Fault diagnosis of squirrel cage induction generator for wind turbine applications using a hybrid deep neural network and decision tree approach

Toshani, Hamid, Abdi, Salman, Khadem, Narges Khadem and Abdi, Ehsan (2022) Fault diagnosis of squirrel cage induction generator for wind turbine applications using a hybrid deep neural network and decision tree approach. In: 2021 International Conference on Electrical, Computer and Energy Technologies (ICECET). IEEE Press, IEEE. ISBN 978-1-6654-4232-9

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Abstract

Phase-to-Phase Fault (PPF) and Phase-to-Ground Fault (PGF) are among common electrical faults in wind turbine generators. Detecting and classifying these faults at early stage are hence vital to improving drivetrain reliability and reduce its maintenance cost. In this paper, a hybrid approach based on the Decision Tree (DT) and Deep Neural Network (DNN) is proposed as a high-performance fault diagnosis method to detect and classify PPF and PGF in the squirrel cage induction generator (SCIG). The DT algorithm is used to detect the faulty conditions in the generator by determining special features in the stator current signals. CNN model will then be used to determine the type of fault by analysing the fault signals. Finally, the accuracy of the proposed fault diagnosis approach is evaluated by simulating a 1.7 MW SCIG wind turbine drivetrain at healthy and faulty conditions.

Item Type: Book Section
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Depositing User: LivePure Connector
Date Deposited: 15 Feb 2022 11:30
Last Modified: 24 May 2022 12:12
URI: https://ueaeprints.uea.ac.uk/id/eprint/83491
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