DyTAM: Accelerating Wind Turbine Inspections with Dynamic UAV Trajectory Adaptation

Serhii Svystun, Lukasz Scislo (), Marcin Pawlik, Oleksandr Melnychenko, Pavlo Radiuk, Oleg Savenko and Anatoliy Sachenko
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Serhii Svystun: Faculty of Information Technologies, Khmelnytskyi National University, 11, Instytuts’ka Str., 29016 Khmelnytskyi, Ukraine
Lukasz Scislo: Faculty of Electrical and Computer Engineering, Cracow University of Technology, 24, Warszawska, 31-155 Cracow, Poland
Marcin Pawlik: Faculty of Electrical and Computer Engineering, Cracow University of Technology, 24, Warszawska, 31-155 Cracow, Poland
Oleksandr Melnychenko: Faculty of Information Technologies, Khmelnytskyi National University, 11, Instytuts’ka Str., 29016 Khmelnytskyi, Ukraine
Pavlo Radiuk: Faculty of Information Technologies, Khmelnytskyi National University, 11, Instytuts’ka Str., 29016 Khmelnytskyi, Ukraine
Oleg Savenko: Faculty of Information Technologies, Khmelnytskyi National University, 11, Instytuts’ka Str., 29016 Khmelnytskyi, Ukraine
Anatoliy Sachenko: Research Institute for Intelligent Computer Systems, West Ukrainian National University, 11, Lvivska Str., 46009 Ternopil, Ukraine

Energies, 2025, vol. 18, issue 7, 1-19

Abstract: Wind energy’s crucial role in global sustainability necessitates efficient wind turbine maintenance, traditionally hindered by labor-intensive, risky manual inspections. UAV-based inspections offer improvements yet often lack adaptability to dynamic conditions like blade pitch and wind. To overcome these limitations and enhance inspection efficacy, we introduce the Dynamic Trajectory Adaptation Method (DyTAM), a novel approach for automated wind turbine inspections using UAVs. Within the proposed DyTAM, real-time image segmentation identifies key turbine components—blades, tower, and nacelle—from the initial viewpoint. Subsequently, the system dynamically computes blade pitch angles, classifying them into acute, vertical, and horizontal tilts. Based on this classification, DyTAM employs specialized, parameterized trajectory models—spiral, helical, and offset-line paths—tailored for each component and blade orientation. DyTAM allows for cutting total inspection time by 78% over manual approaches, decreasing path length by 17%, and boosting blade coverage by 6%. Field trials at a commercial site under challenging wind conditions show that deviations from planned trajectories are lowered by 68%. By integrating advanced path models (spiral, helical, and offset-line) with robust optical sensing, the DyTAM-based system streamlines the inspection process and ensures high-quality data capture. The dynamic adaptation is achieved through a closed-loop control system where real-time visual data from the UAV’s camera is continuously processed to update the flight trajectory on the fly, ensuring optimal inspection angles and distances are maintained regardless of blade position or external disturbances. The proposed method is scalable and can be extended to multi-UAV scenarios, laying a foundation for future efforts in real-time, large-scale wind infrastructure monitoring.

Keywords: DyTAM; UAV-based inspection; wind turbine monitoring; automated path planning; dynamic trajectory adaptation; real-time blade analysis; deep learning segmentation (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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