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Clustering of Wind Speed Time Series as a Tool for Wind Farm Diagnosis

Authors :
Ana Alexandra Martins
Daniel C. Vaz
Tiago A. N. Silva
Margarida Cardoso
Alda Carvalho
Source :
Mathematical and Computational Applications, Vol 29, Iss 3, p 35 (2024)
Publication Year :
2024
Publisher :
MDPI AG, 2024.

Abstract

In several industrial fields, environmental and operational data are acquired with numerous purposes, potentially generating a huge quantity of data containing valuable information for management actions. This work proposes a methodology for clustering time series based on the K-medoids algorithm using a convex combination of different time series correlation metrics, the COMB distance. The multidimensional scaling procedure is used to enhance the visualization of the clustering results, and a matrix plot display is proposed as an efficient visualization tool to interpret the COMB distance components. This is a general-purpose methodology that is intended to ease time series interpretation; however, due to the relevance of the field, this study explores the clustering of time series judiciously collected from data of a wind farm located on a complex terrain. Using the COMB distance for wind speed time bands, clustering exposes operational similarities and dissimilarities among neighboring turbines which are influenced by the turbines’ relative positions and terrain features and regarding the direction of oncoming wind. In a significant number of cases, clustering does not coincide with the natural geographic grouping of the turbines. A novel representation of the contributing distances—the COMB distance matrix plot—provides a quick way to compare pairs of time bands (turbines) regarding various features.

Details

Language :
English
ISSN :
22978747 and 1300686X
Volume :
29
Issue :
3
Database :
Directory of Open Access Journals
Journal :
Mathematical and Computational Applications
Publication Type :
Academic Journal
Accession number :
edsdoj.7ce01f7df2b24c6e860b00cf86af24ed
Document Type :
article
Full Text :
https://doi.org/10.3390/mca29030035