Anomaly detection in wind turbine SCADA data for power curve cleaning

Morrison, R., Liu, X. and Lin, Z. (2022) Anomaly detection in wind turbine SCADA data for power curve cleaning. Renewable Energy, 184, pp. 473-486. (doi: 10.1016/j.renene.2021.11.118)

Abstract

Wind turbine power curve cleaning, by way of removing curtailment, stoppage, and other anomalies, is an essential step in making raw data useable for further analysis, such as determining turbine performance, site characteristics, or improving forecasting models. Typically, data comes as SCADA (Supervisory Control and Data Acquisition) data, so contains not only environmental and turbine performance data but also the control action imposed on the turbine by the operator. Many different anomaly detection (AD) methods have been proposed to clean power curves; however, few papers have explored filtering explicit and obvious anomalies from the SCADA prior to running AD. This paper actively explores this filtering impact by comparing the performances of 4 different AD methods with/without filtering. These are: iForest, Local Outlier Factor, Gaussian Mixture Models, and k-Nearest Neighbours. Each approach is evaluated in terms of prediction error, data removal rates, and ability to maintain the underlying wind statistical characteristics. The results show the effectiveness of filtering with every technique showing improvement compared to its unfiltered counterpart. Furthermore, Gaussian Mixture Models are shown to provide favourable accuracy whilst maintaining wind variability, however, with the wide range of performances of methods, a user's choice may be different depending on their needs.

• Item Type: Articles
• Additional Information: This research was funded by the EPSRC Doctoral Training Partnership (EP/R513222/1 ENG).
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Morrison, Rory and Liu, Dr Xiaolei
• Authors: Morrison, R., Liu, X., and Lin, Z.
• Subjects: T Technology > TA Engineering (General). Civil engineering (General)
• College/School: College of Science and Engineering > School of Engineering > Systems Power and Energy
• Journal Name: Renewable Energy
• Publisher: Elsevier
• ISSN: 0960-1481
• ISSN (Online): 1879-0682
• Published Online: 02 December 2021
• Copyright Holders: Copyright © 2021 Elsevier Ltd
• First Published: First published in Renewable Energy 184: 473-486
• Publisher Policy: Reproduced in accordance with the publisher copyright policy

Project Code	Award No	Project Name	Principal Investigator	Funder's Name	Funder Ref	Lead Dept
305200		DTP 2018-19 University of Glasgow	Mary Beth Kneafsey	Engineering and Physical Sciences Research Council (EPSRC)	EP/R513222/1	MVLS - Graduate School