Condition monitoring and early diagnostics methodologies for hydropower plants
This work addresses predictive maintenance for hydropower plant operators, but it appears incremental as it builds on existing ICT and machine learning advancements.
The authors tackled condition monitoring and early diagnostics for hydropower plants by proposing a novel Key Performance Indicator (KPI), which identified several faults over more than a year of operation and outperformed conventional methods like the Hotelling t2 index.
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 $t_2$ index.