Prognostic and Health Management in Ocean Energy System A Self-Healing
Framework based on Reinforcement Learning
Abstract
In this paper, for minimizing the cost from the ocean generator power
production by optimizing the operation and maintenance (O&M) policy
over an infinite time horizon, while considering the uncertainty of the
renewable sources and components failure behaviors, we develop a
self-healing framework for ocean energy systems. It consists of three
major modules: data manipulation, health assessment, and
decision-making. Specifically, a graph-theoretic approach is first
proposed for ocean generator health monitoring utilizing multivariate
time-series data, then, reinforcement learning (RL) based technique
exploits the health states of the system that provides decision support
for optimal O&M management.