• Energy Research

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.

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.