Condition monitoring and early diagnostics methodologies for hydropower plants

TL;DR

This paper introduces a novel KPI for hydropower plant condition monitoring, demonstrating its effectiveness in fault detection and maintenance support over a year of real-world operation, outperforming traditional control charts.

Contribution

The paper presents a new KPI for hydropower plants that enhances fault detection and maintenance, validated through long-term real-world testing.

Findings

The KPI successfully identified multiple faults in operating hydropower plants.

It outperformed conventional control charts like Hotelling t2.

The KPI supports operational and maintenance decision-making.

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.