{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T17:25:28Z","timestamp":1778347528737,"version":"3.51.4"},"reference-count":36,"publisher":"Springer Science and Business Media LLC","issue":"23","license":[{"start":{"date-parts":[[2024,9,10]],"date-time":"2024-09-10T00:00:00Z","timestamp":1725926400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,9,10]],"date-time":"2024-09-10T00:00:00Z","timestamp":1725926400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100004731","name":"Natural Science Foundation of Zhejiang Province","doi-asserted-by":"publisher","award":["LY22E070006"],"award-info":[{"award-number":["LY22E070006"]}],"id":[{"id":"10.13039\/501100004731","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2024,12]]},"DOI":"10.1007\/s10489-024-05772-9","type":"journal-article","created":{"date-parts":[[2024,9,10]],"date-time":"2024-09-10T07:02:21Z","timestamp":1725951741000},"page":"12112-12127","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":43,"title":["Meta-transfer learning-based method for multi-fault analysis and assessment in power system"],"prefix":"10.1007","volume":"54","author":[{"given":"Lingfeng","family":"Zheng","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuhong","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0305-7267","authenticated-orcid":false,"given":"Yongzhi","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,9,10]]},"reference":[{"key":"5772_CR1","doi-asserted-by":"publisher","first-page":"107650","DOI":"10.1016\/j.ymssp.2021.107650","volume":"156","author":"H Wang","year":"2021","unstructured":"Wang H, Liu C, Jiang D, Jiang Z (2021) Collaborative deep learning framework for fault diagnosis in distributed complex systems. Mech Syst Signal Process 156:107650. https:\/\/doi.org\/10.1016\/j.ymssp.2021.107650","journal-title":"Mech Syst Signal Process"},{"key":"5772_CR2","doi-asserted-by":"publisher","first-page":"107702","DOI":"10.1016\/j.ijepes.2021.107702","volume":"137","author":"AS Gazafroudi","year":"2022","unstructured":"Gazafroudi AS, Neumann F, Brown T (2022) Topology-based approximations for n-1 contingency constraints in power transmission networks. Int J Electrical Power Energy Syst 137:107702. https:\/\/doi.org\/10.1016\/j.ijepes.2021.107702","journal-title":"Int J Electrical Power Energy Syst"},{"key":"5772_CR3","doi-asserted-by":"publisher","unstructured":"Tinney WF, Hart CE (1967) Power flow solution by newton\u2019s method. IEEE Trans Power Apparatus Syst PAS-86(11):1449\u20131460. https:\/\/doi.org\/10.1109\/TPAS.1967.291823","DOI":"10.1109\/TPAS.1967.291823"},{"key":"5772_CR4","doi-asserted-by":"publisher","first-page":"4027","DOI":"10.1007\/s00500-020-05431-4","volume":"25","author":"S Abd El-sattar","year":"2021","unstructured":"Abd El-sattar S, Kamel S, Ebeed M, Jurado F (2021) An improved version of salp swarm algorithm for solving optimal power flow problem. Soft Comput 25:4027\u20134052. https:\/\/doi.org\/10.1007\/s00500-020-05431-4","journal-title":"Soft Comput"},{"key":"5772_CR5","doi-asserted-by":"publisher","first-page":"105869","DOI":"10.1016\/j.ijepes.2020.105869","volume":"119","author":"M Tostado-V\u00e9liz","year":"2020","unstructured":"Tostado-V\u00e9liz M, Kamel S, Jurado F (2020) An efficient power-flow approach based on heun and king-werner\u2019s methods for solving both well and ill-conditioned cases. Int J Electrical Power Energy Syst 119:105869. https:\/\/doi.org\/10.1016\/j.ijepes.2020.105869","journal-title":"Int J Electrical Power Energy Syst"},{"issue":"3","key":"5772_CR6","doi-asserted-by":"publisher","first-page":"1725","DOI":"10.1109\/TPWRS.2020.3026379","volume":"36","author":"X Pan","year":"2020","unstructured":"Pan X, Zhao T, Chen M, Zhang S (2020) Deepopf: a deep neural network approach for security-constrained dc optimal power flow. IEEE Trans Power Syst 36(3):1725\u20131735. https:\/\/doi.org\/10.1109\/TPWRS.2020.3026379","journal-title":"IEEE Trans Power Syst"},{"key":"5772_CR7","doi-asserted-by":"crossref","unstructured":"Zamzam AS, Baker K (2020) Learning optimal solutions for extremely fast ac optimal power flow. Paper Presented at the 2020 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm), Tempe, AZ, USA, 11\u201313 November 2020","DOI":"10.1109\/SmartGridComm47815.2020.9303008"},{"issue":"1","key":"5772_CR8","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1109\/JPROC.2020.3004555","volume":"109","author":"F Zhuang","year":"2020","unstructured":"Zhuang F, Qi Z, Duan K, Xi D, Zhu Y, Zhu H, Xiong H, He Q (2020) A comprehensive survey on transfer learning. Proc IEEE 109(1):43\u201376. https:\/\/doi.org\/10.1109\/JPROC.2020.3004555","journal-title":"Proc IEEE"},{"key":"5772_CR9","doi-asserted-by":"publisher","unstructured":"Zhu Y, Zhou Y, Wei W, Wang N (2022) Cascading failure analysis based on a physics-informed graph neural network. IEEE Trans Power Syst. https:\/\/doi.org\/10.1109\/TPWRS.2022.3205043","DOI":"10.1109\/TPWRS.2022.3205043"},{"key":"5772_CR10","doi-asserted-by":"publisher","first-page":"75712","DOI":"10.1109\/ACCESS.2021.3082001","volume":"9","author":"J Xie","year":"2021","unstructured":"Xie J, Sun W (2021) A transfer and deep learning-based method for online frequency stability assessment and control. IEEE Access 9:75712\u201375721. https:\/\/doi.org\/10.1109\/ACCESS.2021.3082001","journal-title":"IEEE Access"},{"issue":"6","key":"5772_CR11","doi-asserted-by":"publisher","first-page":"4424","DOI":"10.1109\/TPWRS.2022.3153445","volume":"37","author":"H Li","year":"2022","unstructured":"Li H, Ma Z, Weng Y (2022) A transfer learning framework for power system event identification. IEEE Trans Power Syst 37(6):4424\u20134435. https:\/\/doi.org\/10.1109\/TPWRS.2022.3153445","journal-title":"IEEE Trans Power Syst"},{"issue":"5","key":"5772_CR12","doi-asserted-by":"publisher","first-page":"4856","DOI":"10.1109\/TPWRS.2021.3089042","volume":"36","author":"C Ren","year":"2021","unstructured":"Ren C, Xu Y, Dai B, Zhang R (2021) An integrated transfer learning method for power system dynamic security assessment of unlearned faults with missing data. IEEE Trans Power Syst 36(5):4856\u20134859. https:\/\/doi.org\/10.1109\/TPWRS.2021.3089042","journal-title":"IEEE Trans Power Syst"},{"key":"5772_CR13","doi-asserted-by":"publisher","first-page":"105650","DOI":"10.1016\/j.ijepes.2019.105650","volume":"117","author":"M Xiang","year":"2020","unstructured":"Xiang M, Yu J, Yang Z, Yang Y, Yu H, He H (2020) Probabilistic power flow with topology changes based on deep neural network. Int J Electrical Power Energy Syst 117:105650. https:\/\/doi.org\/10.1016\/j.ijepes.2019.105650","journal-title":"Int J Electrical Power Energy Syst"},{"key":"5772_CR14","doi-asserted-by":"publisher","unstructured":"Chen B, Sun D, Zhu Y, Liu D, Zhou Y (2023) Real-time risk assessment of cascading failure in power system with high proportion of renewable energy based on fault graph chains. Engineering Reports p e12631. https:\/\/doi.org\/10.1002\/eng2.12631","DOI":"10.1002\/eng2.12631"},{"key":"5772_CR15","doi-asserted-by":"publisher","first-page":"105567","DOI":"10.1016\/j.engappai.2022.105567","volume":"117","author":"TB Lopez-Garcia","year":"2023","unstructured":"Lopez-Garcia TB, Dom\u00ednguez-Navarro JA (2023) Power flow analysis via typed graph neural networks. Eng Appl Artif Intell 117:105567. https:\/\/doi.org\/10.1016\/j.engappai.2022.105567","journal-title":"Eng Appl Artif Intell"},{"key":"5772_CR16","doi-asserted-by":"publisher","first-page":"93283","DOI":"10.1109\/ACCESS.2020.2991263","volume":"8","author":"J Huang","year":"2020","unstructured":"Huang J, Guan L, Su Y, Yao H, Guo M, Zhong Z (2020) Recurrent graph convolutional network-based multi-task transient stability assessment framework in power system. IEEE Access 8:93283\u201393296. https:\/\/doi.org\/10.1109\/ACCESS.2020.2991263","journal-title":"IEEE Access"},{"issue":"5","key":"5772_CR17","doi-asserted-by":"publisher","first-page":"4122","DOI":"10.1109\/TPWRS.2022.3213800","volume":"38","author":"Y Zhu","year":"2023","unstructured":"Zhu Y, Zhou Y, Wei W, Zhang L (2023) Real-time cascading failure risk evaluation with high penetration of renewable energy based on a graph convolutional network. IEEE Trans Power Syst 38(5):4122\u20134133. https:\/\/doi.org\/10.1109\/TPWRS.2022.3213800","journal-title":"IEEE Trans Power Syst"},{"issue":"6","key":"5772_CR18","doi-asserted-by":"publisher","first-page":"7068","DOI":"10.1109\/TIA.2022.3202159","volume":"58","author":"H Wu","year":"2022","unstructured":"Wu H, Wang M, Xu Z, Jia Y (2022) Graph attention enabled convolutional network for distribution system probabilistic power flow. IEEE Trans Ind Appl 58(6):7068\u20137078. https:\/\/doi.org\/10.1109\/TIA.2022.3202159","journal-title":"IEEE Trans Ind Appl"},{"key":"5772_CR19","doi-asserted-by":"publisher","first-page":"106547","DOI":"10.1016\/j.epsr.2020.106547","volume":"189","author":"B Donon","year":"2020","unstructured":"Donon B, Cl\u00e9ment R, Donnot B, Marot A, Guyon I, Schoenauer M (2020) Neural networks for power flow: graph neural solver. Electric Power Syst Res 189:106547. https:\/\/doi.org\/10.1016\/j.epsr.2020.106547","journal-title":"Electric Power Syst Res"},{"issue":"3","key":"5772_CR20","doi-asserted-by":"publisher","first-page":"2082","DOI":"10.1109\/TPWRS.2020.3029557","volume":"36","author":"X Hu","year":"2020","unstructured":"Hu X, Hu H, Verma S, Zhang ZL (2020) Physics-guided deep neural networks for power flow analysis. IEEE Trans Power Syst 36(3):2082\u20132092. https:\/\/doi.org\/10.1109\/TPWRS.2020.3029557","journal-title":"IEEE Trans Power Syst"},{"key":"5772_CR21","doi-asserted-by":"publisher","unstructured":"Zhou J, Cui G, Hu S, Zhang Z, Yang C, Liu Z, Wang L, Li C, Sun M (2020) Graph neural networks: a review of methods and applications. AI open 1:57\u201381. https:\/\/doi.org\/10.1016\/j.aiopen.2021.01.001","DOI":"10.1016\/j.aiopen.2021.01.001"},{"issue":"1","key":"5772_CR22","doi-asserted-by":"publisher","first-page":"117","DOI":"10.1080\/02626667.2021.2003367","volume":"67","author":"SA Hosseini","year":"2022","unstructured":"Hosseini SA, Abbaszadeh Shahri A, Asheghi R (2022) Prediction of bedload transport rate using a block combined network structure. Hydrol Sci J 67(1):117\u2013128. https:\/\/doi.org\/10.1080\/02626667.2021.2003367","journal-title":"Hydrol Sci J"},{"key":"5772_CR23","doi-asserted-by":"publisher","first-page":"121399","DOI":"10.1016\/j.apenergy.2023.121399","volume":"349","author":"Y Zhao","year":"2023","unstructured":"Zhao Y, Zhang G, Hu W, Huang Q, Chen Z, Blaabjerg F (2023) Meta-learning based voltage control strategy for emergency faults of active distribution networks. Appl Energy 349:121399. https:\/\/doi.org\/10.1016\/j.apenergy.2023.121399","journal-title":"Appl Energy"},{"issue":"9","key":"5772_CR24","doi-asserted-by":"publisher","first-page":"5149","DOI":"10.1109\/TPAMI.2021.3079209","volume":"44","author":"T Hospedales","year":"2021","unstructured":"Hospedales T, Antoniou A, Micaelli P, Storkey A (2021) Meta-learning in neural networks: a survey. IEEE Trans Pattern Anal Mach Intell 44(9):5149\u20135169. https:\/\/doi.org\/10.1109\/TPAMI.2021.3079209","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"1","key":"5772_CR25","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40537-016-0043-6","volume":"3","author":"K Weiss","year":"2016","unstructured":"Weiss K, Khoshgoftaar TM, Wang D (2016) A survey of transfer learning. J Big data 3(1):1\u201340. https:\/\/doi.org\/10.1186\/s40537-016-0043-6","journal-title":"J Big data"},{"issue":"3","key":"5772_CR26","doi-asserted-by":"publisher","first-page":"1443","DOI":"10.1109\/TPAMI.2020.3018506","volume":"44","author":"Q Sun","year":"2020","unstructured":"Sun Q, Liu Y, Chen Z, Chua TS, Schiele B (2020) Meta-transfer learning through hard tasks. IEEE Trans Pattern Anal Mach Intell 44(3):1443\u20131456. https:\/\/doi.org\/10.1109\/TPAMI.2020.3018506","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"2","key":"5772_CR27","doi-asserted-by":"publisher","first-page":"949","DOI":"10.1007\/s11063-022-10918-2","volume":"55","author":"J Zhao","year":"2023","unstructured":"Zhao J, Tang T, Yu Y, Wang J, Yang T, Chen M, Wu J (2023) Adaptive meta transfer learning with efficient self-attention for few-shot bearing fault diagnosis. Neural Process Lett 55(2):949\u2013968. https:\/\/doi.org\/10.1007\/s11063-022-10918-2","journal-title":"Neural Process Lett"},{"key":"5772_CR28","doi-asserted-by":"crossref","unstructured":"Soh JW, Cho S, Cho NI (2020) Meta-transfer learning for zero-shot super-resolution. Paper Presented at the Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA, 13\u201319 June 2020","DOI":"10.1109\/CVPR42600.2020.00357"},{"key":"5772_CR29","doi-asserted-by":"crossref","unstructured":"Murty P (2017) Power systems analysis. butterworth-heinemann, Oxford","DOI":"10.1016\/B978-0-08-101111-9.00013-6"},{"key":"5772_CR30","unstructured":"Goodfellow I, Bengio Y, Courville A (2016) Deep learning. MIT press"},{"key":"5772_CR31","doi-asserted-by":"publisher","first-page":"100296","DOI":"10.1016\/j.cosrev.2020.100296","volume":"38","author":"M Hou","year":"2020","unstructured":"Hou M, Ren J, Zhang D, Kong X, Zhang D, Xia F (2020) Network embedding: taxonomies, frameworks and applications. Comput Sci Rev 38:100296. https:\/\/doi.org\/10.1016\/j.cosrev.2020.100296","journal-title":"Comput Sci Rev"},{"key":"5772_CR32","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser \u0141, Polosukhin I (2017) Attention is all you need. Advan Neural Inform Process Syst 30"},{"issue":"1","key":"5772_CR33","doi-asserted-by":"publisher","first-page":"161","DOI":"10.1080\/24709360.2017.1396742","volume":"1","author":"YC Chen","year":"2017","unstructured":"Chen YC (2017) A tutorial on kernel density estimation and recent advances. Biostatistics & Epidemiology 1(1):161\u2013187. https:\/\/doi.org\/10.1080\/24709360.2017.1396742","journal-title":"Biostatistics & Epidemiology"},{"issue":"3","key":"5772_CR34","doi-asserted-by":"publisher","first-page":"1351","DOI":"10.1007\/s11053-022-10051-w","volume":"31","author":"A Abbaszadeh Shahri","year":"2022","unstructured":"Abbaszadeh Shahri A, Shan C, Larsson S (2022) A novel approach to uncertainty quantification in groundwater table modeling by automated predictive deep learning. Nat Resour Res 31(3):1351\u20131373. https:\/\/doi.org\/10.1007\/s11053-022-10051-w","journal-title":"Nat Resour Res"},{"issue":"3","key":"5772_CR35","doi-asserted-by":"publisher","first-page":"562","DOI":"10.2166\/hydro.2020.098","volume":"22","author":"R Asheghi","year":"2020","unstructured":"Asheghi R, Hosseini SA, Saneie M, Shahri AA (2020) Updating the neural network sediment load models using different sensitivity analysis methods: a regional application. J Hydroinf 22(3):562\u2013577. https:\/\/doi.org\/10.2166\/hydro.2020.098","journal-title":"J Hydroinf"},{"issue":"6","key":"5772_CR36","doi-asserted-by":"publisher","first-page":"1300","DOI":"10.1016\/j.jrmge.2021.07.006","volume":"13","author":"A Abbaszadeh Shahri","year":"2021","unstructured":"Abbaszadeh Shahri A, Shan C, Z\u00e4ll E, Larsson S (2021) Spatial distribution modeling of subsurface bedrock using a developed automated intelligence deep learning procedure: a case study in Sweden. J Rock Mech Geotechnical Eng 13(6):1300\u20131310. https:\/\/doi.org\/10.1016\/j.jrmge.2021.07.006","journal-title":"J Rock Mech Geotechnical Eng"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-024-05772-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-024-05772-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-024-05772-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,30]],"date-time":"2024-09-30T13:08:07Z","timestamp":1727701687000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-024-05772-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,10]]},"references-count":36,"journal-issue":{"issue":"23","published-print":{"date-parts":[[2024,12]]}},"alternative-id":["5772"],"URL":"https:\/\/doi.org\/10.1007\/s10489-024-05772-9","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,9,10]]},"assertion":[{"value":"12 August 2024","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 September 2024","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"No applicable","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics Approval"}},{"value":"No applicable","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to Participate"}},{"value":"No applicable","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for Publication"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of Interest\/Competing Interests"}}]}}