A hydrogenerator model-based failure detection framework to support asset management

Abstract

Electrical utilities in North America significantly increased their installed capacities between 1960 and 1990. This ageing fleet is now forcing the producers to begin to use a holistic asset management in a more systematic way by introducing diagnostic and prognostic tools to support them in their decision-making process. For the last few decades, the Hydro-Quebec Research Institute has been working to understand ageing mechanisms and developing a diagnostic and prognostic causal graph model for hydrogenerators based on expert knowledge and diagnostic data. This paper proposes asset and asset system metrics based on graph theory to estimate the probability of detecting a failure using the number of detectable early warning signs. Proposed indicators intend to inform operators and decision makers on the failure detection probability for each individual asset and to identify critical failure detection of assets at an asset system level. An analysis has been carried out on a real hydropower plant for each of its sixteen hydrogenerators. Some results will be presented and critical failure detection rates for hydrogenerators will be identified. A framework will be proposed to improve asset management.