Two-stage deep learning-based wind turbine condition monitoring using SCADA data

Shahabodin Afrasiabi, Mousa Afrasiabi, Benyamin Parang, Mohammad Mohammadi, Solmaz Kahourzade, Amin Mahmoudi

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

This paper proposes a two-stage data-driven methodology in condition monitoring (CM) of wind turbines (WTs). In the first stage, a fast and powerful network, namely a parallel generative adversarial network (PGAN) is proposed to resolve the problem of limited available information by generating artificial data. In the second stage, a robust deep network is designed based on a one-module Gabor filter oriented convolutional neural network and reformulation of a new loss function, namely robust deep Gabor network (RDGN). The experimental dataset of 3MW wind turbines in Ireland is used to verify the effectiveness of the proposed method and demonstrate the superiority of the proposed two-stage method in comparison with several state-of-the-art methods in terms of accuracy and reliability.

Original languageEnglish
Title of host publication9th IEEE International Conference on Power Electronics, Drives and Energy Systems, PEDES 2020
Place of PublicationJaipur, India
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9781728156729
DOIs
Publication statusPublished - 16 Dec 2020
Event9th IEEE International Conference on Power Electronics, Drives and Energy Systems, PEDES 2020 - Malaviya National Institute of Technology Jaipur, Jaipur, India
Duration: 16 Dec 202019 Dec 2020

Publication series

Name9th IEEE International Conference on Power Electronics, Drives and Energy Systems, PEDES 2020

Conference

Conference9th IEEE International Conference on Power Electronics, Drives and Energy Systems, PEDES 2020
CountryIndia
CityJaipur
Period16/12/2019/12/20

Keywords

  • Condition Monitoring (CM)
  • Parallel generative adversarial network (PGAN)
  • Robust deep Gabor network (RDGN)
  • Wind turbine (WT)

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