Predictive Maintenance for Wind Turbines
Updated: Mar 12
For all the hope that lies in them, wind turbines (WTGs) bring challenges that demand urgent solutions.
1. High maintenance costs: Up to 30% of levelized costs per kWh generated during the lifetime of a wind turbine can be attributed to operation and maintenance.
2.Downtime: Turbine failure can easily result in downtime of a week or more – resulting in respective downtime costs.
3.Tricky and time-intensive repairs: Transporting spare parts is expensive and, depending on location, not without difficulty. Transportation often takes more time than the repair itself. It is estimated that over 70% of WTG downtime comes from unplanned repairs.
Early defect detection, maintenance and repair optimization, and downtime prevention are therefore key factors in achieving a positive return on investment for wind turbines.
But how can wind farm operators reliably detect damages at an early stage?
The Solution: Predictive Maintenance Through Artificial Intelligence
Since in-depth information on Predictive Maintenance (PdM) in wind power is hard to come by, we decided to cover the following points in this article:
- Scope and opportunities of artificial intelligence (AI) in PdM
- Potential use cases of AI-based PdM
- Benefits of for owners
You’re looking for some real-life examples? We share four case studies from our everyday work in this whitepaper:
Definition of Predictive Maintenance for Wind Turbines
Predictive maintenance includes a series of activities that detect changes in the physical condition of components (signs of failure) to enable timely maintenance activities, maximize component life, and reduce downtime.
Predictive maintenance thus aims to achieve three goals:
Longer runtime of wind turbines in normal operation, limiting abnormal behavior, and minimizing downtime
Prevention of expensive major incidents, extending the service life of individual components through taking early action
Monitoring of repair measures and success rate tracking
The Challenge of Predictive Maintenance in Practice
Every wind turbine behaves differently. However, these differences aren’t usually considered in predictive maintenance analyses.
