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This paper evaluates the effectiveness of various clustering algorithms for filtering SCADA data from wind farms, aiming to automate the process of identifying normal operational measurements amidst numerous anomalies and outliers. By introducing robust evaluation metrics tailored for unlabeled data, the authors compare the filtering accuracy of these algorithms against traditional manual methods. The findings reveal that cluster-based approaches significantly outperform manual filtering in detecting both evident and subtle outliers, although the accuracy varies by model, indicating that expert oversight is still beneficial but less critical than before.
Cluster-based filtering methods can outperform human experts in detecting subtle anomalies in wind farm SCADA data, but model choice significantly impacts accuracy.
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.