4.7 Article

Data annotation and feature extraction in fault detection in a wind turbine hydraulic pitch system

期刊

RENEWABLE ENERGY
卷 185, 期 -, 页码 692-703

出版社

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.renene.2021.12.047

关键词

Pitch system; Wind turbine; Fault detection; ANFIS; SCADA

资金

  1. Doctoral School of Industry Innovations (DSII) of Tampere University
  2. Suomen Hyotytuuli Oy

向作者/读者索取更多资源

The performance of wind turbines can be improved by processing SCADA data, which also enhances decisions about maintenance schedules. The pitch system plays a critical role in analyzing relevant data for improving wind turbine operation. This study demonstrates the potential of the adaptive neuro fuzzy inference system technique for detecting pitch faults by gathering significant pitch faults and implementing the ANFIS technique. The proposed approach includes detailed preprocessing of SCADA data and emphasizes the labeling process.
The performance of wind turbines can be improved by processing supervisory control and data acquisition (SCADA) data. SCADA data can be processed in a reasonable time to enhance decisions made about maintenance schedules. The pitch system is critical in improving wind turbine operation by analysing data of the most relevant SCADA features. This study gathers the most significant pitch faults, and by implementing the adaptive neuro fuzzy inference system (ANFIS) technique it demonstrates the fault detection potential of this technique. The proposed approach includes the detailed pre-processing of SCADA data, emphasising the labelling process, in which a modified power curve monitoring method is used. During the implementation of the ANFIS, different combinations of the selected parameters were tested for their effects on the performance of fault detection. This methodology was implemented at a windfarm, commissioned in 2004, in five 2.3 MW fixed-speed onshore wind turbines equipped with a traditional servo-valve controlled hydraulic pitch system. Overall, data on 10 years of the operation of each wind turbine were utilised, and a total of nine pitch events were considered. Individual measurement for each blade angle was available for detecting pitch faults. Results demonstrated above 86% achievement of F1-score for pitch fault detection. (C) 2021 The Authors. Published by Elsevier Ltd.

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