Wind Turbine Generator Short Circuit Fault Detection Using a Hybrid Approach of Wavelet Transform and Naïve Bayes Classifier

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Toshani, Hamid, Abdi Jalebi, Salman, Khadem, Narges and Abdi, Ehsan (2021) Wind Turbine Generator Short Circuit Fault Detection Using a Hybrid Approach of Wavelet Transform and Naïve Bayes Classifier. In: IEEE 15th International Conference on Compatibility, Power Electronics and Power Engineering (CPE-POWERENG). 2021 IEEE 15th International Conference on Compatibility, Power Electronics and Power Engineering, CPE-POWERENG 2021 . UNSPECIFIED. ISBN 9781728180717

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Abstract

Wind turbines are subjected to several failure modes during their operation. A wind turbine drivetrain generally consists of rotor, bearings, low and high-speed shafts, gearbox, brakes, and generator. Single phase-to-phase and single phase-to-ground faults are among common electrical failure modes in the generator. In this paper, feature extraction has been performed using the Discrete Wavelet Transform (DWT) to detect the electrical faults in the wind turbine generator. A two-stage prediction process is proposed using Naïve Bayes Classifier (NBC), where the healthy and faulty modes are first determined, followed by classifying the types of electrical faults. Three-phase stator currents are used as fault detection signals. The performance of the proposed algorithm has been evaluated in Simulink for a 1659 kW wind turbine drivetrain.

Item Type:	Book Section
Uncontrolled Keywords:	electrical faults,fault detection,naïve bayes classifier,wavelet transform,wind turbine drivetrain,energy engineering and power technology,electrical and electronic engineering ,/dk/atira/pure/subjectarea/asjc/2100/2102
Faculty \ School:	Faculty of Science > School of Engineering (former - to 2024)
UEA Research Groups:	Faculty of Science > Research Groups > Sustainable Energy
Related URLs:	http://www.scopus.com/inward/record.url?... https://ieeexplore.ieee.org/document/950...
Depositing User:	LivePure Connector
Date Deposited:	01 Sep 2021 23:40
Last Modified:	07 Nov 2024 12:49
URI:	https://ueaeprints.uea.ac.uk/id/eprint/81265
DOI:	10.1109/CPE-POWERENG50821.2021.9501211

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