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

被引：1

作者：

Toshani, Hamid ^{[1
]}

Abdi, Salman ^{[1
]}

Khadem, Narges ^{[2
]}

Abdi, Ehsan ^{[3
]}

机构：

[1] Univ East Anglia, Sch Engn, Norwich, England

[2] Iran Univ Sci & Technol, Engn Dept, Tehran, Iran

[3] Wind Technol Ltd, Cambridge, England

来源：

2021 IEEE 15TH INTERNATIONAL CONFERENCE ON COMPATIBILITY, POWER ELECTRONICS AND POWER ENGINEERING (CPE-POWERENG) | 2021年

关键词：

Wind Turbine Drivetrain; Fault Detection; Electrical Faults; Wavelet Transform; Naive Bayes Classifier; DIAGNOSIS;

D O I：

10.1109/CPE-POWERENG50821.2021.9501211

中图分类号：

TE [石油、天然气工业]; TK [能源与动力工程];

学科分类号：

0807 ; 0820 ;

摘要：

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-toground 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 Naive 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.

引用

页数：7

共 16 条

[1]

[Anonymous], C INT SCI TECHNOLOGI

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