Evidence-Calibrated Work-Order Prioritization for Wind-Turbine Fleets

Abstract

The global transition toward renewable energy has significantly accelerated the deployment of wind turbine fleets, particularly in remote onshore and harsh offshore environments. As the scale of these fleets expands, operations and maintenance costs have emerged as a dominant factor influencing the levelized cost of energy. Central to operational efficiency is the management of maintenance work orders, which are frequently generated by automated condition monitoring systems, supervisory control and data acquisition alarms, and routine manual inspections. However, the sheer volume of these alerts often overwhelms maintenance personnel, leading to suboptimal scheduling, deferred critical repairs, and unnecessary site visits. This paper proposes a comprehensive framework for evidence-calibrated work-order prioritization designed to systematically rank maintenance tasks based on synthesized heterogeneous data. By employing principles derived from evidence theory, the proposed methodology integrates multi-source diagnostic confidence, dynamic environmental risk factors, and logistical constraints to calculate a calibrated priority score for each pending work order. The approach addresses the inherent uncertainty and conflicting information common in sensor networks and historical maintenance logs. Extensive validation using a simulated fleet of wind turbines demonstrates that the evidence-calibrated prioritization model significantly enhances fleet availability, reduces the incidence of catastrophic component failures, and optimizes the utilization of maintenance resources compared to traditional first-in-first-out or purely prognostic scheduling methods. The findings provide a scalable, robust architecture for modern wind farm operators to transition from reactive alarm management to intelligent, risk-informed maintenance execution.