Optimizing Power Quality Signal Compression: Harnessing Compressed Sensing and Reconstruction Techniques for Big Data Measurement

Milton Ruiz; Edwin Garcia

doi:10.1109/ACCESS.2025.3544147

Optimizing Power Quality Signal Compression: Harnessing Compressed Sensing and Reconstruction Techniques for Big Data Measurement

Grupo de Investigación en Redes Eléctricas Inteligentes (GIREI)

Research output: Article › peer-review

Abstract

The following research proposes a compression technique that combines traditional lossy compression methods with newer ones to identify properties of power quality signals. The data collected undergoes biorthogonal wavelet transformation and filter integration to remove the ripple added to the signal. The system utilizes Matching Pursuit to create an orthogonal dictionary, achieving compression ratios of 846:1. The quality indicators achieved are Percentage of Retained Energy (RTE) = 0.9969, Normalized Mean Squared Error NMSE = 0.0030, and Correlation (COR) = 0.9969, demonstrating the technique’s efficiency. This research’s results surpass the most relevant papers in Q1 journals.

Original language	English
Pages (from-to)	36339-36347
Number of pages	9
Journal	IEEE Access
Volume	13
DOIs	https://doi.org/10.1109/ACCESS.2025.3544147
Publication status	Published - 2025

Bibliographical note

Publisher Copyright:
© 2013 IEEE.

Areas de Conocimiento del CACES

317A Electricidad y energía

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10.1109/ACCESS.2025.3544147

Cite this

@article{d18305458fcb4b169982b76a396c789c,

title = "Optimizing Power Quality Signal Compression: Harnessing Compressed Sensing and Reconstruction Techniques for Big Data Measurement",

abstract = "The following research proposes a compression technique that combines traditional lossy compression methods with newer ones to identify properties of power quality signals. The data collected undergoes biorthogonal wavelet transformation and filter integration to remove the ripple added to the signal. The system utilizes Matching Pursuit to create an orthogonal dictionary, achieving compression ratios of 846:1. The quality indicators achieved are Percentage of Retained Energy (RTE) = 0.9969, Normalized Mean Squared Error NMSE = 0.0030, and Correlation (COR) = 0.9969, demonstrating the technique{\textquoteright}s efficiency. This research{\textquoteright}s results surpass the most relevant papers in Q1 journals.",

keywords = "big data, lossless compression, matching pursuit, Power quality, signal processing",

author = "Milton Ruiz and Edwin Garcia",

note = "Publisher Copyright: {\textcopyright} 2013 IEEE.",

year = "2025",

doi = "10.1109/ACCESS.2025.3544147",

language = "English",

volume = "13",

pages = "36339--36347",

journal = "IEEE Access",

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T1 - Optimizing Power Quality Signal Compression

T2 - Harnessing Compressed Sensing and Reconstruction Techniques for Big Data Measurement

AU - Ruiz, Milton

AU - Garcia, Edwin

PY - 2025

Y1 - 2025

N2 - The following research proposes a compression technique that combines traditional lossy compression methods with newer ones to identify properties of power quality signals. The data collected undergoes biorthogonal wavelet transformation and filter integration to remove the ripple added to the signal. The system utilizes Matching Pursuit to create an orthogonal dictionary, achieving compression ratios of 846:1. The quality indicators achieved are Percentage of Retained Energy (RTE) = 0.9969, Normalized Mean Squared Error NMSE = 0.0030, and Correlation (COR) = 0.9969, demonstrating the technique’s efficiency. This research’s results surpass the most relevant papers in Q1 journals.

AB - The following research proposes a compression technique that combines traditional lossy compression methods with newer ones to identify properties of power quality signals. The data collected undergoes biorthogonal wavelet transformation and filter integration to remove the ripple added to the signal. The system utilizes Matching Pursuit to create an orthogonal dictionary, achieving compression ratios of 846:1. The quality indicators achieved are Percentage of Retained Energy (RTE) = 0.9969, Normalized Mean Squared Error NMSE = 0.0030, and Correlation (COR) = 0.9969, demonstrating the technique’s efficiency. This research’s results surpass the most relevant papers in Q1 journals.

KW - big data

KW - lossless compression

KW - matching pursuit

KW - Power quality

KW - signal processing

UR - https://www.scopus.com/pages/publications/85218789843

U2 - 10.1109/ACCESS.2025.3544147

DO - 10.1109/ACCESS.2025.3544147

M3 - Article

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SN - 2169-3536

VL - 13

SP - 36339

EP - 36347

JO - IEEE Access

JF - IEEE Access

ER -

Optimizing Power Quality Signal Compression: Harnessing Compressed Sensing and Reconstruction Techniques for Big Data Measurement

Abstract

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