skip to main content


Title: Statistical and clustering analysis for disturbances: A case study of voltage dips in wind farms

This study proposes and evaluates an alternative statistical methodology to analyze a large number of voltage dips. For a given voltage dip, a set of lengths is first identified to characterize the root mean square (rms) voltage evolution along the disturbance, deduced from partial linearized time intervals and trajectories. Principal component analysis and K-means clustering processes are then applied to identify rms-voltage patterns and propose a reduced number of representative rms-voltage profiles from the linearized trajectories. This reduced group of averaged rms-voltage profiles enables the representation of a large amount of disturbances, which offers a visual and graphical representation of their evolution along the events, aspects that were not previously considered in other contributions. The complete process is evaluated on real voltage dips collected in intense field-measurement campaigns carried out in a wind farm in Spain among different years. The results are included in this paper.
 [1] ;  [1] ;  [2] ;  [3] ;  [3]
  1. Renewable Energy Research Institute and DIEEAC/EDII-AB Univ. de Castilla-La Mancha, Albacete (Spain)
  2. National Renewable Energy Lab. (NREL), Golden, CO (United States)
  3. Univ. Politecnica de Cartagena, Cartagena (Spain)
Publication Date:
Report Number(s):
Journal ID: ISSN 0885-8977
Grant/Contract Number:
Accepted Manuscript
Journal Name:
IEEE Transactions on Power Delivery
Additional Journal Information:
Journal Volume: 31; Journal Issue: 6; Journal ID: ISSN 0885-8977
Research Org:
National Renewable Energy Lab. (NREL), Golden, CO (United States)
Sponsoring Org:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Wind and Water Technologies Office (EE-4W)
Country of Publication:
United States
24 POWER TRANSMISSION AND DISTRIBUTION; voltage dip; principal component analysis; clustering methods
OSTI Identifier: