A decentralized algorithm to help drivers find the best charging station
Energy
e-Vehicles home-charging: an algorithm to protect power systems
Home charging stations and power system stability
Looking at the Sky for Data
Satellites: what they are and how we use them
WORD OF THE DAY: Satellite
A brief recap of satellites world
WORD OF THE DAY: PR (Performance Ratio)
Floating Photovoltaic applications state of the art
Ollague (Chile): hybrid plant management challenge
Innovation and sustainability even under extreme conditions: photovoltaic and wind power and storage, microgrid...
Big Data Hydro: the digital future for hydro power plants
Hydropower fleet value creation by data gathering combined to big data analytics development and application on pilot...
Data Analysis for Solar Forecasting: “(I) Miss Sunshine!”. How to deliver the optimum output from Solar Forecasting
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Data Analysis: “The Treasure Hunt”. Uncover hidden gems in your data
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SP4GO services: focus on satellite backup Communication System
Goal This solution is a backup platform for normal and emergency scenarios capable to answer the following needs: i)...
SP4GO services: focus on smart grid and e-Mobility
Goal The g-EM smart grid area-solution is developed to tackle many challenges related to the smart grid context, such...
SP4GO services: focus on Consumption monitoring system
Goal This solution is a monitoring service of the electrical consumption on the distribution grid. To avoid...
SP4GO services: focus on Vegetation monitoring system
This service will provide a risk management tool to avoid incidents due to the vegetation close-by to the power lines...
Condition monitoring and early diagnostics methodologies for hydropower plants
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.
So far, so good. But: failure is coming… Predictive maintenance for PV solar assets
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