Optimizing Power Quality Signal Compression: Harnessing Compressed Sensing and Reconstruction Techniques for Big Data Measurement
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 signa...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10896674/ |
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| author | Milton Ruiz Edwin Garcia |
| author_facet | Milton Ruiz Edwin Garcia |
| author_sort | Milton Ruiz |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-d0da2c5e54094899b97fefba239d4728 |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-d0da2c5e54094899b97fefba239d47282025-08-20T03:00:02ZengIEEEIEEE Access2169-35362025-01-0113363393634710.1109/ACCESS.2025.354414710896674Optimizing Power Quality Signal Compression: Harnessing Compressed Sensing and Reconstruction Techniques for Big Data MeasurementMilton Ruiz0https://orcid.org/0000-0001-7478-7545Edwin Garcia1Department of Electrical Engineering, GIREI Research Group, Universidad Politécnica Salesiana, Quito, EcuadorDepartment of Electrical Engineering, GIREI Research Group, Universidad Politécnica Salesiana, Quito, EcuadorThe 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.https://ieeexplore.ieee.org/document/10896674/Power qualitybig datamatching pursuitsignal processinglossless compression |
| spellingShingle | Milton Ruiz Edwin Garcia Optimizing Power Quality Signal Compression: Harnessing Compressed Sensing and Reconstruction Techniques for Big Data Measurement IEEE Access Power quality big data matching pursuit signal processing lossless compression |
| title | Optimizing Power Quality Signal Compression: Harnessing Compressed Sensing and Reconstruction Techniques for Big Data Measurement |
| title_full | Optimizing Power Quality Signal Compression: Harnessing Compressed Sensing and Reconstruction Techniques for Big Data Measurement |
| title_fullStr | Optimizing Power Quality Signal Compression: Harnessing Compressed Sensing and Reconstruction Techniques for Big Data Measurement |
| title_full_unstemmed | Optimizing Power Quality Signal Compression: Harnessing Compressed Sensing and Reconstruction Techniques for Big Data Measurement |
| title_short | Optimizing Power Quality Signal Compression: Harnessing Compressed Sensing and Reconstruction Techniques for Big Data Measurement |
| title_sort | optimizing power quality signal compression harnessing compressed sensing and reconstruction techniques for big data measurement |
| topic | Power quality big data matching pursuit signal processing lossless compression |
| url | https://ieeexplore.ieee.org/document/10896674/ |
| work_keys_str_mv | AT miltonruiz optimizingpowerqualitysignalcompressionharnessingcompressedsensingandreconstructiontechniquesforbigdatameasurement AT edwingarcia optimizingpowerqualitysignalcompressionharnessingcompressedsensingandreconstructiontechniquesforbigdatameasurement |