Open AccessApproach to fitting parameters and clustering for characterising measured voltage dips based on two‐dimensional polarisation ellipses
Author(s) -
GarcíaSánchez Tania,
GómezLázaro Emilio,
Muljadi E.,
Kessler Mathieu,
MolinaGarcía Angel
Publication year - 2017
Publication title -
iet renewable power generation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.005
H-Index - 76
eISSN - 1752-1424
pISSN - 1752-1416
DOI - 10.1049/iet-rpg.2016.0813
Subject(s) - ellipse , cluster analysis , voltage , computer science , pattern recognition (psychology) , data mining , artificial intelligence , mathematics , engineering , electrical engineering , geometry
An alternative approach to characterise real voltage dips is proposed and evaluated in this study. The proposed methodology is based on voltage‐space vector solutions, identifying parameters for ellipses trajectories by using the least‐squares algorithm applied on a sliding window along the disturbance. The most likely patterns are then estimated through a clustering process based on the k ‐means algorithm. The objective is to offer an efficient and easily implemented alternative to characterise faults and visualise the most likely instantaneous phase‐voltage evolution during events through their corresponding voltage‐space vector trajectories. This novel solution minimises the data to be stored but maintains extensive information about the dips including starting and ending transients. The proposed methodology has been applied satisfactorily to real voltage dips obtained from intensive field‐measurement campaigns carried out in a Spanish wind power plant up to a time period of several years. A comparison to traditional minimum root mean square‐voltage and time‐duration classifications is also included in this study.