Dr. Alessandro Betti
FlySight srl
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10Years (2017-Present)
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Representative Publications
View All Publications12 October 2017 in IEEE Transactions on Sustainable Energy
Journal Article
LGLorenzo Gigoni
Lorenzo GigoniAlessandro BettiECEmanuele Crisostomi
Emanuele Crisostomi
Alessandro FrancoMTMauro Tucci
Mauro TucciDMDebora Mucci
Debora Mucci +1
The ability to accurately forecast power generation from renewable sources is nowadays recognised as a fundamental skill to improve the operation of power systems. Despite the general interest of the power community in this topic, it is not always simple to compare different forecasting methodologies, and infer the impact of single components in providing accurate predictions. In this paper we extensively compare simple forecasting methodologies with more sophisticated ones over 32 photovoltaic plants of different size and technology over a whole year. Also, we try to evaluate the impact of weather forecasts on the prediction of PV power generation.
Journal Article
Alessandro BettiMTMauro Tucci
Mauro TucciECEmanuele Crisostomi
Emanuele CrisostomiAPAntonio Piazzi
Antonio PiazziSBSami Barmada
Sami BarmadaDTDimitri Thomopulos
Dimitri ThomopulosIn this paper, a novel and flexible solution for fault prediction based on data collected from Supervisory Control and Data Acquisition (SCADA) system is presented. Generic fault/status prediction is offered by means of a data driven approach based on a self-organizing map (SOM) and the definition of an original Key Performance Indicator (KPI). The model has been assessed on a park of three photovoltaic (PV) plants with installed capacity up to 10 MW, and on more than sixty inverter modules of three different technology brands. The results indicate that the proposed method is effective in predicting incipient generic faults in average up to 7 days in advance with true positives rate up to 95%. The model is easily deployable for on-line monitoring of anomalies on new PV plants and technologies, requiring only the availability of historical SCADA data, fault taxonomy and inverter electrical datasheet.
Preprint
Alessandro BettiMTMauro Tucci
Mauro TucciECEmanuele Crisostomi
Emanuele CrisostomiAPAntonio Piazzi
Antonio PiazziSBSami Barmada
Sami BarmadaDTDimitri Thomopulos
Dimitri ThomopulosIn this paper a novel and flexible solution for fault prediction based on data collected from Supervisory Control and Data Acquisition (SCADA) system is presented. Generic fault/status prediction is offered by means of a data driven approach based on a self-organizing map (SOM)and the definition of an original Key Performance Indicator (KPI). The model has been assessed on a park of three photovoltaic (PV) plants with installed capacity up to 10 MW, and on more than sixty inverter modules of three different technology brands. The results indicate that the proposed method is effective in predicting incipient generic faults in average up to 7 days in advance with true positives rate up to 95%. The model is easily deployable for on-line monitoring of anomalies on new PV plants and technologies, requiring only the availability of historical SCADA data, fault taxonomy and inverter electrical datasheet.
About Me
Dr. Alessandro Betti received the M.S. degree in physics and the Ph.D. degree in electronics engineering from the University of Pisa, Italy, in 2007 and 2011, respectively. His main field of research was modeling of noise and transport in quasi-one dimensional devices. His work has been published in more than 25 papers published in peer-reviewed journals in the field of solid state electronics and condensed matter physics and in conference papers, presenting for 3 straight years his research at the top IEEE International Conference in electron devices IEDM in USA. From February 2021 he works as a senior data scientist and R&D Manager at the company FlySight srl (Flyby Group) with main interest in the application of Deep Learning methods in energy, defense and health sectors. He has also a many year experience in developing power generation forecasting, nowcasting and predictive maintenance models based on Machine Learning algorithms, achieved in his previous experience at the company i-EM srl (Flyby Group).