Convolutional Neural Network for Dust and Hotspot Classification in PV Modules

Giovanni Cipriani, Antonino D’Amico, Stefania Guarino, Donatella Manno, Marzia Traverso and Vincenzo Di Dio
Additional contact information
Giovanni Cipriani: Department of Engineering, University of Palermo, 90133 Palermo, Italy
Antonino D’Amico: Department of Engineering, University of Palermo, 90133 Palermo, Italy
Stefania Guarino: Department of Engineering, University of Palermo, 90133 Palermo, Italy
Donatella Manno: Department of Engineering, University of Palermo, 90133 Palermo, Italy
Marzia Traverso: Institute of Sustainability in Civil Engineering (INaB), RWTH Aachen University, D-52074 Aachen, Germany
Vincenzo Di Dio: Department of Engineering, University of Palermo, 90133 Palermo, Italy

Energies, 2020, vol. 13, issue 23, 1-17

Abstract: This paper proposes an innovative approach to classify the losses related to photovoltaic (PV) systems, through the use of thermographic non-destructive tests (TNDTs) supported by artificial intelligence techniques. Low electricity production in PV systems can be caused by an efficiency decrease in PV modules due to abnormal operating conditions such as failures or malfunctions. The most common performance decreases are due to the presence of dirt on the surface of the module, the impact of which depends on many parameters and conditions, and can be identified through the use of the TNDTs. The proposed approach allows one to automatically classify the thermographic images from the convolutional neural network (CNN) of the system, achieving an accuracy of 98% in tests that last a couple of minutes. This approach, compared to approaches in literature, offers numerous advantages, including speed of execution, speed of diagnosis, reduced costs, reduction in electricity production losses.

Keywords: infrared thermography; diagnostics; renewable energy; photovoltaic energy; energy efficient; artificial intelligence; convolutional neural network; dust; hot spot (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
https://www.mdpi.com/1996-1073/13/23/6357/pdf (application/pdf)
https://www.mdpi.com/1996-1073/13/23/6357/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:13:y:2020:i:23:p:6357-:d:454796

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().