This paper proposes a novel prognostics-driven approach to optimize
operations and maintenance (O&M) decisions in hydropower systems. Our approach
harnesses the insights from sensor data to accurately predict the remaining
lifetime distribution of critical generation assets in hydropower systems,
i.e., thrust bearings, and use these predictions to optimally schedule O&M
actions for a fleet of hydro generators. We consider complex interdependencies
across hydro generator failure risks, reservoir, production, and demand
management decisions. We propose a stochastic joint O&M scheduling model to
tackle the unique challenges of hydropower O&M including the interdependency of
generation capacities, the nonlinear nature of power production, operational
requirements, and uncertainties. We develop a two-level decomposition-based
solution algorithm to effectively handle large-scale cases. The algorithm
incorporates a combination of Benders optimality cuts and integer cuts to solve
the problem in an efficient manner. We design an experimental framework to
evaluate the proposed prognostics-driven O&M scheduling framework, using
real-world condition monitoring data from hydropower systems, historical market
prices, and water inflow data. The developed framework can be partially
implemented for a phased-in approach. Our experiments demonstrate the
significant benefits of the sensor-driven O&M framework in improving
reliability, availability, effective usage of resources, and system
profitability, especially when gradually shifting from traditional time-based
maintenance policies to condition-based prognostics-driven maintenance
policies.

Este artículo explora los viajes en el tiempo y sus implicaciones.

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