Clustering algorithms for multivariate wind farm SCADA data filtering

2026-07-15

AI Summary

This study addresses the challenge of contaminated SCADA data from wind turbines, which often includes anomalies, transients, and non-steady-state operating points, rendering traditional expert-driven manual filtering inefficient. To overcome this limitation, the authors propose an unsupervised filtering approach that leverages multivariate feature engineering combined with multiple clustering algorithms to automatically detect both explicit and implicit abnormal operating conditions. A key innovation lies in the design of robust evaluation metrics tailored for unlabeled SCADA data, moving beyond the conventional reliance on power curves alone. Experimental results demonstrate that the proposed method consistently outperforms manual filtering across most scenarios, preserving a higher proportion of valid operational data while requiring only minimal expert calibration for deployment.

Abstract

During wind farm operation, Supervisory Control and Data Acquisition (SCADA) systems record numerous anomalies, transients, and specific operational modes, leading to large datasets. However, for a wide range of applications, only measurements corresponding to normal operation are required and, therefore, the SCADA data must be filtered. For this purpose, several methods have been proposed to automate and replace manual filtering conducted by experts via visual inspection of the data. In this paper, we compare the filtering accuracy of multiple clustering algorithms against manual filtering, introducing evaluation metrics that are suitable for unlabeled data and robust across potential applications. Based on the results, we provide recommendations for generalizing model calibration to different datasets and discuss potential use cases for each model. The models are applied to the SCADA data of three turbines of an existing offshore wind farm, using 10-minute statistics across multiple data channels. In addition to the anomalies and operational modes typically recorded, the dataset presents a large number of non-evident outliers due to several field tests. Overall, the results highlight the importance of extending the analysis beyond the power curve, both in feature selection and in the design of evaluation metrics. In most cases, cluster-based methods are able to detect both evident and subtle outliers, achieving higher accuracy than manual filtering. However, the accuracy and the amount of data retained vary considerably depending on the model, and expert involvement remains necessary, though to a reduced extent compared to manual filtering.

Problem

• SCADA data filtering
• wind farm
• clustering algorithms
• anomaly detection
• normal operation

Innovation

• clustering algorithms
• SCADA data filtering
• unsupervised evaluation metrics
• multivariate wind farm data
• outlier detection