KR102431935B1 - Artificial Intelligence based Solar Module Diagnosis Method and System using Thermal Images Taken by Drone - Google Patents

Abstract

Provided are an artificial intelligence-based solar module diagnosis method and system using thermal images taken by a drone. The solar module diagnosis method according to an embodiment of the present invention comprises: acquiring thermal images of a solar module; and inputting the acquired thermal images into an artificial intelligence model to analyze faults and degradation of the solar modules. The artificial intelligence model is an artificial intelligence model learned with the thermal images as inputs and regions and modes of faults and degradation of solar modules as outputs. As a result, by utilizing thermal images of solar modules captured by a drone and analyzing faults and degradation based on artificial intelligence, predicting outputs, and determining grades, solar modules can be diagnosed objectively, quickly, and easily without the need for experts.

Description

Artificial Intelligence based Solar Module Diagnosis Method and System using Thermal Images Taken by Drone}

The present invention relates to a photovoltaic module management technology, and more particularly, a photovoltaic module for analyzing failure/deterioration with a thermal image of a photovoltaic module taken by a drone at a photovoltaic power plant, predicting output, and judging a grade It relates to a diagnostic method and system.

The solar module of the solar power plant is a stable product that has been operated for more than 25 years. However, failure/deterioration of the solar module may occur due to incorrect installation of the operator, initial failure, natural disaster, negligent management, and the like. The failure/deterioration of the solar module appears in a variety of ways as shown in FIG. 1 .

In order to understand the state of the photovoltaic module, it is possible to diagnose the failure/deterioration by photographing the photovoltaic module with a thermal imaging camera. However, there are problems such as the fact that the diagnosis has to be manually performed by an expert, and that it takes a lot of time and effort even if it is done.

In addition, even by an expert, it cannot be completely free from subjective diagnosis. In other words, there is a problem that the diagnosis results are different depending on who the expert is, so objectivity cannot be guaranteed.

The present invention has been devised to solve the above problems, and an object of the present invention is to analyze failure/deterioration based on artificial intelligence using thermal images of solar modules photographed by a drone, predict output, and grade To provide a solar module diagnostic method and system for determining

According to an embodiment of the present invention for achieving the above object, a method for diagnosing a solar module includes: acquiring thermal images of the solar modules; An analysis step of inputting the thermal image obtained in the acquisition step into the artificial intelligence model to analyze the failure and deterioration of the solar modules, and the artificial intelligence model takes the thermal image as an input, It is an artificial intelligence model trained with the deterioration region and mode as output.

A method for diagnosing a solar module according to an embodiment of the present invention includes: predicting the output of the solar modules by using the thermal image acquired in the acquisition step and the analysis result in the analysis step; determining a rating for each of the solar modules based on the output predicted in the predicting step; and visualizing and outputting the analysis result in the analysis step, the prediction result in the prediction step, and the determination result in the determination step;

The artificial intelligence model is a Generative Adversarial Network (GAN), and GANs for Anomaly Detection (GAN-AD) may be applied.

The analysis step may include: a first analysis step of inputting the thermal image obtained in the acquisition step into the first artificial intelligence model, and analyzing the failure and deterioration of the solar modules; converting the thermal image acquired in the acquisition step by image processing based on the analysis result in the first analysis step; a second analysis step of inputting the thermal image converted in the conversion step into the second artificial intelligence model, and analyzing the failure and deterioration of the solar modules.

The conversion step may perform image processing for emphasizing faulty and deteriorated areas.

The failure and degradation modes may include at least one of hot spots, bypass diode failure (BDF), shading, contamination, potential induced degradation (PID), short circuit, and disconnection.

In the conversion step, image processing is performed to detect and emphasize the point shape for the failure and degradation areas analyzed as hotspots, PIDs or shorts in the failure and degradation modes in the first analysis step, and failure and degradation in the first analysis step Image processing is performed to detect and emphasize the bar shape for the failure and deterioration area analyzed by BDF mode, and the plane shape is detected for the failure and deterioration area analyzed as a single line in the failure and deterioration mode in the first analysis step. to perform image processing for emphasis, and image processing for emphasizing by detecting irregular shapes for the failure and deterioration regions analyzed as shading or contamination in the first analysis step may be performed.

The conversion step may further perform image processing for smoothing the area in which the failure and deterioration appear.

The analysis step may further include a third analysis step of collecting the analysis results in the first analysis step and the analysis results in the second analysis step, and analyzing the failure and deterioration of the solar modules.

On the other hand, according to another embodiment of the present invention, a solar module diagnostic system, the acquisition unit for acquiring thermal images of the solar modules; and an analysis unit that inputs the thermal image obtained from the acquisition unit into the artificial intelligence model and analyzes the failure and deterioration of the solar modules, wherein the artificial intelligence model receives the thermal image as an input, and It is an artificial intelligence model trained with the deterioration region and mode as output.

As described above, according to embodiments of the present invention, by analyzing failure/deterioration based on artificial intelligence using thermal images of photovoltaic modules photographed by a drone, predicting output, and judging a grade, objectively without an expert At the same time, it is possible to diagnose the solar module quickly and easily.

1 is a diagram illustrating faulty/degraded solar modules;
2 is a view provided for conceptual explanation of a solar module diagnostic system according to an embodiment of the present invention;
3 is a block diagram of the solar module diagnostic system shown in FIG. 2;
4 is a detailed block diagram of the solar module diagnostic platform 200 shown in FIG. 3 ;
5 is a diagram provided for explanation of failure/degradation mode;
6 is a diagram illustrating visual information output as a diagnosis result;
7 is a block diagram of a solar module diagnostic platform according to another embodiment of the present invention, and
8 is a block diagram of a solar module diagnostic platform according to another embodiment of the present invention.

Hereinafter, the present invention will be described in more detail with reference to the drawings.

2 is a diagram provided to explain the concept of a solar module diagnosis system according to an embodiment of the present invention.

In the solar module diagnostic system according to an embodiment of the present invention, as shown, when thermal images of solar modules are taken with the drone 100, the solar module diagnostic platform 200 analyzes the photographed thermal images. It is a system built to diagnose a solar module and provide a diagnostic result through the solar power plant manager terminal 300 .

FIG. 3 is a block diagram of the solar module diagnostic system shown in FIG. 2 . The solar module diagnosis system according to an embodiment of the present invention, as shown, is configured to include a drone 100 , a solar module diagnosis platform 200 , and a solar power plant manager terminal 300 .

The drone 100 creates thermal images of the solar modules by photographing as if scanning the sky above the solar power plant. The thermal image taken by the drone 100 is transmitted to the solar module diagnosis platform 200 .

The solar module diagnosis platform 200 analyzes the failure/deterioration of the solar modules based on artificial intelligence using the thermal image received from the drone 100, predicts the output based on this, and determines the grade.

The photovoltaic power plant manager terminal 300 is a terminal owned/carried by the photovoltaic power plant manager, and receives and displays a diagnosis result from the photovoltaic module diagnosis platform 200 . Through this, the photovoltaic power plant manager can understand the status of the photovoltaic modules, and establish an after-sales service and preventive maintenance plan.

Hereinafter, the detailed structure and operation of the solar module diagnostic platform 200 will be described in detail with reference to FIG. 4 . 4 is a detailed block diagram of the solar module diagnostic platform 200 shown in FIG. 3 .

The solar module diagnosis platform 200, as shown, includes a thermal image acquisition unit 210, a failure/degradation analysis unit 220, a module output prediction unit 230 and a module grade determination unit 240, and a diagnosis result It is configured to include an output unit 250 , a diagnosis result storage unit 260 , and a diagnosis result analysis unit 270 .

The thermal image acquisition unit 210 receives and acquires thermal images of solar modules photographed by the drone 100 , and uses the acquired thermal images to the failure/degradation analysis unit 220 and the module output prediction unit 230 . Enter

The failure/deterioration analysis unit 220 inputs the thermal image input by the thermal image acquisition unit 210 into the artificial intelligence model for failure/deterioration analysis, and identifies the failure and deterioration of the solar modules.

The artificial intelligence model provided in the failure/deterioration analysis unit 220 is an artificial intelligence model learned by inputting thermal images of solar modules and outputting failure/deterioration areas and modes of the solar modules.

The failure/degradation mode can be classified into seven types as shown in FIG. 5 .

1) Hot-spot

Cell cracks, snail trails, appearing as dots or multiple dots in a cell in a module

2) BDF (Bypass Diode Failure)

Appears as module substring due to bypass diode failure, uniform high temperature, 1/3, 2/3 uniform brightness of rod (rectangular)-shaped module

3) Shading

Appears in irregular shape with shadows caused by telephone poles, weeds, fallen leaves, snow, etc.

4) Contamination

Appears in irregular shape due to surface contamination by dust adsorption, water scale, bird droppings, insect carcass, etc.

5) PID (Potential Induced Degradation)

A gradual increase in the number of hot-spot modules in the string (-) region, represented by multiple dots of multiple connected modules.

6) Short circuit

Non-uniform high temperature of the string, represented by multiple dots on multiple connected modules

7) Disconnection

A uniform high temperature of the string, manifested by the uniform brightness of a number of connected modules

The AI model can be implemented as a Generative Adversarial Network (GAN), which is the most suitable model for detecting failure/deterioration areas, and GAN-AD (GANs for Anomaly Detection) can be applied. However, this is exemplary and does not exclude implementation in other ways.

Accordingly, when the thermal image is input, the failure/deterioration analysis unit 220 outputs the failure/deterioration region and mode of the solar modules, so that the failure/deterioration status and mode can be analyzed for each solar module.

The module output prediction unit 230 predicts the output of the solar modules using the following data.

1) Thermal images of solar modules input from the thermal image acquisition unit 210

2) Analysis result by the failure/deterioration analysis unit 220

3) Weather/environment data at the time of thermal imaging by drone 100

4) Photovoltaic module specification/specification data

The module output prediction unit 230 may be implemented as an artificial intelligence model learned by using the above data as an input and the expected output of the solar modules as an output, or may be implemented as an algorithm using a decision engine.

The module grade determination unit 240 determines a grade for each of the solar modules based on the output predicted by the module output prediction unit 230 .

The module grade determination unit 240 may be implemented as an artificial intelligence model learned by inputting the prediction result of the module output prediction unit 230 and outputting the grades of solar modules, or may be implemented as an algorithm using a decision engine. .

The diagnosis result output unit 250 visualizes and outputs the analysis result of the failure/degradation analysis unit 220 , the prediction result of the module output prediction unit 230 , and the determination result of the module grade determination unit 240 .

Specifically, the diagnosis result output unit 250 displays the corresponding information on the photovoltaic power plant map showing the arrangement state of the photovoltaic modules. 6 exemplifies information output by the diagnosis result output unit 250 .

Specifically, in the upper part of FIG. 6, information on the failure/deterioration status and mode of the solar module is displayed in module unit, and in the center of FIG. 6, information on the expected output for each of the solar modules, in FIG. In the lower part, information about the grade determined for each of the solar modules is shown as a visualization result.

The diagnosis result output unit 250 transmits the visualized information shown in FIG. 6 to the solar power plant manager terminal 300 .

The diagnosis result storage unit 260 receives the analysis result of the failure/degradation analysis unit 220 , the prediction result of the module output prediction unit 230 , and the determination result of the module grade determination unit 240 from the thermal image acquisition unit 210 . It is saved together with the input thermal image.

The diagnosis result analysis unit 270 analyzes data stored in the diagnosis result storage unit 260 to manage preventive maintenance and A/S of the solar modules.

7 is a block diagram of a solar module diagnostic platform according to another embodiment of the present invention. The solar module diagnosis platform 200 according to the embodiment of the present invention, as shown, includes a thermal image acquisition unit 210 , a failure/degradation analysis unit-1 221 , an image processing unit 222 , and a failure/deterioration including an analysis unit-2 223 , a module output prediction unit 230 , a module grade determination unit 240 , a diagnosis result output unit 250 , a diagnosis result storage unit 260 , and a diagnosis result analysis unit 270 . is composed

The solar module diagnosis platform shown in FIG. 7 includes the 'failure/deterioration analysis unit 220' of the solar module diagnosis platform shown in FIG. ) and failure/deterioration analysis unit-2(223)'.

Accordingly, in FIG. 7 , the rest of the configurations except for the 'failure/deterioration analysis unit-1 (221), the image processing unit 222, and the failure/degradation analysis unit-2(223)' can be inferred from the description of FIG. In , only the 'failure/degradation analysis unit-1 (221), the image processing unit 222, and the failure/degradation analysis unit-2(223)' will be described, and detailed descriptions of the remaining components will be omitted.

The failure/deterioration analysis unit-1 221 inputs the thermal image input by the thermal image acquisition unit 210 into the artificial intelligence model for failure/deterioration analysis, and identifies the failure and deterioration of the solar modules.

The artificial intelligence model provided in the failure/degradation analysis unit-1 (221) is an artificial intelligence model learned by taking thermal images of solar modules as input and outputting failure/deterioration areas and modes of the solar modules. The failure/deterioration mode is classified into seven types as described above.

The image processing unit 222 converts the thermal image input by the thermal image acquisition unit 210 by image processing, and transmits the converted thermal image to the failure/degradation analysis unit-2 223 . The image processing performed is different according to the analysis result by the failure/deterioration analysis unit-1 (221). Specifically,

1) For the photovoltaic module whose failure mode is analyzed as hotspot, PID, and short by the failure/degradation analysis unit-1 (221), image processing for detecting and emphasizing (reinforcing) the point shape is performed,

2) For the photovoltaic module whose failure mode is analyzed as BDF by the failure/degradation analysis unit-1 (221), image processing is performed to detect and emphasize the bar shape,

3) For the solar module whose failure mode is analyzed as disconnection by the failure/degradation analysis unit-1 (221), image processing is performed to detect and emphasize the shape of the surface,

4) Image processing for detecting and emphasizing irregular shapes is performed on the photovoltaic module whose failure mode is analyzed as shadow and pollution by the failure/degradation analysis unit-1 (221).

The failure/deterioration analysis unit-2 223 inputs the thermal image converted by the image processing unit 222 into the artificial intelligence model for failure/deterioration analysis, and identifies the failure and deterioration of the solar modules.

The artificial intelligence model provided in the failure/degradation analysis unit-2 (222) is an artificial intelligence model learned by inputting the thermal image converted by thermal image processing and outputting the failure/deterioration region and mode of the solar modules as an output. . Failure/deterioration modes are classified into 7 categories.

The failure/degradation analysis unit-2 (223) can utilize an artificial intelligence model having the same structure as the failure/degradation analysis unit-1 (221), but the input image is different during learning (original thermal image vs. image processing converted to thermal image), the parameters of the AI model are set differently.

The failure/deterioration region and mode of the photovoltaic modules output from the failure/deterioration analysis unit-2 223 are output to the module output prediction unit 230 as a final analysis result for the failure and deterioration of the photovoltaic modules.

In the above embodiment, it is assumed that the image processing unit 222 performs image processing for emphasizing by detecting a point shape, a bar shape, a planar shape, and an irregular shape, but variations are possible. For instance,

1) For the photovoltaic module whose failure mode is analyzed as hotspot, PID, or short by the failure/degradation analysis unit-1 (221), for smoothing (weakening) in addition to image processing that detects and emphasizes (enhanced) the dot shape image processing is further performed,

2) For the photovoltaic module whose failure mode is analyzed as BDF by the failure/degradation analysis unit-1 (221), image processing for smoothing is further performed in addition to image processing to detect and emphasize the rod shape,

3) For the photovoltaic module whose failure mode is analyzed as disconnection by the failure/degradation analysis unit-1 (221), image processing for smoothing is further performed in addition to image processing to detect and emphasize the surface shape,

4) An irregular shape is detected for the solar module whose failure mode is analyzed as shading and contamination by the failure/degradation analysis unit-1 (221), and image processing for smoothing is further performed in addition to image processing to emphasize .

In this case, the image processing unit 222 further generates a thermal image in which the corresponding shape is smoothed in addition to the thermal image in which the corresponding shape is emphasized, and the failure/deterioration analysis unit-2 222 uses both the thermal images to generate a solar module. Analyze their failure/deterioration areas and modes.

8 is a block diagram of a solar module diagnostic platform according to another embodiment of the present invention. The solar module diagnosis platform 200 according to the embodiment of the present invention, as shown, includes a thermal image acquisition unit 210 , a failure/degradation analysis unit-1 221 , an image processing unit 222 , and a failure/deterioration Analysis unit-2 (223), failure/degradation analysis unit-3 (224), module output prediction unit 230 and module grade determination unit 240, diagnosis result output unit 250, diagnosis result storage unit 260 and a diagnostic result analysis unit 270 .

The solar module diagnostic platform illustrated in FIG. 8 is a failure/deterioration analysis unit-3 224 added to the solar module diagnostic platform illustrated in FIG. 7 .

The failure/deterioration analysis unit-3 (224) collects the failure/deterioration analysis result in the failure/degradation analysis unit-1 (221) and the failure/deterioration analysis result in the failure/deterioration analysis unit-2 (223), The failure/deterioration region and mode of the solar modules are finally analyzed, and the result is output to the module output prediction unit 230 .

In the previous embodiment, there is a difference in that the failure/deterioration analysis result of the failure/degradation analysis unit-2 223 is output to the module output prediction unit 230 as a final result.

The failure/deterioration analysis unit-3 (224) receives the failure/deterioration analysis results of the previous failure/degradation analysis units (221 and 223) as input, and an artificial intelligence model learned by outputting the failure/deterioration region and mode of the solar modules. It can be implemented as an algorithm using Decision Engine.

So far, a preferred embodiment has been described in detail for a method and system for diagnosing an artificial intelligence-based solar module using a thermal image taken by a drone.

In the above embodiment, the failure/deterioration analyzers 220 , 221 , and 223 take the thermal images of the Taeyoung Kwang modules as input and analyze the failure/deterioration regions and modes of the solar modules, and input data may be added. For example, weather/environment data at the time of thermal image shooting by the drone 100 and/or standard/specification data of a solar module may be added to the input.

On the other hand, it goes without saying that the technical idea of the present invention can also be applied to a computer-readable recording medium containing a computer program for performing the functions of the apparatus and method according to the present embodiment. In addition, the technical ideas according to various embodiments of the present invention may be implemented in the form of computer-readable codes recorded on a computer-readable recording medium. The computer-readable recording medium may be any data storage device readable by the computer and capable of storing data. For example, the computer-readable recording medium may be a ROM, RAM, CD-ROM, magnetic tape, floppy disk, optical disk, hard disk drive, or the like. In addition, the computer-readable code or program stored in the computer-readable recording medium may be transmitted through a network connected between computers.

In addition, although preferred embodiments of the present invention have been illustrated and described above, the present invention is not limited to the specific embodiments described above, and the technical field to which the present invention belongs without departing from the gist of the present invention as claimed in the claims In addition, various modifications may be made by those of ordinary skill in the art, and these modifications should not be individually understood from the technical spirit or perspective of the present invention.

100 : drone
200: solar module diagnostic platform
210: thermal image acquisition unit
220: failure/deterioration analysis unit
230: module output prediction unit
240: module grade determination unit
250: diagnostic result output unit
260: diagnosis result storage unit
270: diagnostic result analysis unit
300: solar power plant manager terminal

Claims (10)

obtaining thermal images of solar modules;
An analysis step of analyzing the failure and deterioration of solar modules by inputting the thermal image acquired in the acquisition step into the artificial intelligence model;
artificial intelligence model,
It is an artificial intelligence model learned by taking thermal images as input and outputting the areas and modes of failure and deterioration of solar modules.
The analysis stage is
a first analysis step of inputting the thermal image obtained in the acquisition step into the first artificial intelligence model, and analyzing the failure and deterioration of the solar modules; and
converting the thermal image acquired in the acquisition step by image processing based on the analysis result in the first analysis step;
a second analysis step of inputting the thermal image converted in the conversion step into the second artificial intelligence model, and analyzing the failure and deterioration of the solar modules;
The conversion step is
Perform image processing to highlight areas of failure and deterioration;
Failure and deterioration modes are
including at least one of hotspots, Bypass Diode Failure (BDF), shading, contamination, Potential Induced Degradation (PID), short circuit, and open circuit;
The conversion step is
In the first analysis step, image processing is performed to detect and emphasize the point shape for the failure and deterioration areas analyzed as hotspots, PIDs, or short circuits in failure and deterioration modes;
In the first analysis step, image processing is performed to detect and emphasize the bar shape for the failure and deterioration areas analyzed by BDF in the failure and deterioration modes,
In the first analysis step, image processing is performed to detect and emphasize the shape of the failure and deterioration areas analyzed as disconnection in the failure and deterioration modes;
A method for diagnosing a photovoltaic module, characterized in that in the first analysis step, image processing for detecting and emphasizing irregular shapes is performed on the failure and deterioration areas analyzed as shading or contamination in the failure and deterioration modes.
The method according to claim 1,
predicting the output of the solar modules by using the thermal image acquired in the acquisition step and the analysis result in the analysis step;
determining a rating for each of the solar modules based on the output predicted in the predicting step; and
Visualizing and outputting the analysis result in the analysis step, the prediction result in the prediction step, and the determination result in the determination step; Solar module diagnosis method further comprising a.
The method according to claim 1,
artificial intelligence model,
A solar module diagnosis method, characterized in that it is a Generative Adversarial Network (GAN) and GAN-AD (GANs for Anomaly Detection) is applied.
delete delete delete delete The method according to claim 1,
The conversion step is
A method for diagnosing a photovoltaic module, characterized in that further image processing is performed for smoothing the area in which the failure and deterioration appear.
The method according to claim 1,
The analysis step is
A photovoltaic module diagnosis method further comprising a; a third analysis step of collecting the analysis result in the first analysis step and the analysis result in the second analysis step, and analyzing the failure and deterioration of the solar modules.
an acquisition unit for acquiring thermal images of solar modules;
An analysis unit that inputs the thermal image obtained by the acquisition unit to the artificial intelligence model and analyzes the failure and deterioration of the solar modules;
artificial intelligence model,
It is an artificial intelligence model learned by taking thermal images as input and outputting the areas and modes of failure and deterioration of solar modules.
analysis department,
a first analysis unit that inputs the thermal image obtained by the acquisition unit into the first artificial intelligence model, and analyzes the failure and deterioration of the solar modules; and
an image processing unit for image processing and converting the thermal image acquired by the acquisition unit based on the analysis result of the first analysis unit;
a second analysis unit that inputs the thermal image converted by the image processing unit into the second artificial intelligence model, and analyzes the failure and deterioration of the solar modules;
The image processing unit,
Perform image processing to highlight areas of failure and deterioration;
Failure and deterioration modes are
including at least one of hotspots, Bypass Diode Failure (BDF), shading, contamination, Potential Induced Degradation (PID), short circuit, and open circuit;
The image processing unit,
The first analysis unit performs image processing for detecting and emphasizing the point shape for the failure and deterioration areas analyzed as hotspots, PIDs, or short circuits in the failure and deterioration modes;
The first analysis unit performs image processing for detecting and emphasizing the bar shape for the failure and deterioration areas analyzed by BDF in the failure and deterioration modes,
The first analysis unit performs image processing for detecting and emphasizing the shape of a failure and deterioration area analyzed as a disconnection in the failure and deterioration mode;
A photovoltaic module diagnostic system, characterized in that the first analysis unit detects and performs image processing for highlighting irregular shapes for the failure and deterioration areas analyzed as shadows or contamination in the failure and deterioration modes.

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