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.

Giovanni Cipriani, Antonino D’Amico, Stefania Guarino, Donatella Manno, Marzia Traverso, & Vincenzo Di Dio (2020). Convolutional Neural Network for Dust and Hotspot Classification in PV Modules. ENERGIES, 13(23) [10.3390/en13236357].

Convolutional Neural Network for Dust and Hotspot Classification in PV Modules

Giovanni Cipriani;Antonino D’Amico;Stefania Guarino;Donatella Manno;Vincenzo Di Dio
2020

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.
Settore ING-IND/32 - Convertitori, Macchine E Azionamenti Elettrici
Settore ING-IND/11 - Fisica Tecnica Ambientale
Giovanni Cipriani, Antonino D’Amico, Stefania Guarino, Donatella Manno, Marzia Traverso, & Vincenzo Di Dio (2020). Convolutional Neural Network for Dust and Hotspot Classification in PV Modules. ENERGIES, 13(23) [10.3390/en13236357].
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/10447/466959
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