PO.250

Using UAV Imagery and Deep Learningfor Wind Turbine Inspection
1Barrett M. Sather, 2Joshua M. Riedy & 3Thomas Bahr
1Harris Geospatial Solutions Inc., 2EdgeData, 3Harris Geospatial Solutions GmbH

Introduction

Methods – Capture Assurance

When it comes to using an Unmanned Aerial
Vehicle (UAV) to collect imagery for windturbine inspections, maneuvering a drone
around a huge turbine
isn’t the only challenge
involved. There's also
the issue of capturing
extra
positional
information so images
can be projected ontoa plane, which isnecessary since thecamera
isnotpositioned to look directly down as in most
UAV imagery applications. Then there's
ensuring the quality of the capture while in
the field to avoid the costs associated withhaving to re-fly a job. Finally, there's the timeand rigor required to analyze all the data toidentify and locate damage and otherabnormalities on turbine blades.

The two main metrics that we use to validate
if a capture is good or not, and what they help
with preventing are:
• Image overlap: ensures uniformity of flight,
eliminates gaps, ensures mosaics arecontinuous.
• Pixel spacing: ensures uniformity of flight,
flags cases where the data doesn’t match,
distance to the blade (too far/close).

On-site validation of image metadata, GPS information, image overlap andpixel spacing using the BladeEdgeSM Capture Assurance Tool (BECAT).

Wind turbine blades affected by leading edge erosion with (left) leading edge
delamination, (right) pits and gouges.

Objectives
We present a workflow currently in use thattakes UAV imagery from flights around windturbine blades, and automatically assesses
those images for defects.

Methods – Damage Detection
Damage detection is performed on the pixel
data using Harris Deep Learning technologies,
and damage locations are reported for eachimage. Current models are trained on chipand crack damage types.

Labeling of imagery for the generation of models of blade edges, various
defects, and background using Harris Deep Learning technologies.

Results
A web application, the BladeEdgeSM Portal,provides user friendly access to wind turbine
blade inspection information generated by
the aforementioned workflow. Specifically,imagery is organized by turbine, blade, andblade side with anomalies and damage
graphically represented.
From this application users have thecapability to modify findings as well asorganize and output inspection results.

Conclusions
This method for wind turbine inspection
significantly reduces the cost of wind turbine
management and inspection, and allows toinspect more turbines in less time.
With an accuracy of 95+ %, there is littlevariation
betweenthese
automatic
inspections as compared to inspections done
manually by human technicians.
Future work on the project includes adding
more damage types, and assigning damage
severity classifications based on standards set
forth by the Electric Power Research Institute(EPRI).
These enhancements will further informturbine managers on how to prioritize therepair and maintenance of their windturbines.

References
Methods – Image Capture
Multiple images are captured along the windturbine for all four sides, and all three bladesof a wind turbine. The current procedure is to
fly one side of the blade at a time. Four flights
are required for a single blade. Blades areflown from root to tip, and the root and tip
locations are captured from directly
underneath the feature.

Blade edge, background model, and resulting heatmap of leading edge
erosion, showing location and type of the defects.

The Deep Learning technologies are achieving
95-99 % accuracy when identifying damage
on wind turbine blades.

1. Sareen, A., Sapre, C. A. & Selig M. S. (2014): Effects of leadingedge erosion on wind turbine blade performance. ̶ Wind
Energy 2014; 17:1531–1542. DOI: 10.1002/we
2. Lusk, R. M. & Monday, W. H. (2017): An early survey of bestpractices for the use of small unmanned aerial systems by theElectric Utility Industry. ̶ Oak Ridge National Laboratory,
ORNL/TM-2017/93.
3. Ehrlich, M., Justice, B. & Baldridge B. (2016): Harris Deep
Learning technology. ̶
http://www.harrisgeospatial.com/portals/0/pdfs/1216_deep_learning_whitepaper_FA.pdf [Accessed 12 July 2018]www.harrisgeospatial.com
windeurope.org/summit2018#GlobalWind2018

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