2018 IEEE 7th World Conference on Photovoltaic Energy Conversion (WCPEC) (A Joint Conference of 45th IEEE PVSC, 28th PVSEC & 34th EU PVSEC), 2018
The experimentation with a movable outdoor electroluminescence (EL) testbed is performed in this ... more The experimentation with a movable outdoor electroluminescence (EL) testbed is performed in this work. For EL inspections of PV power plants, the fastest scenario will include the use of unmanned aerial vehicle (UAV) performing image acquisition in continuous motion. With this motivation, we investigate the EL image quality of an acquisition in motion and the extent of image processing required to correct scene displacement. The results show processed EL images with a high level of information even when acquired at 1 m/s camera speed and at frame rate of 120 fps.
Users may download and print one copy of any publication from the public portal for the purpose... more Users may download and print one copy of any publication from the public portal for the purpose of private study or research. You may not further distribute the material or use it for any profit-making activity or commercial gain You may freely distribute the URL identifying the publication in the public portal If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim.
2018 IEEE 7th World Conference on Photovoltaic Energy Conversion (WCPEC) (A Joint Conference of 45th IEEE PVSC, 28th PVSEC & 34th EU PVSEC), 2018
Electroluminescence (EL) imaging inspections of PV power plants can bring a huge improvement in a... more Electroluminescence (EL) imaging inspections of PV power plants can bring a huge improvement in accuracy. The use of InGaAs camera will also make such inspections fast, but the restriction to acquire the images during dusk or evening is a limitation. Performing lock-in EL is a way to go for daylight EL. This paper proposes an extension of the SNR50 quality measure to estimate the quality of a stack of N images and evaluates the impact of some factors over the measured and visual quality of images acquired with InGaAs sensors. The factors analyzed are the characteristics of the noise in the acquired images, the influence of the sun variations and the averaging over multiple acquired images.
2018 IEEE 7th World Conference on Photovoltaic Energy Conversion (WCPEC) (A Joint Conference of 45th IEEE PVSC, 28th PVSEC & 34th EU PVSEC), 2018
With the significant growth in the number of photovoltaic (PV) installations and their size, regu... more With the significant growth in the number of photovoltaic (PV) installations and their size, regular PV system inspection has become a challenge. Aerial drone imaging, based on visual, thermographic, and more recently luminescence, can be viable solutions for PV inspection. However, to achieve effective detection and quantification of failure based on images acquired form Unmanned Aerial Vehicle, there is need for image quality enhancement and correction of distortions, inherent to the drone measurement process. In this work we propose methods to automatically correct the perspective distortion in electroluminescent (EL) images of PV panels. We identified two main cases of perspective distortion: when the imaging plane is parallel to the panel plane or not, and propose methods to correct both. For both cases, the proposed method yields good results, as assessed by visual evaluation.
The goal of this work is to perform outdoor defect detection imaging that will be used in a fast,... more The goal of this work is to perform outdoor defect detection imaging that will be used in a fast, accurate and automatic drone-based survey system for PV power plants. The imaging development focuses on techniques that do not require electrical contact, permitting automatic drone inspections to be perform quicker and with less manpower. The final inspection method will combine several techniques such as, infrared (IR), electroluminescence (EL), photoluminescence (PL), and visual imaging. Solar plant inspection in the future can be restricted only by imaging speed requirements, allowing an entire new perspective in large-scale PV inspection.
We demonstrate a method to quantify the extent of solar cell cracks, shunting, or damaged cell in... more We demonstrate a method to quantify the extent of solar cell cracks, shunting, or damaged cell interconnects, present in crystalline silicon photovoltaic (PV) modules by statistical analysis of the electroluminescence (EL) intensity distributions of individual cells within the module. From the EL intensity distributions (ELID) of each cell, we calculated summary statistics such as standard deviation, median, skewness and kurtosis, and analyzed how they correlate with the magnitude of the solar cell degradation. We found that the dispersion of the ELID increases with the size and severity of the solar cell cracks, correlating with an increase in standard deviation and decrease in kurtosis. For shunted cells, we found that the ELID median is strongly correlated with the extent of cell shunting. Last, cells with damaged interconnect ribbons show current crowding and increased series resistance regions, characterized by increased dispersion and skewness of the ELID. These cell-level dia...
Regular photovoltaic (PV) system inspections have become a challenge with the significant growth ... more Regular photovoltaic (PV) system inspections have become a challenge with the significant growth in thenumber of modules and peak power capacity of PV installations. Image acquisition using drones, based on visual, thermographic, and more recently luminescence, can be a viable solution for large-scale PV inspections. As luminescence can provide a highly detailed and accurate PV module failure diagnosis, the development of a daylight electroluminescence (EL) imaging capability is of high importance. EL imaging performed in the field during the day requires the enhancement of the relatively weak luminescence signal over the noise from the sun. This is accomplished by image averaging and background subtraction, which requires the highly accurate registering of the of individual module images. A sequential EL image acquisition at high frame rates in continuous motion at different angles will be the realworld situation for a drone-based PV inspection in daylight, and to account for this ...
Electroluminescence (EL) imaging of photovoltaic (PV) modules requires careful selection of the m... more Electroluminescence (EL) imaging of photovoltaic (PV) modules requires careful selection of the measurement parameters, such as camera sensor exposure time, for obtaining accurate images for qualitative and quantitative evaluation of PV module failures. In this work we propose a method for determining the optimal exposure time for EL imaging of PV panels, with respect to maximizing the utilized camera sensitivity range and limiting image saturation due to overexposure. The method requires two EL measurements of the PV panel, at two distinct exposure times, that are used to calculate the optimal exposure time. The main advantage of the proposed method over manually calibrating the exposure time of the camera is that it can be integrated in the camera image acquisition application, for automatic exposure time calibration.
In this paper, we present a study of daylight EL acquisition and the results of a sunlight variat... more In this paper, we present a study of daylight EL acquisition and the results of a sunlight variation study in a scenario necessary to assure the increase of EL image quality with denoising by averaging for the robustness of the drone system when bright and intermittently cloudy days occur. It was verified that the indicator of image quality based on the signal-to-noise ratio of EL images has a linear behavior with the amount of averaged images when there is no sun variation. When there are sun irradiance variation, it is observed that the quality decrease even with the increased number of images being averaged, turning to increase again only with further additional images.
A wide range of defects, failures, and degradation can develop at different stages in the lifetim... more A wide range of defects, failures, and degradation can develop at different stages in the lifetime of photovoltaic modules. To accurately assess their effect on the module performance, these failures need to be quantified. Electroluminescence (EL) imaging is a powerful diagnostic method, providing high spatial resolution images of solar cells and modules. EL images allow the identification and quantification of different types of failures, including those in high recombination regions, as well as series resistance-related problems. In this study, almost 46,000 EL cell images are extracted from photovoltaic modules with different defects. We present a method that extracts statistical parameters from the histogram of these images and utilizes them as a feature descriptor. Machine learning algorithms are then trained using this descriptor to classify the detected defects into three categories: (i) cracks (Mode B and C), (ii) micro-cracks (Mode A) and finger failures, and (iii) no failu...
The number of photovoltaic panels installed globally is continuously growing, requiring an automa... more The number of photovoltaic panels installed globally is continuously growing, requiring an automatic inspection procedure for operation and maintenance. Drones can be a useful tool to this aim as they enable fast acquisition of various imaging modalities: visual, infrared, or electroluminescence (EL). Image distortions due to perspective must be corrected to allow further automatic processing. It can be done by estimating the corresponding rotation angles to control the camera gimbal or as postprocessing to rectify the images. This article presents two methods to achieve both goals by identifying known points in the acquired image. The first method detects the four panel corners, whereas the second method finds the corners of each cell. The performance evaluation is performed first quantitatively on a validation dataset composed of 113 EL images and their corresponding ground-truth orientations. A qualitative evaluation shows satisfying performance of the rectification similarly for both methods. The quantitative performance is varying for each rotation axis. The average absolute error is 2.78 • along the x-axis, 2.64 • along the y-axis, and 1.28 • along the z-axis for the panel method and 3.26 • , 2.05 • , and 1.24 • for the cell method. As a proof of concept, a final test on drone-acquired EL images shows good performance for the image rectification in real-life conditions.
Electroluminescence (EL) imaging is a PV module characterization technique, which provides high a... more Electroluminescence (EL) imaging is a PV module characterization technique, which provides high accuracy in detecting defects and faults such as cracks, broken cells interconnections, shunts, among many others; furthermore, the EL technique is used extensively due to a high level of detail and direct relationship to injected carrier density. However, this technique is commonly practiced only indoors-or outdoors from dusk to dawn-because the crystalline silicon luminescence signal is several orders of magnitude lower than sunlight. This limits the potential of such a powerful technique to be used in utility scale inspections, and therefore the interest in the development of electrical biasing tools to make outdoor EL imaging truly fast and efficient. With the focus of quickly acquiring EL images in daylight, we present in this article a drone-based system capable of acquiring EL images at a framerate of 120 frames per second. In a single second during high irradiance conditions, this system can capture enough EL and background image pairs to create an EL PV module image that has sufficient diagnostic information to identify faults associated with power loss. The final EL images shown in this work reached representative quality SNRAVG of 4.6, obtained with algorithms developed in previous works. These drone-based EL images were acquired with global horizontal solar irradiance close to one sun in the plane of the array.
2018 IEEE 7th World Conference on Photovoltaic Energy Conversion (WCPEC) (A Joint Conference of 45th IEEE PVSC, 28th PVSEC & 34th EU PVSEC), 2018
The experimentation with a movable outdoor electroluminescence (EL) testbed is performed in this ... more The experimentation with a movable outdoor electroluminescence (EL) testbed is performed in this work. For EL inspections of PV power plants, the fastest scenario will include the use of unmanned aerial vehicle (UAV) performing image acquisition in continuous motion. With this motivation, we investigate the EL image quality of an acquisition in motion and the extent of image processing required to correct scene displacement. The results show processed EL images with a high level of information even when acquired at 1 m/s camera speed and at frame rate of 120 fps.
Users may download and print one copy of any publication from the public portal for the purpose... more Users may download and print one copy of any publication from the public portal for the purpose of private study or research. You may not further distribute the material or use it for any profit-making activity or commercial gain You may freely distribute the URL identifying the publication in the public portal If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim.
2018 IEEE 7th World Conference on Photovoltaic Energy Conversion (WCPEC) (A Joint Conference of 45th IEEE PVSC, 28th PVSEC & 34th EU PVSEC), 2018
Electroluminescence (EL) imaging inspections of PV power plants can bring a huge improvement in a... more Electroluminescence (EL) imaging inspections of PV power plants can bring a huge improvement in accuracy. The use of InGaAs camera will also make such inspections fast, but the restriction to acquire the images during dusk or evening is a limitation. Performing lock-in EL is a way to go for daylight EL. This paper proposes an extension of the SNR50 quality measure to estimate the quality of a stack of N images and evaluates the impact of some factors over the measured and visual quality of images acquired with InGaAs sensors. The factors analyzed are the characteristics of the noise in the acquired images, the influence of the sun variations and the averaging over multiple acquired images.
2018 IEEE 7th World Conference on Photovoltaic Energy Conversion (WCPEC) (A Joint Conference of 45th IEEE PVSC, 28th PVSEC & 34th EU PVSEC), 2018
With the significant growth in the number of photovoltaic (PV) installations and their size, regu... more With the significant growth in the number of photovoltaic (PV) installations and their size, regular PV system inspection has become a challenge. Aerial drone imaging, based on visual, thermographic, and more recently luminescence, can be viable solutions for PV inspection. However, to achieve effective detection and quantification of failure based on images acquired form Unmanned Aerial Vehicle, there is need for image quality enhancement and correction of distortions, inherent to the drone measurement process. In this work we propose methods to automatically correct the perspective distortion in electroluminescent (EL) images of PV panels. We identified two main cases of perspective distortion: when the imaging plane is parallel to the panel plane or not, and propose methods to correct both. For both cases, the proposed method yields good results, as assessed by visual evaluation.
The goal of this work is to perform outdoor defect detection imaging that will be used in a fast,... more The goal of this work is to perform outdoor defect detection imaging that will be used in a fast, accurate and automatic drone-based survey system for PV power plants. The imaging development focuses on techniques that do not require electrical contact, permitting automatic drone inspections to be perform quicker and with less manpower. The final inspection method will combine several techniques such as, infrared (IR), electroluminescence (EL), photoluminescence (PL), and visual imaging. Solar plant inspection in the future can be restricted only by imaging speed requirements, allowing an entire new perspective in large-scale PV inspection.
We demonstrate a method to quantify the extent of solar cell cracks, shunting, or damaged cell in... more We demonstrate a method to quantify the extent of solar cell cracks, shunting, or damaged cell interconnects, present in crystalline silicon photovoltaic (PV) modules by statistical analysis of the electroluminescence (EL) intensity distributions of individual cells within the module. From the EL intensity distributions (ELID) of each cell, we calculated summary statistics such as standard deviation, median, skewness and kurtosis, and analyzed how they correlate with the magnitude of the solar cell degradation. We found that the dispersion of the ELID increases with the size and severity of the solar cell cracks, correlating with an increase in standard deviation and decrease in kurtosis. For shunted cells, we found that the ELID median is strongly correlated with the extent of cell shunting. Last, cells with damaged interconnect ribbons show current crowding and increased series resistance regions, characterized by increased dispersion and skewness of the ELID. These cell-level dia...
Regular photovoltaic (PV) system inspections have become a challenge with the significant growth ... more Regular photovoltaic (PV) system inspections have become a challenge with the significant growth in thenumber of modules and peak power capacity of PV installations. Image acquisition using drones, based on visual, thermographic, and more recently luminescence, can be a viable solution for large-scale PV inspections. As luminescence can provide a highly detailed and accurate PV module failure diagnosis, the development of a daylight electroluminescence (EL) imaging capability is of high importance. EL imaging performed in the field during the day requires the enhancement of the relatively weak luminescence signal over the noise from the sun. This is accomplished by image averaging and background subtraction, which requires the highly accurate registering of the of individual module images. A sequential EL image acquisition at high frame rates in continuous motion at different angles will be the realworld situation for a drone-based PV inspection in daylight, and to account for this ...
Electroluminescence (EL) imaging of photovoltaic (PV) modules requires careful selection of the m... more Electroluminescence (EL) imaging of photovoltaic (PV) modules requires careful selection of the measurement parameters, such as camera sensor exposure time, for obtaining accurate images for qualitative and quantitative evaluation of PV module failures. In this work we propose a method for determining the optimal exposure time for EL imaging of PV panels, with respect to maximizing the utilized camera sensitivity range and limiting image saturation due to overexposure. The method requires two EL measurements of the PV panel, at two distinct exposure times, that are used to calculate the optimal exposure time. The main advantage of the proposed method over manually calibrating the exposure time of the camera is that it can be integrated in the camera image acquisition application, for automatic exposure time calibration.
In this paper, we present a study of daylight EL acquisition and the results of a sunlight variat... more In this paper, we present a study of daylight EL acquisition and the results of a sunlight variation study in a scenario necessary to assure the increase of EL image quality with denoising by averaging for the robustness of the drone system when bright and intermittently cloudy days occur. It was verified that the indicator of image quality based on the signal-to-noise ratio of EL images has a linear behavior with the amount of averaged images when there is no sun variation. When there are sun irradiance variation, it is observed that the quality decrease even with the increased number of images being averaged, turning to increase again only with further additional images.
A wide range of defects, failures, and degradation can develop at different stages in the lifetim... more A wide range of defects, failures, and degradation can develop at different stages in the lifetime of photovoltaic modules. To accurately assess their effect on the module performance, these failures need to be quantified. Electroluminescence (EL) imaging is a powerful diagnostic method, providing high spatial resolution images of solar cells and modules. EL images allow the identification and quantification of different types of failures, including those in high recombination regions, as well as series resistance-related problems. In this study, almost 46,000 EL cell images are extracted from photovoltaic modules with different defects. We present a method that extracts statistical parameters from the histogram of these images and utilizes them as a feature descriptor. Machine learning algorithms are then trained using this descriptor to classify the detected defects into three categories: (i) cracks (Mode B and C), (ii) micro-cracks (Mode A) and finger failures, and (iii) no failu...
The number of photovoltaic panels installed globally is continuously growing, requiring an automa... more The number of photovoltaic panels installed globally is continuously growing, requiring an automatic inspection procedure for operation and maintenance. Drones can be a useful tool to this aim as they enable fast acquisition of various imaging modalities: visual, infrared, or electroluminescence (EL). Image distortions due to perspective must be corrected to allow further automatic processing. It can be done by estimating the corresponding rotation angles to control the camera gimbal or as postprocessing to rectify the images. This article presents two methods to achieve both goals by identifying known points in the acquired image. The first method detects the four panel corners, whereas the second method finds the corners of each cell. The performance evaluation is performed first quantitatively on a validation dataset composed of 113 EL images and their corresponding ground-truth orientations. A qualitative evaluation shows satisfying performance of the rectification similarly for both methods. The quantitative performance is varying for each rotation axis. The average absolute error is 2.78 • along the x-axis, 2.64 • along the y-axis, and 1.28 • along the z-axis for the panel method and 3.26 • , 2.05 • , and 1.24 • for the cell method. As a proof of concept, a final test on drone-acquired EL images shows good performance for the image rectification in real-life conditions.
Electroluminescence (EL) imaging is a PV module characterization technique, which provides high a... more Electroluminescence (EL) imaging is a PV module characterization technique, which provides high accuracy in detecting defects and faults such as cracks, broken cells interconnections, shunts, among many others; furthermore, the EL technique is used extensively due to a high level of detail and direct relationship to injected carrier density. However, this technique is commonly practiced only indoors-or outdoors from dusk to dawn-because the crystalline silicon luminescence signal is several orders of magnitude lower than sunlight. This limits the potential of such a powerful technique to be used in utility scale inspections, and therefore the interest in the development of electrical biasing tools to make outdoor EL imaging truly fast and efficient. With the focus of quickly acquiring EL images in daylight, we present in this article a drone-based system capable of acquiring EL images at a framerate of 120 frames per second. In a single second during high irradiance conditions, this system can capture enough EL and background image pairs to create an EL PV module image that has sufficient diagnostic information to identify faults associated with power loss. The final EL images shown in this work reached representative quality SNRAVG of 4.6, obtained with algorithms developed in previous works. These drone-based EL images were acquired with global horizontal solar irradiance close to one sun in the plane of the array.
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