Condition monitoring and predictive maintenance methodologies for hydropower plants equipment. (June 2021)

• Record Type: Journal Article
• Authors: Betti, Alessandro; Crisostomi, Emanuele; Paolinelli, Gianluca; Piazzi, Antonio; Ruffini, Fabrizio; Tucci, Mauro
• Is Part Of: Renewable energy. Volume 171(2021)
• Page Start: 246
• Page End: 253
• Publication Date: 2021-06
• Subjects: Hydropower plants -- Self-organizing maps -- Control charts -- Machine learning -- Neural networks -- Condition monitoring; Renewable energy sources -- Periodicals; Power resources -- Periodicals; Énergies renouvelables -- Périodiques; Ressources énergétiques -- Périodiques; 333.794
• DOI: 10.1016/j.renene.2021.02.102
• Languages: English
• ISSNs: 0960-1481

Abstract

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 t 2 index. Highlights: A novel SOM-based indicator is proposed for condition monitoring in hydropower plants. The indicator detects anomalous operating conditions and specific faulty components. The proposed methodology is tested in a real case study for more than one year. The proposed KPI outperforms classic process control tools such as Hotelling charts. It is a first step for digitalizated and unsupervised operation of hydropower plants.