Identifying wind component failures before they occur

Predict, Greenbyte Energy Cloud's new feature 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 R&D Department, 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 2 to 9 months in advance, achieved 94% accuracy and showed a 23% reduction of cost, and the software keeps learning and outperforming itself. 

Greenbyte |




Volume: 2019 January/February