A Machine Learning and Internet of Things-Based Online Fault Diagnosis Method for Photovoltaic Arrays
Adel Mellit,
Omar Herrak,
Catalina Rus Casas and
Alessandro Massi Pavan
Additional contact information
Adel Mellit: Renewable Energy Laboratory, Jijel University, Jijel 18000, Algeria
Omar Herrak: Renewable Energy Laboratory, Jijel University, Jijel 18000, Algeria
Catalina Rus Casas: Departamento de Ingeniería Electrónica y Automática, Universidad de Jaén, 23071 Jaén, Spain
Alessandro Massi Pavan: Department of Engineering and Architecture, Center for Energy, Environment and Transport Giacomo Ciamician, University of Trieste, 34127 Trieste, Italy
Sustainability, 2021, vol. 13, issue 23, 1-14
Abstract:
In this paper, a novel fault detection and classification method for photovoltaic (PV) arrays is introduced. The method has been developed using a dataset of voltage and current measurements (I–V curves) which were collected from a small-scale PV system at the RELab, the University of Jijel (Algeria). Two different machine learning-based algorithms have been used in order to detect and classify the faults. An Internet of Things-based application has been used in order to send data to the cloud, while the machine learning codes have been run on a Raspberry Pi 4. A webpage which shows the results and informs the user about the state of the PV array has also been developed. The results show the ability and the feasibility of the developed method, which detects and classifies a number of faults and anomalies (e.g., the accumulation of dust on the PV module surface, permanent shading, the disconnection of a PV module, and the presence of a short-circuited bypass diode in a PV module) with a pretty good accuracy (98% for detection and 96% classification).
Keywords: photovoltaic array; machine learning; Internet of Things; fault detection; fault classification (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)
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Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:13:y:2021:i:23:p:13203-:d:690524
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