Up to 30% of the lifecycle costs of wind farms is due to wind-turbine component failures and maintenance. Predict, Greenbyte Energy Cloud’s new innovative feature is now on commercial release and available for new and potential users. Greenbyte is launching an all-informative campaign to showcase to the renewable energy industry how the accessible feature Predict enables wind-farm operators and owners to avoid unscheduled downtime and decrease unforeseen expenditures.
Predict uses statistical models, artificial neural networks, and machine learning to identify wind turbine component failures before they occur. The feature alarms users on changes in temperature that indicate need for maintenance.
Predict’s advanced statistical models developed by Greenbyte’s Head of Research, Dr. Pramod Bangalore have been optimized for high accuracy and in collaboration with Greenbyte’s Head of Technology, Mikael Baros, been put to vigorous testing to ensure high accuracy.
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 two to nine months in advance, achieved 94% accuracy and showed a 23% reduction of cost, and the software keeps learning and outperforming itself.
Multiple benefits accrue from this heavily researched feature. Early indication for component failure can reduce downtime, maintenance cost and increase component life. It enables operators and managers to act with a plan instead of acting within a crisis, and allows them agency on making informed maintenance decisions.
Developing Predict has been a journey of knowledge for Greenbyte and an evidence of innovation for the industry. Director of Technology, Mikael Baros 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, seven days a week. If we did the same with a car it would only last us 8 months! 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.”
The rest of the series continues to unravel how the data crunching process was applied to the first test customer. Stay tuned for the big and final reveal of Greenbyte’s Predict recipe, published on September 4th!
In the meantime, Head of Research at Greenbyte, Dr. Pramod Bangalore is holding a compelling webinar on Predict on August, 29th, where he unveils the science behind the technology. This webinar is a valuable source of knowledge for users of Greenbyte Energy Cloud, industry professionals and data scientists alike.
Interesting parties can sign up to attend the webinar here.
Filed Under: News, O&M, Software
