Enhancing Empirical Modal Extraction by Logarithmic Scaling and Normalization of Multi‐Area Signal Measurements.
Saved in:
| Title: | Enhancing Empirical Modal Extraction by Logarithmic Scaling and Normalization of Multi‐Area Signal Measurements. |
|---|---|
| Authors: | Esquivel, Pedro1 (AUTHOR), Castañeda, Carlos E.2 (AUTHOR) carlose.castanedah@academicos.udg.mx, Romero, Gerardo3 (AUTHOR) gromero@uat.edu.mx, Ornelas-Tellez, Fernando4 (AUTHOR), Reyes, Evaristo Noe5 (AUTHOR), Shi, Xiasheng (AUTHOR) shixiasheng@zju.edu.cn |
| Source: | Journal of Applied Mathematics. 1/20/2026, Vol. 2026, p1-15. 15p. |
| Subjects: | Hilbert transform, Data analysis, Calibration, Phase oscillations, Scientific computing, Hilbert-Huang transform |
| Abstract: | This paper presents a hierarchical scaling and normalization method to multi‐area signal measurements that dynamically relates both the angular phase domain and its amplitude in empirical analysis of power system oscillations. The proposed approach combines logarithmic relations and the Hilbert transform to derive an effective multi‐area data scaling and normalization method, improving objectively the numerical performance and reliability of data‐based modal extraction algorithms. This method is developed in order to numerically minimize multiscale angular phase and amplitude effects in decomposition and identification processes of inter‐area oscillation modes. It employs conventional data‐based analysis algorithms on interconnected power systems to achieve this objective. Results show that the presented method guarantees the most effective description of interscale interaction effects and fluctuations among detected modal oscillation patterns, enhancing empirical modal extraction for inter‐area electromechanical modes. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Applied Mathematics is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
|
Full text is not displayed to guests.
Login for full access.
|
|
| FullText | Links: – Type: pdflink Text: Availability: 1 |
|---|---|
| Header | DbId: egs DbLabel: Engineering Source An: 190987660 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: Enhancing Empirical Modal Extraction by Logarithmic Scaling and Normalization of Multi‐Area Signal Measurements. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Esquivel%2C+Pedro%22">Esquivel, Pedro</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Castañeda%2C+Carlos+E%2E%22">Castañeda, Carlos E.</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> carlose.castanedah@academicos.udg.mx</i><br /><searchLink fieldCode="AR" term="%22Romero%2C+Gerardo%22">Romero, Gerardo</searchLink><relatesTo>3</relatesTo> (AUTHOR)<i> gromero@uat.edu.mx</i><br /><searchLink fieldCode="AR" term="%22Ornelas-Tellez%2C+Fernando%22">Ornelas-Tellez, Fernando</searchLink><relatesTo>4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Reyes%2C+Evaristo+Noe%22">Reyes, Evaristo Noe</searchLink><relatesTo>5</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Shi%2C+Xiasheng%22">Shi, Xiasheng</searchLink> (AUTHOR)<i> shixiasheng@zju.edu.cn</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+Applied+Mathematics%22">Journal of Applied Mathematics</searchLink>. 1/20/2026, Vol. 2026, p1-15. 15p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Hilbert+transform%22">Hilbert transform</searchLink><br /><searchLink fieldCode="DE" term="%22Data+analysis%22">Data analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Calibration%22">Calibration</searchLink><br /><searchLink fieldCode="DE" term="%22Phase+oscillations%22">Phase oscillations</searchLink><br /><searchLink fieldCode="DE" term="%22Scientific+computing%22">Scientific computing</searchLink><br /><searchLink fieldCode="DE" term="%22Hilbert-Huang+transform%22">Hilbert-Huang transform</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: This paper presents a hierarchical scaling and normalization method to multi‐area signal measurements that dynamically relates both the angular phase domain and its amplitude in empirical analysis of power system oscillations. The proposed approach combines logarithmic relations and the Hilbert transform to derive an effective multi‐area data scaling and normalization method, improving objectively the numerical performance and reliability of data‐based modal extraction algorithms. This method is developed in order to numerically minimize multiscale angular phase and amplitude effects in decomposition and identification processes of inter‐area oscillation modes. It employs conventional data‐based analysis algorithms on interconnected power systems to achieve this objective. Results show that the presented method guarantees the most effective description of interscale interaction effects and fluctuations among detected modal oscillation patterns, enhancing empirical modal extraction for inter‐area electromechanical modes. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Journal of Applied Mathematics is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=190987660 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1155/jama/8884983 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 15 StartPage: 1 Subjects: – SubjectFull: Hilbert transform Type: general – SubjectFull: Data analysis Type: general – SubjectFull: Calibration Type: general – SubjectFull: Phase oscillations Type: general – SubjectFull: Scientific computing Type: general – SubjectFull: Hilbert-Huang transform Type: general Titles: – TitleFull: Enhancing Empirical Modal Extraction by Logarithmic Scaling and Normalization of Multi‐Area Signal Measurements. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Esquivel, Pedro – PersonEntity: Name: NameFull: Castañeda, Carlos E. – PersonEntity: Name: NameFull: Romero, Gerardo – PersonEntity: Name: NameFull: Ornelas-Tellez, Fernando – PersonEntity: Name: NameFull: Reyes, Evaristo Noe – PersonEntity: Name: NameFull: Shi, Xiasheng IsPartOfRelationships: – BibEntity: Dates: – D: 20 M: 01 Text: 1/20/2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 1110757X Numbering: – Type: volume Value: 2026 Titles: – TitleFull: Journal of Applied Mathematics Type: main |
| ResultId | 1 |
