Online Prognostic Condition Monitoring for Wind Turbines Using Current Signals Slides

Abstract- This webinar will present the pioneering work conducted by the Power and Energy System Laboratory at the University of Nebraska-Lincoln, Lincoln, NE, USA on low-cost, online, prognostic condition monitoring (PCM) for wind turbines and their mechanical and electrical subassemblies, such as gearbox, bearing, blade, drivetrain, and generator. The PCM methods primarily use current signals acquired from generator terminals or in the power electronic converter control system to online diagnose and prognose the faults and predict the RUL of the wind turbines in all operating conditions. In addition to current signals, the PCM can also be used to include vibration signals in wind turbines to provide additional PCM capability and improved diagnosis and prognosis accuracy. The PCM methods developed have been successfully applied for fault diagnosis and prognosis and RUL prediction of wind turbines of different types and different sizes from kW to MW scale in both the laboratory and field. The PCM methods developed outperformed the state-of-the-art wind turbine condition monitoring technologies in terms of cost, hardware complexity, implementation, accuracy, capability, and reliability. They provide a low-cost, reliable solution for the wind industry to reduce the failure rate and level, O&M costs, and downtime, improve reliability, and extend the life of wind turbines