Please use this identifier to cite or link to this item: http://dx.doi.org/10.14279/depositonce-10975
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Main Title: Change-point detection in wind turbine SCADA data for robust condition monitoring with normal behaviour models
Author(s): Letzgus, Simon
Type: Article
Language Code: en
Abstract: Analysis of data from wind turbine supervisory control and data acquisition (SCADA) systems has attracted considerable research interest in recent years. Its predominant application is to monitor turbine condition without the need for additional sensing equipment. Most approaches apply semi-supervised anomaly detection methods, also called normal behaviour models, that require clean training data sets to establish healthy component baseline models. In practice, however, the presence of change points induced by malfunctions or maintenance actions poses a major challenge. Even though this problem is well described in literature, this contribution is the first to systematically evaluate and address the issue. A total of 600 signals from 33 turbines are analysed over an operational period of more than 2 years. During this time one-third of the signals were affected by change points, which highlights the necessity of an automated detection method. Kernel-based change-point detection methods have shown promising results in similar settings. We, therefore, introduce an appropriate SCADA data preprocessing procedure to ensure their feasibility and conduct comprehensive comparisons across several hyperparameter choices. The results show that the combination of Laplace kernels with a newly introduced bandwidth and regularisation-penalty selection heuristic robustly outperforms existing methods. More than 90 % of the signals were classified correctly regarding the presence or absence of change points, resulting in an F1 score of 0.86. For an automated change-point-free sequence selection, the most severe 60 % of all change points (CPs) could be automatically removed with a precision of more than 0.96 and therefore without any significant loss of training data. These results indicate that the algorithm can be a meaningful step towards automated SCADA data preprocessing, which is key for data-driven methods to reach their full potential. The algorithm is open source and its implementation in Python is publicly available.
URI: https://depositonce.tu-berlin.de/handle/11303/12100
http://dx.doi.org/10.14279/depositonce-10975
Issue Date: 27-Oct-2020
Date Available: 2-Dec-2020
DDC Class: 004 Datenverarbeitung; Informatik
Subject(s): SCADA
wind turbines
normal behaviour models
Sponsor/Funder: TU Berlin, Open-Access-Mittel – 2020
License: https://creativecommons.org/licenses/by/4.0/
Journal Title: Wind Energy Science
Publisher: Copernicus
Publisher Place: Göttingen
Volume: 5
Issue: 4
Publisher DOI: 10.5194/wes-5-1375-2020
Page Start: 1375
Page End: 1397
EISSN: 2366-7451
ISSN: 2366-7443
Appears in Collections:FG Maschinelles Lernen » Publications

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