Posters

Abstract

As global wind fleets continue to scale and turbine model complexity increases, operators face mounting challenges in extending turbine life, optimising maintenance, and controlling costs. Yet, most reliability analysis remains fragmented, with data siloed by site, owner, or OEM. This limits the sector’s ability to anticipate systemic risks and unlock full lifecycle value. This study introduces an independent reliability benchmarking initiative built from anonymised data across a diverse global fleet built on 28,000+ turbines and 160GW of due diligence projects, across 35 countries. By integrating sensor, operational, and environmental datasets, it provides a system-level view of turbine reliability and risk patterns. The emphasis is practical: capture data that enable operators, OEMs, and investors to make smarter decisions about topics such as inspection timing, spare part allocation, and financial planning. This approach supports the hard work of operators and colleagues across the industry. In the short-term it reduces downtime and costs and in the long-term gains it enables improved accountability, higher reliability standards, and more informed investment decisions. To tie this diverse data set together, an innovative hybrid analytics framework was developed. This involved the combination of expert engineering review with automated classification and advanced statistical modelling. This approach transforms disparate data into actionable intelligence without raw data exchange between vendors, making it scalable and secure. Key outcomes include benchmarking turbine reliability across OEMs with performance variations of around fourfold across major component groups, identifying major factors in failure rate variability and highlighting data biases from issue-driven reporting. These findings not only highlight the value of fleetwide analytics but also demonstrate its potential to underpin predictive maintenance programmes, spare part optimisation, and financial risk modelling. This work invites the industry to reimagine how data-driven decision support can improve reliability, reduce costs, and extend turbine lifetimes globally.