21 Feb 2023
 | 21 Feb 2023
Status: this preprint is currently under review for the journal WES.

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

Xavier Chesterman, Timothy Verstraeten, Pieter-Jan Daems, Ann Nowé, and Jan Helsen

Abstract. Condition monitoring and failure prediction for wind turbines is currently 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 Modelling framework. The first part of the paper consists of an in-depth overview of 5 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, 6 demonstration experiments are designed. The first 5 experiments test different techniques for the modeling of the normal behavior. The sixth experiment compares several techniques that can be used for identifying anomalous patterns in the prediction error. The paper concludes with several directions for future work.

Xavier Chesterman et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on wes-2022-120', Anonymous Referee #1, 09 Mar 2023
  • RC2: 'Comment on wes-2022-120', Anonymous Referee #2, 10 Mar 2023

Xavier Chesterman et al.

Xavier Chesterman et al.


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Short summary
This paper reviews and implements several techniques that can be used for condition monitoring and failure prediction on wind turbines using SCADA data. The focus lies on techniques that answer to requirements of the industry, e.g. robustness, transparency, computationally efficiency, and maintainability. The end result of this research is a pipeline that can accurately detect three types of failures, i.e. generator bearing failures, generator fan failures, and generator stator failures.