Online Prediction and Correction of Static Voltage Stability Index Based on Extreme Gradient Boosting Algorithm
With the increasing integration of renewable energy sources into the power grid and the continuous expansion of grid infrastructure, real-time preventive control becomes crucial. This article proposes a real-time prediction and correction method based on the extreme gradient boosting (XGBoost) algor...
Gespeichert in:
| Veröffentlicht in: | Energies (Basel) Jg. 17; H. 22; S. 5710 |
|---|---|
| Hauptverfasser: | , , , , , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
Basel
MDPI AG
01.11.2024
|
| Schlagworte: | |
| ISSN: | 1996-1073, 1996-1073 |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| Abstract | With the increasing integration of renewable energy sources into the power grid and the continuous expansion of grid infrastructure, real-time preventive control becomes crucial. This article proposes a real-time prediction and correction method based on the extreme gradient boosting (XGBoost) algorithm. The XGBoost algorithm is utilized to evaluate the real-time stability of grid static voltage, with the voltage stability L-index as the prediction target. A correction model is established with the objective of minimizing correction costs while considering the operational constraints of the grid. When the L-index exceeds the warning value, the XGBoost algorithm can obtain the importance of each feature of the system and calculate the sensitivity approximation of highly important characteristics. The model corrects these characteristics to maintain the system’s operation within a reasonably secure range. The methodology is demonstrated using the IEEE-14 and IEEE-118 systems. The results show that the XGBoost algorithm has higher prediction accuracy and computational efficiency in assessing the static voltage stability of the power grid. It is also shown that the proposed approach has the potential to greatly improve the operational dependability of the power grid. |
|---|---|
| AbstractList | With the increasing integration of renewable energy sources into the power grid and the continuous expansion of grid infrastructure, real-time preventive control becomes crucial. This article proposes a real-time prediction and correction method based on the extreme gradient boosting (XGBoost) algorithm. The XGBoost algorithm is utilized to evaluate the real-time stability of grid static voltage, with the voltage stability L-index as the prediction target. A correction model is established with the objective of minimizing correction costs while considering the operational constraints of the grid. When the L-index exceeds the warning value, the XGBoost algorithm can obtain the importance of each feature of the system and calculate the sensitivity approximation of highly important characteristics. The model corrects these characteristics to maintain the system’s operation within a reasonably secure range. The methodology is demonstrated using the IEEE-14 and IEEE-118 systems. The results show that the XGBoost algorithm has higher prediction accuracy and computational efficiency in assessing the static voltage stability of the power grid. It is also shown that the proposed approach has the potential to greatly improve the operational dependability of the power grid. |
| Audience | Academic |
| Author | Li, Bo Qin, Huiling Zhang, Juncheng He, Chengyu Rao, Zhi Li, Shuang Chen, Zhijun |
| Author_xml | – sequence: 1 givenname: Huiling surname: Qin fullname: Qin, Huiling – sequence: 2 givenname: Shuang surname: Li fullname: Li, Shuang – sequence: 3 givenname: Juncheng surname: Zhang fullname: Zhang, Juncheng – sequence: 4 givenname: Zhi surname: Rao fullname: Rao, Zhi – sequence: 5 givenname: Chengyu surname: He fullname: He, Chengyu – sequence: 6 givenname: Zhijun surname: Chen fullname: Chen, Zhijun – sequence: 7 givenname: Bo orcidid: 0000-0001-7320-7297 surname: Li fullname: Li, Bo |
| BookMark | eNpNUcFq3DAQFSWFpptc-gWC3gqbSB7Zko6bJU0XAimk6dWM5bGrxSulsgLJ31cbl6aag2aGN4838z6ykxADMfZJigsAKy4pSF1VtZbiHTuV1jZrKTSc_Jd_YOfzvBflAUgAOGXxLkw-EP-eqPcu-xg4hp5vY0q0lHHg9xmzd_xnnDKOdCw7P_n8wnehp2d-hTP1vECvn3OiA_GbhL2nkPlVjHP2YeSbaYzJ51-HM_Z-wGmm87__ij18vf6x_ba-vbvZbTe3awe1zWvZdXUHqlNoHQ0EjZbYDSCMLsKNUxqlIGc6XXXaWbLUGCOkcqYG5QgQVmy38PYR9-1j8gdML21E3742YhpbTGWpiVotKm0acBL1oDQJNIqErKU1lQVdTrVinxeuxxR_P9Gc2318SqHIb49XFFA3lSqoiwU1YiH1YYg5oSvR08G74tTgS39jpKlEZZUtA1-WAZfiPCca_smUoj0a2r4ZCn8AwNGSuw |
| Cites_doi | 10.1109/TPWRS.2009.2038059 10.1109/TPWRS.2018.2872505 10.1109/TSG.2017.2693394 10.1016/j.egyr.2020.11.006 10.1109/AEEES51875.2021.9403146 10.1016/j.ijepes.2015.05.002 10.1016/j.epsr.2023.109562 10.1016/j.eswa.2022.117184 10.1016/j.ijepes.2011.06.008 10.3390/su16093621 10.1016/j.apenergy.2020.115733 10.1016/j.engappai.2024.109368 10.1109/SCEMS48876.2020.9352249 10.3390/en17091999 10.1109/TPWRS.2018.2849717 10.1016/j.eswa.2007.08.059 10.1016/j.egyr.2020.10.005 10.1109/TPWRS.2004.826018 10.1145/2939672.2939785 10.1016/j.epsr.2022.108915 10.1109/ICTFCEN.2016.8052703 10.1109/TPWRD.1986.4308013 |
| ContentType | Journal Article |
| Copyright | COPYRIGHT 2024 MDPI AG 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
| Copyright_xml | – notice: COPYRIGHT 2024 MDPI AG – notice: 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
| DBID | AAYXX CITATION ABUWG AFKRA AZQEC BENPR CCPQU DWQXO PHGZM PHGZT PIMPY PKEHL PQEST PQQKQ PQUKI PRINS DOA |
| DOI | 10.3390/en17225710 |
| DatabaseName | CrossRef ProQuest Central (Alumni) ProQuest Central UK/Ireland ProQuest Central Essentials ProQuest Central ProQuest One ProQuest Central Korea ProQuest Central Premium ProQuest One Academic (New) Publicly Available Content Database ProQuest One Academic Middle East (New) ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Academic (retired) ProQuest One Academic UKI Edition ProQuest Central China DOAJ Directory of Open Access Journals |
| DatabaseTitle | CrossRef Publicly Available Content Database ProQuest One Academic Middle East (New) ProQuest Central Essentials ProQuest One Academic Eastern Edition ProQuest Central (Alumni Edition) ProQuest One Community College ProQuest Central China ProQuest Central ProQuest One Academic UKI Edition ProQuest Central Korea ProQuest Central (New) ProQuest One Academic ProQuest One Academic (New) |
| DatabaseTitleList | CrossRef Publicly Available Content Database |
| Database_xml | – sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: PIMPY name: Publicly Available Content Database url: http://search.proquest.com/publiccontent sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering |
| EISSN | 1996-1073 |
| ExternalDocumentID | oai_doaj_org_article_7027863c1a7f47e0a84e015198293700 A818202949 10_3390_en17225710 |
| GroupedDBID | 29G 2WC 2XV 5GY 5VS 7XC 8FE 8FG 8FH AADQD AAHBH AAYXX ABDBF ACUHS ADBBV ADMLS AENEX AFFHD AFKRA AFZYC ALMA_UNASSIGNED_HOLDINGS BCNDV BENPR CCPQU CITATION CS3 DU5 EBS ESX FRP GROUPED_DOAJ GX1 I-F IAO ITC KQ8 L6V L8X MODMG M~E OK1 OVT P2P PHGZM PHGZT PIMPY PROAC TR2 TUS ABUWG AZQEC DWQXO PKEHL PQEST PQQKQ PQUKI PRINS |
| ID | FETCH-LOGICAL-c359t-1bb5b34b4a9cefe3671abf30873318c47a10ec8b72b7c9e9e688014c8534ce3a3 |
| IEDL.DBID | DOA |
| ISICitedReferencesCount | 0 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001364364800001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1996-1073 |
| IngestDate | Mon Nov 10 04:29:49 EST 2025 Mon Jun 30 13:59:54 EDT 2025 Tue Nov 04 18:14:42 EST 2025 Sat Nov 29 07:12:37 EST 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 22 |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c359t-1bb5b34b4a9cefe3671abf30873318c47a10ec8b72b7c9e9e688014c8534ce3a3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| ORCID | 0000-0001-7320-7297 |
| OpenAccessLink | https://doaj.org/article/7027863c1a7f47e0a84e015198293700 |
| PQID | 3133035624 |
| PQPubID | 2032402 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_7027863c1a7f47e0a84e015198293700 proquest_journals_3133035624 gale_infotracacademiconefile_A818202949 crossref_primary_10_3390_en17225710 |
| PublicationCentury | 2000 |
| PublicationDate | 2024-11-01 |
| PublicationDateYYYYMMDD | 2024-11-01 |
| PublicationDate_xml | – month: 11 year: 2024 text: 2024-11-01 day: 01 |
| PublicationDecade | 2020 |
| PublicationPlace | Basel |
| PublicationPlace_xml | – name: Basel |
| PublicationTitle | Energies (Basel) |
| PublicationYear | 2024 |
| Publisher | MDPI AG |
| Publisher_xml | – name: MDPI AG |
| References | Gao (ref_17) 2023; 214 ref_14 Yang (ref_22) 2022; 199 ref_23 Gupta (ref_10) 2019; 34 Su (ref_7) 2018; 33 Devaraj (ref_8) 2011; 33 ref_21 Anthony (ref_19) 2024; 138 ref_1 ref_3 Cao (ref_13) 2020; 6 ref_18 Zhou (ref_5) 2010; 25 Malbasa (ref_6) 2017; 8 ref_15 Shi (ref_2) 2020; 278 Moulin (ref_4) 2004; 19 Guo (ref_12) 2020; 6 Kessel (ref_20) 1986; 1 Shahriyari (ref_16) 2023; 223 Chakrabarti (ref_9) 2008; 35 Sajan (ref_11) 2015; 73 |
| References_xml | – volume: 25 start-page: 1566 year: 2010 ident: ref_5 article-title: Online Monitoring of Voltage Stability Margin Using an Artificial Neural Network publication-title: IEEE Trans. Power Syst. doi: 10.1109/TPWRS.2009.2038059 – ident: ref_3 – volume: 34 start-page: 864 year: 2019 ident: ref_10 article-title: An Online Power System Stability Monitoring System Using Convolutional Neural Networks publication-title: IEEE Trans. Power Syst. doi: 10.1109/TPWRS.2018.2872505 – volume: 8 start-page: 3117 year: 2017 ident: ref_6 article-title: Voltage Stability Prediction Using Active Machine Learning publication-title: IEEE Trans. Smart Grid doi: 10.1109/TSG.2017.2693394 – volume: 6 start-page: 1424 year: 2020 ident: ref_12 article-title: Study on short-term photovoltaic power prediction model based on the Stacking ensemble learning publication-title: Energy Rep. doi: 10.1016/j.egyr.2020.11.006 – ident: ref_15 doi: 10.1109/AEEES51875.2021.9403146 – volume: 73 start-page: 200 year: 2015 ident: ref_11 article-title: Genetic algorithm based support vector machine for on-line voltage stability monitoring publication-title: Int. J. Electr. Power Energy Syst. doi: 10.1016/j.ijepes.2015.05.002 – volume: 223 start-page: 109562 year: 2023 ident: ref_16 article-title: A short-term voltage stability online assessment based on multi-layer perceptron learning publication-title: Electr. Power Syst. Res. doi: 10.1016/j.epsr.2023.109562 – volume: 199 start-page: 117184 year: 2022 ident: ref_22 article-title: Online prediction and correction control of static voltage stability index based on Broad Learning System publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2022.117184 – volume: 33 start-page: 1550 year: 2011 ident: ref_8 article-title: On-line voltage stability assessment using radial basis function network model with reduced input features publication-title: Int. J. Electr. Power Energy Syst. doi: 10.1016/j.ijepes.2011.06.008 – ident: ref_23 doi: 10.3390/su16093621 – volume: 278 start-page: 115733 year: 2020 ident: ref_2 article-title: Artificial intelligence techniques for stability analysis and control in smart grids: Methodologies, applications, challenges and future directions publication-title: Appl. Energy doi: 10.1016/j.apenergy.2020.115733 – volume: 138 start-page: 109368 year: 2024 ident: ref_19 article-title: Application of cascaded neural network for prediction of voltage stability margin in a solar and wind integrated power system publication-title: Eng. Appl. Artif. Intell. doi: 10.1016/j.engappai.2024.109368 – ident: ref_14 doi: 10.1109/SCEMS48876.2020.9352249 – ident: ref_18 doi: 10.3390/en17091999 – volume: 33 start-page: 6696 year: 2018 ident: ref_7 article-title: Enhanced-Online-Random-Forest Model for Static Voltage Stability Assessment Using Wide Area Measurements publication-title: IEEE Trans. Power Syst. doi: 10.1109/TPWRS.2018.2849717 – volume: 35 start-page: 1802 year: 2008 ident: ref_9 article-title: Voltage stability monitoring by artificial neural network using a regression-based feature selection method publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2007.08.059 – volume: 6 start-page: 2751 year: 2020 ident: ref_13 article-title: Electrical load prediction of healthcare buildings through single and ensemble learning publication-title: Energy Rep. doi: 10.1016/j.egyr.2020.10.005 – volume: 19 start-page: 818 year: 2004 ident: ref_4 article-title: Support vector machines for transient stability analysis of large-scale power systems publication-title: IEEE Trans. Power Syst. doi: 10.1109/TPWRS.2004.826018 – ident: ref_21 doi: 10.1145/2939672.2939785 – volume: 214 start-page: 108915 year: 2023 ident: ref_17 article-title: Real-time long-term voltage stability assessment based on eGBDT for large-scale power system with high renewables penetration publication-title: Electr. Power Syst. Res. doi: 10.1016/j.epsr.2022.108915 – ident: ref_1 doi: 10.1109/ICTFCEN.2016.8052703 – volume: 1 start-page: 346 year: 1986 ident: ref_20 article-title: Estimating the Voltage Stability of a Power System publication-title: IEEE Trans. Power Deliv. doi: 10.1109/TPWRD.1986.4308013 |
| SSID | ssj0000331333 |
| Score | 2.3783772 |
| Snippet | With the increasing integration of renewable energy sources into the power grid and the continuous expansion of grid infrastructure, real-time preventive... |
| SourceID | doaj proquest gale crossref |
| SourceType | Open Website Aggregation Database Index Database |
| StartPage | 5710 |
| SubjectTerms | Accuracy Algorithms Alternative energy sources Analysis Decision trees Electricity distribution Energy consumption extreme gradient boosting (XGBoost) algorithm Feature selection Genetic algorithms Infrastructure (Economics) Machine learning Methods Neural networks preventive control real-time prediction sensitivity approximation static voltage stability Support vector machines Wind power |
| SummonAdditionalLinks | – databaseName: Publicly Available Content Database dbid: PIMPY link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1LT9wwEB61Sw_tgb7FUqgstVJP0SY7JnZOaBfxOhTtoa3oybIdhyJBQrNpBf-emcQLXMqpxySOY-Wz52HPfAPw2aIsnWNPdWoxkaXME41eJZmvbFYxHZ50fbEJdXKiT0-LRUyPXsawypVM7AX1wPbMcdskhCdl43nHfILkWqVIulvuXv1OuIYUn7XGghpPYY2Jt9IRrC2Ovy5-3u25pMjv4cBSiuTtT0JNCpxmLSfQPtBLPX3_v4R0r3kOXv7fMb-C9WiBitkwZV7Dk1C_gRcPeAnfQjMQkIpFy8c4DJ2wdSn2uJLHcNlUgs3Ucy9-NBcdySS-7ONsb8QxEzCKOanHUlDT_euO9yDFYdtHl3Vi3jRLDrYWs4szGl_36_IdfD_Y_7Z3lMTKDInHnaJLMud2HEonbeFDFTBXmXUVkwvSH9ZeKpulwWunpk75IhQh18xS48k2kD6gxfcwqps6bIBwWiPZlBqzvJLkf2pVaGaVL6ceyRxzY_i0wsVcDQQchhwXRs_cozeGOUN214JJs_sbTXtm4ho0ik9Zc_SZVZVUIbVaBrKGMvogGWkpdfKFATe8tLvWehszFGigTJJlZprZ7qeFLMawtQLcxDW_NPf4bj7--AM8p27kkNG4BaOu_RO24Zn_250v249x0t4CRbn7wA priority: 102 providerName: ProQuest |
| Title | Online Prediction and Correction of Static Voltage Stability Index Based on Extreme Gradient Boosting Algorithm |
| URI | https://www.proquest.com/docview/3133035624 https://doaj.org/article/7027863c1a7f47e0a84e015198293700 |
| Volume | 17 |
| WOSCitedRecordID | wos001364364800001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 1996-1073 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000331333 issn: 1996-1073 databaseCode: DOA dateStart: 20080101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 1996-1073 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000331333 issn: 1996-1073 databaseCode: M~E dateStart: 20080101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 1996-1073 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000331333 issn: 1996-1073 databaseCode: BENPR dateStart: 20080301 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: Publicly Available Content Database customDbUrl: eissn: 1996-1073 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000331333 issn: 1996-1073 databaseCode: PIMPY dateStart: 20080301 isFulltext: true titleUrlDefault: http://search.proquest.com/publiccontent providerName: ProQuest |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1Nb9QwEB2h0gMcKlpALC2VJZA4RU123Ng57lbbj0NXEQJUTpbtOLBSm6BsiuiF386Mk9K9IC5cIjmyFOuN7ZkXj98AvLMoK-eYqU4tJrKSeaLRqyTztc1qlsOTLhabUMulvroqyo1SX5wTNsgDD8AdKT4ay9FnVtVShdRqGciFEVcmR6XSyNZTVWyQqbgHIxL5wkGPFInXH4WGXDXNT74qu-GBolD_37bj6GNOn8HOGByK2TCoXXgUmj14uiEZ-BzaQRtUlB2fsDCqwjaVOOEiG0OzrQVHkCsvPrfXPW0X3IwpsHfigrURxZw8VyWo6-Jnz78HxVkXE796MW_bNedBi9n117Zb9d9uXsCn08XHk_NkLJqQeDwu-iRz7tihdNIWPtQBc5VZV7PuH0GivVQ2S4PXTk2d8kUoQq5ZQMaT25Y-oMWXsNW0TXgFwmmNFO5pzPJaEjXUikAnPlJNPVKk5Cbw9h5I833QxjDEKRhu8wD3BOaM8Z8erGcdX5CVzWhl8y8rT-A9W8jwqus76-14eYAGyvpVZqZZiH5ayGICB_dGNONyXBueDClSqCdf_4_R7MMT-pgcriQewFbf3YY3sO1_9Kt1dwiP54tl-eEwzkh6Xv5a0Lvy4rL88hsfsuM9 |
| linkProvider | Directory of Open Access Journals |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V3NU9QwFH-DizPKwW-HVdTMqOOpQ9uEJj0wzi6C7AA7e0AHTyFJU2QGWuhWhX_Kv9H3-gFc9MbBY9pMmia_vI8k7_cA3hkuMmvJU40ND0QmkkBxJ4PI5SbKiQ5P2CbZhJxO1cFBOluA330sDF2r7GViI6iz0tEe-SpHZyrkqK3Fx7PzgLJG0elqn0KjhcWOv_yFLtt8ffIJ5_d9HG9t7m9sB11WgcDxtbQOImvXLBdWmNT53PNERsbmRIzHEd9OSBOF3ikrYytd6lOfKGJYcajXhPPccGz3DiwKBHs4gMXZZG_27WpXJ-TUT97yoHKehqu-QBMB1wWF6N7QfE2CgL-pgUa3bT3830blETzorGg2amH_GBZ88QSWbnArPoWyJVFls4qOogh-zBQZ26BsJG2xzBmZ2seOfS1PapSrVGzuCl-yCZFIsjGq-Ixh1c2LmvZR2eequSFXs3FZzunCOBudHOF41N9Pn8GXW_nj5zAoysIvA7NKcbSLFY-SXKAPrWSqiBk_ix1Hk9IO4W0_8_qsJRHR6HwRPvQ1PoYwJlBc1SDi7-ZBWR3pTo5oSSfFCXeRkbmQPjRKeLToIvwgGpohNvKBIKVJPNWVcaaLssCOEtGXHili7I9TkQ5hpYeU7uTWXF_j6cW_X7-Be9v7e7t6dzLdeQn3sUnRRmiuwKCufvhXcNf9rI_n1etuiTA4vG38_QHVKU2Q |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Lb9QwEB6VLUJw4I1YKGAJEKdok4y7dg4I7ba7sCparRCg3lLbcUqlkpRsePSv8euYyaPtBW49cHRiOYnzeR72zDcALwzKzFr2VGODgczkONDoVBC53EQ50-FJ2xSbUMul3t9PVhvwu8-F4bDKXiY2gjorHe-Rj5CcqRBJW8tR3oVFrHbnb06-BVxBik9a-3IaLUT2_OlPct_Wrxe79K9fxvF89nHnXdBVGAgcbid1EFm7bVFaaRLnc49jFRmbM0keEtadVCYKvdNWxVa5xCd-rJltxZGOk86jQRr3CmwqJKdnAJvT2XL14WyHJ0R-Z2w5URGTcOQLMhdojXC67gUt2BQL-JtKaPTc_Nb_PEO34WZnXYtJuxzuwIYv7sKNC5yL96BsyVXFquIjKoalMEUmdrhKSdssc8Em-JETn8vjmuQtN5sY4lOxYHJJMSXVnwnqOvtV8_6qeFs1kXO1mJblmgPJxeT4kOaj_vL1Pny6lC9-AIOiLPxDEFZrJHtZYzTOJfnWWiWaGfOz2CGZmnYIz3sUpCctuUhKThljJT3HyhCmDJCzHkwI3lwoq8O0ky-p4hPkMbrIqFwqHxotPVl6ET2QDNCQBnnF8EpZbNWVcabLvqAXZQKwdKKZyT9OZDKErR5eaSfP1uk5th79-_YzuEagS98vlnuP4TqNKNvEzS0Y1NV3_wSuuh_10bp62q0WAQeXDb8_N7BWKg |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Online+Prediction+and+Correction+of+Static+Voltage+Stability+Index+Based+on+Extreme+Gradient+Boosting+Algorithm&rft.jtitle=Energies+%28Basel%29&rft.au=Huiling+Qin&rft.au=Shuang+Li&rft.au=Juncheng+Zhang&rft.au=Zhi+Rao&rft.date=2024-11-01&rft.pub=MDPI+AG&rft.eissn=1996-1073&rft.volume=17&rft.issue=22&rft.spage=5710&rft_id=info:doi/10.3390%2Fen17225710&rft.externalDBID=DOA&rft.externalDocID=oai_doaj_org_article_7027863c1a7f47e0a84e015198293700 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1996-1073&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1996-1073&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1996-1073&client=summon |