An Approach of Drivetrain Degradation Assessment Applied in Wind Turbines

DOI

10.2991/iwmecs-15.2015.128How to use a DOI?

Keywords

Feature extraction, Self-organizing map, Minimum quantization error, Contribution value, Faulty localization, Drivetrain degradation assessment.

Abstract

As wind energy is widely used nowadays, it is more and more important to predict wind turbine downtime and organize maintenance just in time. Two types of data systems have been widely adopted for monitoring wind turbine condition: supervisory control and data acquisition (SCADA) and condition monitoring system (CMS). In this paper, a systematic framework is designed to combine CMS and SCADA data and assess drivetrain degradation. Advanced feature extraction techniques are applied to calculate health indicators. A pattern recognition algorithm is used to model baseline and actual condition, Self-organizing Map (SOM) and minimum quantization error (MQE) method is selected to do the degradation assessment. Eventually, the contribution of each component is calculated to achieve component-level fault localization. The approach is validated on a 2 MW wind turbine, where an incipient fault is detected before the wind turbine shuts down.

Copyright

© 2015, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Menghang Zhang
PY  - 2015/10
DA  - 2015/10
TI  - An Approach of Drivetrain Degradation Assessment Applied in Wind Turbines
BT  - Proceedings of the 2015 2nd International Workshop on Materials Engineering and Computer Sciences
PB  - Atlantis Press
SP  - 642
EP  - 645
SN  - 2352-538X
UR  - https://doi.org/10.2991/iwmecs-15.2015.128
DO  - 10.2991/iwmecs-15.2015.128
ID  - Zhang2015/10
ER  -