Optimal preventive maintenance scheduling for wind turbines under condition monitoring

TL;DR

This paper presents a mathematical model for optimizing preventive maintenance scheduling of wind turbines, incorporating condition monitoring data, component ages, and costs to improve maintenance efficiency.

Contribution

It introduces a novel optimization framework that dynamically updates maintenance plans based on real-time condition data and component aging.

Findings

Preventive maintenance improves efficiency when component lifespan is shorter than turbine lifespan.

The model effectively integrates condition monitoring data into maintenance scheduling.

Case study demonstrates practical applicability in Swedish wind farms.

Abstract

We suggest a mathematical model for computing and regularly updating the next preventive maintenance plan for a wind farm. Our optimization criterium takes into account the current ages of the key components, the major maintenance costs including eventual energy production losses as well as the available data monitoring the condition of the wind turbines. We illustrate our approach with a case study based on data collected from several wind farms located in Sweden. Our results show that preventive maintenance planning gives some effect, if the wind turbine components in question live significantly shorter than the turbine itself.