Posters
Come meet the poster presenters to ask them questions and discuss their work
We would like to invite you to come and see the posters at our upcoming conference. The posters will showcase a diverse range of research topics, and will give delegates an opportunity to engage with the authors and learn more about their work. Whether you are a seasoned researcher or simply curious about the latest developments in your field, we believe that the posters will offer something of interest to everyone. So please join us at the conference and take advantage of this opportunity to learn and engage with your peers in industry and the academic community.
On 9 April at 17:15, we’ll also hold the main poster session and distinguish the 7 best posters of this year’s edition with our traditional Poster Awards Ceremony. Join us at the poster area to cheer and meet the laureates, and enjoy some drinks with all poster presenters!
We look forward to seeing you there!
PO218: Advancing Wind Turbine Reliability: Early Fault Detection in Electrical Components Through High-Resolution Monitoring
Zuri Zugasti, Sr. Technical Leader, WindESCo
Abstract
The wind industry has made significant progress in health condition monitoring for mechanical components, particularly through the implementation of Condition Monitoring Systems (CMS) for main bearings, gearboxes, and generator bearings. However, there remains a critical gap in the monitoring of electrical components, which account for approximately between 20 and 40% of all wind turbine failures. Among these, converters and generators are particularly impactful, contributing significantly to annual turbine downtime. Early fault detection in these components is vital for improving maintenance planning, ensuring the availability of long-lead-time parts, reducing downtime, and preventing catastrophic failures that necessitate full component replacement. While some generator repairs can be performed on-site, undetected issues often require costly crane operations for complete component replacement. The financial impact of unplanned maintenance for electrical components ranges between 30,000€ and 60,000€ per turbine annually, highlighting the urgent need for advanced, scalable monitoring systems. This abstract presents an innovative approach for monitoring the health of wind turbine generators through advanced data acquisition techniques. The solution involves deploying an Internet of Things (IoT) platform to capture high-frequency data (+20 kHz) from sensors monitoring current, voltage, and vibration within the generator. This comprehensive data stream enables precise detection and analysis of common failures in induction generators, such as stator wedge and winding failures, bearing damage, and rotor bar degradation. Additionally, for Doubly Fed Induction Generators (DFIG), it facilitates the identification of rotor winding and carbon brush failures.
No recording available for this poster.
