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Dr. Harsh Parikh
Aalborg University

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Research Keywords & Expertise

0 Electroluminescence
0 Photoluminescence
0 fault diagnosis
0 machine learning
0 Modular Multilevel Converters

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Journal article
Published: 10 December 2020 in Applied Sciences
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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 failures. By comparing the developed methods with the commonly used one, this study demonstrates that the pre-processing of images into a feature vector of statistical parameters provides a higher classification accuracy than would be obtained by raw images alone. The proposed method can autonomously detect cracks and finger failures, enabling outdoor EL inspection using a drone-mounted system for quick assessments of photovoltaic fields.

ACS Style

Harsh Rajesh Parikh; Yoann Buratti; Sergiu Spataru; Frederik Villebro; Gisele Alves Dos Reis Benatto; Peter B. Poulsen; Stefan Wendlandt; Tamas Kerekes; Dezso Sera; Ziv Hameiri. Solar Cell Cracks and Finger Failure Detection Using Statistical Parameters of Electroluminescence Images and Machine Learning. Applied Sciences 2020, 10, 8834 .

AMA Style

Harsh Rajesh Parikh, Yoann Buratti, Sergiu Spataru, Frederik Villebro, Gisele Alves Dos Reis Benatto, Peter B. Poulsen, Stefan Wendlandt, Tamas Kerekes, Dezso Sera, Ziv Hameiri. Solar Cell Cracks and Finger Failure Detection Using Statistical Parameters of Electroluminescence Images and Machine Learning. Applied Sciences. 2020; 10 (24):8834.

Chicago/Turabian Style

Harsh Rajesh Parikh; Yoann Buratti; Sergiu Spataru; Frederik Villebro; Gisele Alves Dos Reis Benatto; Peter B. Poulsen; Stefan Wendlandt; Tamas Kerekes; Dezso Sera; Ziv Hameiri. 2020. "Solar Cell Cracks and Finger Failure Detection Using Statistical Parameters of Electroluminescence Images and Machine Learning." Applied Sciences 10, no. 24: 8834.

Journal article
Published: 10 September 2020 in IEEE Journal of Photovoltaics
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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 $^{\circ }$ along the $x$ -axis, 2.64 $^{\circ }$ along the $y$ -axis, and 1.28 $^{\circ }$ along the $z$ -axis for the panel method and 3.26 $^{\circ }$ , 2.05 $^{\circ }$ , and 1.24 $^{\circ }$ 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.

ACS Style

Claire Mantel; Frederik Villebro; Harsh Rajesh Parikh; Sergiu Spataru; Gisele A. Dos Reis Benatto; Dezso Sera; Peter B. Poulsen; Soren Forchhammer. Method for Estimation and Correction of Perspective Distortion of Electroluminescence Images of Photovoltaic Panels. IEEE Journal of Photovoltaics 2020, 10, 1797 -1802.

AMA Style

Claire Mantel, Frederik Villebro, Harsh Rajesh Parikh, Sergiu Spataru, Gisele A. Dos Reis Benatto, Dezso Sera, Peter B. Poulsen, Soren Forchhammer. Method for Estimation and Correction of Perspective Distortion of Electroluminescence Images of Photovoltaic Panels. IEEE Journal of Photovoltaics. 2020; 10 (6):1797-1802.

Chicago/Turabian Style

Claire Mantel; Frederik Villebro; Harsh Rajesh Parikh; Sergiu Spataru; Gisele A. Dos Reis Benatto; Dezso Sera; Peter B. Poulsen; Soren Forchhammer. 2020. "Method for Estimation and Correction of Perspective Distortion of Electroluminescence Images of Photovoltaic Panels." IEEE Journal of Photovoltaics 10, no. 6: 1797-1802.

Journal article
Published: 19 March 2020 in IEEE Journal of Photovoltaics
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Electroluminescence (EL) imaging is a photovoltaic (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 frame rate 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 ${rm{SNR_{AVG}}$ 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.

ACS Style

Gisele Alves Dos Reis Benatto; Claire Mantel; Sergiu Spataru; Adrian Alejo Santamaria Lancia; Nicholas Riedel; Sune Thorsteinsson; Peter Behrensdorff Poulsen; Harsh Parikh; Soren Forchhammer; Dezso Sera. Drone-Based Daylight Electroluminescence Imaging of PV Modules. IEEE Journal of Photovoltaics 2020, 10, 872 -877.

AMA Style

Gisele Alves Dos Reis Benatto, Claire Mantel, Sergiu Spataru, Adrian Alejo Santamaria Lancia, Nicholas Riedel, Sune Thorsteinsson, Peter Behrensdorff Poulsen, Harsh Parikh, Soren Forchhammer, Dezso Sera. Drone-Based Daylight Electroluminescence Imaging of PV Modules. IEEE Journal of Photovoltaics. 2020; 10 (3):872-877.

Chicago/Turabian Style

Gisele Alves Dos Reis Benatto; Claire Mantel; Sergiu Spataru; Adrian Alejo Santamaria Lancia; Nicholas Riedel; Sune Thorsteinsson; Peter Behrensdorff Poulsen; Harsh Parikh; Soren Forchhammer; Dezso Sera. 2020. "Drone-Based Daylight Electroluminescence Imaging of PV Modules." IEEE Journal of Photovoltaics 10, no. 3: 872-877.

Conference paper
Published: 01 June 2018 in 2018 IEEE 7th World Conference on Photovoltaic Energy Conversion (WCPEC) (A Joint Conference of 45th IEEE PVSC, 28th PVSEC & 34th EU PVSEC)
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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.

ACS Style

Claire Mantel; Sergiu Spataru; Harsh Parikh; Dezso Sera; Gisele Alves Dos Reis Benatto; Nicholas Riedel; Sune Thorsteinsson; Peter Behrensdorff Poulsen; Søren Forchhammer. Correcting for Perspective Distortion in Electroluminescence Images of Photovoltaic Panels. 2018 IEEE 7th World Conference on Photovoltaic Energy Conversion (WCPEC) (A Joint Conference of 45th IEEE PVSC, 28th PVSEC & 34th EU PVSEC) 2018, 0433 -0437.

AMA Style

Claire Mantel, Sergiu Spataru, Harsh Parikh, Dezso Sera, Gisele Alves Dos Reis Benatto, Nicholas Riedel, Sune Thorsteinsson, Peter Behrensdorff Poulsen, Søren Forchhammer. Correcting for Perspective Distortion in Electroluminescence Images of Photovoltaic Panels. 2018 IEEE 7th World Conference on Photovoltaic Energy Conversion (WCPEC) (A Joint Conference of 45th IEEE PVSC, 28th PVSEC & 34th EU PVSEC). 2018; ():0433-0437.

Chicago/Turabian Style

Claire Mantel; Sergiu Spataru; Harsh Parikh; Dezso Sera; Gisele Alves Dos Reis Benatto; Nicholas Riedel; Sune Thorsteinsson; Peter Behrensdorff Poulsen; Søren Forchhammer. 2018. "Correcting for Perspective Distortion in Electroluminescence Images of Photovoltaic Panels." 2018 IEEE 7th World Conference on Photovoltaic Energy Conversion (WCPEC) (A Joint Conference of 45th IEEE PVSC, 28th PVSEC & 34th EU PVSEC) , no. : 0433-0437.

Conference paper
Published: 01 June 2018 in 2018 IEEE 7th World Conference on Photovoltaic Energy Conversion (WCPEC) (A Joint Conference of 45th IEEE PVSC, 28th PVSEC & 34th EU PVSEC)
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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.

ACS Style

Gisele Alves Dos Reis Benatto; Claire Mantel; Nicholas Riedel; Adrian A. Santamaria Lancia; Sune Thorsteinsson; Peter Behrensdorff Poulsen; Søren Forchhammer; Anders Thorseth; Carsten Dam-Hansen; Kenn H. B. Frederiksen; Jan Vedde; Michael Larsen; Henrik Voss; Harsh Parikh; Sergiu Spataru; Dezso Sera. Outdoor electroluminescence acquisition using a movable testbed. 2018 IEEE 7th World Conference on Photovoltaic Energy Conversion (WCPEC) (A Joint Conference of 45th IEEE PVSC, 28th PVSEC & 34th EU PVSEC) 2018, 0400 -0404.

AMA Style

Gisele Alves Dos Reis Benatto, Claire Mantel, Nicholas Riedel, Adrian A. Santamaria Lancia, Sune Thorsteinsson, Peter Behrensdorff Poulsen, Søren Forchhammer, Anders Thorseth, Carsten Dam-Hansen, Kenn H. B. Frederiksen, Jan Vedde, Michael Larsen, Henrik Voss, Harsh Parikh, Sergiu Spataru, Dezso Sera. Outdoor electroluminescence acquisition using a movable testbed. 2018 IEEE 7th World Conference on Photovoltaic Energy Conversion (WCPEC) (A Joint Conference of 45th IEEE PVSC, 28th PVSEC & 34th EU PVSEC). 2018; ():0400-0404.

Chicago/Turabian Style

Gisele Alves Dos Reis Benatto; Claire Mantel; Nicholas Riedel; Adrian A. Santamaria Lancia; Sune Thorsteinsson; Peter Behrensdorff Poulsen; Søren Forchhammer; Anders Thorseth; Carsten Dam-Hansen; Kenn H. B. Frederiksen; Jan Vedde; Michael Larsen; Henrik Voss; Harsh Parikh; Sergiu Spataru; Dezso Sera. 2018. "Outdoor electroluminescence acquisition using a movable testbed." 2018 IEEE 7th World Conference on Photovoltaic Energy Conversion (WCPEC) (A Joint Conference of 45th IEEE PVSC, 28th PVSEC & 34th EU PVSEC) , no. : 0400-0404.

Conference paper
Published: 01 June 2018 in 2018 IEEE 7th World Conference on Photovoltaic Energy Conversion (WCPEC) (A Joint Conference of 45th IEEE PVSC, 28th PVSEC & 34th EU PVSEC)
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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 SNR 50 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.

ACS Style

Claire Mantel; Gisele Alves Dos Reis Benatto; Nicholas Riedel; Sune Thorsteinsson; Peter Behrensdorff Poulsen; Harsh Parikh; Sergiu Spataru; Dezso Sera; Søren Forchhammer. SNR Study of Outdoor Electroluminescence Images under High Sun Irradiation. 2018 IEEE 7th World Conference on Photovoltaic Energy Conversion (WCPEC) (A Joint Conference of 45th IEEE PVSC, 28th PVSEC & 34th EU PVSEC) 2018, 3285 -3289.

AMA Style

Claire Mantel, Gisele Alves Dos Reis Benatto, Nicholas Riedel, Sune Thorsteinsson, Peter Behrensdorff Poulsen, Harsh Parikh, Sergiu Spataru, Dezso Sera, Søren Forchhammer. SNR Study of Outdoor Electroluminescence Images under High Sun Irradiation. 2018 IEEE 7th World Conference on Photovoltaic Energy Conversion (WCPEC) (A Joint Conference of 45th IEEE PVSC, 28th PVSEC & 34th EU PVSEC). 2018; ():3285-3289.

Chicago/Turabian Style

Claire Mantel; Gisele Alves Dos Reis Benatto; Nicholas Riedel; Sune Thorsteinsson; Peter Behrensdorff Poulsen; Harsh Parikh; Sergiu Spataru; Dezso Sera; Søren Forchhammer. 2018. "SNR Study of Outdoor Electroluminescence Images under High Sun Irradiation." 2018 IEEE 7th World Conference on Photovoltaic Energy Conversion (WCPEC) (A Joint Conference of 45th IEEE PVSC, 28th PVSEC & 34th EU PVSEC) , no. : 3285-3289.

Conference paper
Published: 01 June 2017 in 2017 IEEE 44th Photovoltaic Specialist Conference (PVSC)
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In this work we investigate and present preliminary results for two methods for luminescence imaging of photovoltaic (PV) modules in outdoor conditions, with the aim of choosing the most suitable method for implementation on a drone PV plant inspection system. We examined experimentally both electroluminescence (EL) and photoluminescence (PL) PV module imaging methods under natural light conditions, and determined that fast pulsed EL imaging with InGaAs detector cameras can yield reasonably accurate results under daylight conditions. Moreover, we formulated the necessary requirement for a PL light source, which would allow PL imaging of modules under daylight conditions.

ACS Style

Gisele Alves Dos Reis Benatto; Jan Vedde; Michael Petersen; Henrik Voss; Michael Messerschmidt; Harsh Parikh; Sergiu Spataru; Dezso Sera; Nicholas Riedel; Sune Thorsteinsson; Peter Behrensdorff Poulsen; Anders Thorseth; Carsten Dam-Hansen; Claire Mantel; Søren Forchhammer; Kenn H. B. Frederiksen. Development of outdoor luminescence imaging for drone-based PV array inspection. 2017 IEEE 44th Photovoltaic Specialist Conference (PVSC) 2017, 2682 -2687.

AMA Style

Gisele Alves Dos Reis Benatto, Jan Vedde, Michael Petersen, Henrik Voss, Michael Messerschmidt, Harsh Parikh, Sergiu Spataru, Dezso Sera, Nicholas Riedel, Sune Thorsteinsson, Peter Behrensdorff Poulsen, Anders Thorseth, Carsten Dam-Hansen, Claire Mantel, Søren Forchhammer, Kenn H. B. Frederiksen. Development of outdoor luminescence imaging for drone-based PV array inspection. 2017 IEEE 44th Photovoltaic Specialist Conference (PVSC). 2017; ():2682-2687.

Chicago/Turabian Style

Gisele Alves Dos Reis Benatto; Jan Vedde; Michael Petersen; Henrik Voss; Michael Messerschmidt; Harsh Parikh; Sergiu Spataru; Dezso Sera; Nicholas Riedel; Sune Thorsteinsson; Peter Behrensdorff Poulsen; Anders Thorseth; Carsten Dam-Hansen; Claire Mantel; Søren Forchhammer; Kenn H. B. Frederiksen. 2017. "Development of outdoor luminescence imaging for drone-based PV array inspection." 2017 IEEE 44th Photovoltaic Specialist Conference (PVSC) , no. : 2682-2687.