A scalable predictive maintenance model for detecting wind turbine component failures based on SCADA data

Jun 8, 2019

AUTHORS
Lorenzo Gigoni, Alessandro Betti, Emanuele Crisostomi and Mauro Tucci.

ABSTRACT
This work presents a novel Predictive Maintenance model for the main Wind Turbine components based on SCADA data and combination of Machine Learning and Classical Statistical approach. According to the impact of maintenance cost on the wind power Levelized Cost of Energy (LCOE), the components Gearbox, Generator, and Main Bearing have been modelled. The model was trained on historical nominal behavior periods of components specific SCADA tags. Test campaign was divided in two stages: test on historical faults for model training and validation, and Real-Time test for proper integration in the plant operators’ activities. Test on historical faults of six wind farms located in Italy and Romania, corresponding to an overall installed nominal power of 283 MW, demonstrate outstanding capabilities of anomaly prediction up to 2 months before device unscheduled downtime. Additionally, test on 12- months Real-Time phase confirm its ability to detect several anomalies, therefore allowing the operators to identify root case and schedule maintenance before reaching the catastrophic stage.

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One year Operation of an Innovative Condition Monitoring Technique in Four Hydropower Plants

One year Operation of an Innovative Condition Monitoring Technique in Four Hydropower Plants

Feb 14, 2022

AUTHORS:
Lorenzo Gigoni, Alessandro Betti, Emanuele Crisostomi, Alessandro Franco, Mauro Tucci, Fabrizio Bizzarri and Debora Mucci.
ABSTRACT:
The ability to accurately forecast power generation from renewable sources is nowadays recognized 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 (PV) plants of different sizes and technology over a whole year. Also, we try to evaluate the impact of weather conditions and weather forecasts on the prediction of PV power generation.

Web tools concerning performance analysis and planning support for solar energy plants starting from remotely sensed optical images

Web tools concerning performance analysis and planning support for solar energy plants starting from remotely sensed optical images

Feb 14, 2022

AUTHORS:
Lorenzo Gigoni, Alessandro Betti, Emanuele Crisostomi, Alessandro Franco, Mauro Tucci, Fabrizio Bizzarri and Debora Mucci.
ABSTRACT:
The ability to accurately forecast power generation from renewable sources is nowadays recognized 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 (PV) plants of different sizes and technology over a whole year. Also, we try to evaluate the impact of weather conditions and weather forecasts on the prediction of PV power generation.

NREL Best Practices Handbook for the Collection and Use of Solar Resource Data for Solar Energy Applications

NREL Best Practices Handbook for the Collection and Use of Solar Resource Data for Solar Energy Applications

Apr 27, 2021

AUTHORS:
Lorenzo Gigoni, Alessandro Betti, Emanuele Crisostomi, Alessandro Franco, Mauro Tucci, Fabrizio Bizzarri and Debora Mucci.
ABSTRACT:
The ability to accurately forecast power generation from renewable sources is nowadays recognized 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 (PV) plants of different sizes and technology over a whole year. Also, we try to evaluate the impact of weather conditions and weather forecasts on the prediction of PV power generation.

Condition monitoring and early diagnostics methodologies for hydropower plants

Condition monitoring and early diagnostics methodologies for hydropower plants

Nov 15, 2019

AUTHORS:
Alessandro Betti, Emanuele Crisostomi, Gianluca Paolinelli, Antonio Piazzi, Fabrizio Ruffini and Mauro Tucci.
ABSTRACT:
Hydropower plants are one of the most convenient option for power generation, as they generate energy exploiting a renewable source, they have relatively low operating and maintenance costs, and they may be used to provide ancillary services, exploiting the large reservoirs of available water. The recent advances in Information and Communication Technologies (ICT) and in machine learning methodologies are seen as fundamental enablers to upgrade and modernize the current operation of most hydropower plants, in terms of condition monitoring, early diagnostics and eventually predictive maintenance. While very few works, or running technologies, have been documented so far for the hydro case, in this paper we propose a novel Key Performance Indicator (KPI) that we have recently developed and tested on operating hydropower plants. In particular, we show that after more than one year of operation it has been able to identify several faults, and to support the operation and maintenance tasks of plant operators. Also we show that the proposed KPI outperforms conventional multivariable process control charts, like the Hotelling t2 index.

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