Article
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Preserved in Portico This version is not peer-reviewed
Dynamic Wind Turbines clustering according to SCADA signals shapes
Version 1
: Received: 2 May 2024 / Approved: 7 May 2024 / Online: 7 May 2024 (11:06:48 CEST)
A peer-reviewed article of this Preprint also exists.
Marti-Puig, P.; Núñez-Vilaplana, C. Dynamic Clustering of Wind Turbines Using SCADA Signal Analysis. Energies 2024, 17, 2514. Marti-Puig, P.; Núñez-Vilaplana, C. Dynamic Clustering of Wind Turbines Using SCADA Signal Analysis. Energies 2024, 17, 2514.
Abstract
In this work, we explore the ability to dynamically group the Wind Turbine (WT) of a Wind Farm (WF) based on the behaviour of some of their Supervisory Control And Data Acquisition (SCADA) signals to detect the turbines that exhibit abnormal behaviour. We centre this study on a small WF of five WTs. We use the fact that the same signals from different turbines in the same WF coherently evolve temporally in a time domain, describing very similar waveforms. In this contribution, we use averaged signals from the SCADA system and omit maximums, minimums and standard deviations, focusing mainly on velocities and other slowly varying signals. For the temporal analysis, sliding windows of different temporal durations and overlapings are explored. The capability to automatically identify WTs whose signals differ from the group’s behaviour can alert and program preventive maintenance operations on such WTs before a major breakdown occurs.
Keywords
Hierarchical Clustering (HC); Wind Turbine (WT); SCADA Data; Industrial AI; Dynamic Clustering (DC)
Subject
Engineering, Energy and Fuel Technology
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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