File(s) under permanent embargo

Reason: This item is currently closed access.

Adaptive fault detection and tracking for a wind turbine generator using Kalman filter

conference contribution
posted on 25.10.2016, 13:42 by Raed Ibrahim, Abdullahi Daniyan, Simon Watson
This paper describes a wind turbine (WT) condition monitoring technique that uses the measurement of stator current and rotational speed to derive a fault detection signal. The detection algorithm uses a Kalman filter (KF) to extract and track the strength of particular frequency components, characteristic of faults in the stator current signal. This has been done by an extensive simulation studies to develop an on-line detection and monitoring of mechanical faults in permanent magnet synchronous generators (PMSGs), recentlly used in modern variable-speed WTs. The model is developed and validated with operational data of five 2.5MW turbines were recorded by the supervisory control and data acquisition (SCADA) system over the period of 1 year. The simulation results show that the KF algorithm can provide a reliable indication of the presence of a fault with low computational times, from director indirectdrive fixed- or variable-speed WTs. The proposed algorithm can indicate the severity of the fault, where in contrast with traditional methods, they failed to extract the fault features from non-stationary current measurements, due to variable-speed operating conditions of WTs.

History

School

  • Mechanical, Electrical and Manufacturing Engineering

Published in

2nd International Conference on Offshore Renewable Energy - CORE 2016

Citation

IBRAHIM, R.K., DANIYAN, A. and WATSON, S.J., 2016. Adaptive fault detection and tracking for a wind turbine generator using Kalman filter. Presented at the 2nd International Conference on Offshore Renewable Energy (CORE 2016), Glasgow, UK, 12-14th Sept.

Publisher

ASRANet Ltd

Version

AM (Accepted Manuscript)

Publisher statement

This work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/

Acceptance date

14/09/2016

Publication date

2016

Notes

This conference paper is in closed access.

Language

en

Location

Glasgow, UK