AI provides predictive maintenance of wind turbines

August 23, 2018 //By Nick Flaherty
AI provides predictive maintenance of wind turbines
Cloud software developer Greenbyte has combined statistical models, artificial neural networks and machine learning to identify wind turbine component failures before they occur.

Up to 30% of the life-cycle cost of wind farms is due to wind turbine component failures and maintenance. Predict from Greenbyte Energy Cloud  enables wind farm operators and owners to avoid unscheduled downtime and decrease unforeseen expenditures.

Predict estimates the expected temperature for critical components, compares that estimated data to the actual measured values, and enables intelligent and early detection of developing failures. The pilot study on Predict detected faults 2 to 9 months in advance, achieved 94% accuracy and showed a 23% reduction of cost, and the software keeps learning and outperforming itself. 

This early indication for component failure can reduce downtime, maintenance cost and increase component life and enables operators and managers to act with a plan instead of acting within a crisis to make informed maintenance decisions. 

Director of Technology, has been describing the Artificial Intelligence and machine learning part of the journey in a thrilling blog series The Greenbyte recipe for Artificial Intelligence in renewable energy. More specifically in the first article, he narrates the imminence of component failures in the lifetime of a wind turbine:

"We expect turbines to operate 24 hours a day, 7 days a week. If we did the same with a car it would only last us 8 months," said Mikael Baros, director of technology. "Hence it is not surprising that these poor turbines fail too often. It is estimated that up to 30% of the total life-cycle cost of a wind farm is due to failure and maintenance activities." 

www.greenbyte.com

 


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