Overview of normal behavior modeling approaches for SCADA-based wind turbine condition monitoring demonstrated on data from operational wind farms

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Abstract

Condition monitoring and failure prediction for wind turbines currently comprise a hot research topic. This follows from the fact that investments in the wind energy sector have increased dramatically due to the transition to renewable energy production. This paper reviews and implements several techniques from state-of-the-art research on condition monitoring for wind turbines using SCADA data and the normal behavior modeling framework. The first part of the paper consists of an in-depth overview of the current state of the art. In the second part, several techniques from the overview are implemented and compared using data (SCADA and failure data) from five operational wind farms. To this end, six demonstration experiments are designed. The first five experiments test different techniques for the modeling of normal behavior. The sixth experiment compares several techniques that can be used for identifying anomalous patterns in the prediction error. The selection of the tested techniques is driven by requirements from industrial partners, e.g., a limited number of training data and low training and maintenance costs of the models. The paper concludes with several directions for future work.
Original languageEnglish
Pages (from-to)893–924
Number of pages32
JournalWind Energy Science
Volume8
Issue number6
DOIs
Publication statusPublished - 5 Jun 2023

Bibliographical note

Funding Information:
This research has been supported by the Flemish Government (AI Research Program), H2020 Energy (grant no. 872592), the Fonds Wetenschappelijk Onderzoek (through SBO Robustify, grant no. S006119N), and the Agentschap Innoveren en Ondernemen (through ICON project Supersized 4.0, grant no. HBC.2019.0135).

Funding Information:
Xavier Chesterman, Timothy Verstraeten, Pieter-Jan Daems, Ann Nowé, and Jan Helsen would like to thank the Flemish AI Research Program, H2020 Energy, Fonds Wetenschappelijk Onderzoek and Agentschap Innoveren en Ondernemen for their financial support.

Publisher Copyright:
© 2023 Xavier Chesterman et al.

Keywords

  • condition monitoring
  • wind turbine
  • SCADA
  • anomaly detection
  • failure prediction

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