Identification of electromechanical oscillatory modes based on variational mode decomposition
• A novel strategy for identifying dominant oscillatory modes’ features including high noise tolerance is proposed by an optimal and recursive formulation. • This paper introduces variational mode decomposition from biomedical engineering applications to power systems with the aim of estimating osci...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Arrieta Paternina, Mario R. [verfasserIn] |
|---|
| Format: |
E-Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2019 |
|---|
| Schlagwörter: |
|---|
| Umfang: |
15 |
|---|
| Übergeordnetes Werk: |
Enthalten in: MEALPY: An open-source library for latest meta-heuristic algorithms in Python - Van Thieu, Nguyen ELSEVIER, 2023, an international journal devoted to research and new applications in generation, transmission, distribution and utilization of electric power, Amsterdam [u.a.] |
|---|---|
| Übergeordnetes Werk: |
volume:167 ; year:2019 ; pages:71-85 ; extent:15 |
| Links: |
|---|
| DOI / URN: |
10.1016/j.epsr.2018.10.014 |
|---|
| Katalog-ID: |
ELV045108978 |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | ELV045108978 | ||
| 003 | DE-627 | ||
| 005 | 20230624113627.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 190205s2019 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1016/j.epsr.2018.10.014 |2 doi | |
| 028 | 5 | 2 | |a GBV00000000000500.pica |
| 035 | |a (DE-627)ELV045108978 | ||
| 035 | |a (ELSEVIER)S0378-7796(18)30332-8 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 082 | 0 | 4 | |a 004 |q VZ |
| 084 | |a 54.30 |2 bkl | ||
| 100 | 1 | |a Arrieta Paternina, Mario R. |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Identification of electromechanical oscillatory modes based on variational mode decomposition |
| 264 | 1 | |c 2019 | |
| 300 | |a 15 | ||
| 336 | |a nicht spezifiziert |b zzz |2 rdacontent | ||
| 337 | |a nicht spezifiziert |b z |2 rdamedia | ||
| 338 | |a nicht spezifiziert |b zu |2 rdacarrier | ||
| 520 | |a • A novel strategy for identifying dominant oscillatory modes’ features including high noise tolerance is proposed by an optimal and recursive formulation. • This paper introduces variational mode decomposition from biomedical engineering applications to power systems with the aim of estimating oscillatory modes. • The paper corroborates the ability of Hilbert's subspace for tracking modal parameters. • The proposed approach reduces the requirement for pre-processing tools, which increases its potential for real-time applications. | ||
| 650 | 7 | |a Hilbert transform |2 Elsevier | |
| 650 | 7 | |a Power oscillations |2 Elsevier | |
| 650 | 7 | |a Variational mode decomposition |2 Elsevier | |
| 650 | 7 | |a Electromechanical modes |2 Elsevier | |
| 650 | 7 | |a Signal decomposition |2 Elsevier | |
| 700 | 1 | |a Tripathy, Rajesh Kumar |4 oth | |
| 700 | 1 | |a Zamora-Mendez, Alejandro |4 oth | |
| 700 | 1 | |a Dotta, Daniel |4 oth | |
| 773 | 0 | 8 | |i Enthalten in |n Elsevier Science |a Van Thieu, Nguyen ELSEVIER |t MEALPY: An open-source library for latest meta-heuristic algorithms in Python |d 2023 |d an international journal devoted to research and new applications in generation, transmission, distribution and utilization of electric power |g Amsterdam [u.a.] |w (DE-627)ELV009857966 |
| 773 | 1 | 8 | |g volume:167 |g year:2019 |g pages:71-85 |g extent:15 |
| 856 | 4 | 0 | |u https://doi.org/10.1016/j.epsr.2018.10.014 |3 Volltext |
| 912 | |a GBV_USEFLAG_U | ||
| 912 | |a GBV_ELV | ||
| 912 | |a SYSFLAG_U | ||
| 936 | b | k | |a 54.30 |j Systemarchitektur: Allgemeines |x Informatik |q VZ |
| 951 | |a AR | ||
| 952 | |d 167 |j 2019 |h 71-85 |g 15 | ||
| author_variant |
p m r a pmr pmra |
|---|---|
| matchkey_str |
arrietapaterninamariortripathyrajeshkuma:2019----:dniiainflcrmcaiaoclaoyoebsdnait |
| hierarchy_sort_str |
2019 |
| bklnumber |
54.30 |
| publishDate |
2019 |
| allfields |
10.1016/j.epsr.2018.10.014 doi GBV00000000000500.pica (DE-627)ELV045108978 (ELSEVIER)S0378-7796(18)30332-8 DE-627 ger DE-627 rakwb eng 004 VZ 54.30 bkl Arrieta Paternina, Mario R. verfasserin aut Identification of electromechanical oscillatory modes based on variational mode decomposition 2019 15 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier • A novel strategy for identifying dominant oscillatory modes’ features including high noise tolerance is proposed by an optimal and recursive formulation. • This paper introduces variational mode decomposition from biomedical engineering applications to power systems with the aim of estimating oscillatory modes. • The paper corroborates the ability of Hilbert's subspace for tracking modal parameters. • The proposed approach reduces the requirement for pre-processing tools, which increases its potential for real-time applications. Hilbert transform Elsevier Power oscillations Elsevier Variational mode decomposition Elsevier Electromechanical modes Elsevier Signal decomposition Elsevier Tripathy, Rajesh Kumar oth Zamora-Mendez, Alejandro oth Dotta, Daniel oth Enthalten in Elsevier Science Van Thieu, Nguyen ELSEVIER MEALPY: An open-source library for latest meta-heuristic algorithms in Python 2023 an international journal devoted to research and new applications in generation, transmission, distribution and utilization of electric power Amsterdam [u.a.] (DE-627)ELV009857966 volume:167 year:2019 pages:71-85 extent:15 https://doi.org/10.1016/j.epsr.2018.10.014 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 54.30 Systemarchitektur: Allgemeines Informatik VZ AR 167 2019 71-85 15 |
| spelling |
10.1016/j.epsr.2018.10.014 doi GBV00000000000500.pica (DE-627)ELV045108978 (ELSEVIER)S0378-7796(18)30332-8 DE-627 ger DE-627 rakwb eng 004 VZ 54.30 bkl Arrieta Paternina, Mario R. verfasserin aut Identification of electromechanical oscillatory modes based on variational mode decomposition 2019 15 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier • A novel strategy for identifying dominant oscillatory modes’ features including high noise tolerance is proposed by an optimal and recursive formulation. • This paper introduces variational mode decomposition from biomedical engineering applications to power systems with the aim of estimating oscillatory modes. • The paper corroborates the ability of Hilbert's subspace for tracking modal parameters. • The proposed approach reduces the requirement for pre-processing tools, which increases its potential for real-time applications. Hilbert transform Elsevier Power oscillations Elsevier Variational mode decomposition Elsevier Electromechanical modes Elsevier Signal decomposition Elsevier Tripathy, Rajesh Kumar oth Zamora-Mendez, Alejandro oth Dotta, Daniel oth Enthalten in Elsevier Science Van Thieu, Nguyen ELSEVIER MEALPY: An open-source library for latest meta-heuristic algorithms in Python 2023 an international journal devoted to research and new applications in generation, transmission, distribution and utilization of electric power Amsterdam [u.a.] (DE-627)ELV009857966 volume:167 year:2019 pages:71-85 extent:15 https://doi.org/10.1016/j.epsr.2018.10.014 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 54.30 Systemarchitektur: Allgemeines Informatik VZ AR 167 2019 71-85 15 |
| allfields_unstemmed |
10.1016/j.epsr.2018.10.014 doi GBV00000000000500.pica (DE-627)ELV045108978 (ELSEVIER)S0378-7796(18)30332-8 DE-627 ger DE-627 rakwb eng 004 VZ 54.30 bkl Arrieta Paternina, Mario R. verfasserin aut Identification of electromechanical oscillatory modes based on variational mode decomposition 2019 15 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier • A novel strategy for identifying dominant oscillatory modes’ features including high noise tolerance is proposed by an optimal and recursive formulation. • This paper introduces variational mode decomposition from biomedical engineering applications to power systems with the aim of estimating oscillatory modes. • The paper corroborates the ability of Hilbert's subspace for tracking modal parameters. • The proposed approach reduces the requirement for pre-processing tools, which increases its potential for real-time applications. Hilbert transform Elsevier Power oscillations Elsevier Variational mode decomposition Elsevier Electromechanical modes Elsevier Signal decomposition Elsevier Tripathy, Rajesh Kumar oth Zamora-Mendez, Alejandro oth Dotta, Daniel oth Enthalten in Elsevier Science Van Thieu, Nguyen ELSEVIER MEALPY: An open-source library for latest meta-heuristic algorithms in Python 2023 an international journal devoted to research and new applications in generation, transmission, distribution and utilization of electric power Amsterdam [u.a.] (DE-627)ELV009857966 volume:167 year:2019 pages:71-85 extent:15 https://doi.org/10.1016/j.epsr.2018.10.014 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 54.30 Systemarchitektur: Allgemeines Informatik VZ AR 167 2019 71-85 15 |
| allfieldsGer |
10.1016/j.epsr.2018.10.014 doi GBV00000000000500.pica (DE-627)ELV045108978 (ELSEVIER)S0378-7796(18)30332-8 DE-627 ger DE-627 rakwb eng 004 VZ 54.30 bkl Arrieta Paternina, Mario R. verfasserin aut Identification of electromechanical oscillatory modes based on variational mode decomposition 2019 15 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier • A novel strategy for identifying dominant oscillatory modes’ features including high noise tolerance is proposed by an optimal and recursive formulation. • This paper introduces variational mode decomposition from biomedical engineering applications to power systems with the aim of estimating oscillatory modes. • The paper corroborates the ability of Hilbert's subspace for tracking modal parameters. • The proposed approach reduces the requirement for pre-processing tools, which increases its potential for real-time applications. Hilbert transform Elsevier Power oscillations Elsevier Variational mode decomposition Elsevier Electromechanical modes Elsevier Signal decomposition Elsevier Tripathy, Rajesh Kumar oth Zamora-Mendez, Alejandro oth Dotta, Daniel oth Enthalten in Elsevier Science Van Thieu, Nguyen ELSEVIER MEALPY: An open-source library for latest meta-heuristic algorithms in Python 2023 an international journal devoted to research and new applications in generation, transmission, distribution and utilization of electric power Amsterdam [u.a.] (DE-627)ELV009857966 volume:167 year:2019 pages:71-85 extent:15 https://doi.org/10.1016/j.epsr.2018.10.014 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 54.30 Systemarchitektur: Allgemeines Informatik VZ AR 167 2019 71-85 15 |
| allfieldsSound |
10.1016/j.epsr.2018.10.014 doi GBV00000000000500.pica (DE-627)ELV045108978 (ELSEVIER)S0378-7796(18)30332-8 DE-627 ger DE-627 rakwb eng 004 VZ 54.30 bkl Arrieta Paternina, Mario R. verfasserin aut Identification of electromechanical oscillatory modes based on variational mode decomposition 2019 15 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier • A novel strategy for identifying dominant oscillatory modes’ features including high noise tolerance is proposed by an optimal and recursive formulation. • This paper introduces variational mode decomposition from biomedical engineering applications to power systems with the aim of estimating oscillatory modes. • The paper corroborates the ability of Hilbert's subspace for tracking modal parameters. • The proposed approach reduces the requirement for pre-processing tools, which increases its potential for real-time applications. Hilbert transform Elsevier Power oscillations Elsevier Variational mode decomposition Elsevier Electromechanical modes Elsevier Signal decomposition Elsevier Tripathy, Rajesh Kumar oth Zamora-Mendez, Alejandro oth Dotta, Daniel oth Enthalten in Elsevier Science Van Thieu, Nguyen ELSEVIER MEALPY: An open-source library for latest meta-heuristic algorithms in Python 2023 an international journal devoted to research and new applications in generation, transmission, distribution and utilization of electric power Amsterdam [u.a.] (DE-627)ELV009857966 volume:167 year:2019 pages:71-85 extent:15 https://doi.org/10.1016/j.epsr.2018.10.014 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 54.30 Systemarchitektur: Allgemeines Informatik VZ AR 167 2019 71-85 15 |
| language |
English |
| source |
Enthalten in MEALPY: An open-source library for latest meta-heuristic algorithms in Python Amsterdam [u.a.] volume:167 year:2019 pages:71-85 extent:15 |
| sourceStr |
Enthalten in MEALPY: An open-source library for latest meta-heuristic algorithms in Python Amsterdam [u.a.] volume:167 year:2019 pages:71-85 extent:15 |
| format_phy_str_mv |
Article |
| bklname |
Systemarchitektur: Allgemeines |
| institution |
findex.gbv.de |
| topic_facet |
Hilbert transform Power oscillations Variational mode decomposition Electromechanical modes Signal decomposition |
| dewey-raw |
004 |
| isfreeaccess_bool |
false |
| container_title |
MEALPY: An open-source library for latest meta-heuristic algorithms in Python |
| authorswithroles_txt_mv |
Arrieta Paternina, Mario R. @@aut@@ Tripathy, Rajesh Kumar @@oth@@ Zamora-Mendez, Alejandro @@oth@@ Dotta, Daniel @@oth@@ |
| publishDateDaySort_date |
2019-01-01T00:00:00Z |
| hierarchy_top_id |
ELV009857966 |
| dewey-sort |
14 |
| id |
ELV045108978 |
| language_de |
englisch |
| fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">ELV045108978</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230624113627.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">190205s2019 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.epsr.2018.10.014</subfield><subfield code="2">doi</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">GBV00000000000500.pica</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV045108978</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S0378-7796(18)30332-8</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">004</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">54.30</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Arrieta Paternina, Mario R.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Identification of electromechanical oscillatory modes based on variational mode decomposition</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2019</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">15</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">z</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zu</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">• A novel strategy for identifying dominant oscillatory modes’ features including high noise tolerance is proposed by an optimal and recursive formulation. • This paper introduces variational mode decomposition from biomedical engineering applications to power systems with the aim of estimating oscillatory modes. • The paper corroborates the ability of Hilbert's subspace for tracking modal parameters. • The proposed approach reduces the requirement for pre-processing tools, which increases its potential for real-time applications.</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Hilbert transform</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Power oscillations</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Variational mode decomposition</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Electromechanical modes</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Signal decomposition</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Tripathy, Rajesh Kumar</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Zamora-Mendez, Alejandro</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Dotta, Daniel</subfield><subfield code="4">oth</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="n">Elsevier Science</subfield><subfield code="a">Van Thieu, Nguyen ELSEVIER</subfield><subfield code="t">MEALPY: An open-source library for latest meta-heuristic algorithms in Python</subfield><subfield code="d">2023</subfield><subfield code="d">an international journal devoted to research and new applications in generation, transmission, distribution and utilization of electric power</subfield><subfield code="g">Amsterdam [u.a.]</subfield><subfield code="w">(DE-627)ELV009857966</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:167</subfield><subfield code="g">year:2019</subfield><subfield code="g">pages:71-85</subfield><subfield code="g">extent:15</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1016/j.epsr.2018.10.014</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ELV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_U</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">54.30</subfield><subfield code="j">Systemarchitektur: Allgemeines</subfield><subfield code="x">Informatik</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">167</subfield><subfield code="j">2019</subfield><subfield code="h">71-85</subfield><subfield code="g">15</subfield></datafield></record></collection>
|
| author |
Arrieta Paternina, Mario R. |
| spellingShingle |
Arrieta Paternina, Mario R. ddc 004 bkl 54.30 Elsevier Hilbert transform Elsevier Power oscillations Elsevier Variational mode decomposition Elsevier Electromechanical modes Elsevier Signal decomposition Identification of electromechanical oscillatory modes based on variational mode decomposition |
| authorStr |
Arrieta Paternina, Mario R. |
| ppnlink_with_tag_str_mv |
@@773@@(DE-627)ELV009857966 |
| format |
electronic Article |
| dewey-ones |
004 - Data processing & computer science |
| delete_txt_mv |
keep |
| author_role |
aut |
| collection |
elsevier |
| remote_str |
true |
| illustrated |
Not Illustrated |
| topic_title |
004 VZ 54.30 bkl Identification of electromechanical oscillatory modes based on variational mode decomposition Hilbert transform Elsevier Power oscillations Elsevier Variational mode decomposition Elsevier Electromechanical modes Elsevier Signal decomposition Elsevier |
| topic |
ddc 004 bkl 54.30 Elsevier Hilbert transform Elsevier Power oscillations Elsevier Variational mode decomposition Elsevier Electromechanical modes Elsevier Signal decomposition |
| topic_unstemmed |
ddc 004 bkl 54.30 Elsevier Hilbert transform Elsevier Power oscillations Elsevier Variational mode decomposition Elsevier Electromechanical modes Elsevier Signal decomposition |
| topic_browse |
ddc 004 bkl 54.30 Elsevier Hilbert transform Elsevier Power oscillations Elsevier Variational mode decomposition Elsevier Electromechanical modes Elsevier Signal decomposition |
| format_facet |
Elektronische Aufsätze Aufsätze Elektronische Ressource |
| format_main_str_mv |
Text Zeitschrift/Artikel |
| carriertype_str_mv |
zu |
| author2_variant |
r k t rk rkt a z m azm d d dd |
| hierarchy_parent_title |
MEALPY: An open-source library for latest meta-heuristic algorithms in Python |
| hierarchy_parent_id |
ELV009857966 |
| dewey-tens |
000 - Computer science, knowledge & systems |
| hierarchy_top_title |
MEALPY: An open-source library for latest meta-heuristic algorithms in Python |
| isfreeaccess_txt |
false |
| familylinks_str_mv |
(DE-627)ELV009857966 |
| title |
Identification of electromechanical oscillatory modes based on variational mode decomposition |
| ctrlnum |
(DE-627)ELV045108978 (ELSEVIER)S0378-7796(18)30332-8 |
| title_full |
Identification of electromechanical oscillatory modes based on variational mode decomposition |
| author_sort |
Arrieta Paternina, Mario R. |
| journal |
MEALPY: An open-source library for latest meta-heuristic algorithms in Python |
| journalStr |
MEALPY: An open-source library for latest meta-heuristic algorithms in Python |
| lang_code |
eng |
| isOA_bool |
false |
| dewey-hundreds |
000 - Computer science, information & general works |
| recordtype |
marc |
| publishDateSort |
2019 |
| contenttype_str_mv |
zzz |
| container_start_page |
71 |
| author_browse |
Arrieta Paternina, Mario R. |
| container_volume |
167 |
| physical |
15 |
| class |
004 VZ 54.30 bkl |
| format_se |
Elektronische Aufsätze |
| author-letter |
Arrieta Paternina, Mario R. |
| doi_str_mv |
10.1016/j.epsr.2018.10.014 |
| dewey-full |
004 |
| title_sort |
identification of electromechanical oscillatory modes based on variational mode decomposition |
| title_auth |
Identification of electromechanical oscillatory modes based on variational mode decomposition |
| abstract |
• A novel strategy for identifying dominant oscillatory modes’ features including high noise tolerance is proposed by an optimal and recursive formulation. • This paper introduces variational mode decomposition from biomedical engineering applications to power systems with the aim of estimating oscillatory modes. • The paper corroborates the ability of Hilbert's subspace for tracking modal parameters. • The proposed approach reduces the requirement for pre-processing tools, which increases its potential for real-time applications. |
| abstractGer |
• A novel strategy for identifying dominant oscillatory modes’ features including high noise tolerance is proposed by an optimal and recursive formulation. • This paper introduces variational mode decomposition from biomedical engineering applications to power systems with the aim of estimating oscillatory modes. • The paper corroborates the ability of Hilbert's subspace for tracking modal parameters. • The proposed approach reduces the requirement for pre-processing tools, which increases its potential for real-time applications. |
| abstract_unstemmed |
• A novel strategy for identifying dominant oscillatory modes’ features including high noise tolerance is proposed by an optimal and recursive formulation. • This paper introduces variational mode decomposition from biomedical engineering applications to power systems with the aim of estimating oscillatory modes. • The paper corroborates the ability of Hilbert's subspace for tracking modal parameters. • The proposed approach reduces the requirement for pre-processing tools, which increases its potential for real-time applications. |
| collection_details |
GBV_USEFLAG_U GBV_ELV SYSFLAG_U |
| title_short |
Identification of electromechanical oscillatory modes based on variational mode decomposition |
| url |
https://doi.org/10.1016/j.epsr.2018.10.014 |
| remote_bool |
true |
| author2 |
Tripathy, Rajesh Kumar Zamora-Mendez, Alejandro Dotta, Daniel |
| author2Str |
Tripathy, Rajesh Kumar Zamora-Mendez, Alejandro Dotta, Daniel |
| ppnlink |
ELV009857966 |
| mediatype_str_mv |
z |
| isOA_txt |
false |
| hochschulschrift_bool |
false |
| author2_role |
oth oth oth |
| doi_str |
10.1016/j.epsr.2018.10.014 |
| up_date |
2024-07-06T16:37:51.660Z |
| _version_ |
1803848381013950464 |
| fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">ELV045108978</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230624113627.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">190205s2019 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.epsr.2018.10.014</subfield><subfield code="2">doi</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">GBV00000000000500.pica</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV045108978</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S0378-7796(18)30332-8</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">004</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">54.30</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Arrieta Paternina, Mario R.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Identification of electromechanical oscillatory modes based on variational mode decomposition</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2019</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">15</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">z</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zu</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">• A novel strategy for identifying dominant oscillatory modes’ features including high noise tolerance is proposed by an optimal and recursive formulation. • This paper introduces variational mode decomposition from biomedical engineering applications to power systems with the aim of estimating oscillatory modes. • The paper corroborates the ability of Hilbert's subspace for tracking modal parameters. • The proposed approach reduces the requirement for pre-processing tools, which increases its potential for real-time applications.</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Hilbert transform</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Power oscillations</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Variational mode decomposition</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Electromechanical modes</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Signal decomposition</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Tripathy, Rajesh Kumar</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Zamora-Mendez, Alejandro</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Dotta, Daniel</subfield><subfield code="4">oth</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="n">Elsevier Science</subfield><subfield code="a">Van Thieu, Nguyen ELSEVIER</subfield><subfield code="t">MEALPY: An open-source library for latest meta-heuristic algorithms in Python</subfield><subfield code="d">2023</subfield><subfield code="d">an international journal devoted to research and new applications in generation, transmission, distribution and utilization of electric power</subfield><subfield code="g">Amsterdam [u.a.]</subfield><subfield code="w">(DE-627)ELV009857966</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:167</subfield><subfield code="g">year:2019</subfield><subfield code="g">pages:71-85</subfield><subfield code="g">extent:15</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1016/j.epsr.2018.10.014</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ELV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_U</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">54.30</subfield><subfield code="j">Systemarchitektur: Allgemeines</subfield><subfield code="x">Informatik</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">167</subfield><subfield code="j">2019</subfield><subfield code="h">71-85</subfield><subfield code="g">15</subfield></datafield></record></collection>
|
| score |
7.39787 |