Data-driven leading edge erosion detection for wind turbine blades using SCADA data
T.J.S. Gertsen (TU Delft - Aerospace Engineering)
D. Zarouchas – Mentor (TU Delft - Structural Integrity & Composites)
Nikolay Dimitrov – Mentor (Technical University of Denmark (DTU))
Mihai Florian – Mentor (Vattenfall)
Nando Timmer – Graduation committee member (TU Delft - Wind Energy)
Ioannis Antoniou – Graduation committee member
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Abstract
Wind turbines are operating in harsh environmental conditions, especially offshore. An implication of these conditions, caused by the impact of precipitation, is the development of leading edge erosion (LEE). This leads to degraded blade surfaces that result in lower aerodynamic performance. Leading edge erosion is researched in many ways but remote detection is still underdeveloped. Therefore, this thesis investigates the possibility to develop a LEE detection method by analysing real-life data from operational turbines in the field.