Investigation on Diverse Sparse Signal Decomposition Techniques for Power Signal Representation.
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| Title: | Investigation on Diverse Sparse Signal Decomposition Techniques for Power Signal Representation. |
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| Authors: | Anjali, Vivek1 (AUTHOR) viveka@am.amrita.edu, Parakkatu Kesava Panikkar, Preetha1 (AUTHOR) preethapk@am.amrita.edu |
| Source: | Energies (19961073). May2026, Vol. 19 Issue 10, p2399. 20p. |
| Subject Terms: | *Orthogonal matching pursuit, *Sparse approximations, *Optimization algorithms, *Data compression, *Electric power systems, *Power quality disturbances, *Compressed sensing |
| Abstract: | Power quality disturbance signals must be continuously monitored, stored, and transmitted for effective analysis, protection, and system planning in modern power systems. The large volume of data generated during power quality monitoring necessitates efficient storage techniques. The sparse representation of power quality signals can significantly reduce memory requirements while preserving important signal characteristics. Since several techniques exist for obtaining sparse representations, it is important to identify the most suitable Sparse Signal Decomposition (SSD) technique for different power quality disturbances. This paper presents a comparative study of various SSD techniques, including Orthogonal Matching Pursuit (OMP), Matching Pursuit (MP), Least Squares–Orthogonal Matching Pursuit (LS-OMP), and Thresholding and Basis Pursuit (BP), along with diverse dictionaries for the representation of power quality disturbances such as sag, swell, transients, and harmonics. Mean Square Error (MSE) and the ratio between the actual signals and reconstructed signals (A/R ratio) are used to evaluate the accuracy, while computation time is considered to compare the computational speed of different techniques. Simulation studies are carried out in MATLAB to evaluate the effectiveness of the SSD techniques. From the simulation results, it is observed that OMP and LS-OMP provide accurate representations of power quality disturbance signals. For sag, swell, and transients, the impulse dictionary performs best with OMP, offering faster computation. However, for harmonics, OMP with DCT dictionary is found to be more effective. [ABSTRACT FROM AUTHOR] |
| Database: | Energy & Power Source |
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| Header | DbId: enr DbLabel: Energy & Power Source An: 194141514 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Investigation on Diverse Sparse Signal Decomposition Techniques for Power Signal Representation. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Anjali%2C+Vivek%22">Anjali, Vivek</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> viveka@am.amrita.edu</i><br /><searchLink fieldCode="AR" term="%22Parakkatu+Kesava+Panikkar%2C+Preetha%22">Parakkatu Kesava Panikkar, Preetha</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> preethapk@am.amrita.edu</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Energies+%2819961073%29%22">Energies (19961073)</searchLink>. May2026, Vol. 19 Issue 10, p2399. 20p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Orthogonal+matching+pursuit%22">Orthogonal matching pursuit</searchLink><br />*<searchLink fieldCode="DE" term="%22Sparse+approximations%22">Sparse approximations</searchLink><br />*<searchLink fieldCode="DE" term="%22Optimization+algorithms%22">Optimization algorithms</searchLink><br />*<searchLink fieldCode="DE" term="%22Data+compression%22">Data compression</searchLink><br />*<searchLink fieldCode="DE" term="%22Electric+power+systems%22">Electric power systems</searchLink><br />*<searchLink fieldCode="DE" term="%22Power+quality+disturbances%22">Power quality disturbances</searchLink><br />*<searchLink fieldCode="DE" term="%22Compressed+sensing%22">Compressed sensing</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Power quality disturbance signals must be continuously monitored, stored, and transmitted for effective analysis, protection, and system planning in modern power systems. The large volume of data generated during power quality monitoring necessitates efficient storage techniques. The sparse representation of power quality signals can significantly reduce memory requirements while preserving important signal characteristics. Since several techniques exist for obtaining sparse representations, it is important to identify the most suitable Sparse Signal Decomposition (SSD) technique for different power quality disturbances. This paper presents a comparative study of various SSD techniques, including Orthogonal Matching Pursuit (OMP), Matching Pursuit (MP), Least Squares–Orthogonal Matching Pursuit (LS-OMP), and Thresholding and Basis Pursuit (BP), along with diverse dictionaries for the representation of power quality disturbances such as sag, swell, transients, and harmonics. Mean Square Error (MSE) and the ratio between the actual signals and reconstructed signals (A/R ratio) are used to evaluate the accuracy, while computation time is considered to compare the computational speed of different techniques. Simulation studies are carried out in MATLAB to evaluate the effectiveness of the SSD techniques. From the simulation results, it is observed that OMP and LS-OMP provide accurate representations of power quality disturbance signals. For sag, swell, and transients, the impulse dictionary performs best with OMP, offering faster computation. However, for harmonics, OMP with DCT dictionary is found to be more effective. [ABSTRACT FROM AUTHOR] |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=enr&AN=194141514 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/en19102399 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 20 StartPage: 2399 Subjects: – SubjectFull: Orthogonal matching pursuit Type: general – SubjectFull: Sparse approximations Type: general – SubjectFull: Optimization algorithms Type: general – SubjectFull: Data compression Type: general – SubjectFull: Electric power systems Type: general – SubjectFull: Power quality disturbances Type: general – SubjectFull: Compressed sensing Type: general Titles: – TitleFull: Investigation on Diverse Sparse Signal Decomposition Techniques for Power Signal Representation. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Anjali, Vivek – PersonEntity: Name: NameFull: Parakkatu Kesava Panikkar, Preetha IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 05 Text: May2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 19961073 Numbering: – Type: volume Value: 19 – Type: issue Value: 10 Titles: – TitleFull: Energies (19961073) Type: main |
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