Autonomous Sensor System for Wind Turbine Blade Collision Detection

Kyle Clocker, Congcong Hu, Jason Roadman, Roberto Albertani, Matthew Johnston

Research output: Contribution to journalArticlepeer-review

1 Scopus Citations

Abstract

This paper presents an automated blade collision detection system for use on wind turbines, toward the goal of supporting monitoring and quantitative assessment of wind energy impacts on wildlife. A wireless, multisensor module mounted at the blade root measures surface vibrations, and a blade-mounted camera provides image capture of colliding objects. Using sensor data recorded during field testing of the system on an operational wind turbine, we present the development, training, and testing of automated detection algorithms for collision detection using machine-learning approaches. In particular, we compare the use of a new two-step, anomaly-based classification algorithm with conventional adaptive boosting and amplitude-based detection techniques, where the two-step approach improves average precision for the experimental data set. This integrated sensor and classification systems demonstrates a new approach for automated, on-blade collision detection for wind turbines, with broad utility across structural health monitoring applications.

Original languageAmerican English
Pages (from-to)11382-11392
Number of pages11
JournalIEEE Sensors Journal
Volume22
Issue number12
DOIs
StatePublished - 2022

Bibliographical note

Publisher Copyright:
© 2001-2012 IEEE.

NREL Publication Number

  • NREL/JA-5000-79428

Keywords

  • Autonomous sensors
  • sensor systems at the edge
  • wind energy

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