| KEYWORD |
Use of Self-Organizing Maps (SOM) for degradation analysis of solar photovoltaic power generation systems
Reference persons BARTOLOMEO MONTRUCCHIO
Description The objective of the present research work concerns the investigation of the potential of Self-Organizing Maps (SOM), an unsupervised learning technique belonging to Machine Learning, for Condition-Based Maintenance (CBM) of solar photovoltaic power generation systems of utility-scale, i.e., 1 MW or more. In particular, they will be attempted to be used to:
Recognize and identify periods of poor performance.
Recognize and identify performance trends.
Detect any anomalies.
The thesis will take place at the Sirius s.r.l. company, located in via Frejus 106, Turin (https://www.sirius.to.it/). Basic knowledge of Python and machine learning is required.
For more information, don't hesitate to get in touch with Bartolomeo Montrucchio, Antonio Marceddu, Matteo Di Salvo, and Fabio Bima at the following emails:
bartolomeo.montrucchio@polito.it
antonio.marceddu@polito.it
disalvo@sirius-ea.com
bima@sirius-ea.com
Deadline 18/07/2025
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