Anomaly detection in wind turbine SCADA data for power curve cleaning

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 perfor-mance, site characteristics, or improving forecasting models. Typically, data comes as SCADA (Supervi-sory 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 un-derlying wind statistical characteristics. The results show the effectiveness of filtering with every tech-nique 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. (c) 2021 Elsevier Ltd. All rights reserved.

Automated summary

This paper investigates the impact of filtering anomalies from SCADA before running anomaly detection methods for cleaning wind turbine power curves. Four different anomaly detection methods are compared, and the results show improvement in prediction error and data removal rates with filtering. The ability to maintain underlying wind statistical characteristics is also considered. Gaussian Mixture Models are found to provide favorable accuracy while preserving wind variability.