Comparative analysis of segmentation methods, including adaptive and HOS based algorithms

Alameda-Hernández, E.; Aznar, Fernando; Botella, Guillermo

doi:10.1109/ichqp.2016.7783445

Cited by 1 publication

(1 citation statement)

References 18 publications

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“…Compared to RLS algorithms, LMS algorithms are computationally lighter, so in this sense, are better candidates for real-time implementation, and hence are worth considering. LMS algorithms were analysed by the authors previously [5], but not compared to any other pure adaptive algorithm, like the RLS here. Finally, due to the estimation process inherent in this kind of problems, where in some cases statistical magnitudes are computed from time averages of signal samples assuming ergodicity [6], considering more equations than model order parameters -that are the unknowns in this problem-is likely to yield more accurate results.…”

Section: Introductionmentioning

confidence: 99%

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

Alameda-Hernández¹,

Aznar²,

Gil³

et al. 2017

REPQJ

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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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Section: Introductionmentioning

confidence: 99%