{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T00:48:33Z","timestamp":1791420513627,"version":"4.3.3"},"reference-count":39,"publisher":"Elsevier BV","issue":"4","license":[{"start":{"date-parts":[[2025,4,1]],"date-time":"2025-04-01T00:00:00Z","timestamp":1743465600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2025,4,1]],"date-time":"2025-04-01T00:00:00Z","timestamp":1743465600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2025,3,3]],"date-time":"2025-03-03T00:00:00Z","timestamp":1740960000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U1866602"],"award-info":[{"award-number":["U1866602"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["cell.com","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["iScience"],"published-print":{"date-parts":[[2025,4]]},"DOI":"10.1016\/j.isci.2025.112172","type":"journal-article","created":{"date-parts":[[2025,3,6]],"date-time":"2025-03-06T11:51:59Z","timestamp":1741261919000},"page":"112172","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":5,"title":["An adaptive power system transient stability assessment method based on shared feature extraction"],"prefix":"10.1016","volume":"28","author":[{"given":"Jiexiang","family":"Hu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Le","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0054-0288","authenticated-orcid":false,"given":"Wei","family":"Ai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yansong","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jun","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinglei","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.isci.2025.112172_bib1","doi-asserted-by":"crossref","first-page":"1430","DOI":"10.1109\/TPWRS.2015.2413895","article-title":"Time Series Shapelet Classification Based Online Short-Term Voltage Stability Assessment","volume":"31","author":"Zhu","year":"2016","journal-title":"IEEE Trans. Power Syst."},{"key":"10.1016\/j.isci.2025.112172_bib2","doi-asserted-by":"crossref","first-page":"2818","DOI":"10.1109\/TPWRS.2014.2357212","article-title":"PMU-Based Model-Free Approach for Real-Time Rotor Angle Monitoring","volume":"30","author":"Dasgupta","year":"2015","journal-title":"IEEE Trans. Power Syst."},{"key":"10.1016\/j.isci.2025.112172_bib3","doi-asserted-by":"crossref","first-page":"747","DOI":"10.1109\/T-AIEE.1947.5059502","article-title":"The transient-energy method of calculating stability","volume":"66","author":"Magnusson","year":"1947","journal-title":"Trans. Am. Inst. Electr. Eng."},{"key":"10.1016\/j.isci.2025.112172_bib4","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1109\/TII.2018.2829818","article-title":"A hierarchical self-adaptive data-analytics method for real-time power system short-term voltage stability assessment","volume":"15","author":"Zhang","year":"2019","journal-title":"IEEE Trans. Industr. Inform."},{"key":"10.1016\/j.isci.2025.112172_bib5","doi-asserted-by":"crossref","first-page":"1686","DOI":"10.1109\/TNNLS.2015.2441706","article-title":"Assessing short-term voltage stability of electric power systems by a hierarchical intelligent system","volume":"27","author":"Xu","year":"2016","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"10.1016\/j.isci.2025.112172_bib6","doi-asserted-by":"crossref","first-page":"1275","DOI":"10.1109\/TPWRS.2007.901667","article-title":"Transient stability prediction by a hybrid intelligent system","volume":"22","author":"Amjady","year":"2007","journal-title":"IEEE Trans. Power Syst."},{"key":"10.1016\/j.isci.2025.112172_bib7","doi-asserted-by":"crossref","first-page":"1049","DOI":"10.1109\/TPWRS.2017.2707501","article-title":"Intelligent Time-Adaptive Transient Stability Assessment System","volume":"33","author":"Yu","year":"2018","journal-title":"IEEE Trans. Power Syst."},{"key":"10.1016\/j.isci.2025.112172_bib8","doi-asserted-by":"crossref","first-page":"27","DOI":"10.35833\/MPCE.2019.000058","article-title":"Improved Deep Belief Network and Model Interpretation Method for Power System Transient Stability Assessment","volume":"8","author":"Wu","year":"2020","journal-title":"J.\u00a0Mod. Power Syst. Clean Energy"},{"key":"10.1016\/j.isci.2025.112172_bib9","doi-asserted-by":"crossref","DOI":"10.1016\/j.ijepes.2022.108010","article-title":"Deep learning-based transient stability assessment framework for large-scale modern power system","volume":"139","author":"Li","year":"2022","journal-title":"Int. J. Electr. Power Energy Syst."},{"key":"10.1016\/j.isci.2025.112172_bib10","first-page":"1664","article-title":"Hybrid analytical and data-driven modeling based instance-transfer method for power system online transient stability assessment","volume":"10","author":"Li","year":"2021","journal-title":"CSEE J. Power Energy Syst."},{"key":"10.1016\/j.isci.2025.112172_bib11","doi-asserted-by":"crossref","first-page":"5128","DOI":"10.1109\/TPWRS.2022.3220955","article-title":"Bidirectional Active Transfer Learning for Adaptive Power System Stability Assessment and Dominant Instability Mode Identification","volume":"38","author":"Shi","year":"2023","journal-title":"IEEE Trans. Power Syst."},{"key":"10.1016\/j.isci.2025.112172_bib12","doi-asserted-by":"crossref","first-page":"3274","DOI":"10.1109\/TASE.2023.3277536","article-title":"Transfer Learning for Transient Stability Predictions in Modern Power Systems Under Enduring Topological Changes","volume":"21","author":"Hijazi","year":"2024","journal-title":"IEEE Trans. Autom. Sci. Eng."},{"key":"10.1016\/j.isci.2025.112172_bib13","doi-asserted-by":"crossref","DOI":"10.1016\/j.epsr.2022.108795","article-title":"An image-based deep transfer learning approach to classify power quality disturbances","volume":"213","author":"Todeschini","year":"2022","journal-title":"Elec. Power Syst. Res."},{"key":"10.1016\/j.isci.2025.112172_bib14","doi-asserted-by":"crossref","first-page":"821","DOI":"10.1109\/TPWRS.2019.2947781","article-title":"Transfer Learning-Based Power System Online Dynamic Security Assessment: Using One Model to Assess Many Unlearned Faults","volume":"35","author":"Ren","year":"2020","journal-title":"IEEE Trans. Power Syst."},{"key":"10.1016\/j.isci.2025.112172_bib15","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2023.122696","article-title":"Human Activity Recognition based on Local Linear Embedding and Geodesic Flow Kernel on Grassmann manifolds","volume":"241","author":"Wang","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.isci.2025.112172_bib16","doi-asserted-by":"crossref","first-page":"1540","DOI":"10.1109\/TETCI.2023.3296747","article-title":"Evolutionary multitasking optimization enhanced by geodesic flow kernel","volume":"8","author":"Gao","year":"2023","journal-title":"IEEE Trans. Emerg. Topics Comput. Intell."},{"key":"10.1016\/j.isci.2025.112172_bib17","doi-asserted-by":"crossref","DOI":"10.1016\/j.ijepes.2023.109642","article-title":"Power system transfer learning to identify event types with or without event labels","volume":"155","author":"Ma","year":"2024","journal-title":"Int. J. Electr. Power Energy Syst."},{"key":"10.1016\/j.isci.2025.112172_bib18","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1007\/s10845-020-01657-z","article-title":"A novel transfer learning fault diagnosis method based on manifold embedded distribution alignment with a little labeled data","volume":"33","author":"Zhao","year":"2022","journal-title":"J.\u00a0Intell. Manuf."},{"key":"10.1016\/j.isci.2025.112172_bib19","series-title":"2019 IEEE international conference on data mining (ICDM)","first-page":"778","article-title":"Transfer Learning with Dynamic Adversarial Adaptation Network","author":"Yu","year":"2019"},{"key":"10.1016\/j.isci.2025.112172_bib20","doi-asserted-by":"crossref","first-page":"399","DOI":"10.1109\/TII.2023.3262856","article-title":"SFR Modeling for Hybrid Power Systems Based on Deep Transfer Learning","volume":"20","author":"Zhang","year":"2024","journal-title":"IEEE Trans. Industr. Inform."},{"key":"10.1016\/j.isci.2025.112172_bib21","series-title":"Euclidean Distance Matrices and Applications","author":"Krislock","year":"2012"},{"key":"10.1016\/j.isci.2025.112172_bib22","series-title":"Distance Weighted Cosine Similarity Measure for Text Classification","first-page":"611","author":"Li","year":"2013"},{"key":"10.1016\/j.isci.2025.112172_bib23","first-page":"501","article-title":"Comparison between mahalanobis distance and kullback-leibler divergence in clustering analysis","volume":"3","author":"Martins","year":"2004","journal-title":"WSEAS Trans. Syst."},{"key":"10.1016\/j.isci.2025.112172_bib24","series-title":"Simulation Data of 10-Machine 39-Bus Power System","author":"Li","year":"2024"},{"key":"10.1016\/j.isci.2025.112172_bib25","first-page":"5732","article-title":"Active Transfer Learning for TTC Assessment of Transmission Interfaces with Changing Operating Scenarios","volume":"43","author":"Zhengcheng","year":"2023","journal-title":"Proc. CSEE"},{"key":"10.1016\/j.isci.2025.112172_bib26","first-page":"176","article-title":"Framework of Transient Stability Assessment Based on Two-stage Transfer Learning","volume":"46","author":"Baoluo","year":"2022","journal-title":"Autom. Electr. Power Syst."},{"key":"10.1016\/j.isci.2025.112172_bib27","first-page":"184","article-title":"Adaptive assessment of transient stability for power system based on transfer multi-type of deep learning model","volume":"43","author":"Baoqin","year":"2023","journal-title":"Electr. Power Autom. Equip."},{"key":"10.1016\/j.isci.2025.112172_bib28","first-page":"1","article-title":"Power System Transient Stability Assessment Based on Spatio-Temporal Broad Learning System","author":"Zhao","year":"2025","journal-title":"IEEE Trans. Autom. Sci. Eng."},{"key":"10.1016\/j.isci.2025.112172_bib29","article-title":"Emergency voltage control strategy for power system transient stability enhancement based on edge graph convolutional network reinforcement learning","volume":"40","author":"Jiang","year":"2024","journal-title":"Sustain. Energy Grids Netw."},{"key":"10.1016\/j.isci.2025.112172_bib30","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2023.107254","article-title":"Real-time transient stability estimation of power system considering nonlinear limiters of excitation system using deep machine learning: An actual case study in Iran","volume":"127","author":"Sedghi","year":"2024","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.isci.2025.112172_bib31","doi-asserted-by":"crossref","first-page":"1130","DOI":"10.1109\/TPWRS.2024.3416150","article-title":"A Non-Sampling Time-Domain Simulation Framework for Transient Stability Assessment Under Stochastic Continuous Disturbances","volume":"40","author":"Yang","year":"2025","journal-title":"IEEE Trans. Power Syst."},{"key":"10.1016\/j.isci.2025.112172_bib32","series-title":"Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining","first-page":"1135","article-title":"\u201cWhy should i trust you?\u201d Explaining the predictions of any classifier","author":"Ribeiro","year":"2016"},{"key":"10.1016\/j.isci.2025.112172_bib33","doi-asserted-by":"crossref","DOI":"10.1016\/j.rser.2023.113913","article-title":"Deep learning based on Transformer architecture for power system short-term voltage stability assessment with class imbalance","volume":"189","author":"Li","year":"2024","journal-title":"Renew. Sustain. Energy Rev."},{"key":"10.1016\/j.isci.2025.112172_bib34","doi-asserted-by":"crossref","first-page":"129484","DOI":"10.1109\/ACCESS.2024.3458431","article-title":"Designing a Double Filter-Encircled Wrappers Feature Selection Algorithm for Transient Stability Prediction Based on Transient Time Series Data","volume":"12","author":"Bashiri Mosavi","year":"2024","journal-title":"IEEE Access"},{"key":"10.1016\/j.isci.2025.112172_bib35","doi-asserted-by":"crossref","DOI":"10.1016\/j.apenergy.2021.118347","article-title":"A Deep-Learning intelligent system incorporating data augmentation for Short-Term voltage stability assessment of power systems","volume":"308","author":"Li","year":"2022","journal-title":"Appl. Energy"},{"key":"10.1016\/j.isci.2025.112172_bib36","doi-asserted-by":"crossref","first-page":"125787","DOI":"10.1109\/ACCESS.2021.3111408","article-title":"Wind Speed Ensemble Forecasting Based on Deep Learning Using Adaptive Dynamic Optimization Algorithm","volume":"9","author":"Ibrahim","year":"2021","journal-title":"IEEE Access"},{"key":"10.1016\/j.isci.2025.112172_bib37","first-page":"137","article-title":"Analysis of representations for domain adaptation","volume":"19","author":"Ben-David","year":"2006","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.isci.2025.112172_bib38","doi-asserted-by":"crossref","DOI":"10.1016\/j.energy.2021.121271","article-title":"A novel transfer learning approach for wind power prediction based on a serio-parallel deep learning architecture","volume":"234","author":"Yin","year":"2021","journal-title":"Energy"},{"key":"10.1016\/j.isci.2025.112172_bib39","series-title":"LSTM-CNN-Based Transient Stability Assessment","first-page":"1930","author":"Han","year":"2021"}],"container-title":["iScience"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S258900422500433X?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S258900422500433X?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,7,28]],"date-time":"2025-07-28T20:19:35Z","timestamp":1753733975000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S258900422500433X"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4]]},"references-count":39,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2025,4]]}},"alternative-id":["S258900422500433X"],"URL":"https:\/\/doi.org\/10.1016\/j.isci.2025.112172","relation":{},"ISSN":["2589-0042"],"issn-type":[{"value":"2589-0042","type":"print"}],"subject":[],"published":{"date-parts":[[2025,4]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"An adaptive power system transient stability assessment method based on shared feature extraction","name":"articletitle","label":"Article Title"},{"value":"iScience","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.isci.2025.112172","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2025 The Authors. Published by Elsevier Inc.","name":"copyright","label":"Copyright"}],"article-number":"112172"}}