CN116054741A - Photovoltaic module fault positioning analysis method based on big data on-line monitoring - Google Patents
Photovoltaic module fault positioning analysis method based on big data on-line monitoring Download PDFInfo
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Abstract
The invention relates to the technical field of photovoltaic module fault location, and particularly discloses a photovoltaic module fault location analysis method based on big data on-line monitoring, which comprises the following steps: the method and the device can scientifically screen potential safety hazards of the photovoltaic assembly, further provide reliable support guarantee for safe and stable operation of the photovoltaic panel, greatly improve timeliness of response processing of the photovoltaic panel, guarantee power output stability of a photovoltaic power generation system in a corresponding area, effectively reduce dependence on manual inspection screening, further overcome defects of long personnel feedback period, high cost and the like, and not only facilitate improvement of reasonable and efficient maintenance level of the photovoltaic panel, but also greatly reduce yield loss of the photovoltaic power generation system in the corresponding area.
Description
Technical Field
The invention relates to the technical field of photovoltaic module fault location analysis, in particular to a photovoltaic module fault location analysis method based on big data on-line monitoring.
Background
The rapid consumption of social energy makes people pay more attention to the use of renewable energy, a photovoltaic power generation plate is power generation equipment for converting light energy into electric energy by utilizing a photoelectric effect, when solar rays irradiate on the surface of the photovoltaic power generation plate, free electrons in the photovoltaic power generation plate can be excited out to generate current, so that the output and transmission of electric power are realized, the photovoltaic power generation becomes an important organization structure of a novel electric power system, a large-scale photovoltaic array is often required to be arranged in the photovoltaic power generation system, and the main characteristic of the photovoltaic power generation is that the photovoltaic power generation needs to be displayed in a flat and open area, so that the photovoltaic power generation is greatly influenced by the damage of the environment, and therefore, the photovoltaic module in the photovoltaic power generation system needs to be subjected to fault positioning and analysis.
The prior art has a great degree of limitation on fault detection of the photovoltaic module, and specific layers comprise: 1. most of the photovoltaic module fault points in the existing photovoltaic power generation system are manually subjected to periodic and fixed-point cyclic inspection, and blind areas exist in human eye vision inevitably, so that potential safety hazards of the photovoltaic module cannot be scientifically screened, reliable support guarantee cannot be provided for safe and stable operation of the photovoltaic panel, timeliness of response processing of the fault photovoltaic panel is greatly reduced, power output stability of the photovoltaic power generation system in a corresponding area is broken, meanwhile, inspection is carried out manually, defects of long personnel feedback period, high cost and the like are overcome, and reasonable and efficient maintenance level of the photovoltaic panel is not facilitated to be improved.
2. The existing photovoltaic module fault monitoring lacks of carrying out detailed and accurate analysis on specific structural layer fault conditions, and is often only carried out integral adjustment replacement or movable maintenance on a faulty photovoltaic panel, so that the fault maintenance and replacement cost of the photovoltaic module are increased, the integral photovoltaic panel power generation quality and the power generation efficiency are affected by fluctuation, the gain loss of a photovoltaic power generation system in a corresponding area is increased, the data support is not provided for confirming the cause and the position of the occurrence of the photovoltaic panel fault for relevant staff, the development of the fault maintenance work of the relevant staff is further hindered, the timely analysis and the treatment of fault symptoms are not facilitated, and the stable and reliable operation of the photovoltaic power generation system in the corresponding area cannot be scientifically and efficiently realized.
Disclosure of Invention
In order to overcome the defects in the background technology, the embodiment of the invention provides a photovoltaic module fault positioning analysis method based on big data on-line monitoring, which can effectively solve the problems related to the background technology.
The aim of the invention can be achieved by the following technical scheme: a photovoltaic module fault positioning analysis method based on big data on-line monitoring comprises the following steps: s1, identifying and counting the photovoltaic panels: and identifying the photovoltaic panels in the photovoltaic array to which the set area belongs, and further counting the photovoltaic panels.
S2, obtaining basic parameters of the photovoltaic panel: and obtaining basic parameters of each photovoltaic panel.
S3, monitoring and analyzing the electric power characteristic parameters: and monitoring the electric power characteristic parameters of each photovoltaic panel according to the basic parameters of each photovoltaic panel, and further analyzing to obtain the electric power output stability index of each photovoltaic panel, so as to screen each photovoltaic panel running in different modes.
S4, structural component division statistics: dividing the structural layers of the different operating photovoltaic panels, and counting the structural components of the different operating photovoltaic panels, wherein each structural component comprises an apparent structural component, a basic supporting structural component and an internal structural component.
S5, monitoring and analyzing information parameters of the structural assembly: monitoring information parameters of each structural component of each abnormal operation photovoltaic panel, and evaluating operation risk indexes of each structural component of each abnormal operation photovoltaic panel, wherein each operation risk index of each structural component of each abnormal operation photovoltaic panel comprises an operation risk index of an apparent structural component of the abnormal operation photovoltaic panel, an operation risk index of a basic supporting structural component of the abnormal operation photovoltaic panel and an operation risk index of an internal structural component of the abnormal operation photovoltaic panel.
S6, feedback of abnormal operation of the structural component: according to the running risk indexes of the structural components of the abnormal running photovoltaic panels, further processing to obtain the fault investigation sequence condition information of the abnormal running photovoltaic panels, and feeding back the abnormal running of the structural components through the intelligent display terminal.
As a preferred embodiment, the basic parameters of each photovoltaic panel include type, panel active area
And the application time point, p is the number of each photovoltaic panel, < >>
。
As a preferred design, the monitoring of the electrical characteristic parameter of each photovoltaic panel specifically includes: the preset use time period is divided in equal proportion to obtain each monitoring use time point, and the number is
。
Monitoring the electric power characteristic parameters of the photovoltaic panels in each monitoring use time point, wherein the electric power characteristic parameters comprise output current and output voltage, obtaining the output current and the output voltage of the photovoltaic panels corresponding to each monitoring use time point, and further processing to obtain the output power of the photovoltaic panels corresponding to each monitoring use time point
And calculating the average output power of each photovoltaic panel in the preset use period by means of average value>
。
And monitoring the solar irradiance of the photovoltaic array to which the set area belongs, and taking the solar irradiance as the reference solar irradiance of each photovoltaic panel, so as to count the reference solar irradiance of each photovoltaic panel in each monitoring use time point.
According to the types of the photovoltaic panels, the photovoltaic panels are stored with various types of WEB cloudsMatching the adaptive output power of the photovoltaic panel corresponding to the cell panel with the unit effective area under various reference solar irradiance intervals to obtain the adaptive output power of the cell panel corresponding to the unit effective area of each photovoltaic panel in each monitoring use time point
。
Extracting the application time length of each photovoltaic panel corresponding to each monitoring use time point according to each monitoring use time point and the application time point of each photovoltaic panel
Meanwhile, according to the types of the photovoltaic panels, the output power loss factors of unit time length of the corresponding battery panels of the various types of the photovoltaic panels stored in the WEB cloud are further matched, and the output power loss value +_of the unit time length of the corresponding battery panels of the various photovoltaic panels is obtained>
。
As a preferred design, the power output stability index of each photovoltaic panel is recorded as
The specific calculation formula is as follows:
Wherein
For the preset allowable deviation value of the output power of the photovoltaic panel corresponding to the battery panel, +.>
For the correction compensation value of the output power of the preset photovoltaic panel corresponding to the battery panel, < ->
The power output stability influence factor of the output power of the corresponding battery plate of the preset photovoltaic plate is p is the number of each photovoltaic plate, < ->
。
As a preferred design scheme, the monitoring of the information parameters of the apparent structural components of the photovoltaic panels with different operations comprises the following specific steps: a1: and carrying out live-action scanning on each abnormal operation photovoltaic panel, constructing a solid model of each abnormal operation photovoltaic panel, and extracting information parameters of apparent structural components of each abnormal operation photovoltaic panel from the solid model, wherein the information parameters of the apparent structural components comprise the outline of the outer edge of an aluminum frame, slit lines connected with the covering glass of the aluminum frame and appearance images of the covering glass.
A2: according to the reference solid model of each photovoltaic panel stored in the WEB cloud, further extracting the reference information parameters of the apparent structural components of each photovoltaic panel running abnormally, wherein the reference information parameters comprise the reference outer edge outline of the aluminum frame, the reference slit line of the aluminum frame connected with the covering glass and the reference appearance image of the covering glass.
A3: the outer edge contour of the aluminum frame of each abnormal operation photovoltaic panel is subjected to overlapping comparison with the reference outer edge contour of the aluminum frame of each abnormal operation photovoltaic panel, and the overlapping line length of the outer edge contour of the aluminum frame of each abnormal operation photovoltaic panel is extracted
Simultaneously extracting the length of the outline of the reference outer edge of the aluminum frame of each abnormal operation photovoltaic panel>
J is the number of the photovoltaic panel for each abnormal operation,/->
。
A4: sampling point layout is carried out on slit lines connected with the aluminum frames of the photovoltaic panels with different operation and the covering glass to obtain sampling points, and then line widths of the slits connected with the aluminum frames of the photovoltaic panels with different operation and the covering glass at the sampling points are extracted
Similarly, the reference line width of the corresponding slit at each sampling point is extracted>
D is the number of each sampling point, +.>
。
A5: gray processing is carried out on the appearance images of the cover glass of the photovoltaic panels with different operations to obtain appearance gray images of the cover glass of the photovoltaic panels with different operations, and the appearance gray images of the cover glass of the photovoltaic panels with different operations are arranged at detection points in a system sample point arrangement mode to obtain detection points with different appearances, and then the image gray values of the cover glass of the photovoltaic panels with different operations at the detection points with different appearances are extracted
Similarly, the gray value of the reference image of the corresponding cover glass at each appearance detection point is extracted>
G is the number of each appearance detection point, +.>
。
As a preferred design, the monitoring of the information parameters of the basic supporting structure components of the photovoltaic panels with different operations comprises the following specific steps: b1: extracting the horizontal heights of all the endpoints of the aluminum frame of each abnormal operation photovoltaic panel according to the solid model of each abnormal operation photovoltaic panel
M is the number of each endpoint, +.>
。
B2: according to the initial horizontal height of each endpoint of the aluminum frame of each photovoltaic panel stored in the WEB cloud, further extracting the aluminum edge of each photovoltaic panel running in a different modeInitial level of each endpoint to which a box belongs
。
B3: extracting lower edge contour lines of aluminum frames of all abnormal operation photovoltaic panels, equally dividing the lower edge contour lines to obtain all lower edge reference points of the aluminum frames of all abnormal operation photovoltaic panels, carrying out shortest linear connection on the lower edge reference points and corresponding upper edge contour lines to obtain all outer elevation reference connection lines of all abnormal operation photovoltaic panels, extending the outer elevation reference connection lines to the horizontal ground, extracting included angles between all outer elevation reference connection lines of all abnormal operation photovoltaic panels and the horizontal ground, recording all reference placement angles of all abnormal operation photovoltaic panels as corresponding angles, and extracting corresponding angles
W is the number of each reference placement angle, +.>
。
B4: extracting angles of initial placement angles of all abnormal operation photovoltaic panels from WEB cloud
。
As a preferred design scheme, the monitoring of the information parameters of the internal structural components of the photovoltaic panels with different operations comprises the following specific steps: c1: and (3) distributing temperature monitoring points of the photovoltaic panels with different operations to obtain the temperature monitoring points of the photovoltaic panels with different operations.
C2: infrared temperature detection is carried out on each temperature monitoring point of each abnormal operation photovoltaic panel to obtain the temperature of each temperature monitoring point of each abnormal operation photovoltaic panel
X is the number of each temperature monitoring point, < ->
Synchronizing pairsThe former ambient temperature is monitored to obtain the current ambient temperature, and is recorded as +.>
。
And C3: matching the current ambient temperature with the adaptive operation temperatures of the photovoltaic panels corresponding to various ambient temperature intervals stored in the WEB cloud to obtain the adaptive operation temperatures of the photovoltaic panels with different operations
。
As a preferred design scheme, the operation risk indexes of the structural components of the different operation photovoltaic panels are respectively calculated according to the following steps: d1: calculating the operation risk index of apparent structural components of various abnormal operation photovoltaic panels
The specific formula is as follows:
wherein f and t are the number of sampling points and appearance detection points, respectively, +.>
For the running risk evaluation threshold value corresponding to the length of the outline coincidence line of the outer edge of the set aluminum frame, +.>
For setting value, & lt + & gt>
And->
Respectively the allowable deviation value corresponding to the gray value of the appearance image of the cover glass and the slit line width of the set aluminum frame connected with the cover glass>
、
And->
The operation risk assessment correction value corresponding to the preset outline coincidence line length of the outer edge of the aluminum frame, the slit line width of the aluminum frame connected with the covering glass and the appearance image gray value of the covering glass is respectively obtained.
D2: calculating the running risk index of the basic supporting structure assembly of each abnormal running photovoltaic panel
The specific formula is as follows:
Wherein y is the number of reference placement angles, +.>
And->
And respectively evaluating influence weight factors for the operation risk corresponding to the horizontal height of the endpoint of the aluminum frame of the abnormal operation photovoltaic panel and the angle of the reference placement angle.
D3: calculating operation risk indexes of internal structural components of various abnormal operation photovoltaic panels
The specific formula is as follows:
wherein z is the number of temperature monitoring points, +.>
Temperature correction compensation value for predefined abnormally operated photovoltaic panels, < >>
Deviation threshold value for the operating temperature of a predefined abnormally operating photovoltaic panel, +.>
The running risk influence factor is set corresponding to the unit temperature difference value of the set ambient temperature and the photovoltaic panel temperature.
Compared with the prior art, the embodiment of the invention has at least the following advantages or beneficial effects: 1. the invention provides a photovoltaic module fault positioning analysis method based on big data on-line monitoring, which can intelligently screen abnormally operated photovoltaic panels in a photovoltaic array, further powerfully make up the defects and shortcomings of the current photovoltaic power generation system caused by manually performing timing and fixed-point cyclic inspection on the photovoltaic module fault points, avoid the influence of blind areas existing in the visual line of eyes, scientifically screen potential safety hazards of the photovoltaic modules, and further provide reliable support guarantee for safe and stable operation of the photovoltaic panels.
2. According to the invention, through analyzing the power output stability index of each photovoltaic panel, the photovoltaic panel with potential operation safety hidden trouble can be screened timely, so that the timeliness of response processing of the fault photovoltaic panel is greatly improved, the power output stability of the photovoltaic power generation system in the corresponding area is ensured, the dependence on manual inspection screening is effectively reduced, the defects of long personnel feedback period, high cost and the like are overcome, and the reasonable and efficient maintenance level of the photovoltaic panel is improved.
3. According to the invention, through counting all structural components of the photovoltaic panels running in different manners and performing targeted fault diagnosis and analysis on the structural components, the defect that the fault monitoring of the existing photovoltaic components lacks of performing detailed and accurate analysis on specific structural layer fault conditions is overcome, the phenomenon that the photovoltaic panels only run on faults to be integrally adjusted and replaced or move for maintenance is avoided, and further, the fault maintenance and replacement cost of the photovoltaic components is reduced, the overall photovoltaic panel power generation quality and the stability of power generation efficiency are effectively ensured, and thus, the yield loss of a photovoltaic power generation system in a corresponding area is greatly reduced.
4. According to the invention, the fault troubleshooting sequence condition information of the photovoltaic panel with abnormal operation is obtained through processing, and the abnormal operation feedback of the structural component is carried out through the intelligent display terminal, so that the data support is provided for confirming the reason and the position of the occurrence of the fault of the photovoltaic panel for related staff, the auxiliary effect is provided for the development of the fault maintenance work of the related staff, and further the fault symptoms can be analyzed and processed in time, thereby realizing the stable and reliable operation of the photovoltaic power generation system in the corresponding area scientifically and efficiently.
Drawings
The invention will be further described with reference to the accompanying drawings, in which embodiments do not constitute any limitation of the invention, and other drawings can be obtained by one of ordinary skill in the art without inventive effort from the following drawings.
FIG. 1 is a flow chart of the steps of the method of the present invention.
Fig. 2 is a schematic illustration of the respective reference placement angles of the respective running photovoltaic panels.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, the invention provides a photovoltaic module fault location analysis method based on big data on-line monitoring, which comprises the following steps: s1, identifying and counting the photovoltaic panels: and identifying the photovoltaic panels in the photovoltaic array to which the set area belongs, and further counting the photovoltaic panels.
It should be explained that the above-mentioned identification of the photovoltaic panel in the photovoltaic array to which the set area belongs, the device specifically used is an intelligent unmanned aerial vehicle.
S2, obtaining basic parameters of the photovoltaic panel: and obtaining basic parameters of each photovoltaic panel.
In particular, the basic parameters of each photovoltaic panel comprise the type and the effective area of the panel
And the application time point, p is the number of each photovoltaic panel, < >>
。
It should be explained that the types of the above-mentioned photovoltaic panels include single crystal silicon photovoltaic panels, polycrystalline silicon photovoltaic panels, amorphous silicon photovoltaic panels, and the like.
S3, monitoring and analyzing the electric power characteristic parameters: and monitoring the electric power characteristic parameters of each photovoltaic panel according to the basic parameters of each photovoltaic panel, and further analyzing to obtain the electric power output stability index of each photovoltaic panel, so as to screen each photovoltaic panel running in different modes.
S31, monitoring the electric power characteristic parameters of each photovoltaic panel, which specifically comprises the following steps: the preset use time period is divided in equal proportion to obtain each monitoring use time point, and the number is
。
Monitoring the electric power characteristic parameters of the photovoltaic panels in each monitoring use time point, wherein the electric power characteristic parameters comprise output current and output voltage, obtaining the output current and the output voltage of the photovoltaic panels corresponding to each monitoring use time point, and further processing to obtain the output power of the photovoltaic panels corresponding to each monitoring use time point
And calculating the average output power of each photovoltaic panel in the preset use period by means of average value>
。
And S32, monitoring the solar irradiance of the photovoltaic array to which the set area belongs, and taking the solar irradiance as the reference solar irradiance of each photovoltaic panel, so as to count the reference solar irradiance of each photovoltaic panel in each monitoring use time point.
It should be explained that the above-mentioned monitoring of solar irradiance of the photovoltaic array to which the set area belongs uses a solar radiometer as the monitoring device.
Depending on the type of each photovoltaic panel, it is associated withMatching the adaptive output power of each type of photovoltaic panel stored in the WEB cloud corresponding to the cell panel with the unit effective area under each reference solar irradiance interval to obtain the adaptive output power of each type of photovoltaic panel corresponding to the cell panel with the unit effective area in each monitoring use time point
。
Extracting the application time length of each photovoltaic panel corresponding to each monitoring use time point according to each monitoring use time point and the application time point of each photovoltaic panel
Meanwhile, according to the types of the photovoltaic panels, the output power loss factors of unit time length of the corresponding battery panels of the various types of the photovoltaic panels stored in the WEB cloud are further matched, and the output power loss value +_of the unit time length of the corresponding battery panels of the various photovoltaic panels is obtained>
。
Further, the power output stability index of each photovoltaic panel is recorded as
The specific calculation formula is as follows:
wherein->
For the preset allowable deviation value of the output power of the photovoltaic panel corresponding to the battery panel, +.>
For the correction compensation value of the output power of the preset photovoltaic panel corresponding to the battery panel, < ->
The power output stability influence factor of the output power of the corresponding battery plate of the preset photovoltaic plate is p is the number of each photovoltaic plate, < ->
。
S33, screening the photovoltaic panels with different abnormal operations, wherein the specific process is as follows: comparing the power output stability index of each photovoltaic panel with the power output stability index interval stored in the WEB cloud, and when the power output stability index of a certain photovoltaic panel exceeds the power output stability index interval, marking the photovoltaic panel as an abnormal operation photovoltaic panel, and further counting the abnormal operation photovoltaic panels.
In the specific embodiment of the invention, the photovoltaic panels with potential operation safety hazards can be screened timely by analyzing the power output stability index of each photovoltaic panel, so that the timeliness of response processing of the fault photovoltaic panel is greatly improved, the power output stability of a photovoltaic power generation system in a corresponding area is ensured, the dependence on manual inspection screening is effectively reduced, the defects of long personnel feedback period, high cost and the like are overcome, and the reasonable and efficient maintenance level of the photovoltaic panels is improved.
S4, structural component division statistics: dividing the structural layers of the different operating photovoltaic panels, and counting the structural components of the different operating photovoltaic panels, wherein each structural component comprises an apparent structural component, a basic supporting structural component and an internal structural component.
S5, monitoring and analyzing information parameters of the structural assembly: monitoring information parameters of each structural component of each abnormal operation photovoltaic panel, and evaluating operation risk indexes of each structural component of each abnormal operation photovoltaic panel, wherein each operation risk index of each structural component of each abnormal operation photovoltaic panel comprises an operation risk index of an apparent structural component of the abnormal operation photovoltaic panel, an operation risk index of a basic supporting structural component of the abnormal operation photovoltaic panel and an operation risk index of an internal structural component of the abnormal operation photovoltaic panel.
In the specific embodiment of the invention, through counting each structural component of each abnormal operation photovoltaic panel and respectively carrying out targeted fault diagnosis analysis on each structural component, the defect that the fault monitoring of the existing photovoltaic component lacks of carrying out detailed and accurate analysis on the fault condition of a specific structural layer is overcome, the phenomenon that the whole photovoltaic panel only carries out whole adjustment and replacement or mobile maintenance on the fault is avoided, and further, the fault maintenance and replacement cost of the photovoltaic component is reduced, the whole photovoltaic panel power generation quality and the stability of the power generation efficiency are effectively ensured, and the yield loss of a photovoltaic power generation system in a corresponding area is greatly reduced.
Specifically, the monitoring of the information parameters of the apparent structural components of the photovoltaic panels with different operations comprises the following specific steps: a1: and carrying out live-action scanning on each abnormal operation photovoltaic panel, constructing a solid model of each abnormal operation photovoltaic panel, and extracting information parameters of apparent structural components of each abnormal operation photovoltaic panel from the solid model, wherein the information parameters of the apparent structural components comprise the outline of the outer edge of an aluminum frame, slit lines connected with the covering glass of the aluminum frame and appearance images of the covering glass.
It should be explained that, performing live-action scanning on each abnormal operation photovoltaic panel, and constructing a solid model of each abnormal operation photovoltaic panel, wherein the specific construction process is as follows: by building a laser radar scanner in the intelligent unmanned aerial vehicle, live-action scanning is further carried out on each abnormal operation photovoltaic panel, and solid model construction is further carried out on each abnormal operation photovoltaic panel based on the obtained scanning result.
A2: according to the reference solid model of each photovoltaic panel stored in the WEB cloud, further extracting the reference information parameters of the apparent structural components of each photovoltaic panel running abnormally, wherein the reference information parameters comprise the reference outer edge outline of the aluminum frame, the reference slit line of the aluminum frame connected with the covering glass and the reference appearance image of the covering glass.
A3: the outer edge contour of the aluminum frame of each abnormal operation photovoltaic panel is subjected to overlapping comparison with the reference outer edge contour of the aluminum frame of each abnormal operation photovoltaic panel, and the overlapping line length of the outer edge contour of the aluminum frame of each abnormal operation photovoltaic panel is extracted
Simultaneously extracting the length of the outline line of the reference outer edge to which the aluminum frame of each abnormal operation photovoltaic panel belongs
J is the number of the photovoltaic panel for each abnormal operation,/->
。
A4: sampling point layout is carried out on slit lines connected with the aluminum frames of the photovoltaic panels with different operation and the covering glass to obtain sampling points, and then line widths of the slits connected with the aluminum frames of the photovoltaic panels with different operation and the covering glass at the sampling points are extracted
Similarly, the reference line width of the corresponding slit at each sampling point is extracted>
D is the number of each sampling point, +.>
。
A5: gray processing is carried out on the appearance images of the cover glass of the photovoltaic panels with different operations to obtain appearance gray images of the cover glass of the photovoltaic panels with different operations, and the appearance gray images of the cover glass of the photovoltaic panels with different operations are arranged at detection points in a system sample point arrangement mode to obtain detection points with different appearances, and then the image gray values of the cover glass of the photovoltaic panels with different operations at the detection points with different appearances are extracted
Similarly, the gray value of the reference image of the corresponding cover glass at each appearance detection point is extracted>
G is the number of each appearance detection point, +.>
。
It should be explained that, the above-mentioned detection point layout is performed by a system sample point layout mode, and the specific process is as follows: will be different fromAppearance gray scale image of cover glass of constant operation photovoltaic panel
The distance interval of (2) is divided into grids with the same size, and the intersection points among the grid lines are used as appearance detection points.
Specifically, the monitoring of the information parameters of the basic supporting structure components of the photovoltaic panels with different operations comprises the following specific steps: b1: extracting the horizontal heights of all the endpoints of the aluminum frame of each abnormal operation photovoltaic panel according to the solid model of each abnormal operation photovoltaic panel
M is the number of each endpoint, +.>
。
B2: according to the initial level of each endpoint of the aluminum frame of each photovoltaic panel stored in the WEB cloud, extracting the initial level of each endpoint of the aluminum frame of each photovoltaic panel running in a different mode
。
B3: extracting lower edge contour lines of aluminum frames of all abnormal operation photovoltaic panels, equally dividing the lower edge contour lines to obtain all lower edge reference points of the aluminum frames of all abnormal operation photovoltaic panels, carrying out shortest linear connection on the lower edge reference points and corresponding upper edge contour lines to obtain all outer elevation reference connection lines of all abnormal operation photovoltaic panels, extending the outer elevation reference connection lines to the horizontal ground, extracting included angles between all outer elevation reference connection lines of all abnormal operation photovoltaic panels and the horizontal ground, recording all reference placement angles of all abnormal operation photovoltaic panels as corresponding angles, and extracting corresponding angles
W is the number of each reference placement angle, +.>
。
B4: extracting angles of initial placement angles of all abnormal operation photovoltaic panels from WEB cloud
。
Specifically, the monitoring of the information parameters of the internal structural components of the photovoltaic panels with different operations comprises the following specific steps: c1: and (3) distributing temperature monitoring points of the photovoltaic panels with different operations to obtain the temperature monitoring points of the photovoltaic panels with different operations.
It should be explained that the temperature monitoring points for each abnormal operation photovoltaic panel are arranged, and the specific process is as follows: the surface of the cover glass of each abnormal operation photovoltaic panel is adjusted to
The distance interval of the photovoltaic panel is divided into grids with the same size, then the intersection points among the grid lines and the central point of each grid are marked as temperature monitoring points, and further each temperature monitoring point of each abnormal operation photovoltaic panel is obtained through statistics.
C2: infrared temperature detection is carried out on each temperature monitoring point of each abnormal operation photovoltaic panel to obtain the temperature of each temperature monitoring point of each abnormal operation photovoltaic panel
X is the number of each temperature monitoring point, < ->
Synchronously monitoring the current environment temperature to obtain the current environment temperature, and recording the current environment temperature as +.>
。
It should be explained that the above-mentioned infrared temperature detection is carried out to each temperature monitoring point of each unusual operation photovoltaic board, and the equipment that specifically uses is the thermal infrared imager that builds in intelligent unmanned aerial vehicle.
And C3: adapting photovoltaic panel corresponding to current ambient temperature and various ambient temperature intervals stored in WEB cloudMatching the matched operation temperatures to obtain the matched operation temperatures of the photovoltaic panels with different operations
。
In a specific embodiment, the invention aims to reflect the fault information of the internal components of the photovoltaic panel, such as inverter faults, bus faults, battery string faults and the like, by carrying out infrared temperature detection on each abnormal operation photovoltaic panel in consideration of temperature distribution.
As a further explanation, an inverter refers to a device for converting dc power generated by a photovoltaic panel into ac power, if the inverter has a fault, for example, a problem of reduced conversion efficiency or partial failure, the temperature of some areas of the photovoltaic panel will be higher, a bus refers to a cable for connecting a photovoltaic panel assembly to the inverter, if the bus has a fault, for example, a problem of disconnection or poor contact, the current will be unbalanced, thereby causing a problem that the temperature of some areas of the photovoltaic panel is higher, the photovoltaic panel battery string is damaged or partially fails, the temperature of some areas will be lower, and other areas will be higher, and a phenomenon of uneven temperature distribution will occur.
Further, the operation risk indexes of the structural components of the abnormal operation photovoltaic panels are calculated according to the following steps: d1: calculating the operation risk index of apparent structural components of various abnormal operation photovoltaic panels
The specific formula is as follows:
wherein f and t are the number of sampling points and appearance detection points, respectively, +.>
For the running risk evaluation threshold value corresponding to the length of the outline coincidence line of the outer edge of the set aluminum frame, +.>
For setting value, & lt + & gt>
And->
Respectively the allowable deviation value corresponding to the gray value of the appearance image of the cover glass and the slit line width of the set aluminum frame connected with the cover glass>
、
And->
The operation risk assessment correction value corresponding to the preset outline coincidence line length of the outer edge of the aluminum frame, the slit line width of the aluminum frame connected with the covering glass and the appearance image gray value of the covering glass is respectively obtained.
D2: calculating the running risk index of the basic supporting structure assembly of each abnormal running photovoltaic panel
The specific formula is as follows:
Wherein y is the number of reference placement angles, +.>
And->
And respectively evaluating influence weight factors for the operation risk corresponding to the horizontal height of the endpoint of the aluminum frame of the abnormal operation photovoltaic panel and the angle of the reference placement angle.
D3: calculating operation risk indexes of internal structural components of various abnormal operation photovoltaic panels
The specific formula is as follows:
wherein z is the number of temperature monitoring points, +.>
Temperature correction compensation value for predefined abnormally operated photovoltaic panels, < >>
Deviation threshold value for the operating temperature of a predefined abnormally operating photovoltaic panel, +.>
The running risk influence factor is set corresponding to the unit temperature difference value of the set ambient temperature and the photovoltaic panel temperature.
S6, feedback of abnormal operation of the structural component: according to the running risk indexes of the structural components of the abnormal running photovoltaic panels, further processing to obtain the fault investigation sequence condition information of the abnormal running photovoltaic panels, and feeding back the abnormal running of the structural components through the intelligent display terminal.
In the specific embodiment of the invention, the fault troubleshooting sequence condition information of the photovoltaic panel with abnormal operation is obtained through processing, and the abnormal operation feedback of the structural component is carried out through the intelligent display terminal, so that the data support is provided for confirming the reason and the position of the occurrence of the fault of the photovoltaic panel for related staff, the auxiliary effect is provided for the development of the fault maintenance work of the related staff, and further, the fault symptoms can be analyzed and processed in time, thereby realizing the stable and reliable operation of the photovoltaic power generation system in the corresponding area scientifically and efficiently.
It is to be explained that the above processing obtains the condition information of the troubleshooting sequence of the photovoltaic panel with abnormal operation, and the specific processing process is as follows: step 1, accumulating the operation risk indexes of the structural components of the abnormal operation photovoltaic panels to obtain the operation risk indexes of the structural components of the abnormal operation photovoltaic panels, sequentially arranging the operation risk indexes according to the sequence from large to small to obtain the fault investigation sequence of the abnormal operation photovoltaic panels, and recording the fault investigation sequence as a fault investigation sequence condition 1.
And 2, extracting running risk indexes of the structural components of the abnormal running photovoltaic panels, sequentially arranging the running risk indexes according to the sequence from large to small to obtain a fault investigation sequence of the structural components of the abnormal running photovoltaic panels, and recording the fault investigation sequence as a fault investigation sequence condition 2.
And 3, jointly recording the fault investigation order condition 1 and the fault investigation order condition 2 as fault investigation order condition information of the photovoltaic panel with abnormal operation.
According to the photovoltaic module fault location analysis method based on big data on-line monitoring, intelligent screening can be carried out on the abnormally operated photovoltaic panel in the photovoltaic array, so that defects and defects caused by regular fixed-point cyclic inspection of most of photovoltaic module fault points in a current photovoltaic power generation system are effectively overcome, influence of blind areas existing in human eyes is avoided, potential safety hazards of the photovoltaic module can be screened scientifically, and reliable support guarantee is provided for safe and stable operation of the photovoltaic panel.
The foregoing is merely illustrative of the structures of this invention and various modifications, additions and substitutions for those skilled in the art of describing particular embodiments without departing from the structures of the invention or exceeding the scope of the invention as defined by the claims.
Claims (5)
1. The photovoltaic module fault positioning analysis method based on big data on-line monitoring is characterized by comprising the following steps:
s1, identifying and counting the photovoltaic panels: identifying the photovoltaic panels in the photovoltaic array to which the set area belongs, and further counting the photovoltaic panels;
s2, obtaining basic parameters of the photovoltaic panel: obtaining basic parameters of each photovoltaic panel;
s3, monitoring and analyzing the electric power characteristic parameters: monitoring and analyzing the electric characteristic parameters of each photovoltaic panel according to the basic parameters of each photovoltaic panel, and screening each abnormal operation photovoltaic panel according to the electric characteristic parameters;
s4, structural component division statistics: dividing structural layers of the photovoltaic panels with different operations, and counting structural components of the photovoltaic panels with different operations, wherein each structural component comprises an apparent structural component, a basic supporting structural component and an internal structural component;
s5, monitoring and analyzing information parameters of the structural assembly: monitoring information parameters of each structural component of each abnormal operation photovoltaic panel, and evaluating operation risk indexes of each structural component of each abnormal operation photovoltaic panel;
the method for monitoring the information parameters of the apparent structural components of the photovoltaic panels with different operations comprises the following specific steps:
a1: carrying out live-action scanning on each abnormal operation photovoltaic panel, constructing a solid model of each abnormal operation photovoltaic panel, and extracting information parameters of apparent structural components of each abnormal operation photovoltaic panel from the solid model, wherein the information parameters of the apparent structural components comprise an outer edge contour of an aluminum frame, slit lines formed by connecting the aluminum frame with the covering glass and an appearance image of the covering glass;
a2: according to the reference solid model of each photovoltaic panel stored in the WEB cloud, extracting the reference information parameters of the apparent structural components of each abnormal operation photovoltaic panel, wherein the reference information parameters comprise the reference outer edge outline of the aluminum frame, the reference slit line of the aluminum frame connected with the covering glass and the reference appearance image of the covering glass;
a3: the outer edge contours of the aluminum frames of the different operation photovoltaic panels are subjected to overlapping comparison with corresponding reference outer edge contours, and the overlapping line lengths of the outer edge contours of the aluminum frames of the different operation photovoltaic panels are extracted
Simultaneously extracting the corresponding reference outer edge contour line length +.>
J is the number of the photovoltaic panel for each abnormal operation,/->
；
A4: carrying out slit lines connecting aluminum frames of photovoltaic panels with covering glassThe sampling points are distributed to obtain sampling points, and then the line width of the slit connected with the covering glass at each sampling point of the aluminum frame of each abnormal operation photovoltaic panel is extracted
Similarly, the reference line width of the corresponding slit at each sampling point is extracted>
D is the number of each sampling point, +.>
；
A5: gray processing is carried out on the appearance images of the cover glass of each abnormal operation photovoltaic panel to obtain appearance gray images of the cover glass of each abnormal operation photovoltaic panel, and the appearance gray images are arranged in a system sample point arrangement mode to obtain each appearance detection point, so that the image gray values of the cover glass of each abnormal operation photovoltaic panel at each appearance detection point are extracted
Similarly, the gray value of the reference image of the corresponding cover glass at each appearance detection point is extracted>
G is the number of each appearance detection point,
；
calculating the operation risk index of apparent structural components of various abnormal operation photovoltaic panels
The specific formula is as follows:
wherein f and t are the number of sampling points and appearance detection points, respectively, +.>
For the running risk evaluation threshold value corresponding to the length of the outline coincidence line of the outer edge of the set aluminum frame, +.>
And->
Respectively the allowable deviation value corresponding to the gray value of the appearance image of the cover glass and the slit line width of the set aluminum frame connected with the cover glass>
、
And->
The method comprises the steps that the operation risk assessment correction value corresponding to the preset length of a coincident line of an outline of the outer edge of the aluminum frame, the width of a slit line formed by connecting the aluminum frame with the cover glass and the gray value of an appearance image of the cover glass is respectively calculated, and e is expressed as a natural constant;
s6, feedback of abnormal operation of the structural component: according to the running risk indexes of the structural components of the abnormal running photovoltaic panels, further processing to obtain the fault investigation sequence condition information of the abnormal running photovoltaic panels, and feeding back the abnormal running of the structural components through the intelligent display terminal.
2. The photovoltaic module fault location analysis method based on big data on-line monitoring according to claim 1, which is characterized in that: the basic parameters of each photovoltaic panel comprise the type and the effective area of the panel
And the application time point, p is the number of each photovoltaic panel, < >>
。
3. The photovoltaic module fault location analysis method based on big data on-line monitoring according to claim 2, which is characterized in that: the monitoring of the electric power characteristic parameters of each photovoltaic panel specifically comprises the following steps:
the preset use time period is divided in equal proportion to obtain each monitoring use time point, and the number is
；
Monitoring the electric power characteristic parameters of the photovoltaic panels in each monitoring use time point, wherein the electric power characteristic parameters comprise output current and output voltage, obtaining the output current and the output voltage of the photovoltaic panels corresponding to each monitoring use time point, and further processing to obtain the output power of the photovoltaic panels corresponding to each monitoring use time point
And calculating the average output power of each photovoltaic panel in the preset use period by means of average value>
；
Monitoring solar irradiance of the photovoltaic array to which the set area belongs, and taking the solar irradiance as reference solar irradiance of each photovoltaic panel, so as to count the reference solar irradiance of each photovoltaic panel in each monitoring use time point;
according to the types of the photovoltaic panels, matching the photovoltaic panels with the adaptive output power of the corresponding unit effective area of the photovoltaic panels stored in the WEB cloud with the adaptive output power of the corresponding unit effective area of the photovoltaic panels in various reference solar irradiance intervals to obtain the adaptive output power of the corresponding unit effective area of the photovoltaic panels in each monitoring use time point
；
According to each monitoring usageThe time point is used for extracting the application time length of each photovoltaic panel corresponding to each monitoring use time point according to the application time point of each photovoltaic panel
Meanwhile, according to the types of the photovoltaic panels, the output power loss factors of unit time length of the corresponding battery panels of the various types of the photovoltaic panels stored in the WEB cloud are further matched, and the output power loss value +_of the unit time length of the corresponding battery panels of the various photovoltaic panels is obtained>
；
Analyzing the electric power characteristic parameters of each photovoltaic panel to obtain the electric power stable output index of each photovoltaic panel
The specific calculation formula is as follows:
Wherein->
For the preset allowable deviation value of the output power of the photovoltaic panel corresponding to the battery panel, +.>
For the correction compensation value of the output power of the preset photovoltaic panel corresponding to the battery panel, < ->
And the power output stability influence factor is the preset power output stability influence factor of the photovoltaic panel corresponding to the output power of the battery panel.
4. The photovoltaic module fault location analysis method based on big data on-line monitoring according to claim 1, which is characterized in that: the information parameters of the foundation support structure components of the photovoltaic panels running in different modes are monitored, and the specific steps are as follows:
b1: extracting the horizontal heights of all the endpoints of the aluminum frame of each abnormal operation photovoltaic panel according to the solid model of each abnormal operation photovoltaic panel
M is the number of each endpoint, +.>
；
B2: according to the initial level of each endpoint of the aluminum frame of each photovoltaic panel stored in the WEB cloud, extracting the initial level of each endpoint of the aluminum frame of each photovoltaic panel running in a different mode
；
B3: extracting the outline of the lower edge of the aluminum frame of each abnormal operation photovoltaic panel, dividing the outline at equal intervals to obtain each lower edge reference point of the aluminum frame of each abnormal operation photovoltaic panel, connecting the outline with the corresponding upper edge outline in a shortest straight line to obtain each outer elevation reference connecting line of each abnormal operation photovoltaic panel, extending the connecting line to the horizontal ground, extracting the included angle between each outer elevation reference connecting line of each abnormal operation photovoltaic panel and the horizontal ground, recording the included angle as each reference placing angle of each abnormal operation photovoltaic panel, and extracting the corresponding angle
W is the number of each reference placement angle,
；
b4: extracting angles of initial placement angles of all abnormal operation photovoltaic panels from WEB cloud
；
Calculating the running risk index of the basic supporting structure assembly of each abnormal running photovoltaic panel
The specific formula is as follows:
wherein y is the number of reference placement angles, +.>
And->
And respectively evaluating influence weight factors for the operation risk corresponding to the horizontal height of the endpoint of the aluminum frame of the abnormal operation photovoltaic panel and the angle of the reference placement angle.
5. The photovoltaic module fault location analysis method based on big data on-line monitoring according to claim 1, which is characterized in that: the method for monitoring the information parameters of the internal structural components of the photovoltaic panels comprises the following specific steps:
c1: temperature monitoring points are distributed on the abnormal operation photovoltaic panels, and each temperature monitoring point of each abnormal operation photovoltaic panel is obtained;
c2: infrared temperature detection is carried out on each temperature monitoring point of each abnormal operation photovoltaic panel to obtain the temperature of each temperature monitoring point of each abnormal operation photovoltaic panel
X is the number of each temperature monitoring point, < ->
Synchronously monitoring the current environment temperature to obtain the current environment temperature, and recording the current environment temperature as +.>
；
And C3: matching the current ambient temperature with the adaptive operation temperature of the photovoltaic panel corresponding to various ambient temperature intervals stored in the WEB cloud to obtain various abnormalitiesAdaptive operating temperature for operating photovoltaic panels
；
Calculating operation risk indexes of internal structural components of various abnormal operation photovoltaic panels
The specific formula is as follows:
wherein z is the number of temperature monitoring points, +.>
Temperature correction compensation value for predefined abnormally operated photovoltaic panels, < >>
Deviation threshold value for the operating temperature of a predefined abnormally operating photovoltaic panel, +.>
The running risk influence factor is set corresponding to the unit temperature difference value of the set ambient temperature and the photovoltaic panel temperature. />
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