Automating the inspection of wind turbines using unmanned vehicles is one area that has emerged over the past years but still needs wider acceptance by the industry and regulators.
With digitalisation expected to increase the value of renewable energy projects for operators and developers through real-time operation and monitoring of assets, there is a need for the development of industry-accepted practices and standards.
This pushed engineering and certification firm DNV, the University of Bristol and technology company Perceptual Robotics to launch a research programme aimed at generating broader acceptance of automated inspection of wind turbines using drones in the UK market.
Over a period of 12 months, the three parties will develop an automated data processing procedure for verification of detected wind turbine blade defects. To do so, they will investigate the automated verification, validation, and processing of inspection data, collected by autonomous drones, to improve inspection quality and performance.
Lessons learned from the research project will be used to inform the industry and government in developing and enacting regulations that will enable wider acceptance of automated inspection of renewables assets.
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The University of Bristol will provide its Visual Information Lab and experts in 3D computer vision and image processing to create automated localisation of inspection images and defects. Perceptual Robotics will perform drone inspections and create AI-based models for defect detection. DNV will provide inspection expertise, verify data collected, validate the methodology and performance of the AI algorithms. Data collected will be used to improve an existing DNV and IEC recommended best practices for automated wind turbine inspection.
The project will be funded using a grant secured from the UK Research and Innovation.
Using unmanned autonomous vehicles to get high-definition videos and images and to geo-position assets that are located in extreme environments helps improve the inspection processes for asset owners and operators, in turn, resulting in enhanced management, operation and maintenance of these assets. The processes enable quick detection of faulty blades and other equipment, prompting operators to quickly respond to avoid asset failure and continued generation of wind resulting in a secure energy supply to meet demand. In addition, the lifespan of these renewables assets is increased through improved maintenance and the safety of workers ensured without them having to visit the harsh and dangerous environments in person.
Dr. Elizabeth Traiger, a DNV senior researcher in digital assurance said: “With many inspections still being carried out manually, visual inspection of offshore wind turbines, is expensive, labour-intensive, and hazardous. Automatic visual inspections can address these issues.
“This collaboration will develop and demonstrate an automated processing pipeline alongside a general framework with the aim of generating broader acceptance across the industry and informing future regulation. This project should provide a stepping-stone to the growth of the automated inspection industry.”
Pierre C Sames, group research and development director at DNV, adds: “With the number of installed wind turbines worldwide increasing, including those in remote and harsh environments, the volume of inspection data collected is quickly outpacing the capacity of skilled inspectors who can competently review it. This research project will develop means to tackle this challenge through machine learning algorithms and process automation.”