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

Feb 14, 2022

AUTHORS
Antonio Piazzi, Mauro Tucci, Fabrizio Ruffini, Emanuele Crisostomi

ABSTRACT
This work presents the results of the applicationto four hydropower plants in Europe, with a total power of1.4GW, of a recently developed monitoring and early diagnosticmethodology. The innovative approach is based on data-drivenand machine learning tools, such as Self-Organizing Maps,allowing an unsupervised learning of the global health stateof the plant, and, at the same time, allowing to discriminatethe plant variables involved in a faulty behaviour. A number ofrelevant incipient malfunctions were detected in early stage byour approach, during one year of operation in four plants, whichare of different size and use different technologies. The feedbackfrom the plant operators was very positive, with respect to thecapacity of the system to reveal incipient faults, which were, inmost cases, not properly detected by the traditional monitoringsystems installed in the plants.Index Terms—Hydropower plants, Self-Organizing Maps, ma-chine learning, condition monitoring, early detection.

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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.

MOWGLI (MicrO reneWable Grid for ruraL Indian areas) project on ETN magazine

MOWGLI (MicrO reneWable Grid for ruraL Indian areas) project on ETN magazine

Oct 15, 2019

AUTHORS:
Ciro Lanzetta and Fabrizio Ruffini.
ABSTRACT:
The Mowgli feasibility study started in 2018 funded by the European Space Agency (ESA) with the involvement of the India Energy Storage Alliance (IESA) as stakeholder. The aim is to evaluate the technical and economic feasibility of satellite-based services for microgrids. Designed by i-EM, Mowgli is a solution that provides a set of services for optimal microgrid planning, designing and operational and maintenance (O&M) applications in urban and rural areas developing countries, with focus on India as a user case.

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