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

SCADA Performance Analysis

Since 2017, it is mandatory that newly planned wind energy projects must be announced in an auction system leading to an increasing cost pressure. Therefore, every cost factor needs to be considered and reduced if possible. Especially yearly expenses such as maintenance. To reduce the (un-)scheduled maintenance time and resulting down time, it is relevant to ensure a proper monitoring of the turbine.

Condition monitoring of technical assets aims at detecting changes and trends that represent deviations from normal operational behavior and thus indicating a developing fault. In case of wind turbines, the monitoring of structural components as the support structure, the tower or rotor blades is often referred to as Structural Health Monitoring (SHM), while systems for monitoring other components, like e.g. the rotating drive train, are usually called Condition Monitoring Systems (CMS). An additional approach would be a CBM based on Supervisory Control and Data Acquisition (SCADA) signals. It has the advantage of utilizing existing data acquisitions without high investment costs.

The objective of the project is to develop methods to reduce the levelized cost of energy of wind turbines through reduced maintenance cost by a data-driven approach. This will be achieved by “normal behavior models” and “alarm and fault analyses” based on the historic SCADA data available of field turbines.

The expected benefits are:

  • Development of a fault detection method for different wind turbine components without additional hardware costs
  • Identification of abnormal behavior in the pitch and yaw system
  • Identification of critical operating conditions with high failure probability considering the interaction of several components such as pitch, yaw, tower and drive train
  • Reduction of downtime and OPEX

Contact:

Björn Roscher, M. Sc.
Email: bjoern.roscher@cwd.rwth-aachen.de
Phone: +49 241 80 90563

Duration:

01. 07. 2018 - 01. 07. 2020

Project funded by: