CWD
  • Conference for Wind Power Drives 2017
    Conference for Wind Power Drives 2017
  • Aufbau FVA-Gondel
    Aufbau FVA-Gondel
  • Conference for Wind Power Drives 2017
    Conference for Wind Power Drives 2017
  • Center for Wind power drives
    Center for Wind power drives
  • Conference for Wind Power Drives 2017
    Conference for Wind Power Drives 2017
  • Aufbau FVA-Gondel
    Aufbau FVA-Gondel
  • Vorstellung des 4MW Prüfstandes
    Vorstellung des 4MW Prüfstandes
  • 4MW-Prüfstand mit HybridDrive
    4MW-Prüfstand mit HybridDrive
  • 1MW-Prüfstand
    1MW-Prüfstand
  • Campus Melaten
    Campus Melaten

Observer-based condition monitoring system for main gears in wind power plants

Due to the continuous increase of power in off- and onshore wind power plants, the load on the single components is increasing. Furthermore, the cost for blackout time grows with the installed power. Condition monitoring systems (CMS) that are currently installed in wind power plants use a signal-based monitoring approach, at which impact sound serves as the processed signal. Due to their cost-efficiency and their ability to detect damage on fast spinning rolling bearings and gearing, signal-based systems are well established on the market. However, they are afflicted with some deficit when it comes to the detection of damage in slowly spinning components. Furthermore, the degree of automatization in CMS is relatively small, since the automatic generation of damage notifications does not work properly and requires an additional signal analysis performed by experts. In addition, the suitability of impact sound signals for life span assessments is limited, as these signals do not feature adequate correlation to essential dynamic load parameters.

Observer-Based condition monitoring system (BCMS)

Within this project, a novel and integrated state-acquisition and prediction system for main gears of wind turbines will be developed. Thereby, the information accuracy and volume of CMS in main gears of wind turbines should be increased and automated, and reliable damage notifications be enabled.

The BCMS that will be developed represents a combination of a model-based observer and a load-oriented sensor technology. The model will deduce parameters that are hard to measure from the attained process parameters.

By comparing the measured data to the results of the model-based calculation, predictions about the condition of gear components should be enabled. Thus, the chronology of local loads on gear wheels and rolling bearings can be determined in order to predict the remaining life span of the components. In this manner, the reliability of wind turbines should be increased and a condition-based maintenance be allowed. Thereby, maintenance incidents will become more projectable, which will result in downtime reduction for wind turbines and, therefore, in an increased energy yield.

Contact person CWD:
Brian Rieckhoff, M.Sc.
Email: brian.rieckhoff@cwd.rwth-aachen.de
Phone: +49 241 80 90893

 

 

Runtime:
01.12.2015 - 30.11.2018

The project is funded by:

Project promoted by: