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A Machine Learning and Internet of Things-Based Online Fault Diagnosis Method for Photovoltaic Arrays
| Content Provider | MDPI |
|---|---|
| Author | Mellit, Adel Herrak, Omar Casas, Catalina Rus Pavan, Alessandro Massi |
| Copyright Year | 2021 |
| Description | 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). |
| Starting Page | 13203 |
| e-ISSN | 20711050 |
| DOI | 10.3390/su132313203 |
| Journal | Sustainability |
| Issue Number | 23 |
| Volume Number | 13 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2021-11-29 |
| Access Restriction | Open |
| Subject Keyword | Sustainability Industrial Engineering Photovoltaic Array Machine Learning Internet of Things Fault Detection Fault Classification |
| Content Type | Text |
| Resource Type | Article |
