Magnetic Signature-Based Model Using Machine Learning for Electrical and Mechanical Faults Classification of Wind Turbine Drive Trains

Akhyurna Swain, Elmouatamid Abdellatif, Philip W.T. Pong

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Signal processing and fault indicators analysis are essential for efficient fault detection, classification, and diagnosis of wind turbines. Accordingly, existing works proposed the installation of multiple intrusive sensors (e.g., current, voltage, and accelerometer) for data collection in order to detect and classify the faults in wind turbine drive trains (WTDT). However, these sensors are scattered on the drive train and have a limited local reach on its components making it technically difficult to install. Therefore, signals from these sensors are not able to detect multi parameter phenomena such as coupling of the mechanical and electrical components of the drive train which contains essential fault information. This work proposes the use of magnetic signatures as fault condition indicators of the complete drive train due to the ability of contactless measurement of this signal without opening the main components of the drive train. This is achieved by performing non-destructive magnetic modeling and analysis of the entire drive train. The air gap magnetic flux density of the wind generator is demonstrated as a good fault condition indicator for different common faults occurring on the gearbox, bearings, and the generator. The proposed model is validated using a supervised machine learning classification algorithm in a way to distinguish between electrical and mechanical faults.

Original languageEnglish (US)
Title of host publication2024 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2024
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350313604
DOIs
StatePublished - 2024
Event2024 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2024 - Washington, United States
Duration: Feb 19 2024Feb 22 2024

Publication series

Name2024 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2024

Conference

Conference2024 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2024
Country/TerritoryUnited States
CityWashington
Period2/19/242/22/24

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Networks and Communications
  • Energy Engineering and Power Technology
  • Renewable Energy, Sustainability and the Environment
  • Electrical and Electronic Engineering
  • Control and Optimization

Keywords

  • Condition Monitoring Systems
  • Fault indicators
  • Magnetic modeling
  • Magnetic Signature
  • Wind turbine drivetrains

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