Piazzi, Antonio (i-EM S.r.l), Ruffini, Fabrizio (i-EM S.r.l), Tucci, Mauro (Department of Energy, Systems, Territory and Constructions Engin), Crisostomi, Emanuele (University of Pisa)

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

Scheduled for presentation during the Regular session "Field trials, user acceptance, and experience" (WeB5), Wednesday, October 28, 2020, 16:00−16:20, Online discussion room 5

2020 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe), October 26-28, 2020, Virtual

This information is tentative and subject to change. Compiled on April 20, 2024

Abstract

This work presents the results of the application to four hydropower plants in Europe, with a total power of 1.4GW, of a recently developed monitoring and early diagnostic methodology. The innovative approach is based on data-driven and machine learning tools, such as Self-Organizing Maps, allowing an unsupervised learning of the global health state of the plant, and, at the same time, allowing to discriminate the plant variables involved in a faulty behaviour. A number of relevant incipient malfunctions were detected in early stage by our approach, during one year of operation in four plants, which are of different size and use different technologies. The feedback from the plant operators was very positive, with respect to the capacity of the system to reveal incipient faults, which were, in most cases, not properly detected by the traditional monitoring systems installed in the plants.