Predictive Maintenance and Anomaly Detection of Wind Turbines Based on Bladed Simulator Models

V. Siva Brahmaiah Rama, Sung Ho Hur, Jung Min Yang

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

Abstract

This paper proposes a novel data-driven scheme for condition monitoring and detecting anomalies in the wind turbine critical components based on advanced deep learning algorithms. The proposed method employs time-series data taken from a Bladed simulator model for the 5MW wind turbine. To emulate the characteristic behavior of essential wind turbine components, we develop supervised and unsupervised deep learning models using self-organizing map (SOM), long short-term memory auto encoder (LSTM-AE), and long short-term memory recurrent neural network (LSTM-RNN). Statistical process control(SPC) charts are used to evaluate the anomalous behavior predicted by the developed data-driven models. The proposed method is tested on a Bladed 5MW wind turbine model with 24 m/sec wind speed for validating its accuracy and applicability.

Original languageEnglish
Title of host publicationIFAC-PapersOnLine
EditorsHideaki Ishii, Yoshio Ebihara, Jun-ichi Imura, Masaki Yamakita
PublisherElsevier B.V.
Pages4633-4638
Number of pages6
Edition2
ISBN (Electronic)9781713872344
DOIs
StatePublished - 1 Jul 2023
Event22nd IFAC World Congress - Yokohama, Japan
Duration: 9 Jul 202314 Jul 2023

Publication series

NameIFAC-PapersOnLine
Number2
Volume56
ISSN (Electronic)2405-8963

Conference

Conference22nd IFAC World Congress
Country/TerritoryJapan
CityYokohama
Period9/07/2314/07/23

Keywords

  • Wind turbine
  • anomaly detection
  • fault prediction
  • long short-term memory auto encoder (LSTM-AE)
  • recurrent neural networks (RNNs)
  • self-organizing maps (SOM)

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