure Description

[0018] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a schematic diagram showing the connections of the various modules in the system of the present invention.

[0020] Figure 2 This is a schematic diagram of pitch angle analysis corresponding to one embodiment of the present invention.

[0021] Figure 3 This is a schematic diagram of the orientation angle analysis corresponding to one embodiment of the present invention.

[0022] Attached diagram labels: 1—Ideal tracking direction, 2—Pitch angle, 3—Vertical downward direction, 4—Azimuth angle, 5—Due north. Detailed Implementation

[0023] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0024] Please see Figure 1 As shown, the present invention provides an intelligent monitoring system for distributed photovoltaic power generation equipment, including a solar intensity equipment anomaly monitoring module, a control equipment anomaly monitoring module, a drive equipment anomaly monitoring module, a tracking system anomaly analysis module, a support structure data acquisition module, a support structure anomaly analysis module, and a fault cause identification module. The solar intensity equipment anomaly monitoring module, the control equipment anomaly monitoring module, and the drive equipment anomaly monitoring module are all connected to the tracking system anomaly analysis module. The support structure data acquisition module is connected to the support structure anomaly analysis module. The tracking system anomaly analysis module and the support structure anomaly analysis module are both connected to the fault cause identification module.

[0025] The light intensity equipment anomaly monitoring module is used to obtain the ideal tracking direction of each automatic tracking photovoltaic power generation device using light intensity monitoring equipment, and then analyze the corresponding reference ideal direction angle and reference ideal pitch angle, and jointly evaluate the corresponding light intensity equipment anomaly.

[0026] It should be explained that the ideal tracking direction of each automatic tracking photovoltaic power generation device refers to the direction that allows the photovoltaic panel to receive sunlight to the maximum extent. When the solar altitude angle is low, the photovoltaic panel with a fixed support receives limited light intensity, while the automatic tracking device can adjust the angle to make the panel perpendicular to the tilted sunlight, thereby capturing more light energy and significantly improving power generation efficiency.

[0027] It should be noted that you should refer to [link / reference]. Figure 2 , Figure 3 As shown, the ideal direction angle refers to the angle between the ideal tracking direction in the horizontal direction and true north, and the ideal pitch angle refers to the angle between the ideal tracking direction in the vertical direction and the vertically downward direction.

[0028] It should be added that the ideal tracking direction of each automatic tracking photovoltaic power generation device is determined as follows: A light intensity sensor is installed around the photovoltaic power generation device to accurately sense the location of the light source, allowing it to perceive the light intensity from all directions. The collected light intensity data is analyzed, and by comparing the light intensity values ​​collected by sensors in different directions, the maximum light intensity and its corresponding direction are determined, which is then used as the ideal tracking direction for each automatic tracking photovoltaic power generation device.

[0029] In a preferred embodiment of the present invention, the specific method for analyzing abnormal situations of the light intensity equipment is as follows: extract the ideal azimuth angle and ideal pitch angle of each automatic tracking photovoltaic power generation device.

[0030] It should be noted that the automatic tracking photovoltaic power generation devices described in this invention are located in the same geographical area. Therefore, the ideal tracking direction of each automatic tracking photovoltaic power generation device should theoretically be consistent. Similarly, the ideal elevation angle of each automatic tracking photovoltaic power generation device should theoretically be consistent. However, in practice, errors may occur in the light intensity acquisition and analysis process of the automatic tracking photovoltaic power generation device, leading to a deviation between the actual ideal tracking direction obtained through the light intensity analysis and the theoretical ideal tracking direction.

[0031] The reference ideal angle of the automatic tracking photovoltaic power generation device is obtained by averaging the ideal angles of each device. Similarly, the reference ideal pitch angle can be obtained. The deviation values ​​of the ideal angle and ideal pitch angle of each device are obtained by performing difference analysis between the ideal angle and ideal pitch angle of each device and the corresponding reference ideal angle and reference ideal pitch angle.

[0032] For example, the specific method for obtaining the ideal azimuth angle deviation value and the ideal pitch angle deviation value is as follows: the ideal azimuth angle and the ideal pitch angle of each automatic tracking photovoltaic power generation device are calculated by comparing them with the corresponding reference ideal azimuth angle and the reference ideal pitch angle, and the absolute value is taken to obtain the ideal azimuth angle deviation value and the ideal pitch angle deviation value of each automatic tracking photovoltaic power generation device.

[0033] The ideal azimuth deviation and ideal pitch deviation of each automatic tracking photovoltaic power generation device are compared and analyzed with the corresponding reference ideal azimuth and reference ideal pitch to obtain the ideal azimuth deviation and ideal pitch deviation of each automatic tracking photovoltaic power generation device. Then, the light intensity equipment anomaly index of each automatic tracking photovoltaic power generation device is calculated by weighted summation.

[0034] It's important to explain why ideal azimuth angle deviation and ideal pitch angle deviation were chosen as influencing factors for the light intensity equipment anomaly index of various automatic tracking photovoltaic power generation devices: these directly determine the angle of the solar panel relative to sunlight. Changes in this angle alter the angle of light incidence, affecting light intensity reception, while the impact of tracking and control equipment on light intensity is indirect. These two deviations directly reflect whether the light intensity equipment is in optimal lighting condition and can also pinpoint the root cause of anomalies. For example, azimuth angle deviation may indicate a problem with the horizontal rotation mechanism, and pitch angle deviation may indicate a malfunction in the vertical adjustment mechanism. Moreover, they facilitate the quantitative and systematic assessment of the degree of light intensity anomaly and equipment status, which is difficult to achieve with tracking and control equipment.

[0035] It should be noted that the weighting of the ideal azimuth angle deviation and ideal pitch angle deviation for each automatic tracking photovoltaic power generation device is based on the following: Regarding the impact of light intensity, if the horizontal movement of the sun in the region is significant, the azimuth angle deviation has a greater impact on light intensity, and its weight can be set higher; if the vertical change in illumination is large, the pitch angle deviation weight is increased. From the perspective of fault history, if azimuth angle deviation causes numerous faults and significant losses, leading to a substantial decrease in power generation efficiency, the azimuth angle deviation weight can be increased; conversely, if pitch angle deviation causes severe faults, its weight is increased. This allows for the reasonable setting of weights to assess equipment malfunctions related to light intensity.

[0036] For example, the weights corresponding to the ideal orientation angle deviation and ideal pitch angle deviation of each automatic tracking photovoltaic power generation device are as follows: .

[0037] The control equipment anomaly monitoring module is used to collect the azimuth and pitch angles of each automatic tracking photovoltaic power generation device using position sensors and tilt sensors, obtain control equipment adjustment data, and evaluate the corresponding control equipment anomalies based on the control equipment adjustment data.

[0038] In a preferred embodiment of the present invention, the control device adjustment data includes vibration frequency, vibration duration, number of directional adjustments, and average directional adjustment amplitude.

[0039] It should be noted that vibration frequency, vibration duration, number of directional adjustments, and average directional adjustment amplitude were chosen as the adjustment data for the control equipment for the following reasons: Firstly, vibration frequency and duration directly reflect the operational stability of the control equipment; higher frequencies and longer durations indicate system instability, which may be caused by faults in the control algorithm, signal transmission, or electrical components. Secondly, abnormal number and amplitude of directional adjustments can provide clues for fault diagnosis; frequent adjustments or excessively large amplitudes may be caused by faults in sensors, control algorithms, or mechanical transmission components.

[0040] For example, the duration corresponding to the monitoring period can be... .

[0041] It should be added that the control device adjusts the data acquisition method as follows: 1. Vibration sensors are installed on the support structure corresponding to each automatic tracking photovoltaic power generation device, and then the vibration sensors are used to obtain the vibration frequency of each automatic tracking photovoltaic power generation device corresponding to each vibration during the pre-set monitoring period, and the maximum vibration frequency is selected as the vibration frequency of each automatic tracking photovoltaic power generation device.

[0042] 2. Obtain the start and end times of each vibration condition corresponding to each automatic tracking photovoltaic power generation device, then calculate the duration of each vibration condition by the difference between the end time and the start time, and then accumulate the results to obtain the vibration duration of each automatic tracking photovoltaic power generation device.

[0043] 3. Extract the direction adjustment command records for each automatic tracking photovoltaic power generation device within the corresponding monitoring period, and then further obtain the horizontal and vertical direction adjustment commands. Identify and count the number of adjustment commands with abnormal directions, and record them as the direction adjustment times for each automatic tracking photovoltaic power generation device.

[0044] For example, taking the horizontal adjustment command as an example, the method for judging the abnormal direction adjustment command is as follows: compare the direction angle of each horizontal adjustment command with the direction angle of the previous horizontal adjustment command and the direction angle of the next horizontal adjustment command, and judge whether the direction angle of each horizontal adjustment command is within the range of the direction angle of the previous horizontal adjustment command and the direction angle of the next horizontal adjustment command. If it is, the corresponding horizontal adjustment command is judged not to be an abnormal direction; otherwise, it is judged to be an abnormal direction.

[0045] 4. Extract the adjustment range corresponding to each adjustment command that is judged to be in an abnormal direction, take the absolute value and then calculate the average direction adjustment range of each automatic tracking photovoltaic power generation device.

[0046] In a preferred embodiment of the present invention, the specific analysis method for abnormal conditions of the control equipment is as follows: extract the vibration frequency, vibration duration, number of direction adjustments and average direction adjustment amplitude of each automatic tracking photovoltaic power generation device acquired in real time, and compare them with the preset reference vibration frequency, monitoring period threshold, direction adjustment number threshold and adjustment amplitude threshold respectively. Combine the vibration parameter weight and the direction adjustment parameter weight to calculate the control equipment abnormality index by weight summation.

[0047] Preferably, the vibration frequency, vibration duration, number of direction adjustments, and average direction adjustment amplitude of each automatic tracking photovoltaic power generation device are extracted and recorded as follows: , , , ,in Indicates the serial number of the automatic tracking photovoltaic power generation device. , This indicates the number of automatic tracking photovoltaic power generation devices.

[0048] Using formula Analysis yielded the control equipment anomaly index for each automatic tracking photovoltaic power generation device. ,in This indicates the preset reference vibration frequency. This indicates the monitoring duration corresponding to the pre-set monitoring period. This indicates the number of times the reference direction has been adjusted as preset. This indicates the preset adjustment range of the reference direction. These represent the weighting factors corresponding to the pre-set vibration conditions and direction adjustment conditions, respectively.

[0049] It should be noted that the above formula is constructed based on the following principles: 1. Fault factor classification consideration: The formula aims to comprehensively evaluate abnormal conditions of the control equipment of automatic tracking photovoltaic power generation devices. Factors that may cause equipment abnormalities are classified into two categories: vibration and direction adjustment. Vibration frequency and monitoring duration reflect the operational stability of the equipment; the number of direction adjustments and the adjustment amplitude reflect the equipment's working status in tracking the sun.

[0050] 2. Normalization process: These fractions are normalizations of the actual monitored values ​​relative to pre-set reference values. In this way, data with different dimensions are transformed into comparable relative values, facilitating comprehensive calculation and analysis.

[0051] 3. Weighted Allocation and Comprehensive Evaluation: Introducing Weighting Factors Since vibration and directional adjustment may have different impacts on equipment malfunctions, appropriate weights should be allocated. By weighted summation, the two types of factors are combined to obtain the control equipment malfunction index. It comprehensively reflects the abnormal status of the equipment.

[0052] It should be noted that the settings for the reference vibration frequency, the number of times the reference direction is adjusted, and the magnitude of the reference direction adjustment are based on the following: 1. The reference vibration frequency is set based on: firstly, the average value of statistical data monitored by vibration sensors during normal operation after equipment commissioning; secondly, the equipment manufacturer's standards are referenced and appropriately adjusted according to the site conditions; and thirdly, the influence of environmental factors is considered, and the vibration frequency is corrected according to common environmental ranges. For example, .

[0053] 2. The basis for setting the number of reference direction adjustments is as follows: First, based on the local solar motion patterns and the equipment's tracking range, the variation in solar angle is converted into the actual adjustment range for the equipment; second, the average or median value of the calibration data during the equipment installation and commissioning phase is used as a reference to determine the error range; third, adjustments are made after considering the impact of environmental and load changes. For example, .

[0054] 3. Basis for setting the reference direction adjustment range: Based on the range of solar angle variation, and according to the local solar motion pattern and the equipment tracking range, the solar angle variation range is converted into the actual adjustment range of the equipment; reference equipment calibration data is used, taking the average or median value of the calibration data during the installation and commissioning phase as a reference, and determining the error range; environmental factors and load changes are considered, their impact on the adjustment range is analyzed, and the reference value is corrected and adjusted accordingly. For example, .

[0055] It should be noted that the weighting factors for vibration and direction adjustment are set based on the following: For vibration, historical equipment failure data must be reviewed, vibration failure frequency and its impact on equipment performance and power generation efficiency must be statistically analyzed, and the severity of failures must be graded. The operating environment must also be considered; environments prone to vibration, such as windy conditions, should have a higher weighting. For direction adjustment, historical failure data is used to statistically analyze the number of failures and their impact on equipment performance. Areas with complex changes in sunlight angle should have a higher weighting due to their significant impact on power generation efficiency.

[0056] For example, .

[0057] In a preferred embodiment, data simulation is performed based on the above formula to obtain data simulation results, and some data simulation results are shown in Table 1.

[0058] Table 1. Data simulation results of the calculation process for the anomaly index of some control equipment

[0059]

[0060] The drive equipment anomaly monitoring module is used to obtain the temperature of the drive motor in real time using a temperature sensor, and at the same time obtain the rotation ratio of each automatic tracking photovoltaic power generation device to evaluate the corresponding drive equipment anomaly.

[0061] In a preferred embodiment of the present invention, the rotation ratio of the automatic tracking photovoltaic power generation device specifically includes the horizontal rotation ratio and the vertical rotation ratio. The specific analysis method is as follows: obtain the change in directional angle and the number of rotations of the horizontal control motor of each automatic tracking photovoltaic power generation device during the monitoring period, and then calculate the ratio to obtain the horizontal rotation ratio of each automatic tracking photovoltaic power generation device.

[0062] It should be added that the specific method for obtaining the change in azimuth angle and the number of rotations of the horizontal control motor for each automatic tracking photovoltaic power generation device during the monitoring period is as follows: 1. Use a position sensor to collect the azimuth angle of each automatic tracking photovoltaic power generation device at the end and start of the monitoring period, and then calculate the difference and take the absolute value to obtain the change in azimuth angle for each automatic tracking photovoltaic power generation device during the monitoring period.

[0063] 2. The rotation process of the horizontal control motor is monitored using a magnetic rotation sensor to obtain the number of rotations of the horizontal control motor for each automatic tracking photovoltaic power generation device during the monitoring period.

[0064] Similarly, the vertical rotation ratio corresponding to each automatic tracking photovoltaic power generation device can be obtained.

[0065] It should be further added that the method for obtaining the vertical rotation ratio of each automatic tracking photovoltaic power generation device is as follows: obtain the pitch angle change and the number of rotations of the vertical control motor of each automatic tracking photovoltaic power generation device during the monitoring period, and then calculate the ratio to obtain the vertical rotation ratio of each automatic tracking photovoltaic power generation device.

[0066] It should be noted that the specific method for obtaining the pitch angle change and the number of rotations of the vertical control motor for each automatic tracking photovoltaic power generation device during the monitoring period is the same as the specific method for obtaining the azimuth angle change and the number of rotations of the horizontal control motor for each automatic tracking photovoltaic power generation device during the monitoring period.

[0067] In a preferred embodiment of the present invention, the specific analysis method for abnormal conditions of the drive equipment is as follows: extract the real-time acquired drive motor temperature, horizontal rotation ratio, and vertical rotation ratio of each automatic tracking photovoltaic power generation device; construct the ratio of the real-time temperature of the drive motor of each device to its preset reference temperature threshold, the normalized value of the absolute deviation between the actual horizontal rotation ratio and the rated rotation ratio, and the normalized value of the absolute deviation between the actual vertical rotation ratio and the rated rotation ratio; and output the drive equipment abnormality index by weighted summation of the above three types of parameters.

[0068] Preferably, the drive motor temperature, horizontal rotation ratio, and vertical rotation ratio of each automatic tracking photovoltaic power generation device are extracted and denoted as follows: , , .

[0069] Using formula Analysis yielded the abnormality index of the drive equipment for each automatic tracking photovoltaic power generation device. ,in This indicates the preset reference drive motor operating temperature threshold. This indicates the preset rated rotational ratio of the horizontal control motor. This indicates the preset rated rotation ratio of the vertical control motor.

[0070] It should be noted that the above formula is constructed based on the following principles: 1. Fault factor selection: This formula is used to evaluate the anomaly index of the drive equipment in an automatic tracking photovoltaic power generation device. During the operation of the drive equipment, the operating temperature of the drive motor... Horizontal control motor rotation ratio Vertical control motor rotation ratio These are key factors, and changes in these factors can reflect abnormal equipment operation, so they are selected to construct the formula.

[0071] 2. Normalization Processing: The actual operating temperature of the drive motor is compared with a pre-set reference drive motor operating temperature threshold to achieve normalization, facilitating the measurement of the relative degree of temperature anomaly. By calculating the absolute value of the difference between the actual rotation ratio and the rated rotation ratio of the control motor in the horizontal and vertical directions, and comparing it with the rated rotation ratio, the relative deviation is obtained, thus achieving normalization of the rotation ratio data.

[0072] 3. Comprehensive Evaluation: Add the three normalized quantities mentioned above together, and comprehensively consider the changes in temperature and the rotation ratio in both directions to obtain the drive equipment anomaly index. The larger the value, the greater the deviation of the drive equipment from the reference state in terms of temperature, rotation ratio, etc., and the higher the degree of abnormality. This allows for a comprehensive assessment of the drive equipment's operating status.

[0073] In a preferred embodiment, data simulation is performed based on the above formula to obtain data simulation results. Partial data simulation results are shown in Table 2, where let... , , .

[0074] Table 2. Data simulation results of the calculation process for the anomaly index of some drive devices

[0075]

[0076] Based on the simulation results above, a larger anomaly index indicates a greater deviation of the drive equipment of the automatic tracking photovoltaic power generation device from the reference state, and a higher degree of anomaly. For example, group 4... The value of 1.58 is relatively high, indicating that the drive equipment deviates significantly from the reference state in terms of temperature, rotation ratio, etc., and the abnormal situation is more obvious.

[0077] It should be noted that the reference drive motor operating temperature threshold, the rated rotation ratio of the horizontal control motor, and the rated rotation ratio of the vertical control motor are obtained as follows: the reference drive motor operating temperature threshold can be provided by the manufacturer or obtained through actual operation testing, and the rated rotation ratio of the horizontal control motor and the rated rotation ratio of the vertical control motor can be obtained from the equipment design documents or factory calibration data.

[0078] The tracking system anomaly analysis module is used to assess the anomaly of the tracking system based on the corresponding anomalies of the light intensity device, control device, and drive device.

[0079] In a preferred embodiment of the present invention, the specific method for analyzing the abnormal situation of the tracking system is as follows: extract the light intensity equipment abnormality index, control equipment abnormality index and drive equipment abnormality index of each automatic tracking photovoltaic power generation device, and then sum them according to weights to obtain the tracking system abnormality index of each automatic tracking photovoltaic power generation device.

[0080] It should be noted that the weights of the solar intensity equipment anomaly index, control equipment anomaly index, and drive equipment anomaly index are set based on the following: Firstly, the importance of the equipment and the degree of impact of the failure are considered. Solar intensity equipment directly affects power generation, control equipment ensures the operation of solar intensity equipment, and drive equipment has a slightly weaker impact. The weights are adjusted according to the impact of each equipment on power generation loss, maintenance costs, and downtime in historical failure data, with higher weights for equipment with greater impact. Secondly, the frequency and repairability of equipment failures are considered. Initial values ​​are set based on the number of failures, and adjustments are made based on the difficulty and duration of repair, with higher weights for equipment that is difficult to repair. The weights are determined comprehensively based on these factors.

[0081] For example, the weights corresponding to the light intensity device anomaly index, the control device anomaly index, and the drive device anomaly index are respectively... .

[0082] The support structure data acquisition module is used to acquire images of the support structure using a high-definition camera and obtain support structure data for each automatic tracking photovoltaic power generation device.

[0083] In a preferred embodiment of the present invention, the support structure data includes a critical component defect evaluation index and a geometric structure anomaly evaluation index, specifically as follows: Images of the support structure of each automatic tracking photovoltaic power generation device are extracted; images of each critical component of each automatic tracking photovoltaic power generation device are obtained; and the number of defects corresponding to each critical component of each automatic tracking photovoltaic power generation device is statistically calculated and recorded as follows: ,in Indicates the number of the key part. , This indicates the number of critical components. Simultaneously, the defect area corresponding to each defect in each critical component of each automatic tracking photovoltaic power generation device is obtained. The summation of the defect areas corresponding to each defect yields the monitored defect area of ​​each critical component of each automatic tracking photovoltaic power generation device, denoted as... .

[0084] For example, the key part can be a welding point or an inflection point, and the defect can be a crack or corrosion.

[0085] It should be added that the specific method for obtaining the number of defects corresponding to each key part of each automatic tracking photovoltaic power generation device is as follows: extract the images of each key part of each automatic tracking photovoltaic power generation device, and then compare them with the performance feature images of each preset reference defect type. If the performance feature corresponding to a certain reference defect type exists at a certain position in the image of a key part of an automatic tracking photovoltaic power generation device, it is determined that the corresponding position in the image of that key part of the automatic tracking photovoltaic power generation device is a defect position. Then, the number of defect positions corresponding to each key part image of each automatic tracking photovoltaic power generation device is counted and recorded as the number of defects corresponding to each key part of each automatic tracking photovoltaic power generation device.

[0086] For example, the cracks are characterized by black or gray lines, and the rust is characterized by reddish-brown or brownish-yellow spots.

[0087] The number of defects and the monitored defect area of ​​each key part of each automatic tracking photovoltaic power generation device are normalized with the corresponding preset reference values, and then the mean is calculated to obtain the defect evaluation index of each key part of the automatic tracking photovoltaic power generation device.

[0088] Preferably, using the formula Analysis yielded the defect evaluation index for key components of each automatic tracking photovoltaic power generation device. ,in This indicates the pre-set reference number of defects. This indicates the pre-set monitoring area.

[0089] Extract the support structure images of each automatic tracking photovoltaic power generation device, and use image processing software to obtain the distance between each key position of each automatic tracking photovoltaic power generation device and the pre-set position reference point, which is recorded as the reference monitoring distance of each key position of each automatic tracking photovoltaic power generation device.

[0090] It should be noted that the location reference point refers to the center point of the solar panel corresponding to each pre-set automatic tracking photovoltaic power generation device.

[0091] By performing deviation analysis and mean calculation on the reference monitoring distance and the corresponding initial monitoring distance at each key location of each automatic tracking photovoltaic power generation device, the geometric structure anomaly evaluation index of each automatic tracking photovoltaic power generation device is obtained.

[0092] For example, the absolute value of the difference between the reference monitoring distance and the corresponding initial monitoring distance of each key position of each automatic tracking photovoltaic power generation device is taken to obtain the reference monitoring distance deviation of each key position of each automatic tracking photovoltaic power generation device. Then, the ratio of this deviation to the corresponding initial monitoring distance is calculated to obtain the displacement of each key position of each automatic tracking photovoltaic power generation device. Finally, the average value is calculated to obtain the geometric structure anomaly evaluation index of each automatic tracking photovoltaic power generation device.

[0093] The stent structure anomaly analysis module is used to analyze the corresponding stent structure anomalies based on the stent structure data.

[0094] In a preferred embodiment of the present invention, the specific analysis method for the abnormal situation of the support structure is as follows: extract the defect evaluation index and geometric structure abnormality evaluation index of the key parts of each automatic tracking photovoltaic power generation device, and then sum them according to weights to obtain the support structure abnormality index of each automatic tracking photovoltaic power generation device.

[0095] It should be noted that the critical component defect evaluation index and the geometric structure anomaly evaluation index were chosen as influencing factors for the support structure anomaly index of each automatic tracking photovoltaic power generation device. The critical component defect evaluation index directly reflects the health status of key areas such as columns and shafts; defects such as cracks and wear in these areas severely affect load-bearing and tracking functions. Furthermore, it has a strong correlation with equipment failures, with over 70% of support structure failures originating from this. The geometric structure anomaly evaluation index reflects the integrity and stability of the support structure; anomalies in dimensions and angles alter the stress distribution. Simultaneously, it affects the overall performance and accuracy of the equipment; geometric deviations reduce power generation efficiency, making both indices crucial.

[0096] It should be noted that the weighting of the critical component defect evaluation index and the geometric structure anomaly evaluation index is based on the following: From a failure history perspective, if critical component defects cause frequent and significant failures (e.g., high maintenance costs and prolonged downtime), their weight can be increased; conversely, if geometric structure anomalies cause relatively minor failures, their weight should be decreased. From a monitoring and maintenance perspective, critical component defects are difficult to monitor and complex to repair, requiring specialized equipment and longer maintenance cycles, and should therefore be given a higher weight; geometric structure anomalies are easier to monitor and repair, and their weight can be relatively lower. This allows for a reasonable weighting of the evaluation of support structure anomalies.

[0097] For example, the weights corresponding to the critical component defect evaluation index and the geometric structure anomaly evaluation index are respectively... .

[0098] The fault cause identification module is used to identify the specific abnormality.

[0099] In a preferred embodiment of the present invention, the method for identifying specific anomalies is as follows: extracting the tracking system anomaly index and the support structure anomaly index of each automatic tracking photovoltaic power generation device, and then comparing them with the preset tracking system anomaly index threshold and the support structure anomaly index threshold, respectively.

[0100] For example, the tracking system anomaly index threshold and the support structure anomaly index threshold are respectively .

[0101] If the tracking system anomaly index of an automatic tracking photovoltaic power generation device is greater than the tracking system anomaly index threshold, the specific anomaly is identified as a tracking system anomaly.

[0102] If the support structure anomaly index of a certain automatic tracking photovoltaic power generation device is greater than the support structure anomaly index threshold, the specific anomaly is identified as a support structure anomaly.

[0103] It should be noted that the specific anomaly indicated by each automatic tracking photovoltaic power generation device can be one or both of the tracking system anomaly and the support structure anomaly. If the specific anomaly indicated by a certain automatic tracking photovoltaic power generation device is not either the tracking system anomaly or the support structure anomaly, then feedback will be provided and monitoring will continue.

[0104] The above content is merely an example and illustration of the concept of the present invention. Those skilled in the art can make various modifications or additions to the specific embodiments described, or use similar methods to replace them, as long as they do not deviate from the concept of the invention or exceed the scope defined by the present invention, and all such modifications or additions should fall within the protection scope of the present invention.

Claims

1. A distributed photovoltaic power generation equipment intelligent monitoring system, characterized in that, Comprise: Light intensity equipment abnormality monitoring module: use light intensity monitoring equipment to obtain the ideal tracking direction of each automatic tracking photovoltaic power generation device, analyze the corresponding reference ideal direction angle and reference ideal pitch angle, and jointly evaluate the corresponding light intensity equipment abnormality; Control equipment abnormality monitoring module: collect the direction angle and pitch angle of each automatic tracking photovoltaic power generation device, obtain control equipment adjustment data, and evaluate the corresponding control equipment abnormality accordingly; Drive equipment abnormality monitoring module: real-time obtain the drive motor temperature, and obtain the corresponding rotation ratio of each automatic tracking photovoltaic power generation device, and comprehensively evaluate the corresponding drive equipment abnormality; Tracking system abnormality analysis module: evaluate the tracking system abnormality according to the corresponding abnormality of the light intensity equipment, control equipment and drive equipment; Support structure data acquisition module: acquire support structure images and obtain support structure data of each automatic tracking photovoltaic power generation device; Support structure abnormality analysis module: analyze the corresponding support structure abnormality based on the support structure data; Fault cause pointing identification module: identify specific abnormality pointing; The specific analysis method of the light intensity equipment abnormality is as follows: Extract the ideal direction angle and ideal pitch angle of each automatic tracking photovoltaic power generation device; Calculate the mean value of the ideal direction angle of each automatic tracking photovoltaic power generation device to obtain the reference ideal direction angle of the automatic tracking photovoltaic power generation device, and similarly obtain the reference ideal pitch angle of the automatic tracking photovoltaic power generation device; Differences between the ideal direction angle and the ideal pitch angle of each automatic tracking photovoltaic power generation device and the corresponding reference ideal direction angle and reference ideal pitch angle are analyzed to obtain the ideal direction angle deviation value and the ideal pitch angle deviation value of each automatic tracking photovoltaic power generation device; Compare the ideal direction angle deviation value and the ideal pitch angle deviation value of each automatic tracking photovoltaic power generation device with the corresponding reference ideal direction angle and reference ideal pitch angle to obtain the ideal direction angle deviation degree and the ideal pitch angle deviation degree of each automatic tracking photovoltaic power generation device, and then perform weight summation calculation to obtain the light intensity equipment abnormality index of each automatic tracking photovoltaic power generation device; The control equipment adjustment data includes vibration frequency, vibration time, direction adjustment times and average direction adjustment amplitude; The specific analysis method of the control equipment abnormality is as follows: Extract the vibration frequency, vibration time, direction adjustment times and average direction adjustment amplitude of each automatic tracking photovoltaic power generation device obtained in real time, compare them with the preset reference vibration frequency, monitoring period threshold, direction adjustment times threshold and adjustment amplitude threshold respectively, combine the vibration parameter weight and the direction adjustment parameter weight, and perform weight summation calculation to obtain the control equipment abnormality index.

2. The distributed photovoltaic power generation equipment intelligent monitoring system according to claim 1, characterized in that: The corresponding rotation ratio of the automatic tracking photovoltaic power generation device specifically includes horizontal direction rotation ratio and vertical direction rotation ratio, and the specific analysis method is as follows: Obtain the direction angle change amount and the horizontal direction control motor rotation number of each automatic tracking photovoltaic power generation device within the monitoring period, and then perform ratio calculation to obtain the horizontal direction rotation ratio of each automatic tracking photovoltaic power generation device; Similarly, the vertical rotation ratio of each automatic tracking photovoltaic power generation device can be obtained.

3. The distributed photovoltaic power plant intelligent monitoring system of claim 2, wherein: The specific analysis method of the drive equipment abnormality is as follows: The temperature of the drive motor, the horizontal rotation ratio and the vertical rotation ratio of each automatic tracking photovoltaic power generation device are extracted, and the ratio of the real-time temperature of the drive motor of each device to the preset reference temperature threshold, the absolute deviation normalized value of the actual horizontal rotation ratio to the rated rotation ratio, and the absolute deviation normalized value of the actual vertical rotation ratio to the rated rotation ratio are constructed. The drive equipment abnormality index is output after weighted summation of the above three types of parameters.

4. The distributed photovoltaic power plant intelligent monitoring system of claim 3, wherein: The specific analysis method of the tracking system abnormality is as follows: The light intensity equipment abnormality index, the control equipment abnormality index and the drive equipment abnormality index of each automatic tracking photovoltaic power generation device are extracted, and the tracking system abnormality index of each automatic tracking photovoltaic power generation device is calculated by weighted summation.

5. The distributed photovoltaic power plant intelligent monitoring system of claim 3, wherein: The support structure data includes key part defect evaluation index and geometric structure abnormality evaluation index, and the specific method is as follows: The support structure image of each automatic tracking photovoltaic power generation device is extracted, the images of each key part of each automatic tracking photovoltaic power generation device are obtained, the number of defects corresponding to each key part of each automatic tracking photovoltaic power generation device is counted, and the defect area corresponding to each defect of each key part of each automatic tracking photovoltaic power generation device is obtained. The monitoring defect area of each key part of each automatic tracking photovoltaic power generation device is calculated by summing the defect area corresponding to each defect; The number of defects and the monitoring defect area corresponding to each key part of each automatic tracking photovoltaic power generation device are normalized with the corresponding preset reference value, and the key part defect evaluation index of each automatic tracking photovoltaic power generation device is calculated by mean value calculation; The support structure image of each automatic tracking photovoltaic power generation device is extracted, and the distance between each key position of each automatic tracking photovoltaic power generation device and the pre-set position reference point is obtained by using image processing software, which is recorded as the reference monitoring distance of each key position of each automatic tracking photovoltaic power generation device. The reference monitoring distance of each key position of each automatic tracking photovoltaic power generation device is analyzed for deviation with the corresponding initial monitoring distance, and the geometric structure abnormality evaluation index of each automatic tracking photovoltaic power generation device is calculated by mean value calculation.

6. The distributed photovoltaic power plant intelligent monitoring system of claim 5, wherein: The specific analysis method of the support structure abnormality is as follows: The key part defect evaluation index and the geometric structure abnormality evaluation index of each automatic tracking photovoltaic power generation device are extracted, and the support structure abnormality index of each automatic tracking photovoltaic power generation device is calculated by weighted summation.

7. The distributed photovoltaic power plant intelligent monitoring system of claim 6, wherein: The specific abnormality direction identification method is as follows: The tracking system abnormality index and the support structure abnormality index of each automatic tracking photovoltaic power generation device are extracted, and then compared with the pre-set tracking system abnormality index threshold and support structure abnormality index threshold respectively; If the tracking system abnormality index of a certain automatic tracking photovoltaic power generation device is greater than the tracking system abnormality index threshold, the specific abnormality direction is identified as tracking system abnormality. If the support structure abnormality index of the automatic tracking photovoltaic power generation device is greater than the support structure abnormality index threshold value, the specific abnormality direction is identified as a support structure abnormality.

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