A State-Age-Dependent Opportunistic Intelligent Maintenance Framework for Wind Turbines Under Dynamic Wind Conditions
Li Yang, Yi Chen, Xiaobing Ma
Abstract
Intelligent maintenance powered by advanced sensor technology is crucial to ensure the safe and reliable operation of wind turbines. Most maintenance models are scheduled solely based on age/degradation conditions while ignoring the dynamics of wind conditions and residual lifetime that significantly affect maintenance executions. This article addresses such challenges by constructing a dynamic age-state-dependent intelligent opportunistic maintenance framework that is capable of integrating 1) degradation and age state, 2) estimation of remaining lifetime, and 3) both the positive (extra maintenance opportunities) and negative impacts (maintenance delays) of wind conditions. Specially, component-level maintenance is allowed to be postponed to balance lifetime extension and resource allocation, whose implementation interval is controlled by real-time estimations of lifetime and dynamic wind velocities. Moreover, both wind- and health-centered opportunistic maintenance are incorporated to mitigate power generation losses. The applicability and superiority of the proposed framework are validated by a case study on an Ontario wind farm.