Fault diagnosis of Wind Turbine Pitch Systems based on Kohonen network

DOI

10.2991/icmra-15.2015.226How to use a DOI?

Keywords

Kohonen neural network; Similarity function;Fault diagnosis;Pitch system

Abstract

The wind turbines has a lot of operational failure parameters and some isolated sample;what’s more , so direct use of neural networks for fault diagnosis easily lead to performance decreased .For this situation, we propose use of similarity function combined with Kohonen neural network for fault diagnosis:first use similarity function method to eliminated the redundant information for samples optimized; then due to the vagueness and uncertainty that exists between fault symptoms and causes of failure,it needs fuzzy clustering based on Kohonen neural network to solve,so the optimized samples input Kohonen network to obtain various type of standard fault model,then put the test samples in the model ,its results were compared with the standard fault sample can get the type of fault . Simulation results show that: in the wind turbine pitch system use the fault diagnostic method, establish the relationship model accuracy is relatively high, able to make quick and accurate diagnosis of the turbine pitch systems operational status and fault type.

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  - Xinli Li
AU  - Wanye Yao
AU  - Qingjie Zhou
AU  - Jianming Wang
PY  - 2015/04
DA  - 2015/04
TI  - Fault diagnosis of Wind Turbine Pitch Systems based on Kohonen network
BT  - Proceedings of the 3rd International Conference on Mechatronics, Robotics and Automation
PB  - Atlantis Press
SP  - 1168
EP  - 1174
SN  - 2352-538X
UR  - https://doi.org/10.2991/icmra-15.2015.226
DO  - 10.2991/icmra-15.2015.226
ID  - Li2015/04
ER  -