A State-Age-Dependent Opportunistic Intelligent Maintenance Framework for Wind Turbines Under Dynamic Wind Conditions

Abstract

Intelligent maintenance powered by advanced sensor technology is crucial to ensure 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 paper addresses such challenges by constructing a dynamic age-state-dependent intelligent opportunistic maintenance framework, which is capable of integrating (a) degradation and age state, (b) estimation of remaining lifetime, (c) both the positive (extra maintenance opportunities) and negative impacts (maintenance delays) of wind conditions. Specially, component-level maintenance is allowed 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.