Article

Least-squares versus LMS parametric approaches for power quality events segmentation

  • Enrique Alameda-Hernandez /
  • Fernando Aznar /
  • Francisco Gil /
  • Antonio Espin
Journal ar
Renewable Energy and Power Quality Journal
  • Volumen: 1
  • Número: 15
  • Fecha: 01 April 2017
  • Páginas: 751-756
  • ISSN: 2172038X
  • Source Type: Journal
  • DOI: 10.24084/repqj15.456
  • Document Type: Article
  • Publisher: European Association for the Development of Renewable Energy, Environment and Power Quality (EA4EPQ)
© 2017, European Association for the Development of Renewable Energy, Environment and Power Quality (EA4EPQ). All rights reserved.Power quality monitoring requires knowing when the start of the perturbation takes place, and also when it ends; in this way, the voltage or current signals are divided into segments. In this work, we follow previously developed ideas in the literature and resort to parametric modelling to achieve the perturbed signal segmentation. What we propose here is the use of adaptive AR modelling identification, in particular Recursive Least Squares and Least Mean Squares, as opposed to a block-based approach used elsewhere. Overdetermined systems, both block-wise and adaptively are also included among the analysed methods. Simulations show that although being computationally lighter, and hence more suitable to real-time implementations, segments limits are accurately located by adaptive algorithms most of the cases.

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