Big data-based intelligent data supervision system and method for photovoltaic power generation system

Abstract

The invention relates to the technical field of photovoltaic power generation, in particular to a photovoltaic power generation system intelligent data supervision system and method based on big data, and the system comprises a light calculation module, an array adjustment module, a waveform analysis module, a shielding judgment module and a cleaning module. The array adjusting module is used for adjusting the angles of the power generation plates and obtaining the total current of the system, the waveform analysis module is used for separating the current waveforms of the power generation plates, the shielding judgment module is used for calculating the shielding conditions of the power generation plates, and the cleaning module is used for judging the power generation plates needing to be cleaned and updating system data. According to the invention, the shielding degree of each power generation panel can be separated through angle adjustment of the photovoltaic array, so that the cost of mounting a sensor is saved, the workload of cleaning personnel is reduced, and intelligent cleaning of the photovoltaic power station is realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of photovoltaic power generation, in particular to an intelligent data monitoring system and method for a photovoltaic power generation system based on big data. BACKGROUND

[0002] Photovoltaic power generation technology is a technology that converts light energy into electrical energy directly through the photoelectric effect of semiconductor solar panels, and is an important part of new energy systems. It has the advantages of clean energy, large power generation capacity, and strong environmental adaptability, and is of great significance to improving energy production structure and achieving energy self-sufficiency.

[0003] Photovoltaic power generation arrays are usually installed in ecological environments such as cities, deserts, and grasslands, which makes it easy for the power generation panels to be blocked by leaves, dust, bird droppings, and other foreign matter. This not only affects the power generation efficiency of the photovoltaic panels, but also easily causes the breakdown of the cells and damages the power generation panels. Therefore, cleaning the power generation panels is a very important part of photovoltaic power generation technology. Due to the different environments of each power generation panel, cleaning personnel cannot accurately determine which power generation panel needs to be cleaned, and can only clean the power generation panels collectively every 30-60 days. This not only wastes time and effort, but also cannot clean the blocked panels in a timely manner, causing safety hazards to the power generation panels.

[0004] The existing patent (CN110768628A) discloses a fault detection method for a photovoltaic array, which relies on detecting the voltage and current on each string of panels to determine the working condition of the panels and provides the coordinates of the power generation panels. However, a complete photovoltaic array has a large number of power generation panels, and installing sensors on each panel would result in a huge cost. In addition, factors such as battery aging and increased battery connection resistance can also cause a decrease in the efficiency of the power generation panels, and the accuracy of this type of technology is not high. SUMMARY

[0005] The present application aims to provide an intelligent data monitoring system and method for a photovoltaic power generation system based on big data to solve the problems raised in the background.

[0006] To solve the above technical problems, the present application provides the following technical solution: an intelligent data monitoring system for a photovoltaic power generation system based on big data, comprising: a light calculation module, an array adjustment module, a waveform analysis module, a blocking judgment module, and a cleaning module.

[0007] The light calculation module is used to determine the solar incident angle of the sunrise and sunset time of the next day according to the positioning information and system time.

[0008] The array adjustment module is used to adjust the angle of each power generation panel in the photovoltaic array, and calculate the power generation intensity of each power generation panel based on historical data.

[0009] The waveform analysis module is used to detect the current output waveform and separate the changing waveforms of each power generation panel based on the power generation intensity of the power generation panel and the waveform changes during the photovoltaic array angle adjustment process.

[0010] The shading judgment module is used to analyze the changes in the waveform and power generation intensity of the power generation panel, combined with the data of the standard power generation panel, and calculate the shading status of the power generation panel.

[0011] The cleaning module is used to identify the power generation panels that need to be cleaned, report their location to the cleaning personnel for cleaning, and then update the system data.

[0012] Furthermore, the light calculation module includes: a GPS positioning unit and an angle setting unit;

[0013] The GPS positioning unit is used to obtain the geographical location of the photovoltaic array;

[0014] The angle setting unit is used to calculate the sunrise time and the angle of incidence of sunlight at sunrise.

[0015] Furthermore, the array adjustment module includes: an angle adjustment unit, a data cleaning unit, and an array self-test unit;

[0016] The angle adjustment unit is used to adjust the tilt angle of the power generation panel according to a set program.

[0017] The data cleaning unit is used to obtain the light-receiving area of ​​each power generation panel based on the total area of ​​the power generation panel, the angle of sunlight, and the tilt angle, and to record the change curve of its light-receiving area.

[0018] The array self-test unit is used to obtain data points based on historical logs and calculate the loss coefficient of the power generation panel through function fitting.

[0019] Furthermore, the waveform analysis module includes: a detection unit, a filtering unit, and an ICA separation unit;

[0020] The detection unit is used to detect current changes in the main circuit and output the waveform of the current change.

[0021] The filtering unit is used to filter out high-frequency components in the main circuit current waveform and retain only the waveform components within the frequency range of the change in the light-receiving area of ​​the power generation panel.

[0022] The ICA separation unit is used to separate the independent waveforms of each power generation panel from the total current waveform based on the changes in the loss coefficient and light-receiving area of ​​each panel using the ICA algorithm.

[0023] Furthermore, the occlusion determination module includes: a coefficient calculation unit and an array positioning unit;

[0024] The coefficient calculation unit is used to compare the independent waveform of each power generation panel with historical data and update the shading coefficient of each power generation panel in the historical data.

[0025] The array positioning unit is used to determine whether the shading coefficient of the photovoltaic panel is higher than the threshold, mark the photovoltaic panel with the shading coefficient higher than the threshold as needing to be cleaned, and give its coordinates in the photovoltaic array.

[0026] Furthermore, the cleaning module includes: an alarm unit and a system update unit;

[0027] The alarm unit is used to send a cleaning alarm to the cleaning personnel and inform them of the coordinates of the power generation panels to be cleaned;

[0028] The system update unit is used to remeasure the power generation data of the power generation panel after the cleaning staff cleans it, and update its loss coefficient.

[0029] The intelligent data monitoring method for photovoltaic power generation systems based on big data includes the following steps:

[0030] S100. Position the photovoltaic array, determine the sunrise time and the incident direction vector of sunlight at sunrise based on the geographical location and the current date, and set the initial angle of each power generation panel in the photovoltaic array according to the incident direction vector;

[0031] S200. At sunrise, based on the characteristic information of the power generation panels, the initial light-receiving area of ​​each power generation panel is obtained, and the loss coefficient of the power generation panels is fitted based on historical records.

[0032] S300. Controls all photovoltaic panels in the photovoltaic array to adjust longitudinally by one angle, resets and then adjusts laterally by one angle, calculates the change function of the light-receiving area of ​​each photovoltaic panel during the adjustment process, reads the current change on the main circuit of the photovoltaic array during the adjustment process, and outputs the waveform of the current change in the two adjustments.

[0033] S400. Filter out high-frequency components in the main circuit current waveform. Based on the change in the light-receiving area of ​​each power generation board, use the ICA algorithm to separate the independent waveforms of each power generation board from the total current waveform, analyze the power generation efficiency of each power generation board, and calculate its shading coefficient after removing the power generation board's own losses.

[0034] S500. Mark the solar panels with shading coefficients higher than the threshold as needing cleaning, provide their coordinates in the photovoltaic array, call cleaning personnel to clean them, and after cleaning, remeasure the power generation data of the solar panel and update its loss coefficient.

[0035] Furthermore, step S100 includes:

[0036] Step S101. Obtain the geographical location of the photovoltaic array through the satellite positioning system, calculate the sunrise time of the next day based on the current date, and obtain the incident direction vector of sunlight at sunrise;

[0037] Step S102. Establish a spatial rectangular coordinate system with the horizontal arrangement direction of the photovoltaic array as the X-axis, the vertical arrangement direction as the Y-axis, and the perpendicular direction between the sun and the ground as the Z-axis. Decompose the incident direction vector into two component vectors, the XZ plane and the YZ plane. Calculate the angle between the two component vectors and the ground. The angle between the XZ plane and the ground is denoted as the horizontal angle θ1, and the angle between the YZ plane and the ground is denoted as the vertical angle θ2. Both θ1 and θ2 are less than 90°.

[0038] Step S103. The photovoltaic panel located in the i-th column and j-th row of the photovoltaic array is denoted as panel (i,j). The angle between panel (i,j) and the ground in the YZ plane is set as V1+i·α, and the angle between panel (i,j) and the ground in the XZ plane is set as V2+j·β. α is the horizontal unit tilt angle, β is the vertical unit tilt angle, V1 is the initial horizontal tilt angle, and V2 is the initial vertical tilt angle. V1, V2, α, and β are all preset by the system. For any panel, it is necessary to satisfy 0°<α<90° and 0°<β<90°.

[0039] Furthermore, step S200 includes:

[0040] Step S201. Obtain the feature information of the power generation panel, the feature information including: the length, width and initial angle of the power generation panel;

[0041] Step S202. Calculate the initial light-receiving area S1 of the solar panel using the following formula:

[0042] A=b·sin(180°-θ1-V1-i·α) / sinθ1

[0043] B=a·sin(180°-θ2-V2-j·β) / sinθ2

[0044] S1=A·B

[0045] Where A is the effective light-receiving width, B is the effective light-receiving length, a is the length of the power generation plate, and b is the width of the power generation plate. All of the above parameters are constants greater than 0.

[0046] Step S203. In the historical log, the loss coefficient of the power generation panel (i,j) measured after each cleaning is recorded as a data point. All data points are fitted as a function of loss coefficient and time. The predicted value of the loss coefficient of the power generation panel on the current date is calculated and recorded as Q. The loss coefficient represents the loss value of power generation efficiency per unit light-receiving area caused by battery aging and increased battery connection resistance.

[0047] Furthermore, step S300 includes:

[0048] Step S301. Control all photovoltaic panels to decrease their tilt angle uniformly in the X-axis direction, and record the angular velocity as r, until a photovoltaic panel is parallel to the ground;

[0049] After the power generation panel is reset, the tilt angle is reduced uniformly in the Y-axis direction at the same angular velocity r until the power generation panel is parallel to the ground.

[0050] Then, the functions of the change of the light-receiving area of ​​the solar panel (i,j) with time during the two tilt angle adjustments are as follows:

[0051] S1(t1)=S1-b·sin(180°-θ1-V1-i·α+r·t1) / sinθ1

[0052] S2(t2)=S1-a·sin(180°-θ2-V2-j·β+r·t2) / sinθ2

[0053] Where S1(t) represents the change function of the light-receiving area of ​​the power generation panel (i,j) with time t during the first adjustment process, r is the angular velocity of the adjustment, t1 is the time during the first adjustment process, and t2 is the time during the second adjustment process;

[0054] Step S302. Using the ammeter on the main circuit, read the waveform of the main circuit changing with time during the two adjustments. The waveform of the first adjustment is recorded as I1(t), and the waveform of the second adjustment is recorded as I2(t).

[0055] Furthermore, step S400 includes:

[0056] Step S401. Use a filter to remove the high-frequency components in the main circuit current waveform and retain only the low-frequency components caused by the change in the light-receiving area of ​​the power generation panel;

[0057] Step S402. Use the ICA algorithm to classify the total current waveforms I1(t) and I2(t). The ICA algorithm is an independent component analysis algorithm, which can separate sub-waveforms with specified frequencies and phases from the superimposed waveforms. Since the phase of the light-receiving area function of each power generation panel is different, the phase difference of the power generation panels is input into the ICA model to obtain the power generation waveforms of each row and column of power generation panels, thereby obtaining the power generation waveforms of each power generation panel in the two adjustments.

[0058] Step S403. The sub-waveform separated in the first adjustment of the power generation plate is denoted as W1(t), and the sub-waveform separated in the second adjustment is denoted as W2(t). Let Z(t) = [W1(t) / S1(t) + W2(t) / S2(t)] / 2, where Z(t) represents the power generation efficiency function of the power generation plate (i,j). Perform amplitude analysis on Z(t), and the average amplitude Z obtained is the average power generation efficiency of the power generation plate (i,j).

[0059] Step S404. Let Z0 be the power generation efficiency of the power generation panel (i,j) when it is first put into use. Calculate the shading coefficient P of the power generation panel (i,j) according to the following formula:

[0060] P = Z0 - QZ

[0061] Where Z is the measured actual power generation efficiency, Q is the predicted loss coefficient, and P is the shading coefficient, representing the power generation efficiency of the solar panel reduced due to shading.

[0062] Furthermore, step S500 includes:

[0063] Step S501. Detect the shading coefficient P of all photovoltaic panels in the photovoltaic array. When the shading coefficient P of the photovoltaic panel (i,j) is greater than the preset threshold P0, issue an alarm to the cleaning staff and inform the cleaning staff of the row and column coordinates (i,j) of the photovoltaic panel.

[0064] Step S502. After cleaning the obscured power generation panel, the cleaning staff immediately retests the power generation efficiency of the power generation panel, calculates the loss coefficient of the power generation panel, and updates the loss coefficient of the power generation panel in the historical log.

[0065] Preferably, in the evening, based on the azimuth angle of the sunlight at sunset, the shading coefficient of all photovoltaic panels is detected again, and the photovoltaic panels with a shading coefficient P greater than the preset threshold P0 are cleaned to ensure the effective operation of the photovoltaic array.

[0066] Compared with the prior art, the beneficial effects achieved by the present invention are:

[0067] 1. This invention can determine the sunrise time and solar azimuth angle using a positioning system and date. During the morning and evening when power generation efficiency is low and electricity is difficult to utilize, it relies on solar energy for angle adjustment and array self-checking without consuming additional energy. By analyzing the changes in the angle of each photovoltaic panel and the overall power generation waveform, the power generation data of each panel can be locked, achieving precise control of the photovoltaic array.

[0068] 2. This invention can identify minute differences in each photovoltaic panel by adjusting the angle of the photovoltaic array. Relying on a filtering algorithm, it can separate the current output curve of each panel from the total current. It can detect the output status of the panels without installing circuit sensors on each panel, saving the cost of installing sensors and reducing the amount of data in the photovoltaic array. This can significantly improve the speed and accuracy of data calculation.

[0069] 3. This invention can analyze data from standard photovoltaic panels to identify those requiring cleaning and notify cleaning personnel to clean them accordingly, thereby increasing cleaning speed, reducing the workload of cleaning personnel, and realizing intelligent cleaning of photovoltaic power stations. Furthermore, this invention can determine shading conditions based on the panel's own power generation capacity, avoiding the impact of battery aging, increased battery connection resistance, and other factors on the detection of shading conditions, thus improving the accuracy of photovoltaic panel data detection. Attached Figure Description

[0070] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0071] Figure 1 This is a schematic diagram of the intelligent data monitoring system for photovoltaic power generation systems based on big data, as described in this invention.

[0072] Figure 2 This is a schematic diagram illustrating the steps of the intelligent data monitoring method for photovoltaic power generation systems based on big data, as described in this invention. Detailed Implementation

[0073] 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.

[0074] Please see Figure 1 The present invention provides a technical solution: an intelligent data monitoring system for photovoltaic power generation systems based on big data, comprising: a light calculation module, an array adjustment module, a waveform analysis module, a shading judgment module, and a cleaning module;

[0075] The light calculation module is used to determine the sunrise and sunset times and the angle of solar incidence at sunrise and sunset for the next day based on the location information and system time.

[0076] The light calculation module includes: a GPS positioning unit and an angle setting unit;

[0077] The GPS positioning unit is used to obtain the geographical location of the photovoltaic array;

[0078] The angle setting unit is used to calculate the sunrise time and the angle of incidence of sunlight at sunrise.

[0079] The array adjustment module is used to adjust the angle of each photovoltaic panel in the photovoltaic array and calculate the power generation intensity of each panel by combining historical data.

[0080] The array adjustment module includes: an angle adjustment unit, a data cleaning unit, and an array self-test unit;

[0081] The angle adjustment unit is used to adjust the tilt angle of the power generation panel according to a set program.

[0082] The data cleaning unit is used to obtain the light-receiving area of ​​each power generation panel based on the total area of ​​the power generation panel, the angle of sunlight, and the tilt angle, and to record the change curve of its light-receiving area.

[0083] The array self-test unit is used to obtain data points based on historical logs and calculate the loss coefficient of the power generation panel through function fitting.

[0084] The waveform analysis module is used to detect the current output waveform and separate the changing waveforms of each power generation panel based on the power generation intensity of the power generation panel and the waveform changes during the photovoltaic array angle adjustment process.

[0085] The waveform analysis module includes: a detection unit, a filtering unit, and an ICA separation unit;

[0086] The detection unit is used to detect current changes in the main circuit and output the waveform of the current change.

[0087] The filtering unit is used to filter out high-frequency components in the main circuit current waveform and retain only the waveform components within the frequency range of the change in the light-receiving area of ​​the power generation panel.

[0088] The ICA separation unit is used to separate the independent waveforms of each power generation panel from the total current waveform based on the changes in the loss coefficient and light-receiving area of ​​each panel using the ICA algorithm.

[0089] The shading judgment module is used to analyze the changes in the waveform and power generation intensity of the power generation panel, combined with the data of the standard power generation panel, and calculate the shading status of the power generation panel.

[0090] The occlusion determination module includes: a coefficient calculation unit and an array positioning unit;

[0091] The coefficient calculation unit is used to compare the independent waveform of each power generation panel with historical data and update the shading coefficient of each power generation panel in the historical data.

[0092] The array positioning unit is used to determine whether the shading coefficient of the photovoltaic panel is higher than the threshold, mark the photovoltaic panel with the shading coefficient higher than the threshold as needing to be cleaned, and give its coordinates in the photovoltaic array.

[0093] The cleaning module is used to identify the power generation panels that need to be cleaned, report their location to the cleaning personnel for cleaning, and then update the system data.

[0094] The cleaning module includes: an alarm unit and a system update unit;

[0095] The alarm unit is used to send a cleaning alarm to the cleaning personnel and inform them of the coordinates of the power generation panels to be cleaned;

[0096] The system update unit is used to remeasure the power generation data of the power generation panel after the cleaning staff cleans it, and update its loss coefficient.

[0097] like Figure 2 As shown, the intelligent data monitoring method for photovoltaic power generation systems based on big data includes the following steps:

[0098] S100. Position the photovoltaic array, determine the sunrise time and the incident direction vector of sunlight at sunrise based on the geographical location and the current date, and set the initial angle of each power generation panel in the photovoltaic array according to the incident direction vector;

[0099] Step S100 includes:

[0100] Step S101. Obtain the geographical location of the photovoltaic array through the satellite positioning system, calculate the sunrise time of the next day based on the current date, and obtain the incident direction vector of sunlight at sunrise;

[0101] Step S102. Establish a spatial rectangular coordinate system with the horizontal arrangement direction of the photovoltaic array as the X-axis, the vertical arrangement direction as the Y-axis, and the perpendicular direction between the sun and the ground as the Z-axis. Decompose the incident direction vector into two component vectors, the XZ plane and the YZ plane. Calculate the angle between the two component vectors and the ground. The angle between the XZ plane and the ground is denoted as the horizontal angle θ1, and the angle between the YZ plane and the ground is denoted as the vertical angle θ2. Both θ1 and θ2 are less than 90°.

[0102] Step S103. The photovoltaic panel located in the i-th column and j-th row of the photovoltaic array is denoted as panel (i,j). The angle between panel (i,j) and the ground in the YZ plane is set as V1+i·α, and the angle between panel (i,j) and the ground in the XZ plane is set as V2+j·β. α is the horizontal unit tilt angle, β is the vertical unit tilt angle, V1 is the initial horizontal tilt angle, and V2 is the initial vertical tilt angle. V1, V2, α, and β are all preset by the system. For any panel, it is necessary to satisfy 0°<α<90° and 0°<β<90°.

[0103] S200. At sunrise, based on the characteristic information of the power generation panels, the initial light-receiving area of ​​each power generation panel is obtained, and the loss coefficient of the power generation panels is fitted based on historical records.

[0104] Step S200 includes:

[0105] Step S201. Obtain the feature information of the power generation panel, the feature information including: the length, width and initial angle of the power generation panel;

[0106] Step S202. Calculate the initial light-receiving area S1 of the solar panel using the following formula:

[0107] A=b·sin(180°-θ1-V1-i·α) / sinθ1

[0108] B=a·sin(180°-θ2-V2-j·β) / sinθ2

[0109] S1=A·B

[0110] Where A is the effective light-receiving width, B is the effective light-receiving length, a is the length of the power generation plate, and b is the width of the power generation plate. All of the above parameters are constants greater than 0.

[0111] Step S203. In the historical log, the loss coefficient of the power generation panel (i,j) measured after each cleaning is recorded as a data point. All data points are fitted as a function of loss coefficient and time. The predicted value of the loss coefficient of the power generation panel on the current date is calculated and recorded as Q. The loss coefficient represents the loss value of power generation efficiency per unit light-receiving area caused by battery aging and increased battery connection resistance.

[0112] S300. Controls all photovoltaic panels in the photovoltaic array to adjust longitudinally by one angle, resets and then adjusts laterally by one angle, calculates the change function of the light-receiving area of ​​each photovoltaic panel during the adjustment process, reads the current change on the main circuit of the photovoltaic array during the adjustment process, and outputs the waveform of the current change in the two adjustments.

[0113] Step S300 includes:

[0114] Step S301. Control all photovoltaic panels to decrease their tilt angle uniformly in the X-axis direction, and record the angular velocity as r, until a photovoltaic panel is parallel to the ground;

[0115] After the power generation panel is reset, the tilt angle is reduced uniformly in the Y-axis direction at the same angular velocity r until the power generation panel is parallel to the ground.

[0116] Then, the functions of the change of the light-receiving area of ​​the solar panel (i,j) with time during the two tilt angle adjustments are as follows:

[0117] S1(t1)=S1-b·sin(180°-θ1-V1-i·α+r·t1) / sinθ1

[0118] S2(t2)=S1-a·sin(180°-θ2-V2-j·β+r·t2) / sinθ2

[0119] Where S1(t) represents the change function of the light-receiving area of ​​the power generation panel (i,j) with time t during the first adjustment process, r is the angular velocity of the adjustment, t1 is the time during the first adjustment process, and t2 is the time during the second adjustment process;

[0120] Step S302. Using the ammeter on the main circuit, read the waveform of the main circuit changing with time during the two adjustments. The waveform of the first adjustment is recorded as I1(t), and the waveform of the second adjustment is recorded as I2(t).

[0121] S400. Filter out high-frequency components in the main circuit current waveform. Based on the change in the light-receiving area of ​​each power generation board, use the ICA algorithm to separate the independent waveforms of each power generation board from the total current waveform, analyze the power generation efficiency of each power generation board, and calculate its shading coefficient after removing the power generation board's own losses.

[0122] Step S400 includes:

[0123] Step S401. Use a filter to remove the high-frequency components in the main circuit current waveform and retain only the low-frequency components caused by the change in the light-receiving area of ​​the power generation panel;

[0124] Step S402. Use the ICA algorithm to classify the total current waveforms I1(t) and I2(t). The ICA algorithm is an independent component analysis algorithm, which can separate sub-waveforms with specified frequencies and phases from the superimposed waveforms. Since the phase of the light-receiving area function of each power generation panel is different, the phase difference of the power generation panels is input into the ICA model to obtain the power generation waveforms of each row and column of power generation panels, thereby obtaining the power generation waveforms of each power generation panel in the two adjustments.

[0125] Step S403. The sub-waveform separated in the first adjustment of the power generation plate is denoted as W1(t), and the sub-waveform separated in the second adjustment is denoted as W2(t). Let Z(t) = [W1(t) / S1(t) + W2(t) / S2(t)] / 2, where Z(t) represents the power generation efficiency function of the power generation plate (i,j). Perform amplitude analysis on Z(t), and the average amplitude Z obtained is the average power generation efficiency of the power generation plate (i,j).

[0126] Step S404. Let Z0 be the power generation efficiency of the power generation panel (i,j) when it is first put into use. Calculate the shading coefficient P of the power generation panel (i,j) according to the following formula:

[0127] P = Z0 - QZ

[0128] Where Z is the measured actual power generation efficiency, Q is the predicted loss coefficient, and P is the shading coefficient, representing the power generation efficiency of the solar panel reduced due to shading.

[0129] S500. Mark the solar panels with shading coefficients higher than the threshold as needing cleaning, provide their coordinates in the photovoltaic array, call cleaning personnel to clean them, and after cleaning, remeasure the power generation data of the solar panel and update its loss coefficient.

[0130] Step S500 includes:

[0131] Step S501. Detect the shading coefficient P of all photovoltaic panels in the photovoltaic array. When the shading coefficient P of the photovoltaic panel (i,j) is greater than the preset threshold P0, issue an alarm to the cleaning staff and inform the cleaning staff of the row and column coordinates (i,j) of the photovoltaic panel.

[0132] Step S502. After cleaning the obscured power generation panel, the cleaning staff immediately retests the power generation efficiency of the power generation panel, calculates the loss coefficient of the power generation panel, and updates the loss coefficient of the power generation panel in the historical log.

[0133] Preferably, in the evening, based on the azimuth angle of the sunlight at sunset, the shading coefficient of all photovoltaic panels is detected again, and the photovoltaic panels with a shading coefficient P greater than the preset threshold P0 are cleaned to ensure the effective operation of the photovoltaic array.

[0134] Example:

[0135] The photovoltaic array obtains its own latitude and longitude position and the current date, and calculates that the sunrise time of the next day is 8:00. At sunrise, the angle between the sunlight and the ground in the horizontal direction of the photovoltaic array is 30° and the angle between the sunlight and the ground in the vertical direction is 45°.

[0136] In the photovoltaic array, the power-generating panel in the 2nd column and 5th row is denoted as power-generating panel (2,5). α and β are both 5°, and V1 = V2 = 20°. Therefore, the angles of power-generating panel (2,3) in the horizontal and vertical directions are 30° and 45° respectively. The power-generating panel is 2m long and 1m wide, so its initial area S1 = 4√2m². 2 ;

[0137] All photovoltaic panels in the array are tilted at a constant speed of r = 1° per second towards the X-axis. After 25 seconds, since the photovoltaic panel in the first row and first column is already parallel to the ground, the tilt angle is stopped and the initial angle is restored. Then, the tilt angle is tilted at a constant speed of r = 1° per second towards the Y-axis. After 25 seconds, the array is reset. The function of the total current changing with time during the two adjustments is read.

[0138] The current variation function of the power generation panel (2,5) was extracted from the total current using the ICA algorithm, and the power generation efficiency of the power generation panel (2,5) per unit area was calculated to be 5W. The power generation efficiency of the power generation panel (2,5) when it was first put into use in the historical log was 10W / m. 2 The loss coefficient obtained by fitting the data points is 2W / m. 2 Therefore, the shading factor of the solar panel is 3W / m. 2 Exceeding 2W / m 2 If the threshold is reached, the cleaning staff will be notified to clean the power generation panel (2,5), and the power generation efficiency of the power generation panel (2,5) will be calculated after cleaning and the data will be updated to the historical record.

[0139] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0140] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for intelligent data monitoring of photovoltaic power generation systems based on big data, characterized in that: The method includes the following steps: S100. Position the photovoltaic array, determine the sunrise time and the incident direction vector of sunlight at sunrise based on the geographical location and the current date, and set the initial angle of each power generation panel in the photovoltaic array according to the incident direction vector; S200. At sunrise, based on the characteristic information of the power generation panels, the initial light-receiving area of ​​each power generation panel is obtained, and the loss coefficient of the power generation panels is fitted based on historical records. S300. Controls all photovoltaic panels in the photovoltaic array to adjust longitudinally by one angle, resets and then adjusts laterally by one angle, calculates the change function of the light-receiving area of ​​each photovoltaic panel during the adjustment process, reads the current change on the main circuit of the photovoltaic array during the adjustment process, and outputs the waveform of the current change in the two adjustments. S400. Filter out high-frequency components in the main circuit current waveform. Based on the change in the light-receiving area of ​​each power generation board, use the ICA algorithm to separate the independent waveforms of each power generation board from the total current waveform, analyze the power generation efficiency of each power generation board, and calculate its shading coefficient after removing the power generation board's own losses. S500. Mark the solar panels with shading coefficients higher than the threshold as needing cleaning, provide their coordinates in the photovoltaic array, call cleaning personnel to clean them, and after cleaning, remeasure the power generation data of the solar panel and update its loss coefficient.

2. The intelligent data monitoring method for photovoltaic power generation systems based on big data according to claim 1, characterized in that: Step S100 includes: Step S101. Obtain the geographical location of the photovoltaic array through the satellite positioning system, calculate the sunrise time of the next day based on the current date, and obtain the incident direction vector of sunlight at sunrise; Step S102. Establish a spatial rectangular coordinate system with the horizontal arrangement direction of the photovoltaic array as the X-axis, the vertical arrangement direction as the Y-axis, and the perpendicular direction between the sun and the ground as the Z-axis. Decompose the incident direction vector into two component vectors, the XZ plane and the YZ plane. Calculate the angle between the two component vectors and the ground. The angle between the XZ plane and the ground is denoted as the horizontal angle θ1, and the angle between the YZ plane and the ground is denoted as the vertical angle θ2. Both θ1 and θ2 are less than 90°. Step S103. The photovoltaic panel located in the i-th column and j-th row of the photovoltaic array is denoted as panel (i,j). The angle between panel (i,j) and the ground in the YZ plane is set as V1+i·α, and the angle between panel (i,j) and the ground in the XZ plane is set as V2+j·β. α is the horizontal unit tilt angle, β is the vertical unit tilt angle, V1 is the initial horizontal tilt angle, and V2 is the initial vertical tilt angle. V1, V2, α, and β are all preset by the system. For any panel, it is necessary to satisfy 0°<α<90° and 0°<β<90°.

3. The intelligent data monitoring method for photovoltaic power generation systems based on big data according to claim 2, characterized in that: Step S200 includes: Step S201. Obtain the feature information of the power generation panel, the feature information including: the length, width and initial angle of the power generation panel; Step S202. Calculate the initial light-receiving area S1 of the solar panel using the following formula: A=b·sin(180°-θ1-V1-i·α) / sinθ1 B=a·sin(180°-θ2-V2-j·β) / sinθ2 S1=A·B Where A is the effective light-receiving width, B is the effective light-receiving length, a is the length of the power generation plate, and b is the width of the power generation plate. All of the above parameters are constants greater than 0. Step S203. In the historical log, the loss coefficient of the power generation panel (i,j) measured after each cleaning is recorded as a data point. All data points are fitted as a function of loss coefficient and time. The predicted value of the loss coefficient of the power generation panel on the current date is calculated and recorded as Q. The loss coefficient represents the loss value of power generation efficiency per unit light-receiving area caused by battery aging and increased battery connection resistance.

4. The intelligent data monitoring method for photovoltaic power generation systems based on big data according to claim 3, characterized in that: Step S300 includes: Step S301. Control all photovoltaic panels to decrease their tilt angle uniformly in the X-axis direction, and record the angular velocity as r, until a photovoltaic panel is parallel to the ground; After the power generation panel is reset, the tilt angle is reduced uniformly in the Y-axis direction at the same angular velocity r until the power generation panel is parallel to the ground. Then, the functions of the change of the light-receiving area of ​​the solar panel (i,j) with time during the two tilt angle adjustments are as follows: S1(t1)=S1-b·sin(180°-θ1-V1-i·α+r·t1) / sinθ1 S2(t2)=S1-a·sin(180°-θ2-V2-j·β+r·t2) / sinθ2 Where S1(t) represents the change function of the light-receiving area of ​​the power generation panel (i,j) with time t during the first adjustment process, r is the angular velocity of the adjustment, t1 is the time during the first adjustment process, and t2 is the time during the second adjustment process; Step S302. Using the ammeter on the main circuit, read the waveform of the main circuit changing with time during the two adjustments. The waveform of the first adjustment is recorded as I1(t), and the waveform of the second adjustment is recorded as I2(t).

5. The intelligent data monitoring method for photovoltaic power generation systems based on big data according to claim 4, characterized in that: Step S400 includes: Step S401. Use a filter to remove the high-frequency components in the main circuit current waveform and retain only the low-frequency components caused by the change in the light-receiving area of ​​the power generation panel; Step S402. Use the ICA algorithm to classify the total current waveforms I1(t) and I2(t). The ICA algorithm is an independent component analysis algorithm, which can separate sub-waveforms with specified frequencies and phases from the superimposed waveforms. Since the phase of the light-receiving area function of each power generation panel is different, the phase difference of the power generation panels is input into the ICA model to obtain the power generation waveforms of each row and column of power generation panels, thereby obtaining the power generation waveforms of each power generation panel in the two adjustments. Step S403. The sub-waveform separated in the first adjustment of the power generation plate is denoted as W1(t), and the sub-waveform separated in the second adjustment is denoted as W2(t). Let Z(t) = [W1(t) / S1(t) + W2(t) / S2(t)] / 2, where Z(t) represents the power generation efficiency function of the power generation plate (i,j). Perform amplitude analysis on Z(t), and the average amplitude Z obtained is the average power generation efficiency of the power generation plate (i,j). Step S404. Let Z0 be the power generation efficiency of the power generation panel (i,j) when it is first put into use. Calculate the shading coefficient P of the power generation panel (i,j) according to the following formula: P = Z0 - QZ Where Z is the measured actual power generation efficiency, Q is the predicted loss coefficient, and P is the shading coefficient, representing the power generation efficiency of the solar panel reduced due to shading. Step S500 includes: Step S501. Detect the shading coefficient P of all photovoltaic panels in the photovoltaic array. When the shading coefficient P of the photovoltaic panel (i,j) is greater than the preset threshold P0, issue an alarm to the cleaning staff and inform the cleaning staff of the row and column coordinates (i,j) of the photovoltaic panel. Step S502. After cleaning the obscured power generation panel, the cleaning staff immediately retests the power generation efficiency of the power generation panel, calculates the loss coefficient of the power generation panel, and updates the loss coefficient of the power generation panel in the historical log.

6. A photovoltaic power generation system intelligent data monitoring system based on big data, characterized in that: The system includes the following modules: a light calculation module, an array adjustment module, a waveform analysis module, an occlusion judgment module, and a cleaning module; The light calculation module is used to determine the sunrise and sunset times and the angle of solar incidence at sunrise and sunset for the next day based on the location information and system time. The array adjustment module is used to adjust the angle of each photovoltaic panel in the photovoltaic array and calculate the power generation intensity of each panel by combining historical data. The waveform analysis module is used to detect the current output waveform and separate the changing waveforms of each power generation panel based on the power generation intensity of the power generation panel and the waveform changes during the photovoltaic array angle adjustment process. The shading judgment module is used to analyze the changes in the waveform and power generation intensity of the power generation panel, combined with the data of the standard power generation panel, and calculate the shading status of the power generation panel. The cleaning module is used to identify the power generation panels that need to be cleaned, report their location to the cleaning personnel for cleaning, and then update the system data.

7. The intelligent data monitoring system for photovoltaic power generation systems based on big data as described in claim 6, characterized in that: The light calculation module includes: a GPS positioning unit and an angle setting unit; The GPS positioning unit is used to obtain the geographical location of the photovoltaic array; The angle setting unit is used to calculate the sunrise time and the angle of incidence of sunlight at sunrise; The array adjustment module includes: an angle adjustment unit, a data cleaning unit, and an array self-test unit; The angle adjustment unit is used to adjust the tilt angle of the power generation panel according to a set program. The data cleaning unit is used to obtain the light-receiving area of ​​each power generation panel based on the total area of ​​the power generation panel, the angle of sunlight, and the tilt angle, and to record the change curve of its light-receiving area. The array self-test unit is used to obtain data points based on historical logs and calculate the loss coefficient of the power generation panel through function fitting.

8. The intelligent data monitoring system for photovoltaic power generation systems based on big data as described in claim 6, characterized in that: The waveform analysis module includes: a detection unit, a filtering unit, and an ICA separation unit; The detection unit is used to detect current changes in the main circuit and output the waveform of the current change. The filtering unit is used to filter out high-frequency components in the main circuit current waveform and retain only the waveform components within the frequency range of the change in the light-receiving area of ​​the power generation panel. The ICA separation unit is used to separate the independent waveforms of each power generation panel from the total current waveform based on the changes in the loss coefficient and light-receiving area of ​​each panel using the ICA algorithm.

9. The intelligent data monitoring system for photovoltaic power generation systems based on big data as described in claim 6, characterized in that: The occlusion determination module includes: a coefficient calculation unit and an array positioning unit; The coefficient calculation unit is used to compare the independent waveform of each power generation panel with historical data and update the shading coefficient of each power generation panel in the historical data. The array positioning unit is used to determine whether the shading coefficient of the photovoltaic panel is higher than the threshold, mark the photovoltaic panel with the shading coefficient higher than the threshold as needing to be cleaned, and give its coordinates in the photovoltaic array.

10. The intelligent data monitoring system for photovoltaic power generation systems based on big data according to claim 6, characterized in that: The cleaning module includes: an alarm unit and a system update unit; The alarm unit is used to send a cleaning alarm to the cleaning personnel and inform them of the coordinates of the power generation panels to be cleaned; The system update unit is used to remeasure the power generation data of the power generation panel after the cleaning staff cleans it, and update its loss coefficient.

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