Distributed photovoltaic power generation equipment intelligent monitoring system

Abstract

The invention belongs to the technical field of photovoltaic power generation equipment fault monitoring, and discloses a distributed photovoltaic power generation equipment intelligent monitoring system. Comprising a light intensity equipment abnormity monitoring module, a control equipment abnormity monitoring module, a driving equipment abnormity monitoring module, a tracking system abnormity analysis module, a support structure data acquisition module, a support structure abnormity analysis module and a fault cause direction identification module. According to the method, the abnormal conditions of the tracking system and the support structure are analyzed, then the health state of each automatic tracking type photovoltaic power generation device is comprehensively evaluated, the analysis mode gives consideration to the tracking system and the support structure and covers key aspects of device operation and support, so that the evaluation accuracy is greatly improved, and the evaluation efficiency is improved. The real condition of equipment can be accurately grasped, potential problems can be found in time, a more targeted maintenance strategy can be formulated, stable operation of the photovoltaic power generation device is effectively guaranteed, the power generation efficiency is improved, and the equipment fault risk is reduced.

Description

Technical Field

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

[0002] The automatic tracking photovoltaic power generation device is a power generation device that can automatically adjust the angle of the photovoltaic panel so that it is always aligned with the sun to receive the maximum amount of sunlight. It is mainly composed of photovoltaic panels, bracket structures and tracking systems. The bracket structure is the foundation, which can provide guarantee for angle adjustment and can adapt to different installation environments. The tracking system is the core, which can significantly improve the power generation efficiency and make the most of sunlight by adjusting the angle of the photovoltaic panel in real time. Therefore, the research on intelligent monitoring of photovoltaic power generation equipment faults based on the automatic tracking photovoltaic power generation device is of great significance.

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

[0004] In addition, a Chinese invention patent application with publication number CN118783876A is for a controllable photovoltaic bracket and installation method, which includes: judging the orientation of the photovoltaic panel according to the light threshold through the optimization module; when it is not optimal, the illumination module determines the steering direction angle, the control module instructs the motor to adjust, and at the same time the energy collection module monitors the energy collection value in real time to realize self-detection during the use of the photovoltaic panel.

[0005] Although the above two schemes have proposed some solutions for photovoltaic power generation equipment fault monitoring technology, there are still certain limitations: on the one hand, some existing technical solutions only focus on unilateral signal collection of the drive system or control system, and lack a comprehensive analysis of the overall performance of the tracking system and the health status of the support structure, resulting in incomplete fault monitoring and omission of potential hidden dangers, which can easily lead to misdiagnosis or missed diagnosis, failure of maintenance strategies and reduced power generation efficiency.

[0006] On the other hand, the existing technical solutions have not established a benchmark reference for the ideal tracking direction in the same area, and cannot achieve accurate anomaly identification by comparing actual light intensity data, resulting in low troubleshooting efficiency, delayed discovery of subtle anomalies, and difficulty in conducting overall monitoring and differentiated management of devices in the area, which weakens the stability and efficiency of the system operation. Summary of the invention

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

[0008] The purpose of the present invention can be achieved through the following technical solutions: a distributed photovoltaic power generation equipment intelligent monitoring system, including: a light intensity equipment abnormality monitoring module, which is used to use the light intensity monitoring equipment to obtain the ideal tracking direction of each automatic tracking photovoltaic power generation device, and then analyze the corresponding reference ideal direction angle and reference ideal pitch angle and jointly evaluate the corresponding light intensity equipment abnormality.

[0009] The control device abnormality monitoring module is used to use a position sensor and an inclination sensor to collect the azimuth angle and pitch angle of each automatic tracking photovoltaic power generation device, obtain control device adjustment data, and evaluate the corresponding control device abnormality based on the control device adjustment data.

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

[0011] The tracking system abnormality analysis module is used to evaluate the abnormality of the tracking system according to the corresponding abnormalities of the light intensity device, the control device and the drive device.

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

[0013] The support structure abnormality analysis module is used to analyze the corresponding support structure abnormality based on the support 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 abnormal conditions of the tracking system and 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, covering the key aspects of device operation and support, which greatly improves the accuracy of the evaluation, can accurately grasp the true condition of the equipment, and discover potential problems in a timely manner. It can formulate more targeted maintenance strategies, effectively ensure the stable operation of photovoltaic power generation devices, improve power generation efficiency, and reduce the risk of equipment failure.

[0016] (2) The present invention analyzes abnormalities in the tracking system of each automatic tracking photovoltaic power generation device by analyzing abnormalities in the light intensity device, control device, and drive device. This analysis method can accurately locate the fault and clearly identify whether it is a problem in the light energy reception, command control, or power transmission link, thus avoiding blind troubleshooting. The analysis is comprehensive, covering key parts such as energy source, coordinated control, and power transmission to ensure that nothing is missed. Moreover, based on this analysis, targeted maintenance can be carried out. Exclusive maintenance plans can be formulated according to the abnormalities of different equipment, thereby improving maintenance efficiency, reducing costs, effectively extending the service life of the equipment, and ensuring the stable operation of the photovoltaic power generation device.

[0017] (3) When performing abnormal monitoring of light intensity equipment, the present invention analyzes the abnormality of the 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 problem 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 the slight deviation of the light intensity equipment can be keenly discovered, 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. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for describing the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0019] Figure 1 It is a schematic diagram of the connection of each module of the system of the present invention.

[0020] Figure 2 A schematic diagram of pitch angle analysis corresponding to an embodiment provided by the present invention.

[0021] Figure 3 A schematic diagram of directional angle analysis corresponding to an embodiment provided by the present invention.

[0022] Reference numerals: 1—ideal tracking direction, 2—pitch angle, 3—vertical downward direction, 4—direction angle, 5—true north direction. DETAILED DESCRIPTION

[0023] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0024] See also Figure 1 As shown, the present invention provides an intelligent monitoring system for distributed photovoltaic power generation equipment, including a light intensity equipment abnormality monitoring module, a control equipment abnormality monitoring module, a drive equipment abnormality monitoring module, a tracking system abnormality analysis module, a bracket structure data acquisition module, a bracket structure abnormality analysis module and a fault cause pointing identification module, wherein the light intensity equipment abnormality monitoring module, the control equipment abnormality monitoring module and the drive equipment abnormality monitoring module are all connected to the tracking system abnormality analysis module, the bracket structure data acquisition module is connected to the bracket structure abnormality analysis module, and the tracking system abnormality analysis module and the bracket structure abnormality analysis module are both connected to the fault cause pointing identification module.

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

[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 the most sunlight. When the solar altitude angle is low, the photovoltaic panel with a fixed bracket receives limited light intensity, and the automatic tracking device can adjust the angle so that the panel is perpendicular to the inclined sunlight, thereby capturing more light energy and greatly improving the power generation efficiency.

[0027] Please note that Figure 2 , Figure 3 As shown, the ideal direction angle refers to the angle of the ideal tracking direction in the horizontal direction relative to the true north direction, and the ideal pitch angle refers to the angle of the ideal tracking direction in the vertical direction relative to the vertical downward direction.

[0028] It should be added that the method for determining the ideal tracking direction of each automatic tracking photovoltaic power generation device is as follows: light intensity sensors are installed around the photovoltaic power generation device at positions where they can accurately sense light, so that they can fully sense light intensities in different 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 value and its corresponding direction are determined, which are then used as the ideal tracking direction of each automatic tracking photovoltaic power generation device.

[0029] In a preferred embodiment of the present invention, the specific analysis method of the abnormal situation of the light intensity device is as follows: extracting the ideal direction 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 the present invention are located in the same geographical area, so the ideal tracking directions of the automatic tracking photovoltaic power generation devices should be consistent in theory. Therefore, the ideal direction angles of the automatic tracking photovoltaic power generation devices in the present invention should be consistent in theory. Similarly, the ideal pitch angles of the automatic tracking photovoltaic power generation devices in the present invention should be consistent in theory. However, in practice, errors may occur in the light intensity equipment of the automatic tracking photovoltaic power generation device during the light intensity collection and analysis process, which may lead to deviations between the ideal tracking direction actually obtained through the light intensity equipment analysis and the theoretical ideal tracking direction.

[0031] The ideal direction angle of each automatic tracking photovoltaic power generation device is averaged to obtain the reference ideal direction angle of the automatic tracking photovoltaic power generation device. Similarly, the reference ideal pitch angle of the automatic tracking photovoltaic power generation device can be obtained. The ideal direction angle and ideal pitch angle of each automatic tracking photovoltaic power generation device are difference analyzed with the corresponding reference ideal direction angle and reference ideal pitch angle to obtain the ideal direction angle deviation value and ideal pitch angle deviation value of each automatic tracking photovoltaic power generation device.

[0032] Exemplarily, the specific method for obtaining the ideal direction angle deviation value and the ideal pitch angle deviation value is as follows: the ideal direction angle and the ideal pitch angle of each automatic tracking photovoltaic power generation device are respectively calculated by difference with the corresponding reference ideal direction angle and the reference ideal pitch angle, and then the absolute values ​​are taken to obtain the ideal direction angle deviation value and the ideal pitch angle deviation value of each automatic tracking photovoltaic power generation device.

[0033] The ideal azimuth deviation value and the ideal pitch angle deviation value of each automatic tracking photovoltaic power generation device are compared and analyzed with the corresponding reference ideal azimuth angle and reference ideal pitch angle respectively to obtain the ideal azimuth deviation degree and the ideal pitch angle deviation degree of each automatic tracking photovoltaic power generation device, and then the light intensity equipment abnormality index of each automatic tracking photovoltaic power generation device is obtained by weighted summation calculation.

[0034] It should be explained that the reason for selecting the ideal azimuth angle deviation and the ideal pitch angle deviation as the influencing factors of the light intensity equipment abnormality index of each automatic tracking photovoltaic power generation device is: they directly determine the angle of the solar panel relative to the sunlight. The change in angle will change the light incident angle and affect the light intensity reception, while the tracking and control equipment has an indirect effect on the light intensity. These two deviations can intuitively reflect whether the light intensity equipment is in the best lighting state, and can also point to the root cause of the abnormality. For example, the azimuth angle deviation may indicate a problem with the horizontal rotation mechanism, and the pitch angle deviation may point to a failure in the vertical adjustment mechanism. Moreover, they facilitate the quantitative and systematic evaluation of the degree of light intensity abnormality and the equipment status, which is difficult for tracking and control equipment to do.

[0035] It should be noted that the basis for setting the corresponding weights of the ideal azimuth deviation and the ideal pitch angle deviation of each automatic tracking photovoltaic power generation device is as follows: in terms of light intensity, if the horizontal movement of the sun in the area is obvious, the azimuth deviation has a great impact on the light intensity, and its deviation weight can be set higher; if the vertical light changes greatly, the pitch angle deviation weight will be increased. From the fault history, if the azimuth deviation causes a large number of faults and large losses, resulting in a significant decrease in power generation efficiency, the azimuth deviation weight can be increased; on the contrary, if the pitch angle deviation causes a serious fault, its deviation weight will be increased, so as to reasonably set the weight to evaluate the abnormality of the light intensity equipment.

[0036] Exemplarily, the corresponding weights of the ideal azimuth angle deviation and the ideal pitch angle deviation of each automatic tracking photovoltaic power generation device are respectively .

[0037] The control device abnormality monitoring module is used to use a position sensor and an inclination sensor to collect the azimuth angle and pitch angle of each automatic tracking photovoltaic power generation device, obtain control device adjustment data, and evaluate the corresponding control device abnormality based on the control device adjustment data.

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

[0039] It should be noted that the reasons for selecting vibration frequency, vibration duration, number of direction adjustments and average direction adjustment amplitude as control device adjustment data are: on the one hand, vibration frequency and vibration duration can directly reflect the operational stability of the control device. Higher frequency and longer duration mean that the system is unstable, which may be caused by control algorithm, signal transmission or electrical component failure. On the other hand, abnormal number and amplitude of direction adjustments can provide clues for fault diagnosis. Frequent adjustments or excessive amplitudes may be caused by sensor, control algorithm or mechanical transmission component failure.

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

[0041] It should be added that the control device adjusts the data acquisition method: 1. A vibration sensor is set on the support structure corresponding to each automatic tracking photovoltaic power generation device, and then the vibration sensor is used to obtain the vibration frequency of each vibration condition corresponding to each automatic tracking photovoltaic power generation device within a preset 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 time and end time of each vibration condition corresponding to each automatic tracking photovoltaic power generation device, and then calculate the difference between the end time and the start time to obtain the duration of each vibration condition, and then perform cumulative calculation to obtain the vibration duration corresponding to each automatic tracking photovoltaic power generation device.

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

[0044] Exemplarily, taking the horizontal direction adjustment instruction as an example, the method for judging the adjustment instruction of the abnormal direction is: comparing the direction angle of each horizontal direction adjustment instruction with the direction angle of the previous horizontal direction adjustment instruction and the direction angle of the next horizontal direction adjustment instruction, and judging whether the direction angle of each horizontal direction adjustment instruction is within the range of the direction angle of the previous horizontal direction adjustment instruction and the direction angle of the next horizontal direction adjustment instruction; if so, judging that the corresponding horizontal direction adjustment instruction does not belong to an abnormal direction; otherwise, judging that it belongs to an abnormal direction.

[0045] 4. Extract the adjustment amplitude corresponding to each adjustment instruction judged to be in an abnormal direction, take the absolute value and perform average calculation to obtain the average direction adjustment amplitude of each automatic tracking photovoltaic power generation device.

[0046] In a preferred embodiment of the present invention, the specific analysis method of the abnormal situation of the control equipment is as follows: the vibration frequency, vibration duration, direction adjustment times and average direction adjustment amplitude of each automatic tracking photovoltaic power generation device acquired in real time are extracted, and compared with the preset reference vibration frequency, monitoring period threshold, direction adjustment times threshold, and adjustment amplitude threshold respectively, and the control equipment abnormality index is calculated by weighted summation in combination with the vibration parameter weight and the direction adjustment parameter weight.

[0047] Preferably, the vibration frequency, vibration duration, direction adjustment times and average direction adjustment amplitude of each automatic tracking photovoltaic power generation device are extracted and recorded as , , , ,in Indicates the number of the automatic tracking photovoltaic power generation device. , Represents the number of automatic tracking photovoltaic power generation devices.

[0048] Using the formula Analyze and obtain the abnormal index of control equipment of each automatic tracking photovoltaic power generation device ,in Indicates the preset reference vibration frequency, Indicates the monitoring duration corresponding to the preset monitoring period. Indicates the preset reference direction adjustment times. Indicates the preset reference direction adjustment range. They respectively represent the weight factors corresponding to the preset vibration conditions and direction adjustment conditions.

[0049] It should be noted that the above formula is constructed with the following ideas: 1. Classification of fault factors: The formula is designed to comprehensively evaluate the abnormal conditions of the control equipment of the automatic tracking photovoltaic power generation device. The factors that may cause equipment abnormalities are classified into two categories: vibration conditions and direction adjustment conditions. The vibration frequency and monitoring time reflect the stability of the equipment operation; the number of direction adjustments and the adjustment range reflect the working status of the equipment tracking the sun.

[0050] 2. Normalization processing: These fractions are the normalization of the actual monitoring values ​​relative to the preset reference values. In this way, data of different dimensions are converted into relatively comparable values, which is convenient for comprehensive calculation and analysis.

[0051] 3. Comprehensive evaluation of weight allocation: introducing weight factors Since vibration and direction adjustment may have different effects on equipment abnormality, the weights should be reasonably allocated. The two factors are combined by weighted summation to obtain the control equipment abnormality index. , comprehensively reflecting the abnormal condition of the equipment.

[0052] It should be noted that the reference vibration frequency, reference direction adjustment times, and reference direction adjustment amplitude are set based on: 1. The reference vibration frequency is set based on the average value of the statistical data monitored by the vibration sensor when the equipment is running normally after debugging; second, it is adjusted appropriately based on the equipment manufacturer's standards and the impact of environmental factors is taken into account, and the vibration frequency is corrected according to the common environmental range. For example, .

[0053] 2. The basis for setting the reference direction adjustment times is: first, combining the local solar motion law and the equipment tracking range, converting the solar angle change amplitude into the actual equipment adjustment amplitude; second, taking the average or median value of the calibration data during the equipment installation and commissioning phase as a reference, and determining the error range; third, making corrections and adjustments after considering the impact of environmental and load changes. For example, .

[0054] 3. The basis for setting the adjustment range of the reference direction: Based on the range of solar angle changes, according to the local solar motion law and the equipment tracking range, convert the solar angle change range into the actual adjustment range of the equipment; refer to the equipment calibration data, take the average or median value of the calibration data in the installation and debugging phase as a reference, and determine the error range; consider environmental factors and load changes, analyze their impact on the adjustment range, and correct and adjust the reference value. For example, .

[0055] It should be noted that the weight factors corresponding to the vibration conditions and direction adjustment conditions are set based on the following: To set the weight factor for vibration conditions, it is necessary to review the historical data of equipment failures, count the frequency of vibration failures, the impact on equipment performance and power generation efficiency, and classify the severity of failures. The equipment operating environment should also be considered. Environments that are prone to vibration, such as windy environments, should have higher weights. To set the weight factor for direction adjustment conditions, the number of failures and the impact on equipment performance are counted based on the historical data of failures. The weight should be increased in areas with complex changes in illumination angles because of their large 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 abnormal index calculation process of some control equipment

[0059]

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

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

[0062] It should be added that the specific method for obtaining the angular change corresponding to each automatic tracking photovoltaic power generation device during the monitoring period and the number of rotations of the horizontal direction control motor is as follows: 1. Using a position sensor to collect the angular direction of each automatic tracking photovoltaic power generation device at the end and start of the monitoring period, and then performing difference calculation and taking the absolute value to obtain the angular change corresponding to each automatic tracking photovoltaic power generation device during the monitoring period.

[0063] 2. Use a magnetic rotation sensor to monitor the rotation process of the horizontal direction control motor to obtain the number of rotations of the horizontal direction control motor corresponding to 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 supplemented that the vertical rotation ratio corresponding to each automatic tracking photovoltaic power generation device is obtained by obtaining the pitch angle change and the number of rotations of the vertical control motor corresponding to each automatic tracking photovoltaic power generation device during the monitoring period, and then performing ratio calculation to obtain the vertical rotation ratio corresponding to each automatic tracking photovoltaic power generation device.

[0066] It should be noted that the specific method of obtaining the pitch angle change and the number of rotations of the vertical control motor corresponding to each automatic tracking photovoltaic power generation device during the monitoring period refers to the specific method of obtaining the direction angle change and the number of rotations of the horizontal control motor corresponding to each automatic tracking photovoltaic power generation device during the monitoring period.

[0067] In a preferred embodiment of the present invention, the specific analysis method of the abnormal situation of the driving device is as follows: extract the real-time acquired driving motor temperature, horizontal rotation ratio and vertical rotation ratio of each automatic tracking photovoltaic power generation device, construct the ratio of the real-time driving motor temperature 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 driving device abnormality index after weighted summation of the above three types of parameters.

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

[0069] Using the formula Analyze and obtain the abnormal index of driving equipment of each automatic tracking photovoltaic power generation device ,in Indicates the preset reference drive motor operating temperature threshold, Indicates the preset rated rotation ratio of the horizontal direction control motor. Indicates the preset rated rotation ratio of the vertical direction control motor.

[0070] It should be noted that the construction ideas of the above formula are as follows: 1. Fault factor selection: This formula is used to evaluate the abnormal index of the drive equipment of the automatic tracking photovoltaic power generation device. When the drive equipment is running, the operating temperature of the drive motor , horizontal direction control motor rotation ratio , Vertical direction control motor rotation ratio They are key factors. Changes in these factors can reflect abnormal equipment operation, so they are selected to construct the formula.

[0071] 2. Normalization: Compare the actual drive motor operating temperature with the preset reference drive motor operating temperature threshold to achieve normalization, which is convenient for measuring the relative abnormality of temperature. By calculating the absolute value of the difference between the actual rotation ratio of the horizontal and vertical control motor and the rated rotation ratio, and comparing it with the rated rotation ratio, the relative deviation degree is obtained to achieve normalization of the rotation ratio data.

[0072] 3. Comprehensive evaluation: Add the above three normalized quantities, comprehensively consider the changes in temperature and the rotation ratio in two directions, and obtain the abnormal index of the drive equipment. 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, which can be used to comprehensively evaluate the operating status of the drive equipment.

[0073] In a preferred embodiment, data simulation is performed based on the above formula to obtain data simulation results. Some data simulation results refer to Table 2, where , , .

[0074] Table 2. Data simulation results of abnormal index calculation process of some driving equipment

[0075]

[0076] For the above simulation results, the larger the abnormality index, the greater the deviation of the driving device of the automatic tracking photovoltaic power generation device from the reference state under the sample, and the higher the abnormality. It is 1.58, which is relatively high, indicating that its driving equipment deviates a lot from the reference state in terms of temperature and rotation ratio, 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 direction control motor and the rated rotation ratio of the vertical direction control motor are obtained as follows: the reference drive motor operating temperature threshold can be provided by the manufacturer or obtained through actual operation tests, and the rated rotation ratio of the horizontal direction control motor and the rated rotation ratio of the vertical direction control motor can be obtained from the equipment design documents or factory calibration data.

[0078] The tracking system abnormality analysis module is used to evaluate the abnormality of the tracking system according to the corresponding abnormalities of the light intensity device, the control device and the drive device.

[0079] In a preferred embodiment of the present invention, the specific analysis method of 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 up according to the weight to obtain the tracking system abnormality index of each automatic tracking photovoltaic power generation device.

[0080] It should be noted that the weights corresponding to the abnormal index of light intensity equipment, abnormal index of control equipment and abnormal index of driving equipment are set based on: on the one hand, based on the importance of equipment and the degree of impact of faults, light intensity equipment directly affects power generation, control equipment ensures the operation of light intensity equipment, and the impact of driving equipment is slightly weaker. The weights are adjusted according to the impact of each device on power generation loss, maintenance cost and downtime in historical fault data, and the weights with greater impact are higher. On the other hand, considering the frequency and repairability of equipment failures, the initial value is determined according to the number of failures, and adjusted according to the difficulty and duration of repair. The weight of difficult repairs is increased, and the weight is determined comprehensively.

[0081] For example, the weights corresponding to the abnormal index of the light intensity device, the abnormal index of the control device, and the abnormal index of the driving device are respectively .

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

[0083] In a preferred embodiment of the present invention, the support structure data includes a key part defect evaluation index and a geometric structure abnormality evaluation index, and the specific method is as follows: extract the support structure image of each automatic tracking photovoltaic power generation device, obtain the key part images of each automatic tracking photovoltaic power generation device, and statistically obtain the number of defects corresponding to each key part of each automatic tracking photovoltaic power generation device, and record it as ,in Indicates the number of the key part, , represents the number of key parts, and at the same time obtains the defect area corresponding to each defect of each key part of each automatic tracking photovoltaic power generation device, and sums up the defect area corresponding to each defect to obtain the monitoring defect area of ​​each key part of each automatic tracking photovoltaic power generation device, which is recorded as .

[0084] Exemplarily, the key parts may be welding points or inflection points, and the defects may be cracks or rust.

[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 by counting is as follows: extracting images of each key part of each automatic tracking photovoltaic power generation device, and then comparing them with the performance feature images of each pre-set reference defect type respectively; if a performance feature corresponding to a reference defect type exists at a certain position in the image of a key part of a certain automatic tracking photovoltaic power generation device, the corresponding position in the image of the key part of the automatic tracking photovoltaic power generation device is judged to be a defect position, and then the number of defect positions corresponding to the images of each key part of each automatic tracking photovoltaic power generation device is counted, which is recorded as the number of defects corresponding to each key part of each automatic tracking photovoltaic power generation device.

[0086] Exemplarily, the cracks may be characterized by black or gray lines, and the rust may be characterized by reddish brown or brownish yellow spots.

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

[0088] Preferably, using the formula The defect evaluation index of key parts of each automatic tracking photovoltaic power generation device is obtained by analysis ,in Indicates the preset reference defect quantity, Indicates the preset monitoring area.

[0089] 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 using image processing software, 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 position reference point refers to the center point of the solar cell panel corresponding to each automatic tracking photovoltaic power generation device that is preset.

[0091] The reference monitoring distance of each key position of each automatic tracking photovoltaic power generation device and the corresponding initial monitoring distance are subjected to deviation analysis and mean calculation to obtain the geometric structure anomaly evaluation index of each automatic tracking photovoltaic power generation device.

[0092] Exemplarily, the reference monitoring distance of each key position of each automatic tracking photovoltaic power generation device is calculated by difference with the corresponding initial monitoring distance, and then the absolute value is taken to obtain the reference monitoring distance deviation of each key position of each automatic tracking photovoltaic power generation device, and then the ratio is calculated with the corresponding initial monitoring distance to obtain the displacement of each key position of each automatic tracking photovoltaic power generation device, and then the mean calculation is performed to obtain the geometric structure abnormality evaluation index of each automatic tracking photovoltaic power generation device.

[0093] The support structure abnormality analysis module is used to analyze the corresponding support structure abnormality based on the support structure data.

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

[0095] It should be noted that the reason for selecting the key part defect evaluation index and the geometric structure abnormality evaluation index as the influencing factors of the bracket structure abnormality index of each automatic tracking photovoltaic power generation device is: the key part defect evaluation index can directly reflect the health status of key areas such as columns and shafts. Cracks, wear and other defects in these parts will seriously affect the load-bearing and tracking functions. Moreover, it is strongly correlated with equipment failures, and more than 70% of bracket structure failures are caused by this. The geometric structure abnormality evaluation index reflects the integrity and stability of the bracket structure. Abnormalities such as size and angle will change the force distribution. At the same time, it affects the overall performance and accuracy of the equipment, and geometric structure deviations will reduce the power generation effect, so both are crucial.

[0096] It should be noted that the weights corresponding to the critical part defect evaluation index and the geometric structure abnormality evaluation index are set based on the following: from the perspective of failure history, if the critical part defects cause high failure frequency and large losses, such as high maintenance costs and long downtime, the weight can be increased; if the geometric structure abnormality causes relatively minor failures, the weight is reduced. From the perspective of monitoring and maintenance, critical part defects are difficult to monitor and complex to repair, requiring professional equipment and a long maintenance cycle, and should be given a higher weight; geometric structure abnormalities are easier to monitor and repair, and the weight can be relatively low, so that the weight can be reasonably set to evaluate the support structure abnormality.

[0097] For example, the weights corresponding to the key part defect evaluation index and the geometric structure abnormality evaluation index are: .

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

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

[0100] Exemplarily, the tracking system abnormality index threshold and the stent structure abnormality index threshold are respectively .

[0101] 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 is identified as a tracking system abnormality.

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

[0103] It should be noted that the specific abnormal direction corresponding to each automatic tracking photovoltaic power generation device can be one or both of the tracking system abnormality and the support structure abnormality. If the specific abnormal direction corresponding to a certain automatic tracking photovoltaic power generation device is not any one of the tracking system abnormality and the support structure abnormality, feedback will be provided and continuous monitoring will be performed.

[0104] The above contents are merely examples and explanations of the concept of the present invention. Those skilled in the art may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined by the present invention, they shall all fall within the protection scope of the present invention.

Claims

1. An intelligent monitoring system for distributed photovoltaic power generation equipment, characterized in that: include: 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 device abnormality monitoring module: collects the azimuth and elevation angles of each automatic tracking photovoltaic power generation device, obtains control device adjustment data, and evaluates the abnormality of the corresponding control device based on this data; Abnormal driving equipment monitoring module: obtains the temperature of the driving motor in real time, obtains the corresponding rotation ratio of each automatic tracking photovoltaic power generation device, and comprehensively evaluates the abnormal situation of the corresponding driving equipment; Tracking system abnormality analysis module: evaluates abnormal conditions of the tracking system according to the corresponding abnormal conditions of the light intensity device, the control device and the drive device; Support structure data acquisition module: collects support structure images and obtains support structure data of each automatic tracking photovoltaic power generation device; A stent structure abnormality analysis module: analyzing the corresponding stent structure abnormality based on the stent structure data; Fault cause identification module: identifies the specific abnormality.

2. The distributed photovoltaic power generation equipment intelligent monitoring system according to claim 1, characterized in that: The specific analysis method of the abnormal situation of the light intensity equipment is as follows: Extracting the ideal direction angle and ideal pitch angle of each automatic tracking photovoltaic power generation device; The ideal direction angles of each automatic tracking photovoltaic power generation device are averaged to obtain a reference ideal direction angle of the automatic tracking photovoltaic power generation device. Similarly, the reference ideal pitch angle of the automatic tracking photovoltaic power generation device can be obtained. The ideal azimuth angle and the ideal pitch angle of each automatic tracking photovoltaic power generation device are analyzed for differences with the corresponding reference ideal azimuth angle and the reference ideal pitch angle to obtain the ideal azimuth angle deviation value and the ideal pitch angle deviation value of each automatic tracking photovoltaic power generation device; The ideal azimuth deviation value and the ideal pitch angle deviation value of each automatic tracking photovoltaic power generation device are compared and analyzed with the corresponding reference ideal azimuth angle and reference ideal pitch angle respectively to obtain the ideal azimuth deviation degree and the ideal pitch angle deviation degree of each automatic tracking photovoltaic power generation device, and then the light intensity equipment abnormality index of each automatic tracking photovoltaic power generation device is obtained by weighted summation calculation.

3. A distributed photovoltaic power generation equipment intelligent monitoring system as claimed in claim 2, characterized in that: The control device adjustment data includes vibration frequency, vibration duration, direction adjustment times and average direction adjustment amplitude.

4. A distributed photovoltaic power generation equipment intelligent monitoring system as claimed in claim 3, characterized in that: The specific analysis method of the abnormal situation of the control device is as follows: The vibration frequency, vibration duration, direction adjustment times and average direction adjustment amplitude of each automatic tracking photovoltaic power generation device acquired in real time are extracted and compared with the preset reference vibration frequency, monitoring period threshold, direction adjustment times threshold and adjustment amplitude threshold respectively. The abnormality index of the control equipment is calculated by weighted summation based on the vibration parameter weight and the direction adjustment parameter weight.

5. A distributed photovoltaic power generation equipment intelligent monitoring system as claimed in claim 4, characterized in that: The rotation ratio corresponding to the automatic tracking photovoltaic power generation device specifically includes a horizontal rotation ratio and a vertical rotation ratio, and the specific analysis method is as follows: Obtain the direction angle change and the number of revolutions of the horizontal direction control motor corresponding to each automatic tracking photovoltaic power generation device during the monitoring period, and then perform ratio calculation to obtain the horizontal direction rotation ratio corresponding to each automatic tracking photovoltaic power generation device; Similarly, the vertical rotation ratio corresponding to each automatic tracking photovoltaic power generation device can be obtained.

6. A distributed photovoltaic power generation equipment intelligent monitoring system as claimed in claim 5, characterized in that: The specific analysis method of the abnormal situation of the driving device is as follows: The real-time drive motor temperature, horizontal rotation ratio and vertical rotation ratio of each automatic tracking photovoltaic power generation device are extracted, and the ratio of the real-time drive motor temperature 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 are constructed. The drive device abnormality index is output after weighted summation of the above three types of parameters.

7. A distributed photovoltaic power generation equipment intelligent monitoring system as claimed in claim 6, characterized in that: The specific analysis method of the abnormal situation of the tracking system is as follows: The abnormal index of light intensity equipment, the abnormal index of control equipment and the abnormal index of driving equipment of each automatic tracking photovoltaic power generation device are extracted, and then the abnormal index of tracking system of each automatic tracking photovoltaic power generation device is obtained by summing them up according to the weights.

8. The distributed photovoltaic power generation equipment intelligent monitoring system according to claim 7, characterized in that: The stent structure data includes a key part defect evaluation index and a geometric structure abnormality evaluation index, and the specific method is as follows: Extract the support structure image of each automatic tracking photovoltaic power generation device, obtain the images of each key part of each automatic tracking photovoltaic power generation device, statistically obtain the number of defects corresponding to each key part of each automatic tracking photovoltaic power generation device, and simultaneously obtain the defect area corresponding to each defect of each key part of each automatic tracking photovoltaic power generation device, and sum up the defect areas corresponding to each defect to obtain the monitoring defect area of ​​each key part of each automatic tracking photovoltaic power generation device; Normalizing the defect quantity and monitored defect area corresponding to each key part of each automatic tracking photovoltaic power generation device with the corresponding preset reference value, and then performing mean calculation to obtain the key part defect evaluation index of each automatic tracking photovoltaic power generation device; Extract the support structure image 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 a 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; The reference monitoring distance of each key position of each automatic tracking photovoltaic power generation device and the corresponding initial monitoring distance are subjected to deviation analysis and mean calculation to obtain the geometric structure anomaly evaluation index of each automatic tracking photovoltaic power generation device.

9. A distributed photovoltaic power generation equipment intelligent monitoring system as claimed in claim 8, characterized in that: The specific analysis method of the abnormal situation of the stent structure is as follows: The defect evaluation index of key parts and the geometric structure abnormality evaluation index of each automatic tracking photovoltaic power generation device are extracted, and then the support structure abnormality index of each automatic tracking photovoltaic power generation device is obtained by summing them up according to the weights.

10. A distributed photovoltaic power generation equipment intelligent monitoring system as claimed in claim 9, characterized in that: The method of identifying the specific abnormality is as follows: Extracting the tracking system abnormality index and the support structure abnormality index of each automatic tracking photovoltaic power generation device, and then comparing them with the preset tracking system abnormality index threshold and the 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 is identified as a tracking system abnormality; If the support structure abnormality index of a certain automatic tracking photovoltaic power generation device is greater than the support structure abnormality index threshold, the specific abnormality is identified as support structure abnormality.

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