Home > Research > Publications & Outputs > Condition monitoring of wind turbines based on ...

Electronic data

  • Condition monitoring of wind turbines based on ELM

    Rights statement: ©2015 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

    Accepted author manuscript, 711 KB, PDF document

    Available under license: CC BY-NC: Creative Commons Attribution-NonCommercial 4.0 International License

Links

Text available via DOI:

View graph of relations

Condition monitoring of wind turbines based on extreme learning machine

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paperpeer-review

Published
Close
NullPointerException

Abstract

Wind turbines have been widely installed in many areas, especially in remote locations on land or offshore. Routine inspection and maintenance of wind turbines has become a challenge in order to improve reliability and reduce the energy of cost; thus adopting an efficient condition monitoring approach of wind turbines is desirable. This paper adopts extreme learning machine
(ELM) algorithms to achieve condition monitoring of wind turbines based on a model-based condition monitoring approach. Compared with the traditional gradient-based training algorithm widely used in the single-hidden layer
feed forward neural network, ELM can randomly choose the input weights and hidden biases and need not be tuned in the training process. Therefore, ELM algorithm can dramatically reduce learning time. Models are identified
using supervisory control and data acquisition (SCADA) data acquired from an operational wind farm, which contains data of the temperature of gearbox oil sump, gearbox oil exchange and generator winding. The results show that the proposed method can efficiently identify faults of wind turbines.

Bibliographic note

©2015 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.