Machinery Lubrication magazine published by Noria Corporation
Issue link: https://www.e-digitaleditions.com/i/1378657
46 | May - June 2021 | www . machinerylubrication.com Improving Wind Turbine Fleet Management with In-line Wear Debris Detection Martin Vincent, Cogentrix Energy | Stephen Steen, Poseidon Systems, LLC WEAR DEBRIS Wind turbines are notori- ously difficult to monitor due to several environmental and mechanical factors. First, the drivetrain consists of elements that change load, speed and torque quickly and continuously as the winds change speed and direc- tion. e gearbox internal elements range from 18 rpm planetary gears to 1400 rpm helical gears. In addition to the turbine mechanics themselves, the distributed nature of wind farms, along with the 250-foot (or higher) climb to access the system, makes remote monitoring an efficient choice. Early wind turbines had no diag- nostic systems included by Original Equipment Manufacturers (OEMs). It is now common to rely on annual or semi-annual oil samples from the gearbox sump using spectrographic iron analysis to alert operators of developing problems. is periodic monitoring is not always successful at avoiding prob- lems, though. e chances of sampling the turbine at the optimum time to detect a problem are unlikely. The Need for Continuous Condition Monitoring e costly repairs and potential for significant downtime associated with inter val-based monitoring are both excellent reasons for considering a continuous condition monitoring system in this appli- cation. e turbines already had a data network for monitoring their basic status, power output and temperatures. ese networks could easily be leveraged to add additional sensor technologies to continuously monitor drivetrain health and condition. To introduce continuous condition-monitoring to the process, two basic technology types were considered: vibration-based systems and wear debris technology. Option 1: Vibration Since many wind operators had roots in the conventional power plant space, vibration-based systems were familiar to operators and many fleets were retrofitted with vibra- tion-based systems already. ese systems were sometimes very adept at finding problems in the high- " The chances of sampling the turbine at the optimum time to detect a problem are unlikely. "