CN111242914A - Photovoltaic module hot spot defect positioning method based on pane detection and linear regression algorithm - Google Patents
Photovoltaic module hot spot defect positioning method based on pane detection and linear regression algorithm Download PDFInfo
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Abstract
The invention discloses a photovoltaic module hot spot defect positioning method based on pane detection and linear regression algorithm, which specifically comprises the following steps: s1, converting the RGB image into a gray-scale image, S2, identifying the outline of the bright spot in the image by using an outline identification algorithm, calculating the area of the bright spot, comparing a threshold value to determine a hot plate area, S3, identifying the internal grid line and the boundary of the photovoltaic module to position the hot spot of the photovoltaic module, S4, displaying in a thermodynamic diagram mode, identifying the internal grid line of the photovoltaic panel by using pane detection, S5, identifying the upper edge and the lower edge of the panel by using the inconsistency of data distribution on two sides of the boundary line of the panel, and S6, smoothing the boundary by using linear regression. The method well solves the problems of low efficiency and inaccuracy of manual analysis of the existing unmanned aerial vehicle photovoltaic inspection picture, greatly improves the fault detection efficiency and accuracy of the photovoltaic module, and reduces the labor cost.
Description
Technical Field
The invention relates to the technical field of inspection of photovoltaic power generation systems, in particular to a photovoltaic module hot spot defect positioning method based on pane detection and linear regression algorithm.
Background
Along with the wide use of unmanned aerial vehicle in photovoltaic patrols and examines, the efficiency of the fault detection of photovoltaic panel improves greatly, the high definition digtal camera that carries on the unmanned aerial vehicle is constantly passed back the photo of several tens of thousands photovoltaic panels in real time, it is difficult to accomplish accurately to rely on the manual work, timely processing, need the computer to carry out real-time detection to the defect, when the photovoltaic panel breaks down, because the mismatch of electric current and voltage, can consume a large amount of electric energy and generate heat in trouble region and near, luminance is higher than normal position on the infrared image, this kind of phenomenon is called "hot spot", it has very important meaning to the fault detection of photovoltaic panel to develop the discernment and the positioning algorithm of a hot spot.
Disclosure of Invention
Technical problem to be solved
Aiming at the defects of the prior art, the invention provides a photovoltaic module hot spot defect positioning method based on pane detection and linear regression algorithm, solves the problem that a photovoltaic panel infrared image transmitted back by unmanned aerial vehicle photovoltaic inspection manually cannot accurately identify a fault photovoltaic panel in real time, and improves the photovoltaic module hot spot detection efficiency and quality by applying a computer to detect the photovoltaic module in real time, thereby realizing intelligent identification and positioning of photovoltaic module faults.
(II) technical scheme
In order to achieve the purpose, the invention is realized by the following technical scheme: the photovoltaic module hot spot defect positioning method based on pane detection and linear regression algorithm specifically comprises the following steps:
s1, converting the RGB image into a gray-scale image, and reflecting the hot spot in the gray-scale image in a bright spot form;
s2, identifying the outline of the hot spot in the picture by using an outline identification algorithm for accurately identifying the hot spot area, calculating the area of the hot spot, reserving the outline of the hot spot when the area is larger than a set threshold value, regarding the outline as the hot spot area, and calculating the central point of the hot spot area;
s3, identifying the internal grid lines and boundaries of the photovoltaic module to position the hot spot of the photovoltaic module;
s4, converting an original image into a gray image by using a calculation mode of internal grid lines, importing an obtained gray array into excel, and displaying by using a thermodynamic diagram mode, wherein the gray value at the grid lines belongs to a small value in the row direction, each point on a photovoltaic assembly is respectively expanded into 4 elements from front to back to form a transverse or longitudinal window pane, the value at the central point of the window pane is the minimum value of the center of the whole grid, and the longitudinal or transverse grid lines can be correctly identified;
s5, a large number of scattered points exist in the area outside the photovoltaic panel in the gray scale image, the upper edge and the lower edge of the panel are identified by using the inconsistency of data distribution on two sides of the boundary line of the panel, an optional point is taken as the center, 5 points are respectively expanded from top to bottom to form a window, the sum of the upper 5 values and the sum of the lower 5 values are calculated, the difference between the upper 5 values and the lower 5 values are respectively substituted for the area of the surface plate and the area of the ground, the point in the image is traversed by the difference between the upper 5 values and the lower 5 values, and;
s6, aiming at the phenomenon that the local points of the boundary line of the photovoltaic panel are identified to have 'disjointed jumping', the boundary is smoothed by linear regression, all the identified boundary points are fitted into a straight line by using the linear regression, and the fitted straight line is the lower boundary line.
Preferably, noise such as light reflection of the battery part exists on the gray scale map in step S1.
Preferably, in step S2, when the area is larger than the set threshold, the contour of the hot spot is retained and regarded as a hot spot region, so as to reduce the influence of noise in the gray scale image and accurately identify the hot spot region.
Preferably, the pane detection method applied in step S4 can accurately identify the grid lines inside the photovoltaic module.
Preferably, the photovoltaic panel boundary line can be identified by applying a pane detection method in step S5.
Preferably, in step S6, the photovoltaic panel boundary line identification local point has a phenomenon of "disjointing jumping", and the boundary is smoothed by linear regression, so as to improve the accuracy of photovoltaic panel boundary identification.
(III) advantageous effects
The invention provides a photovoltaic module hot spot defect positioning method based on pane detection and linear regression algorithm. Compared with the prior art, the method has the following beneficial effects: the photovoltaic module hot spot defect positioning method based on pane detection and linear regression algorithm specifically comprises the following steps: s1, converting the RGB image into a gray-scale image, wherein hot spots are embodied in the gray-scale image in a hot spot form, S2, identifying a hot spot area accurately, identifying the outline of the hot spots in the image by using an outline identification algorithm, calculating the area of the hot spots, keeping the outline of the hot spots when the area is larger than a set threshold value, regarding the outline as the hot spot area, calculating the central point of the hot spot area, S3, identifying the internal grid lines and the boundaries of the photovoltaic module to position the hot spots of the photovoltaic module, S4 and the calculation mode of the internal grid lines, converting the original image into the gray-scale image, introducing the obtained gray-scale array into excel, displaying by adopting a thermodynamic diagram mode, wherein the areas outside the photovoltaic panel and S5 have a large number of scattered points in the gray-scale image, identifying the upper edge and the lower edge of the panel by using the inconsistency of data distribution on two sides of the boundary line of the panel, S6, identifying the phenomenon that local points have 'disjointed jumping', the boundaries are smoothed by linear regression, all the identified boundary points are fitted into a straight line by linear regression, the fitted straight line is the lower boundary line, the problems of low efficiency and inaccuracy of manual analysis of the existing unmanned aerial vehicle photovoltaic inspection picture are well solved, by designing the photovoltaic module hot spot defect positioning method based on pane detection and linear regression algorithm, which can be applied to a computer, the intelligent identification and positioning of the photovoltaic module hot spot defects are realized, the failure detection efficiency and accuracy of the photovoltaic module are greatly improved, the labor cost is reduced, the occurrence of the situation that the failed photovoltaic panel cannot be identified accurately in real time by manually sending back the infrared image of the photovoltaic panel during photovoltaic inspection of the unmanned aerial vehicle is avoided, therefore, the photovoltaic module is detected in real time by using the computer, the hot spot detection efficiency and quality of the photovoltaic module are improved, and the intelligent fault identification and positioning of the photovoltaic module are realized.
Drawings
FIG. 1 is an overall flow chart of the present invention;
FIG. 2 is an effect diagram after RGB image is converted into gray scale image in the hot spot identification process of the present invention;
FIG. 3 is a hot spot area diagram after the gray level value extracted by the threshold method is greater than the set threshold value;
FIG. 4 is a schematic view of hot spot positioning according to the present invention;
FIG. 5 is an effect diagram of the present invention, in which an original image is converted into a gray image, and then the obtained gray array is imported into excel and then displayed in a thermodynamic diagram manner;
FIG. 6 is a schematic depiction of the pane detection identifying the internal grid lines of a photovoltaic panel in accordance with the present invention;
FIG. 7 is a graph of the image effect after the grid lines inside the photovoltaic panel are identified according to the invention;
FIG. 8 is a schematic depiction of pane detection identifying the upper and lower boundaries of a photovoltaic panel in accordance with the present invention;
FIG. 9 is a diagram illustrating the picture effect of the photovoltaic panel after the upper and lower boundaries are identified according to the present invention;
FIG. 10 is a graph illustrating the effect of smoothing the boundary by linear regression according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1 to 10, an embodiment of the present invention provides a technical solution: the photovoltaic module hot spot defect positioning method based on pane detection and linear regression algorithm specifically comprises the following steps:
s1, converting the RGB image into a gray-scale image, wherein the hot spot is reflected in the gray-scale image in a bright spot form, and the gray-scale image has noise points such as light reflection of a battery component;
s2, identifying the outline of the hot spot in the picture by using an outline identification algorithm and calculating the area of the hot spot in order to accurately identify the hot spot area, keeping the outline of the hot spot when the area is larger than a set threshold value, regarding the outline as the hot spot area, calculating the central point of the hot spot area, reducing the influence of noise in the gray scale image and accurately identifying the hot spot area;
s3, identifying the internal grid lines and boundaries of the photovoltaic module to position the hot spot of the photovoltaic module;
s4, converting an original image into a gray image, importing the obtained gray array into excel, displaying the excel in a thermodynamic diagram mode, expanding each point on the photovoltaic assembly into 4 elements forwards and backwards to form a transverse or longitudinal pane, wherein the value of the central point of the pane is the minimum value of the center of the whole grid, the longitudinal or transverse grid line can be correctly identified, and the grid line inside the photovoltaic assembly can be accurately identified by using a pane detection method;
s5, a large number of scattered points exist in a gray scale image in an area outside the photovoltaic panel, the upper edge and the lower edge of the panel are identified by using the inconsistency of data distribution on two sides of the boundary line of the panel, an optional point is taken as a center, 5 points are respectively expanded from top to bottom to form a window, the sum of the upper 5 values and the sum of the lower 5 values are calculated, the difference between the upper 5 values and the lower 5 values are respectively substituted for the area of the surface plate and the area of the ground, the point in the image is traversed by the algorithm, the area with the largest difference is regarded as the boundary line, and the boundary line of the photovoltaic panel;
s6, aiming at the phenomenon that the local points of the boundary line of the photovoltaic panel are identified to have 'disjointed jumping', the boundary is smoothed by linear regression, all the identified boundary points are fitted to form a straight line by using the linear regression, and the fitted straight line is the lower boundary line, so that the accuracy of identifying the boundary of the photovoltaic panel is improved.
Fig. 3 is an image obtained after the gray value extracted by using a threshold value method is greater than a set threshold value, in the image, both a large-area hot spot of the photovoltaic panel and the reflection of the battery component exist, and the hot spot greater than the threshold value is reserved, namely, a hot spot region is obtained.
Fig. 4 is a diagram illustrating the principle of hot spot location, i.e. on which cell the hot spot is located on the photovoltaic module.
In conclusion, the invention well solves the problems of low efficiency and inaccuracy of manual analysis of the existing unmanned aerial vehicle photovoltaic inspection picture, realizes intelligent identification and positioning of the photovoltaic module hot spot defect by designing the photovoltaic module hot spot defect positioning method based on pane detection and linear regression algorithm and applicable to the computer, greatly improves the fault detection efficiency and precision of the photovoltaic module, reduces the labor cost, and avoids the situation that the infrared image of the photovoltaic panel sent back by the unmanned aerial vehicle photovoltaic inspection manually cannot accurately identify the fault photovoltaic panel in real time, thereby applying the computer to detect the photovoltaic module in real time, improving the hot spot detection efficiency and quality of the photovoltaic module and realizing intelligent identification and positioning of the fault of the photovoltaic module.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (6)
1. A photovoltaic module hot spot defect positioning method based on pane detection and linear regression algorithm is characterized by comprising the following steps: the method specifically comprises the following steps:
s1, converting the RGB image into a gray-scale image, and reflecting the hot spot in the gray-scale image in a bright spot form;
s2, identifying the outline of the hot spot in the picture by using an outline identification algorithm for accurately identifying the hot spot area, calculating the area of the hot spot, reserving the outline of the hot spot when the area is larger than a set threshold value, regarding the outline as the hot spot area, and calculating the central point of the hot spot area;
s3, identifying the internal grid lines and boundaries of the photovoltaic module to position the hot spot of the photovoltaic module;
s4, converting an original image into a gray image by using a calculation mode of internal grid lines, importing an obtained gray array into excel, and displaying by using a thermodynamic diagram mode, wherein the gray value at the grid lines belongs to a small value in the row direction, each point on a photovoltaic assembly is respectively expanded into 4 elements from front to back to form a transverse or longitudinal window pane, the value at the central point of the window pane is the minimum value of the center of the whole grid, and the longitudinal or transverse grid lines can be correctly identified;
s5, a large number of scattered points exist in the area outside the photovoltaic panel in the gray scale image, the upper edge and the lower edge of the panel are identified by using the inconsistency of data distribution on two sides of the boundary line of the panel, an optional point is taken as the center, 5 points are respectively expanded from top to bottom to form a window, the sum of the upper 5 values and the sum of the lower 5 values are calculated, the difference between the upper 5 values and the lower 5 values are respectively substituted for the area of the surface plate and the area of the ground, the point in the image is traversed by the difference between the upper 5 values and the lower 5 values, and;
s6, aiming at the phenomenon that the local points of the boundary line of the photovoltaic panel are identified to have 'disjointed jumping', the boundary is smoothed by linear regression, all the identified boundary points are fitted into a straight line by using the linear regression, and the fitted straight line is the lower boundary line.
2. The photovoltaic module hot spot defect positioning method based on pane detection and linear regression algorithm according to claim 1, characterized in that: noise such as light reflection of the battery part exists on the gray scale map in the step S1.
3. The photovoltaic module hot spot defect positioning method based on pane detection and linear regression algorithm according to claim 1, characterized in that: in step S2, when the area is larger than the set threshold, the contour of the hot spot is retained and regarded as a hot spot region, so as to reduce the influence of noise in the gray scale image and accurately identify the hot spot region.
4. The photovoltaic module hot spot defect positioning method based on pane detection and linear regression algorithm according to claim 1, characterized in that: the pane detection method applied in the step S4 can accurately identify the grid lines inside the photovoltaic module.
5. The photovoltaic module hot spot defect positioning method based on pane detection and linear regression algorithm according to claim 1, characterized in that: the photovoltaic panel boundary line can be identified by applying a pane detection method in the step S5.
6. The photovoltaic module hot spot defect positioning method based on pane detection and linear regression algorithm according to claim 1, characterized in that: in the step S6, the photovoltaic panel boundary line identification local point has a phenomenon of "disjointing jumping", and the boundary is smoothed by linear regression, so that the accuracy of photovoltaic panel boundary identification is improved.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113155288A (en) * | 2020-11-30 | 2021-07-23 | 齐鲁工业大学 | Image identification method for hot spots of photovoltaic cell |
| CN113781448A (en) * | 2021-09-14 | 2021-12-10 | 国电四子王旗光伏发电有限公司 | Intelligent photovoltaic power station assembly defect identification method based on infrared image analysis |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2009026661A1 (en) * | 2007-08-30 | 2009-03-05 | Bt Imaging Pty Ltd | Photovoltaic cell manufacturing |
| WO2012019219A1 (en) * | 2010-08-09 | 2012-02-16 | Bt Imaging Pty Ltd | Persistent feature detection |
| CN106815838A (en) * | 2017-01-22 | 2017-06-09 | 晶科电力有限公司 | A kind of method and system of the detection of photovoltaic module hot spot |
| CN107314819A (en) * | 2017-07-03 | 2017-11-03 | 南京绿谷信息科技有限公司 | A kind of detection of photovoltaic plant hot spot and localization method based on infrared image |
| CN108108736A (en) * | 2017-12-22 | 2018-06-01 | 晶科电力科技股份有限公司 | A kind of solar energy photovoltaic panel spot identification method |
| CN108986076A (en) * | 2018-06-15 | 2018-12-11 | 重庆大学 | A kind of photovoltaic array hot spot detection method based on PSO optimization PCNN |
| CN109241923A (en) * | 2018-09-18 | 2019-01-18 | 甘肃启远智能科技有限责任公司 | Photovoltaic module hot spot localization method and device |
| CN109584222A (en) * | 2018-11-19 | 2019-04-05 | 国网江西省电力有限公司电力科学研究院 | A kind of failure modes of the photovoltaic module image based on unmanned plane and discrimination method |
| WO2019214268A1 (en) * | 2018-05-09 | 2019-11-14 | 北京理工大学 | Photovoltaic array fault diagnosis method based on composite information |
-
2020
- 2020-01-09 CN CN202010022375.3A patent/CN111242914B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2009026661A1 (en) * | 2007-08-30 | 2009-03-05 | Bt Imaging Pty Ltd | Photovoltaic cell manufacturing |
| WO2012019219A1 (en) * | 2010-08-09 | 2012-02-16 | Bt Imaging Pty Ltd | Persistent feature detection |
| CN106815838A (en) * | 2017-01-22 | 2017-06-09 | 晶科电力有限公司 | A kind of method and system of the detection of photovoltaic module hot spot |
| CN107314819A (en) * | 2017-07-03 | 2017-11-03 | 南京绿谷信息科技有限公司 | A kind of detection of photovoltaic plant hot spot and localization method based on infrared image |
| CN108108736A (en) * | 2017-12-22 | 2018-06-01 | 晶科电力科技股份有限公司 | A kind of solar energy photovoltaic panel spot identification method |
| WO2019214268A1 (en) * | 2018-05-09 | 2019-11-14 | 北京理工大学 | Photovoltaic array fault diagnosis method based on composite information |
| CN108986076A (en) * | 2018-06-15 | 2018-12-11 | 重庆大学 | A kind of photovoltaic array hot spot detection method based on PSO optimization PCNN |
| CN109241923A (en) * | 2018-09-18 | 2019-01-18 | 甘肃启远智能科技有限责任公司 | Photovoltaic module hot spot localization method and device |
| CN109584222A (en) * | 2018-11-19 | 2019-04-05 | 国网江西省电力有限公司电力科学研究院 | A kind of failure modes of the photovoltaic module image based on unmanned plane and discrimination method |
Non-Patent Citations (2)
| Title |
|---|
| 徐庆;张天文;沈道军;李春阳;罗易;周承军;: "基于光伏电站排布设计数据的无人机热斑巡检系统方案" * |
| 邢涛等: "系统端光伏组件热斑研究及其成因分析" * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113155288A (en) * | 2020-11-30 | 2021-07-23 | 齐鲁工业大学 | Image identification method for hot spots of photovoltaic cell |
| CN113781448A (en) * | 2021-09-14 | 2021-12-10 | 国电四子王旗光伏发电有限公司 | Intelligent photovoltaic power station assembly defect identification method based on infrared image analysis |
| CN113781448B (en) * | 2021-09-14 | 2024-01-23 | 国电四子王旗光伏发电有限公司 | Intelligent defect identification method for photovoltaic power station assembly based on infrared image analysis |
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