Frequency estimation in wind farm integrated systems using artificial neural network
• We model a three-layer neural network using an improved elastic back-propagation algorithm. • We verify the effectiveness of the method with actual wind farm data. • The forecasting curve can respond to the change in system frequency in real time. • We compare the improved method with the other tw...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Jiang, Wang [verfasserIn] |
|---|
| Format: |
E-Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2014 |
|---|
| Schlagwörter: |
|---|
| Umfang: |
8 |
|---|
| Übergeordnetes Werk: |
Enthalten in: Synthesis and characterization of AsO[(W,Mo)O - Goudjil, Meriem ELSEVIER, 2023, Amsterdam [u.a.] |
|---|---|
| Übergeordnetes Werk: |
volume:62 ; year:2014 ; pages:72-79 ; extent:8 |
| Links: |
|---|
| DOI / URN: |
10.1016/j.ijepes.2014.04.027 |
|---|
| Katalog-ID: |
ELV022415238 |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | ELV022415238 | ||
| 003 | DE-627 | ||
| 005 | 20230623163851.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 180603s2014 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1016/j.ijepes.2014.04.027 |2 doi | |
| 028 | 5 | 2 | |a GBVA2014003000017.pica |
| 035 | |a (DE-627)ELV022415238 | ||
| 035 | |a (ELSEVIER)S0142-0615(14)00221-X | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 082 | 0 | |a 620 | |
| 082 | 0 | 4 | |a 620 |q DE-600 |
| 082 | 0 | 4 | |a 540 |q VZ |
| 084 | |a 35.90 |2 bkl | ||
| 100 | 1 | |a Jiang, Wang |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Frequency estimation in wind farm integrated systems using artificial neural network |
| 264 | 1 | |c 2014 | |
| 300 | |a 8 | ||
| 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 • We model a three-layer neural network using an improved elastic back-propagation algorithm. • We verify the effectiveness of the method with actual wind farm data. • The forecasting curve can respond to the change in system frequency in real time. • We compare the improved method with the other two algorithms in training times and error. | ||
| 650 | 7 | |a Frequency prediction |2 Elsevier | |
| 650 | 7 | |a Back-propagation algorithm |2 Elsevier | |
| 650 | 7 | |a Wind farms |2 Elsevier | |
| 700 | 1 | |a Lu, Jiping |4 oth | |
| 773 | 0 | 8 | |i Enthalten in |n Elsevier Science |a Goudjil, Meriem ELSEVIER |t Synthesis and characterization of AsO[(W,Mo)O |d 2023 |g Amsterdam [u.a.] |w (DE-627)ELV009519483 |
| 773 | 1 | 8 | |g volume:62 |g year:2014 |g pages:72-79 |g extent:8 |
| 856 | 4 | 0 | |u https://doi.org/10.1016/j.ijepes.2014.04.027 |3 Volltext |
| 912 | |a GBV_USEFLAG_U | ||
| 912 | |a GBV_ELV | ||
| 912 | |a SYSFLAG_U | ||
| 912 | |a SSG-OLC-PHA | ||
| 936 | b | k | |a 35.90 |j Festkörperchemie |q VZ |
| 951 | |a AR | ||
| 952 | |d 62 |j 2014 |h 72-79 |g 8 | ||
| 953 | |2 045F |a 620 | ||
| author_variant |
w j wj |
|---|---|
| matchkey_str |
jiangwanglujiping:2014----:rqecetmtoiwnfritgaessessnat |
| hierarchy_sort_str |
2014 |
| bklnumber |
35.90 |
| publishDate |
2014 |
| allfields |
10.1016/j.ijepes.2014.04.027 doi GBVA2014003000017.pica (DE-627)ELV022415238 (ELSEVIER)S0142-0615(14)00221-X DE-627 ger DE-627 rakwb eng 620 620 DE-600 540 VZ 35.90 bkl Jiang, Wang verfasserin aut Frequency estimation in wind farm integrated systems using artificial neural network 2014 8 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier • We model a three-layer neural network using an improved elastic back-propagation algorithm. • We verify the effectiveness of the method with actual wind farm data. • The forecasting curve can respond to the change in system frequency in real time. • We compare the improved method with the other two algorithms in training times and error. Frequency prediction Elsevier Back-propagation algorithm Elsevier Wind farms Elsevier Lu, Jiping oth Enthalten in Elsevier Science Goudjil, Meriem ELSEVIER Synthesis and characterization of AsO[(W,Mo)O 2023 Amsterdam [u.a.] (DE-627)ELV009519483 volume:62 year:2014 pages:72-79 extent:8 https://doi.org/10.1016/j.ijepes.2014.04.027 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 35.90 Festkörperchemie VZ AR 62 2014 72-79 8 045F 620 |
| spelling |
10.1016/j.ijepes.2014.04.027 doi GBVA2014003000017.pica (DE-627)ELV022415238 (ELSEVIER)S0142-0615(14)00221-X DE-627 ger DE-627 rakwb eng 620 620 DE-600 540 VZ 35.90 bkl Jiang, Wang verfasserin aut Frequency estimation in wind farm integrated systems using artificial neural network 2014 8 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier • We model a three-layer neural network using an improved elastic back-propagation algorithm. • We verify the effectiveness of the method with actual wind farm data. • The forecasting curve can respond to the change in system frequency in real time. • We compare the improved method with the other two algorithms in training times and error. Frequency prediction Elsevier Back-propagation algorithm Elsevier Wind farms Elsevier Lu, Jiping oth Enthalten in Elsevier Science Goudjil, Meriem ELSEVIER Synthesis and characterization of AsO[(W,Mo)O 2023 Amsterdam [u.a.] (DE-627)ELV009519483 volume:62 year:2014 pages:72-79 extent:8 https://doi.org/10.1016/j.ijepes.2014.04.027 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 35.90 Festkörperchemie VZ AR 62 2014 72-79 8 045F 620 |
| allfields_unstemmed |
10.1016/j.ijepes.2014.04.027 doi GBVA2014003000017.pica (DE-627)ELV022415238 (ELSEVIER)S0142-0615(14)00221-X DE-627 ger DE-627 rakwb eng 620 620 DE-600 540 VZ 35.90 bkl Jiang, Wang verfasserin aut Frequency estimation in wind farm integrated systems using artificial neural network 2014 8 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier • We model a three-layer neural network using an improved elastic back-propagation algorithm. • We verify the effectiveness of the method with actual wind farm data. • The forecasting curve can respond to the change in system frequency in real time. • We compare the improved method with the other two algorithms in training times and error. Frequency prediction Elsevier Back-propagation algorithm Elsevier Wind farms Elsevier Lu, Jiping oth Enthalten in Elsevier Science Goudjil, Meriem ELSEVIER Synthesis and characterization of AsO[(W,Mo)O 2023 Amsterdam [u.a.] (DE-627)ELV009519483 volume:62 year:2014 pages:72-79 extent:8 https://doi.org/10.1016/j.ijepes.2014.04.027 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 35.90 Festkörperchemie VZ AR 62 2014 72-79 8 045F 620 |
| allfieldsGer |
10.1016/j.ijepes.2014.04.027 doi GBVA2014003000017.pica (DE-627)ELV022415238 (ELSEVIER)S0142-0615(14)00221-X DE-627 ger DE-627 rakwb eng 620 620 DE-600 540 VZ 35.90 bkl Jiang, Wang verfasserin aut Frequency estimation in wind farm integrated systems using artificial neural network 2014 8 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier • We model a three-layer neural network using an improved elastic back-propagation algorithm. • We verify the effectiveness of the method with actual wind farm data. • The forecasting curve can respond to the change in system frequency in real time. • We compare the improved method with the other two algorithms in training times and error. Frequency prediction Elsevier Back-propagation algorithm Elsevier Wind farms Elsevier Lu, Jiping oth Enthalten in Elsevier Science Goudjil, Meriem ELSEVIER Synthesis and characterization of AsO[(W,Mo)O 2023 Amsterdam [u.a.] (DE-627)ELV009519483 volume:62 year:2014 pages:72-79 extent:8 https://doi.org/10.1016/j.ijepes.2014.04.027 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 35.90 Festkörperchemie VZ AR 62 2014 72-79 8 045F 620 |
| allfieldsSound |
10.1016/j.ijepes.2014.04.027 doi GBVA2014003000017.pica (DE-627)ELV022415238 (ELSEVIER)S0142-0615(14)00221-X DE-627 ger DE-627 rakwb eng 620 620 DE-600 540 VZ 35.90 bkl Jiang, Wang verfasserin aut Frequency estimation in wind farm integrated systems using artificial neural network 2014 8 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier • We model a three-layer neural network using an improved elastic back-propagation algorithm. • We verify the effectiveness of the method with actual wind farm data. • The forecasting curve can respond to the change in system frequency in real time. • We compare the improved method with the other two algorithms in training times and error. Frequency prediction Elsevier Back-propagation algorithm Elsevier Wind farms Elsevier Lu, Jiping oth Enthalten in Elsevier Science Goudjil, Meriem ELSEVIER Synthesis and characterization of AsO[(W,Mo)O 2023 Amsterdam [u.a.] (DE-627)ELV009519483 volume:62 year:2014 pages:72-79 extent:8 https://doi.org/10.1016/j.ijepes.2014.04.027 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 35.90 Festkörperchemie VZ AR 62 2014 72-79 8 045F 620 |
| language |
English |
| source |
Enthalten in Synthesis and characterization of AsO[(W,Mo)O Amsterdam [u.a.] volume:62 year:2014 pages:72-79 extent:8 |
| sourceStr |
Enthalten in Synthesis and characterization of AsO[(W,Mo)O Amsterdam [u.a.] volume:62 year:2014 pages:72-79 extent:8 |
| format_phy_str_mv |
Article |
| bklname |
Festkörperchemie |
| institution |
findex.gbv.de |
| topic_facet |
Frequency prediction Back-propagation algorithm Wind farms |
| dewey-raw |
620 |
| isfreeaccess_bool |
false |
| container_title |
Synthesis and characterization of AsO[(W,Mo)O |
| authorswithroles_txt_mv |
Jiang, Wang @@aut@@ Lu, Jiping @@oth@@ |
| publishDateDaySort_date |
2014-01-01T00:00:00Z |
| hierarchy_top_id |
ELV009519483 |
| dewey-sort |
3620 |
| id |
ELV022415238 |
| 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">ELV022415238</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230623163851.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">180603s2014 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.ijepes.2014.04.027</subfield><subfield code="2">doi</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">GBVA2014003000017.pica</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV022415238</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S0142-0615(14)00221-X</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=" "><subfield code="a">620</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">620</subfield><subfield code="q">DE-600</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">540</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">35.90</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Jiang, Wang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Frequency estimation in wind farm integrated systems using artificial neural network</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2014</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">8</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">• We model a three-layer neural network using an improved elastic back-propagation algorithm. • We verify the effectiveness of the method with actual wind farm data. • The forecasting curve can respond to the change in system frequency in real time. • We compare the improved method with the other two algorithms in training times and error.</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Frequency prediction</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Back-propagation algorithm</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Wind farms</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Lu, Jiping</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">Goudjil, Meriem ELSEVIER</subfield><subfield code="t">Synthesis and characterization of AsO[(W,Mo)O</subfield><subfield code="d">2023</subfield><subfield code="g">Amsterdam [u.a.]</subfield><subfield code="w">(DE-627)ELV009519483</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:62</subfield><subfield code="g">year:2014</subfield><subfield code="g">pages:72-79</subfield><subfield code="g">extent:8</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1016/j.ijepes.2014.04.027</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="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHA</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">35.90</subfield><subfield code="j">Festkörperchemie</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">62</subfield><subfield code="j">2014</subfield><subfield code="h">72-79</subfield><subfield code="g">8</subfield></datafield><datafield tag="953" ind1=" " ind2=" "><subfield code="2">045F</subfield><subfield code="a">620</subfield></datafield></record></collection>
|
| author |
Jiang, Wang |
| spellingShingle |
Jiang, Wang ddc 620 ddc 540 bkl 35.90 Elsevier Frequency prediction Elsevier Back-propagation algorithm Elsevier Wind farms Frequency estimation in wind farm integrated systems using artificial neural network |
| authorStr |
Jiang, Wang |
| ppnlink_with_tag_str_mv |
@@773@@(DE-627)ELV009519483 |
| format |
electronic Article |
| dewey-ones |
620 - Engineering & allied operations 540 - Chemistry & allied sciences |
| delete_txt_mv |
keep |
| author_role |
aut |
| collection |
elsevier |
| remote_str |
true |
| illustrated |
Not Illustrated |
| topic_title |
620 620 DE-600 540 VZ 35.90 bkl Frequency estimation in wind farm integrated systems using artificial neural network Frequency prediction Elsevier Back-propagation algorithm Elsevier Wind farms Elsevier |
| topic |
ddc 620 ddc 540 bkl 35.90 Elsevier Frequency prediction Elsevier Back-propagation algorithm Elsevier Wind farms |
| topic_unstemmed |
ddc 620 ddc 540 bkl 35.90 Elsevier Frequency prediction Elsevier Back-propagation algorithm Elsevier Wind farms |
| topic_browse |
ddc 620 ddc 540 bkl 35.90 Elsevier Frequency prediction Elsevier Back-propagation algorithm Elsevier Wind farms |
| format_facet |
Elektronische Aufsätze Aufsätze Elektronische Ressource |
| format_main_str_mv |
Text Zeitschrift/Artikel |
| carriertype_str_mv |
zu |
| author2_variant |
j l jl |
| hierarchy_parent_title |
Synthesis and characterization of AsO[(W,Mo)O |
| hierarchy_parent_id |
ELV009519483 |
| dewey-tens |
620 - Engineering 540 - Chemistry |
| hierarchy_top_title |
Synthesis and characterization of AsO[(W,Mo)O |
| isfreeaccess_txt |
false |
| familylinks_str_mv |
(DE-627)ELV009519483 |
| title |
Frequency estimation in wind farm integrated systems using artificial neural network |
| ctrlnum |
(DE-627)ELV022415238 (ELSEVIER)S0142-0615(14)00221-X |
| title_full |
Frequency estimation in wind farm integrated systems using artificial neural network |
| author_sort |
Jiang, Wang |
| journal |
Synthesis and characterization of AsO[(W,Mo)O |
| journalStr |
Synthesis and characterization of AsO[(W,Mo)O |
| lang_code |
eng |
| isOA_bool |
false |
| dewey-hundreds |
600 - Technology 500 - Science |
| recordtype |
marc |
| publishDateSort |
2014 |
| contenttype_str_mv |
zzz |
| container_start_page |
72 |
| author_browse |
Jiang, Wang |
| container_volume |
62 |
| physical |
8 |
| class |
620 620 DE-600 540 VZ 35.90 bkl |
| format_se |
Elektronische Aufsätze |
| author-letter |
Jiang, Wang |
| doi_str_mv |
10.1016/j.ijepes.2014.04.027 |
| dewey-full |
620 540 |
| title_sort |
frequency estimation in wind farm integrated systems using artificial neural network |
| title_auth |
Frequency estimation in wind farm integrated systems using artificial neural network |
| abstract |
• We model a three-layer neural network using an improved elastic back-propagation algorithm. • We verify the effectiveness of the method with actual wind farm data. • The forecasting curve can respond to the change in system frequency in real time. • We compare the improved method with the other two algorithms in training times and error. |
| abstractGer |
• We model a three-layer neural network using an improved elastic back-propagation algorithm. • We verify the effectiveness of the method with actual wind farm data. • The forecasting curve can respond to the change in system frequency in real time. • We compare the improved method with the other two algorithms in training times and error. |
| abstract_unstemmed |
• We model a three-layer neural network using an improved elastic back-propagation algorithm. • We verify the effectiveness of the method with actual wind farm data. • The forecasting curve can respond to the change in system frequency in real time. • We compare the improved method with the other two algorithms in training times and error. |
| collection_details |
GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA |
| title_short |
Frequency estimation in wind farm integrated systems using artificial neural network |
| url |
https://doi.org/10.1016/j.ijepes.2014.04.027 |
| remote_bool |
true |
| author2 |
Lu, Jiping |
| author2Str |
Lu, Jiping |
| ppnlink |
ELV009519483 |
| mediatype_str_mv |
z |
| isOA_txt |
false |
| hochschulschrift_bool |
false |
| author2_role |
oth |
| doi_str |
10.1016/j.ijepes.2014.04.027 |
| up_date |
2024-07-06T21:55:48.563Z |
| _version_ |
1803868384593444864 |
| 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">ELV022415238</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230623163851.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">180603s2014 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.ijepes.2014.04.027</subfield><subfield code="2">doi</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">GBVA2014003000017.pica</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV022415238</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S0142-0615(14)00221-X</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=" "><subfield code="a">620</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">620</subfield><subfield code="q">DE-600</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">540</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">35.90</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Jiang, Wang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Frequency estimation in wind farm integrated systems using artificial neural network</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2014</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">8</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">• We model a three-layer neural network using an improved elastic back-propagation algorithm. • We verify the effectiveness of the method with actual wind farm data. • The forecasting curve can respond to the change in system frequency in real time. • We compare the improved method with the other two algorithms in training times and error.</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Frequency prediction</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Back-propagation algorithm</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Wind farms</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Lu, Jiping</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">Goudjil, Meriem ELSEVIER</subfield><subfield code="t">Synthesis and characterization of AsO[(W,Mo)O</subfield><subfield code="d">2023</subfield><subfield code="g">Amsterdam [u.a.]</subfield><subfield code="w">(DE-627)ELV009519483</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:62</subfield><subfield code="g">year:2014</subfield><subfield code="g">pages:72-79</subfield><subfield code="g">extent:8</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1016/j.ijepes.2014.04.027</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="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHA</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">35.90</subfield><subfield code="j">Festkörperchemie</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">62</subfield><subfield code="j">2014</subfield><subfield code="h">72-79</subfield><subfield code="g">8</subfield></datafield><datafield tag="953" ind1=" " ind2=" "><subfield code="2">045F</subfield><subfield code="a">620</subfield></datafield></record></collection>
|
| score |
7.4000187 |