TripleCMAS

Critical Components Condition Monitoring, fault detection and instant Alarm System (TripleCMASTM) technology application will monitor the aerodynamic efficiency of individual wind turbine blades and also of the entire rotor and critical components located in the nacelle.

To pursue the research and development of the TripleCMASTM, Ventus Engineering has, on the one hand, applied for a research funding program supported by the Austrian Research Promotion Agency (FFG), and, on the other hand, entered into a close collaboration with Technical University of Wien (TU Wien) to develop a new blade sensor technology together with a power harvester located inside the blades.

TripleCMASTM is a completely independent and flexible system, being suitable for all existing wind turbines. Moreover, it offers a modular design where more sensors for gearbox, generator, main shaft bearing, etc., can be added at any time. 

The algorithms developed in this application will monitor the wind turbine (rotor and nacelle), provide real-time evaluation and instant alarm system, when needed, such as:

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  • Wind speed identification
  • Turbulence intensity identification
  • Blades aerodynamic efficiency
  • Relative difference in blades aerodynamic efficiency
    • Buffer zone asymmetry identification (yaw and wake management, monitoring of wind shear)
    • Blade damage alarm (lightning damage, missing stall strips, vortex generator and gurney flaps, others)
    • Blade icing events
    • Blade leading edge erosion measured over time
  • Orientation of the rotor in stopped position, or unbalance by weight
  • Temperature identification inside the blades
  • Rotor speed / over speed identification
  • Relative dynamic blade pitch misalignment
  • Pitch speed / pitching when not supposed to pitch
  • Yaw speed identification / yawing when not supposed to yaw
  • Lightning system grounding passage of pitch bearings and main shaft bearing
  • Unusual noise and vibration (Heavy rain, hit by lightning, foreign object impact, unusual noise, others)
  • Structural differences in blades and development of structural differences over time
  • Blade pitch bearing damage identification
  • Tower movements identification in top of tower
  • Power output characteristics
  • Elasticity in the drive train
  • Monitoring sequence for cut in / generator shifting (small/large generator) / cut out / re-cut in / stop and emergency stop

We are using advanced analytics techniques such as machine learning, image processing, data mining, and statistics to gain new insights from data sources independently or together with existing data.