Fault Detection in PV Tracking Systems Using an Image Processing Algorithm Based on PCA

Photovoltaic power plants nowadays play an important role in the context of energy generation based on renewable sources. With the purpose of obtaining maximum efficiency, the PV modules of these power plants are installed in trackers. However, the mobile structure of the trackers is subject to faul...

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Autores principales:	Tito G. Amaral, Vitor Fernão Pires, Armando J. Pires
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	tracking system two-axis photovoltaic systems (pv) fault detection principal component analysis (PCA) image processing Technology T
Acceso en línea:	https://doaj.org/article/c30e61dc68a6414190c0fcad3dba841c
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spelling	oai:doaj.org-article:c30e61dc68a6414190c0fcad3dba841c2021-11-11T16:01:40ZFault Detection in PV Tracking Systems Using an Image Processing Algorithm Based on PCA10.3390/en142172781996-1073https://doaj.org/article/c30e61dc68a6414190c0fcad3dba841c2021-11-01T00:00:00Zhttps://www.mdpi.com/1996-1073/14/21/7278https://doaj.org/toc/1996-1073Photovoltaic power plants nowadays play an important role in the context of energy generation based on renewable sources. With the purpose of obtaining maximum efficiency, the PV modules of these power plants are installed in trackers. However, the mobile structure of the trackers is subject to faults, which can compromise the desired perpendicular position between the PV modules and the brightest point in the sky. So, the diagnosis of a fault in the trackers is fundamental to ensure the maximum energy production. Approaches based on sensors and statistical methods have been researched but they are expensive and time consuming. To overcome these problems, a new method is proposed for the fault diagnosis in the trackers of the PV systems based on a machine learning approach. In this type of approach the developed method can be classified into two major categories: supervised and unsupervised. In accordance with this, to implement the desired fault diagnosis, an unsupervised method based on a new image processing algorithm to determine the PV slopes is proposed. The fault detection is obtained comparing the slopes of several modules. This algorithm is based on a new image processing approach in which principal component analysis (PCA) is used. Instead of using the PCA to reduce the data dimension, as is usual, it is proposed to use it to determine the slope of an object. The use of the proposed approach presents several benefits, namely, avoiding the use of a wide range of data and specific sensors, fast detection and reliability even with incomplete images due to reflections and other problems. Based on this algorithm, a deviation index is also proposed that will be used to discriminate the panel(s) under fault. Several test cases are used to test and validate the proposed approach. From the obtained results, it is possible to verify that the PCA can successfully be adapted and used in image processing algorithms to determine the slope of the PV modules and so effectively detect a fault in the tracker, even when there are incomplete parts of an object in the image.Tito G. AmaralVitor Fernão PiresArmando J. PiresMDPI AGarticletracking systemtwo-axisphotovoltaic systems (pv)fault detectionprincipal component analysis (PCA)image processingTechnologyTENEnergies, Vol 14, Iss 7278, p 7278 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	tracking system two-axis photovoltaic systems (pv) fault detection principal component analysis (PCA) image processing Technology T
spellingShingle	tracking system two-axis photovoltaic systems (pv) fault detection principal component analysis (PCA) image processing Technology T Tito G. Amaral Vitor Fernão Pires Armando J. Pires Fault Detection in PV Tracking Systems Using an Image Processing Algorithm Based on PCA
description	Photovoltaic power plants nowadays play an important role in the context of energy generation based on renewable sources. With the purpose of obtaining maximum efficiency, the PV modules of these power plants are installed in trackers. However, the mobile structure of the trackers is subject to faults, which can compromise the desired perpendicular position between the PV modules and the brightest point in the sky. So, the diagnosis of a fault in the trackers is fundamental to ensure the maximum energy production. Approaches based on sensors and statistical methods have been researched but they are expensive and time consuming. To overcome these problems, a new method is proposed for the fault diagnosis in the trackers of the PV systems based on a machine learning approach. In this type of approach the developed method can be classified into two major categories: supervised and unsupervised. In accordance with this, to implement the desired fault diagnosis, an unsupervised method based on a new image processing algorithm to determine the PV slopes is proposed. The fault detection is obtained comparing the slopes of several modules. This algorithm is based on a new image processing approach in which principal component analysis (PCA) is used. Instead of using the PCA to reduce the data dimension, as is usual, it is proposed to use it to determine the slope of an object. The use of the proposed approach presents several benefits, namely, avoiding the use of a wide range of data and specific sensors, fast detection and reliability even with incomplete images due to reflections and other problems. Based on this algorithm, a deviation index is also proposed that will be used to discriminate the panel(s) under fault. Several test cases are used to test and validate the proposed approach. From the obtained results, it is possible to verify that the PCA can successfully be adapted and used in image processing algorithms to determine the slope of the PV modules and so effectively detect a fault in the tracker, even when there are incomplete parts of an object in the image.
format	article
author	Tito G. Amaral Vitor Fernão Pires Armando J. Pires
author_facet	Tito G. Amaral Vitor Fernão Pires Armando J. Pires
author_sort	Tito G. Amaral
title	Fault Detection in PV Tracking Systems Using an Image Processing Algorithm Based on PCA
title_short	Fault Detection in PV Tracking Systems Using an Image Processing Algorithm Based on PCA
title_full	Fault Detection in PV Tracking Systems Using an Image Processing Algorithm Based on PCA
title_fullStr	Fault Detection in PV Tracking Systems Using an Image Processing Algorithm Based on PCA
title_full_unstemmed	Fault Detection in PV Tracking Systems Using an Image Processing Algorithm Based on PCA
title_sort	fault detection in pv tracking systems using an image processing algorithm based on pca
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/c30e61dc68a6414190c0fcad3dba841c
work_keys_str_mv	AT titogamaral faultdetectioninpvtrackingsystemsusinganimageprocessingalgorithmbasedonpca AT vitorfernaopires faultdetectioninpvtrackingsystemsusinganimageprocessingalgorithmbasedonpca AT armandojpires faultdetectioninpvtrackingsystemsusinganimageprocessingalgorithmbasedonpca
_version_	1718432438083911680

Fault Detection in PV Tracking Systems Using an Image Processing Algorithm Based on PCA

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