Intelligent Sensors for dc Fault Location Scheme Based on Optimized Intelligent Architecture for HVdc Systems
We develop a probabilistic model for determining the location of dc-link faults in MT-HVdc networks using discrete wavelet transforms (DWTs), Bayesian optimization, and multilayer artificial neural networks (ANNs) based on local information. Likewise, feedforward neural networks (FFNNs) are trained...
Uložené v:
| Vydané v: | Sensors (Basel, Switzerland) Ročník 22; číslo 24; s. 9936 |
|---|---|
| Hlavní autori: | , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
Switzerland
MDPI AG
16.12.2022
MDPI |
| Predmet: | |
| ISSN: | 1424-8220, 1424-8220 |
| On-line prístup: | Získať plný text |
| Tagy: |
Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
|
| Abstract | We develop a probabilistic model for determining the location of dc-link faults in MT-HVdc networks using discrete wavelet transforms (DWTs), Bayesian optimization, and multilayer artificial neural networks (ANNs) based on local information. Likewise, feedforward neural networks (FFNNs) are trained using the Levenberg–Marquardt backpropagation (LMBP) method, which multi-stage BO optimizes for efficiency. During training, the feature vectors at the sending terminal of the dc link are selected based on the norm values of the observed waveforms at various frequency bands. The multilayer ANN is trained using a comprehensive set of offline data that takes the denoising scheme into account. This choice not only helps to reduce the computational load but also provides better accuracy. An overall percentage error of 0.5144% is observed for the proposed algorithm when tested against fault resistances ranging from 10 to 485 Ω. The simulation results show that the proposed method can accurately estimate the fault site to a precision of 485 Ω and is more robust. |
|---|---|
| AbstractList | We develop a probabilistic model for determining the location of dc-link faults in MT-HVdc networks using discrete wavelet transforms (DWTs), Bayesian optimization, and multilayer artificial neural networks (ANNs) based on local information. Likewise, feedforward neural networks (FFNNs) are trained using the Levenberg–Marquardt backpropagation (LMBP) method, which multi-stage BO optimizes for efficiency. During training, the feature vectors at the sending terminal of the dc link are selected based on the norm values of the observed waveforms at various frequency bands. The multilayer ANN is trained using a comprehensive set of offline data that takes the denoising scheme into account. This choice not only helps to reduce the computational load but also provides better accuracy. An overall percentage error of 0.5144% is observed for the proposed algorithm when tested against fault resistances ranging from 10 to 485 Ω. The simulation results show that the proposed method can accurately estimate the fault site to a precision of 485 Ω and is more robust. We develop a probabilistic model for determining the location of dc-link faults in MT-HVdc networks using discrete wavelet transforms (DWTs), Bayesian optimization, and multilayer artificial neural networks (ANNs) based on local information. Likewise, feedforward neural networks (FFNNs) are trained using the Levenberg-Marquardt backpropagation (LMBP) method, which multi-stage BO optimizes for efficiency. During training, the feature vectors at the sending terminal of the dc link are selected based on the norm values of the observed waveforms at various frequency bands. The multilayer ANN is trained using a comprehensive set of offline data that takes the denoising scheme into account. This choice not only helps to reduce the computational load but also provides better accuracy. An overall percentage error of 0.5144% is observed for the proposed algorithm when tested against fault resistances ranging from 10 to 485 Ω. The simulation results show that the proposed method can accurately estimate the fault site to a precision of 485 Ω and is more robust.We develop a probabilistic model for determining the location of dc-link faults in MT-HVdc networks using discrete wavelet transforms (DWTs), Bayesian optimization, and multilayer artificial neural networks (ANNs) based on local information. Likewise, feedforward neural networks (FFNNs) are trained using the Levenberg-Marquardt backpropagation (LMBP) method, which multi-stage BO optimizes for efficiency. During training, the feature vectors at the sending terminal of the dc link are selected based on the norm values of the observed waveforms at various frequency bands. The multilayer ANN is trained using a comprehensive set of offline data that takes the denoising scheme into account. This choice not only helps to reduce the computational load but also provides better accuracy. An overall percentage error of 0.5144% is observed for the proposed algorithm when tested against fault resistances ranging from 10 to 485 Ω. The simulation results show that the proposed method can accurately estimate the fault site to a precision of 485 Ω and is more robust. |
| Audience | Academic |
| Author | Yousaf, Muhammad Zain Khan, Muhammad Ahmad Tahir, Muhammad Faizan Raza, Ali Badshah, Fazal |
| AuthorAffiliation | 1 School of Electrical and Information Engineering, Hubei University of Automotive Technology, Shiyan 442002, China 3 School of Electrical Engineering, University of Engineering and Technology, Lahore 39161, Pakistan 4 School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China 2 School of Electric Power, South China University of Technology, Guangzhou 510630, China |
| AuthorAffiliation_xml | – name: 2 School of Electric Power, South China University of Technology, Guangzhou 510630, China – name: 3 School of Electrical Engineering, University of Engineering and Technology, Lahore 39161, Pakistan – name: 4 School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China – name: 1 School of Electrical and Information Engineering, Hubei University of Automotive Technology, Shiyan 442002, China |
| Author_xml | – sequence: 1 givenname: Muhammad Zain surname: Yousaf fullname: Yousaf, Muhammad Zain – sequence: 2 givenname: Muhammad Faizan orcidid: 0000-0001-9138-3323 surname: Tahir fullname: Tahir, Muhammad Faizan – sequence: 3 givenname: Ali orcidid: 0000-0003-0947-3616 surname: Raza fullname: Raza, Ali – sequence: 4 givenname: Muhammad Ahmad surname: Khan fullname: Khan, Muhammad Ahmad – sequence: 5 givenname: Fazal surname: Badshah fullname: Badshah, Fazal |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/36560301$$D View this record in MEDLINE/PubMed |
| BookMark | eNplkktv1DAQgCNURB9w4A-gSFzgsK1fsZ0L0lJRulKlHha4Wo4z2fUqiRfbQSq_nuluW22LfIhn8s3njDOnxdEYRiiK95Scc16Ti8QYE3XN5avihAomZpoxcnSwPy5OU9oQwjjn-k1xzGUlCSf0pBgWY4a-9ysYc7mEMYWYyi7EsnXllZ36XN4EZ7MPY7l0axig_GoTtCXGt9vsB_8Xg0PHPLq1z-DyFGEnuv6FquVdyjCkt8XrzvYJ3j08z4qfV99-XF7Pbm6_Ly7nNzNXEZ1nSthGitpJJqFhwknlGG1tBxg5ZrVzEmqqtLTMOmWbWtYONGVQIVwpyc-Kxd7bBrsx2-gHG-9MsN7sEiGujI3Zux4M501tgXRor4Skla1FUynRdZpwbhWg68vetZ2aAVqHTUbbP5M-fzP6tVmFP6ZWmmghUPDpQRDD7wlSNoNPDi_MjhCmZJiqNCWKaY3oxxfoJkxxxKu6p6TSgtYMqfM9tbLYgB-7gOc6XC0M3uFodB7zcyVkxSsuKBZ8OGzh6dsfxwCBiz3gYkgpQmecz7u_jmbfG0rM_aCZp0HDis8vKh6l_7P_AD0-0q8 |
| CitedBy_id | crossref_primary_10_1016_j_fraope_2025_100345 crossref_primary_10_1016_j_asej_2025_103736 crossref_primary_10_1016_j_prime_2025_100944 crossref_primary_10_3934_math_2025829 crossref_primary_10_3390_s25051347 crossref_primary_10_1016_j_prime_2025_101031 crossref_primary_10_1016_j_fraope_2025_100284 crossref_primary_10_1038_s41598_024_74300_z crossref_primary_10_3390_su16146102 crossref_primary_10_1007_s00202_024_02625_z crossref_primary_10_3390_electronics13193804 crossref_primary_10_1038_s41598_025_98006_y crossref_primary_10_1016_j_ins_2024_121771 crossref_primary_10_3390_electronics12081766 crossref_primary_10_1016_j_ijepes_2023_109423 crossref_primary_10_1016_j_asej_2025_103596 crossref_primary_10_1016_j_asej_2025_103576 crossref_primary_10_1016_j_ijepes_2025_110709 crossref_primary_10_1038_s41598_024_80033_w crossref_primary_10_3390_en17133171 crossref_primary_10_1155_2024_3713139 crossref_primary_10_1016_j_prime_2025_101043 crossref_primary_10_1016_j_heliyon_2025_e43101 crossref_primary_10_3934_electreng_2025017 crossref_primary_10_1109_ACCESS_2024_3444710 crossref_primary_10_1016_j_engappai_2025_110597 crossref_primary_10_1155_2024_1156761 crossref_primary_10_1038_s41598_025_86554_2 |
| Cites_doi | 10.1016/j.compeleceng.2019.07.022 10.1109/TPWRD.2013.2269769 10.1109/MELCON.2016.7495356 10.1109/TPWRD.2019.2922654 10.35833/MPCE.2021.000218 10.1109/72.329697 10.1109/TPWRD.2012.2211898 10.1109/TPWRD.2013.2248068 10.1109/TVLSI.2017.2784783 10.3390/su141811749 10.1049/cp.2015.0068 10.1109/TPWRD.2009.2033078 10.3390/app10041516 10.1109/ACCESS.2021.3057659 10.1016/j.ijepes.2018.07.044 10.1109/ACCESS.2020.3035905 10.1016/j.asoc.2012.09.010 10.1109/TPWRD.2012.2202405 10.1109/TPWRD.2014.2323356 10.1109/TPWRD.2016.2589265 10.1109/ACCESS.2022.3142534 10.1016/j.eswa.2007.05.011 10.1049/iet-gtd.2019.1414 10.1109/TMTT.2015.2495360 10.1016/j.neucom.2010.02.001 10.1109/ACCESS.2019.2945397 10.1007/978-981-15-2977-1 10.1109/TPWRD.2017.2721903 10.1016/j.epsr.2005.11.003 10.1016/j.epsr.2013.10.006 10.1049/iet-rpg.2018.5097 10.1109/TAI.2021.3097307 10.1109/TPWRS.2004.825883 10.1109/TII.2017.2777460 10.1109/TPWRD.2011.2174067 10.1109/TPWRD.2019.2942016 10.1109/TVLSI.2017.2656843 10.1109/TII.2017.2701823 10.1016/j.rser.2019.03.040 10.1016/j.ijepes.2017.10.040 |
| ContentType | Journal Article |
| Copyright | COPYRIGHT 2022 MDPI AG 2022 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. 2022 by the authors. 2022 |
| Copyright_xml | – notice: COPYRIGHT 2022 MDPI AG – notice: 2022 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. – notice: 2022 by the authors. 2022 |
| DBID | AAYXX CITATION CGR CUY CVF ECM EIF NPM 3V. 7X7 7XB 88E 8FI 8FJ 8FK ABUWG AFKRA AZQEC BENPR CCPQU DWQXO FYUFA GHDGH K9. M0S M1P PHGZM PHGZT PIMPY PJZUB PKEHL PPXIY PQEST PQQKQ PQUKI PRINS 7X8 5PM DOA |
| DOI | 10.3390/s22249936 |
| DatabaseName | CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed ProQuest Central (Corporate) Health & Medical Collection ProQuest Central (purchase pre-March 2016) Medical Database (Alumni Edition) Hospital Premium Collection Hospital Premium Collection (Alumni Edition) ProQuest Central (Alumni) (purchase pre-March 2016) ProQuest Central (Alumni) ProQuest Central UK/Ireland ProQuest Central Essentials ProQuest Central ProQuest One Community College ProQuest Central Korea Proquest Health Research Premium Collection Health Research Premium Collection (Alumni) ProQuest Health & Medical Complete (Alumni) ProQuest Health & Medical Collection Medical Database ProQuest Central Premium ProQuest One Academic (New) ProQuest Publicly Available Content Database ProQuest Health & Medical Research Collection ProQuest One Academic Middle East (New) ProQuest One Health & Nursing ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Academic (retired) ProQuest One Academic UKI Edition ProQuest Central China MEDLINE - Academic PubMed Central (Full Participant titles) DOAJ Directory of Open Access Journals |
| DatabaseTitle | CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) Publicly Available Content Database ProQuest One Academic Middle East (New) ProQuest Central Essentials ProQuest Health & Medical Complete (Alumni) ProQuest Central (Alumni Edition) ProQuest One Community College ProQuest One Health & Nursing ProQuest Central China ProQuest Central ProQuest Health & Medical Research Collection Health Research Premium Collection Health and Medicine Complete (Alumni Edition) ProQuest Central Korea Health & Medical Research Collection ProQuest Central (New) ProQuest Medical Library (Alumni) ProQuest One Academic Eastern Edition ProQuest Hospital Collection Health Research Premium Collection (Alumni) ProQuest Hospital Collection (Alumni) ProQuest Health & Medical Complete ProQuest Medical Library ProQuest One Academic UKI Edition ProQuest One Academic ProQuest One Academic (New) ProQuest Central (Alumni) MEDLINE - Academic |
| DatabaseTitleList | MEDLINE MEDLINE - Academic Publicly Available Content Database CrossRef |
| Database_xml | – sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 3 dbid: PIMPY name: ProQuest Publicly Available Content Database url: http://search.proquest.com/publiccontent sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering |
| EISSN | 1424-8220 |
| ExternalDocumentID | oai_doaj_org_article_33b9ae0fafe54615a94b574ff8033a7e PMC9780844 A746535341 36560301 10_3390_s22249936 |
| Genre | Journal Article |
| GroupedDBID | --- 123 2WC 53G 5VS 7X7 88E 8FE 8FG 8FI 8FJ AADQD AAHBH AAYXX ABDBF ABUWG ACUHS ADBBV ADMLS AENEX AFFHD AFKRA AFZYC ALMA_UNASSIGNED_HOLDINGS BENPR BPHCQ BVXVI CCPQU CITATION CS3 D1I DU5 E3Z EBD ESX F5P FYUFA GROUPED_DOAJ GX1 HH5 HMCUK HYE IAO ITC KQ8 L6V M1P M48 MODMG M~E OK1 OVT P2P P62 PHGZM PHGZT PIMPY PJZUB PPXIY PQQKQ PROAC PSQYO RNS RPM TUS UKHRP XSB ~8M 3V. ABJCF ALIPV ARAPS CGR CUY CVF ECM EIF HCIFZ KB. M7S NPM PDBOC 7XB 8FK AZQEC DWQXO K9. PKEHL PQEST PQUKI PRINS 7X8 PUEGO 5PM |
| ID | FETCH-LOGICAL-c508t-74ab649c626eb24c67c21dafeeb2c2a8cc6e91786a2ac7ab969ce812e524c5763 |
| IEDL.DBID | 7X7 |
| ISICitedReferencesCount | 30 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000904509000001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1424-8220 |
| IngestDate | Fri Oct 03 12:27:58 EDT 2025 Tue Nov 04 02:07:14 EST 2025 Fri Sep 05 09:00:06 EDT 2025 Tue Oct 07 07:20:34 EDT 2025 Tue Nov 04 18:15:22 EST 2025 Wed Feb 19 02:25:00 EST 2025 Sat Nov 29 07:14:53 EST 2025 Tue Nov 18 22:02:32 EST 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 24 |
| Keywords | modular multilevel converter Levenberg–Marquardt backpropagation protection sensor Bayesian optimization |
| Language | English |
| License | 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/). |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c508t-74ab649c626eb24c67c21dafeeb2c2a8cc6e91786a2ac7ab969ce812e524c5763 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
| ORCID | 0000-0001-9138-3323 0000-0003-0947-3616 |
| OpenAccessLink | https://www.proquest.com/docview/2756784192?pq-origsite=%requestingapplication% |
| PMID | 36560301 |
| PQID | 2756784192 |
| PQPubID | 2032333 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_33b9ae0fafe54615a94b574ff8033a7e pubmedcentral_primary_oai_pubmedcentral_nih_gov_9780844 proquest_miscellaneous_2758107288 proquest_journals_2756784192 gale_infotracacademiconefile_A746535341 pubmed_primary_36560301 crossref_citationtrail_10_3390_s22249936 crossref_primary_10_3390_s22249936 |
| PublicationCentury | 2000 |
| PublicationDate | 20221216 |
| PublicationDateYYYYMMDD | 2022-12-16 |
| PublicationDate_xml | – month: 12 year: 2022 text: 20221216 day: 16 |
| PublicationDecade | 2020 |
| PublicationPlace | Switzerland |
| PublicationPlace_xml | – name: Switzerland – name: Basel |
| PublicationTitle | Sensors (Basel, Switzerland) |
| PublicationTitleAlternate | Sensors (Basel) |
| PublicationYear | 2022 |
| Publisher | MDPI AG MDPI |
| Publisher_xml | – name: MDPI AG – name: MDPI |
| References | Ahmadimanesh (ref_9) 2013; 28 Chen (ref_25) 2015; 63 Xiao (ref_5) 2021; 10 ref_14 Torun (ref_24) 2018; 26 ref_35 He (ref_2) 2014; 29 ref_33 ref_10 Merlin (ref_22) 2018; 12 Sharifzadeh (ref_31) 2019; 108 Jayamaha (ref_21) 2019; 7 Soualhi (ref_28) 2017; 14 Ashouri (ref_40) 2019; 35 Suonan (ref_3) 2009; 25 Nanayakkara (ref_36) 2012; 27 Malathi (ref_18) 2010; 73 Fairley (ref_1) 2019; 1 Ekici (ref_20) 2008; 34 Tzelepis (ref_39) 2020; 8 Hagan (ref_29) 1994; 5 Tsotsopoulou (ref_44) 2022; 10 Tzelepis (ref_43) 2018; 97 Bull (ref_32) 2011; 12 Karmacharya (ref_23) 2017; 33 Luo (ref_17) 2019; 2019 Wang (ref_34) 2021; 21 Samantaray (ref_12) 2013; 13 Farshad (ref_19) 2019; 104 Lv (ref_27) 2017; 14 Zhang (ref_7) 2019; 34 Azizi (ref_38) 2014; 29 Nanayakkara (ref_37) 2011; 27 Zhang (ref_4) 2021; 9 Hadaeghi (ref_41) 2019; 78 Livani (ref_42) 2014; 107 Hamidi (ref_8) 2016; 32 Farshad (ref_11) 2012; 27 Ola (ref_13) 2020; 14 Samantaray (ref_15) 2006; 76 Park (ref_26) 2017; 25 Salat (ref_16) 2004; 19 Sadiq (ref_30) 2021; 2 ref_6 |
| References_xml | – volume: 78 start-page: 313 year: 2019 ident: ref_41 article-title: Multi extreme learning machine approach for fault location in multi-terminal high-voltage direct current systems publication-title: Comput. Electr. Eng. doi: 10.1016/j.compeleceng.2019.07.022 – volume: 29 start-page: 851 year: 2014 ident: ref_2 article-title: Natural frequency-based line fault location in HVDC lines publication-title: IEEE Trans. Power Deliv. doi: 10.1109/TPWRD.2013.2269769 – ident: ref_10 doi: 10.1109/MELCON.2016.7495356 – volume: 34 start-page: 2028 year: 2019 ident: ref_7 article-title: Single-ended traveling wave fault location method in DC transmission line based on wave front information publication-title: IEEE Trans. Power Deliv. doi: 10.1109/TPWRD.2019.2922654 – volume: 10 start-page: 1437 year: 2021 ident: ref_5 article-title: Statistical Measure for Risk-Seeking Stochastic Wind Power Offering Strategies in Electricity Markets publication-title: J. Mod. Power Syst. Clean Energy doi: 10.35833/MPCE.2021.000218 – volume: 5 start-page: 989 year: 1994 ident: ref_29 article-title: Training feedforward networks with the Marquardt algorithm publication-title: IEEE Trans. Neural Netw. doi: 10.1109/72.329697 – volume: 12 start-page: 2879 year: 2011 ident: ref_32 article-title: Convergence rates of efficient global optimization algorithms publication-title: J. Mach. Learn. Res. – volume: 2019 start-page: 2414 year: 2019 ident: ref_17 article-title: Transient signal identification of HVDC transmission lines based on wavelet entropy and SVM publication-title: J. Eng. – volume: 27 start-page: 2360 year: 2012 ident: ref_11 article-title: Accurate single-phase fault-location method for transmission lines based on k-nearest neighbor algorithm using one-end voltage publication-title: IEEE Trans. Power Deliv. doi: 10.1109/TPWRD.2012.2211898 – volume: 28 start-page: 1373 year: 2013 ident: ref_9 article-title: Transient-based fault-location method for multiterminal lines employing S-transform publication-title: IEEE Trans. Power Deliv. doi: 10.1109/TPWRD.2013.2248068 – volume: 26 start-page: 792 year: 2018 ident: ref_24 article-title: A global Bayesian optimization algorithm and its application to integrated system design publication-title: IEEE Trans. Very Large Scale Integr. Syst. doi: 10.1109/TVLSI.2017.2784783 – ident: ref_6 doi: 10.3390/su141811749 – ident: ref_33 doi: 10.1049/cp.2015.0068 – volume: 25 start-page: 1203 year: 2009 ident: ref_3 article-title: A novel fault-location method for HVDC transmission lines publication-title: IEEE Trans. Power Deliv. doi: 10.1109/TPWRD.2009.2033078 – ident: ref_14 doi: 10.3390/app10041516 – volume: 9 start-page: 29711 year: 2021 ident: ref_4 article-title: Deep learning for short-term voltage stability assessment of power systems publication-title: IEEE Access doi: 10.1109/ACCESS.2021.3057659 – volume: 104 start-page: 615 year: 2019 ident: ref_19 article-title: Detection and classification of internal faults in bipolar HVDC transmission lines based on K-means data description method publication-title: Int. J. Electr. Power Energy Syst. doi: 10.1016/j.ijepes.2018.07.044 – volume: 8 start-page: 203398 year: 2020 ident: ref_39 article-title: Voltage and current measuring technologies for high voltage direct current supergrids: A technology review identifying the options for protection, fault location and automation applications publication-title: IEEE Access doi: 10.1109/ACCESS.2020.3035905 – volume: 13 start-page: 928 year: 2013 ident: ref_12 article-title: A systematic fuzzy rule based approach for fault classification in transmission lines publication-title: Appl. Soft Comput. doi: 10.1016/j.asoc.2012.09.010 – volume: 27 start-page: 2286 year: 2012 ident: ref_36 article-title: Traveling-wave-based line fault location in star-connected multiterminal HVDC systems publication-title: IEEE Trans. Power Deliv. doi: 10.1109/TPWRD.2012.2202405 – volume: 29 start-page: 2552 year: 2014 ident: ref_38 article-title: A traveling-wave-based methodology for wide-area fault location in multiterminal DC systems publication-title: IEEE Trans. Power Deliv. doi: 10.1109/TPWRD.2014.2323356 – volume: 32 start-page: 135 year: 2016 ident: ref_8 article-title: Traveling-wave-based fault-location algorithm for hybrid multiterminal circuits publication-title: IEEE Trans. Power Deliv. doi: 10.1109/TPWRD.2016.2589265 – volume: 10 start-page: 10124 year: 2022 ident: ref_44 article-title: Time-domain protection of superconducting cables based on artificial intelligence classifiers publication-title: IEEE Access doi: 10.1109/ACCESS.2022.3142534 – volume: 34 start-page: 2937 year: 2008 ident: ref_20 article-title: Energy and entropy-based feature extraction for locating fault on transmission lines by using neural network and wavelet packet decomposition publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2007.05.011 – volume: 14 start-page: 1842 year: 2020 ident: ref_13 article-title: Wigner distribution function and alienation coefficient-based transmission line protection scheme publication-title: IET Gener. Transm. Distrib. doi: 10.1049/iet-gtd.2019.1414 – volume: 63 start-page: 4263 year: 2015 ident: ref_25 article-title: Bayesian optimization for broadband high-efficiency power amplifier designs publication-title: IEEE Trans. Microw. Theory Tech. doi: 10.1109/TMTT.2015.2495360 – volume: 73 start-page: 2160 year: 2010 ident: ref_18 article-title: Intelligent approaches using support vector machine and extreme learning machine for transmission line protection publication-title: Neurocomputing doi: 10.1016/j.neucom.2010.02.001 – volume: 7 start-page: 145371 year: 2019 ident: ref_21 article-title: Wavelet-multi resolution analysis based ANN architecture for fault detection and localization in DC microgrids publication-title: IEEE Access doi: 10.1109/ACCESS.2019.2945397 – ident: ref_35 doi: 10.1007/978-981-15-2977-1 – volume: 1 start-page: 35 year: 2019 ident: ref_1 article-title: China’s ambitious plan to build the world’s biggest supergrid publication-title: IEEE Spectr. – volume: 33 start-page: 549 year: 2017 ident: ref_23 article-title: Fault location in ungrounded photovoltaic system using wavelets and ANN publication-title: IEEE Trans. Power Deliv. doi: 10.1109/TPWRD.2017.2721903 – volume: 76 start-page: 897 year: 2006 ident: ref_15 article-title: Fault classification and location using HS-transform and radial basis function neural network publication-title: Electr. Power Syst. Res. doi: 10.1016/j.epsr.2005.11.003 – volume: 107 start-page: 190 year: 2014 ident: ref_42 article-title: A single-ended fault location method for segmented HVDC transmission line publication-title: Electr. Power Syst. Res. doi: 10.1016/j.epsr.2013.10.006 – volume: 12 start-page: 1555 year: 2018 ident: ref_22 article-title: Efficient and robust ANN-based method for an improved protection of VSC-HVDC systems publication-title: IET Renew. Power Gener. doi: 10.1049/iet-rpg.2018.5097 – volume: 2 start-page: 314 year: 2021 ident: ref_30 article-title: Toward the development of versatile brain–computer interfaces publication-title: IEEE Trans. Artif. Intell. doi: 10.1109/TAI.2021.3097307 – volume: 19 start-page: 979 year: 2004 ident: ref_16 article-title: Accurate fault location in the power transmission line using support vector machine approach publication-title: IEEE Trans. Power Syst. doi: 10.1109/TPWRS.2004.825883 – volume: 14 start-page: 3436 year: 2017 ident: ref_27 article-title: Levenberg–Marquardt backpropagation training of multilayer neural networks for state estimation of a safety-critical cyber-physical system publication-title: IEEE Trans. Ind. Inform. doi: 10.1109/TII.2017.2777460 – volume: 21 start-page: 2285 year: 2021 ident: ref_34 article-title: Fault diagnosis for power cables based on convolutional neural network with chaotic system and discrete wavelet transform publication-title: IEEE Trans. Power Deliv. – volume: 27 start-page: 279 year: 2011 ident: ref_37 article-title: Location of DC line faults in conventional HVDC systems with segments of cables and overhead lines using terminal measurements publication-title: IEEE Trans. Power Deliv. doi: 10.1109/TPWRD.2011.2174067 – volume: 35 start-page: 1365 year: 2019 ident: ref_40 article-title: On the application of modal transient analysis for online fault localization in HVDC cable bundles publication-title: IEEE Trans. Power Deliv. doi: 10.1109/TPWRD.2019.2942016 – volume: 25 start-page: 1856 year: 2017 ident: ref_26 article-title: Application of machine learning for optimization of 3-D integrated circuits and systems publication-title: IEEE Trans. Very Large Scale Integr. Syst. doi: 10.1109/TVLSI.2017.2656843 – volume: 14 start-page: 24 year: 2017 ident: ref_28 article-title: Heath monitoring of capacitors and supercapacitors using the neo-fuzzy neural approach publication-title: IEEE Trans. Ind. Inform. doi: 10.1109/TII.2017.2701823 – volume: 108 start-page: 513 year: 2019 ident: ref_31 article-title: Machine-learning methods for integrated renewable power generation: A comparative study of artificial neural networks, support vector regression, and Gaussian Process Regression publication-title: Renew. Sustain. Energy Rev. doi: 10.1016/j.rser.2019.03.040 – volume: 97 start-page: 319 year: 2018 ident: ref_43 article-title: Advanced fault location in MTDC networks utilising optically-multiplexed current measurements and machine learning approach publication-title: Int. J. Electr. Power Energy Syst. doi: 10.1016/j.ijepes.2017.10.040 |
| SSID | ssj0023338 |
| Score | 2.5592527 |
| Snippet | We develop a probabilistic model for determining the location of dc-link faults in MT-HVdc networks using discrete wavelet transforms (DWTs), Bayesian... |
| SourceID | doaj pubmedcentral proquest gale pubmed crossref |
| SourceType | Open Website Open Access Repository Aggregation Database Index Database Enrichment Source |
| StartPage | 9936 |
| SubjectTerms | Accuracy Algorithms Back propagation Bayes Theorem Bayesian optimization Bias Computer Simulation Electricity distribution Levenberg–Marquardt backpropagation Localization modular multilevel converter Neural networks Neural Networks, Computer Power protection sensor Sensors Signal processing Time Wavelet transforms |
| SummonAdditionalLinks | – databaseName: DOAJ Directory of Open Access Journals dbid: DOA link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV3Pi9QwFH7I4kEP4m-rq0QR9FK206RNcpwVhxVkFVZlbyFNExzY6SwzHQ_-9X5pO7WDghehl7SPkOS95Htf034heh1CqJXzs7SylUtFAGGtQqZS7yXQoPN5f9iEPD9Xl5f68-Sor_hNWC8P3A_cCeeVtj4LNvhCAH6tFlUhRQgq49xKH1ffTOo9mRqoFgfz6nWEOEj9yRYoiNS-02H-jT6dSP-fS_EEiw6_k5wAz-Iu3RkyRjbvW3qPbvjmPt2e6Ag-oNWHUVizZRcgpuvNliEbZbVjC7u7atnHdf9qjl3ASSvPTgFeNUP5E5aM1fInCtM65pPtha6is2-oalA3f0hfF--_vDtLh3MUUof0q02lsFUptAN3AY8WrpQun9UYUJRcbpVzpQdrU6XNrZO20qV2HsDvCxiDj_BHdNSsG_-EWKY9CJ_LuAoyki_lygK1FVntPTKjkNDb_fgaN4iMx7MurgzIRnSFGV2R0KvR9LpX1vib0Wl00mgQxbC7GwgRM4SI-VeIJPQmutjEKYvGODv8eYAuRfErM5dRZK4Anid0vI8CM8zlrYkC-XF3VucJvRwfYxbGrRXb-PWus1Eg0rlSCT3ug2ZsM4_6RlhHE5IH4XTQqcMnzfJ7p_Qd5aGUEE__xyg8o1t5_HVjhqs8pqN2s_PP6ab70S63mxfd9PkFSFYiPg priority: 102 providerName: Directory of Open Access Journals |
| Title | Intelligent Sensors for dc Fault Location Scheme Based on Optimized Intelligent Architecture for HVdc Systems |
| URI | https://www.ncbi.nlm.nih.gov/pubmed/36560301 https://www.proquest.com/docview/2756784192 https://www.proquest.com/docview/2758107288 https://pubmed.ncbi.nlm.nih.gov/PMC9780844 https://doaj.org/article/33b9ae0fafe54615a94b574ff8033a7e |
| Volume | 22 |
| WOSCitedRecordID | wos000904509000001&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: 1424-8220 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0023338 issn: 1424-8220 databaseCode: DOA dateStart: 20010101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources (ISSN International Center) customDbUrl: eissn: 1424-8220 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0023338 issn: 1424-8220 databaseCode: M~E dateStart: 20010101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre – providerCode: PRVPQU databaseName: Health & Medical Collection (ProQuest) customDbUrl: eissn: 1424-8220 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0023338 issn: 1424-8220 databaseCode: 7X7 dateStart: 20010101 isFulltext: true titleUrlDefault: https://search.proquest.com/healthcomplete providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 1424-8220 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0023338 issn: 1424-8220 databaseCode: BENPR dateStart: 20010101 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Publicly Available Content Database customDbUrl: eissn: 1424-8220 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0023338 issn: 1424-8220 databaseCode: PIMPY dateStart: 20010101 isFulltext: true titleUrlDefault: http://search.proquest.com/publiccontent providerName: ProQuest |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1Lj9MwEB7BLgc48F4ILFVASHCJNs3Lzgm1qNWuxJaKBVROkePYUGmbLE3KgQO_nRnHTVOBuCBVkZxMLVsznpnPTr4BeKm1LrhUQy8XufQijYA11z73lGIYDYzO22ITbDbji0U6txtutX2tcusTjaMuKkl75CdEU05nZGnw5uq7R1Wj6HTVltC4DodUNpvsnC12gCtE_NWyCYUI7U9qjIWY4Bs25l0MMlT9fzrkXkTaf1uyF36md_534Hfhtk083VFrKffgmirvw60eHeEDWJ11_JyNe4H4tlrXLia1biHdqdhcNu67qt3hcy9Q1yvljjEGFi6236PnWS1_YqPfx6h3SmE6Ov2MXVmS9IfwaTr5-PbUs-UYPIlZXOOxSORJlEqEQAjHI5kwGQwLoRW2ZCC4lIlC8McTEQjJRJ4mqVSYP6gYhRHWhEdwUFalegyunyrEjdIPuWaE4bhMYuwt9gulMMHSDrzeKiiTlqucSmZcZohZSJdZp0sHXnSiVy1Bx9-ExqTlToA4tc2Nav01s0sU_5KnQvkaZxRHmOiJNMpjFmnN_TAUTDnwimwko5WPg5HCfsCAUyIOrWzEiKsuxrTAgeOtKWTWJdTZzg4ceN49xsVMJzSiVNXGyHDE4wHnDjxqra4bc0g0SeiOHWB79rg3qf0n5fKbIQwnlikeRU_-PayncDOgbzuG-EuO4aBZb9QzuCF_NMt6PTAry1z5AA7Hk9n8w8BsYOD1_NcE783PzudffgP-aDYy |
| linkProvider | ProQuest |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Lj9MwEB4tCxLLgfcjsIBBILhEm-Zl54BQF6habSlIu4t6C45jL5W2ydKmIPhR_EZm8moqELc9IPXiZmrZzeeZ-eL4G4BnxphUKN2zE5ko2zdIWBPjCFtrjtGgvOdVsQk-mYjpNPq4Bb-aszD0WmXjE0tHneaKnpHvkUw57ZFF7uuzrzZVjaLd1aaERgWLA_3jO1K25avRW7y_z1138O7ozdCuqwrYCpORwua-TEI_UpjJI6v0VciV20ul0dhSrhRKhRo5jAilKxWXSRRGSmMY1AEaY3buYb8X4CL6cU5kj0_XBM9DvlepF3le5OwtMfYioSjVn9cxrywN8GcA6ETAzbczO-FucO1_-6Ouw9U6sWb9aiXcgC2d3YQrHbnFWzAftfqjBTtE_p4vlgyTdpYqNpCr04KN8-oJJjtELM8128cYnzJsf0DPOp_9xEa3j35nF6bsaPgJu6pF4G_D8bnM9w5sZ3mm7wFzIo28WDmeMJw4qlBhgL0FTqo1JpDGgpcNIGJVa7FTSZDTGDkZYSdusWPB09b0rBIg-ZvRPqGqNSDN8PKLfHES1y4If5JEUjsGZxT4mMjKyE8C7hsjHM-TXFvwgjAZk2fDwShZH9DAKZFGWNznpMUXYNpjwW4Dvbh2ect4jTsLnrSX0VnRDpTMdL4qbUTP4a4QFtytUN6O2SMZKAw3FvAN_G9MavNKNvtSCqKTipbw_fv_HtZjuDw8ej-Ox6PJwQPYcekcSw8_4S5sF4uVfgiX1Lditlw8Klc1g8_nvTp-A5kMjkU |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1bb9MwFD4aHULsgftYYIBBoPESNU2c2HlAqGNUqzZKpQHanjLHsaHSmowmBcFP49dxnNtSgXjbA1Jf3DhHdnJuX2x_B-C51jrhUg3sWMTSphoBa6wdbivFMBqU77wqNsEmE358HE7X4FdzFsZsq2x8Yumok0yab-R9Q1Nu1shCt6_rbRHTvdHr86-2qSBlVlqbchqVihyoH98RvuWvxnv4rl-47ujthzf7dl1hwJaYmBQ2oyIOaCgxq0eESWXApDtIhFbYkq7gUgYK8QwPhCskE3EYhFJhSFQ-dsZM3UO5V2AdU3Lq9mB9On43PWnhnofor-Iy8rzQ6ecYiRFelFzQFxGwLBTwZzjoxMPVvZqd4De6-T8_tltwo065ybCykduwptI7sNEhYrwL83HLTFqQI0T22SInmM6TRJKRWJ4V5DCrvm2SI9TyuSK7GP0Tgu336HPns5_Y6MoYdtZnSkH7n1BUTQ9_Dz5eynw3oZdmqdoC4oQKEbN0PK6ZQa9cBj5K851EKUwttQUvG-WIZM3SboqFnEWI1oweRa0eWfCs7XpeUZP8rdOu0bC2g2ETL__IFp-j2jnhLXEolKNxRj7FFFeENPYZ1Zo7nieYsmDH6GdkfB4ORor66AZOybCHRUNmWPp8TIgs2G7UMKqdYR5d6KAFT9vL6MbM2pRIVbYs-_CBw1zOLbhfaXw7Zs8QRGEgsoCt2MLKpFavpLMvJVW64dfilD7497CewDU0iuhwPDl4CNddc8BlgL9gG3rFYqkewVX5rZjli8e1iRM4vWzz-A12gZiU |
| 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=Intelligent+Sensors+for+dc+Fault+Location+Scheme+Based+on+Optimized+Intelligent+Architecture+for+HVdc+Systems&rft.jtitle=Sensors+%28Basel%2C+Switzerland%29&rft.au=Yousaf%2C+Muhammad+Zain&rft.au=Tahir%2C+Muhammad+Faizan&rft.au=Raza%2C+Ali&rft.au=Khan%2C+Muhammad+Ahmad&rft.date=2022-12-16&rft.eissn=1424-8220&rft.volume=22&rft.issue=24&rft_id=info:doi/10.3390%2Fs22249936&rft_id=info%3Apmid%2F36560301&rft.externalDocID=36560301 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1424-8220&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1424-8220&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1424-8220&client=summon |