{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,4]],"date-time":"2025-11-04T23:50:24Z","timestamp":1762300224582,"version":"build-2065373602"},"reference-count":42,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2022,11,20]],"date-time":"2022-11-20T00:00:00Z","timestamp":1668902400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the Engineering Research Center of New Electric Power System and its Digital Technology of Guizhou Provincial Department of Education","award":["QIAN TEACH TECHNOLOGY [2022] 043","0674002022030103NY00005"],"award-info":[{"award-number":["QIAN TEACH TECHNOLOGY [2022] 043","0674002022030103NY00005"]}]},{"name":"The Second National New Engineering Research and Practice Project","award":["QIAN TEACH TECHNOLOGY [2022] 043","0674002022030103NY00005"],"award-info":[{"award-number":["QIAN TEACH TECHNOLOGY [2022] 043","0674002022030103NY00005"]}]},{"name":"the Construction of Multi-Energy System Intelligent Control","award":["QIAN TEACH TECHNOLOGY [2022] 043","0674002022030103NY00005"],"award-info":[{"award-number":["QIAN TEACH TECHNOLOGY [2022] 043","0674002022030103NY00005"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In recent years, the power system transient stability assessment (TSA) based on a data-driven method has been widely studied. However, the topology and modes of operation of power systems may change frequently due to the complex time-varying characteristics of power systems. which makes it difficult for prediction models trained on stationary distributed data to meet the requirements of online applications. When a new working situation scenario causes the prediction model accuracy not to meet the requirements, the model needs to be updated in real-time. With limited storage space, model capacity, and infinite new scenarios to be updated for learning, the model updates must be sustainable and scalable. Therefore, to address this problem, this paper introduces the continual learning Sliced Cram\u00e9r Preservation (SCP) algorithm to perform update operations on the model. A deep residual shrinkage network (DRSN) is selected as a classifier to construct the TSA model of SCP-DRSN at the same time. With the SCP, the model can be extended and updated just by using the new scenarios data. The updated prediction model not only complements the prediction capability for new scenarios but also retains the prediction ability under old scenarios, which can avoid frequent updates of the model. The test results on a modified New England 10-machine 39-bus system and an IEEE 118-bus system show that the proposed method in this paper can effectively update and extend the prediction model under the condition of using only new scenarios data. The coverage of the updated model for new scenarios is improving.<\/jats:p>","DOI":"10.3390\/s22228982","type":"journal-article","created":{"date-parts":[[2022,11,21]],"date-time":"2022-11-21T04:39:59Z","timestamp":1669005599000},"page":"8982","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Combination with Continual Learning Update Scheme for Power System Transient Stability Assessment"],"prefix":"10.3390","volume":"22","author":[{"given":"Bowen","family":"Hu","sequence":"first","affiliation":[{"name":"Department of Electrical Engineering, Guizhou University, Guiyang 550025, China"}]},{"given":"Zhenghang","family":"Hao","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Guizhou University, Guiyang 550025, China"}]},{"given":"Zhuo","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Guizhou University, Guiyang 550025, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3732-7432","authenticated-orcid":false,"given":"Jing","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Guizhou University, Guiyang 550025, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"6630","DOI":"10.1109\/TPWRS.2018.2834461","article-title":"The Role of Concentrating Solar Power Toward High Renewable Energy Penetrated Power Systems","volume":"33","author":"Du","year":"2018","journal-title":"IEEE Trans. Power Syst."},{"key":"ref_2","unstructured":"Huy, N.D., Huy, C.D., Chien, N.D., and Viet, N.X.H. (2015, January 26\u201330). Simulation of a Power Grid Blackout Event in Vietnam. Proceedings of the General Meeting of the IEEE-Power-and-Energy-Society, Denver, CO, USA."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"5374","DOI":"10.1109\/TPWRS.2018.2820150","article-title":"The Anatomy of the 2016 South Australia Blackout: A Catastrophic Event in a High Renewable Network","volume":"33","author":"Yan","year":"2018","journal-title":"IEEE Trans. Power Syst."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1007\/s40565-013-0014-2","article-title":"Generalized congestion of power systems: Insights from the massive blackouts in India","volume":"1","author":"Xue","year":"2013","journal-title":"J. Mod. Power Syst. Clean Energy"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"645","DOI":"10.1109\/TSG.2012.2187220","article-title":"An Intelligent Wide Area Synchrophasor Based System for Predicting and Mitigating Transient Instabilities","volume":"3","author":"Hashiesh","year":"2012","journal-title":"IEEE Trans. Smart Grid"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"3739","DOI":"10.1109\/TPWRS.2019.2901654","article-title":"Power System Time Domain Simulation Using a Differential Transformation Method","volume":"34","author":"Liu","year":"2019","journal-title":"IEEE Trans. Power Syst."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"2301","DOI":"10.1109\/TPWRS.2014.2361529","article-title":"A Multi-Decomposition Approach for Accelerated Time-Domain Simulation of Transient Stability Problems","volume":"30","author":"Zadkhast","year":"2015","journal-title":"IEEE Trans. Power Syst."},{"key":"ref_8","first-page":"923","article-title":"Real-Time Prediction and Control of Transient Stability Using Transient Energy Function","volume":"32","author":"Bhui","year":"2017","journal-title":"IEEE Trans. Power Syst."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1455","DOI":"10.1109\/TPWRS.2014.2350476","article-title":"A Decomposition-Based Practical Approach to Transient Stability-Constrained Unit Commitment","volume":"30","author":"Xu","year":"2015","journal-title":"IEEE Trans. Power Syst."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"983","DOI":"10.1016\/j.ijepes.2011.01.012","article-title":"Power system transient stability margin estimation using neural networks","volume":"33","author":"Karami","year":"2011","journal-title":"Int. J. Electr. Power Energy Syst."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1109\/TSG.2010.2044815","article-title":"Synchronized Phasor Measurement Applications in Power Systems","volume":"1","author":"Centeno","year":"2010","journal-title":"IEEE Trans. Smart Grid"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1483","DOI":"10.1109\/TPWRD.2010.2041797","article-title":"Reliability Analysis of Wide-Area Measurement System","volume":"25","author":"Wang","year":"2010","journal-title":"IEEE Trans. Power Deliv."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Zhou, Y.Z., Wu, J.Y., Yu, Z.H., Ji, L.Y., and Hao, L.L. (2016). A Hierarchical Method for Transient Stability Prediction of Power Systems Using the Confidence of a SVM-Based Ensemble Classifier. Energies, 9.","DOI":"10.3390\/en9100778"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"717","DOI":"10.1109\/TPWRS.2013.2283064","article-title":"A Systematic Approach for Dynamic Security Assessment and the Corresponding Preventive Control Scheme Based on Decision Trees","volume":"29","author":"Liu","year":"2014","journal-title":"IEEE Trans. Power Syst."},{"key":"ref_15","first-page":"17","article-title":"An Accurate Online Dynamic Security Assessment Scheme Based on Random Forest","volume":"11","author":"Liu","year":"2018","journal-title":"Energies"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"685","DOI":"10.1109\/TIA.2017.2753176","article-title":"Real-Time Monitoring of Post-Fault Scenario for Determining Generator Coherency and Transient Stability Through ANN","volume":"54","author":"Siddiqui","year":"2018","journal-title":"IEEE Trans. Ind. Appl."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Zheng, L., Hu, W., Zhou, Y.F., Min, Y., Xu, X.L., Wang, C.M., and Yu, R. (2017, January 16\u201320). Deep Belief Network Based Nonlinear Representation Learning for Transient Stability Assessment. Proceedings of the 2017 IEEE Power & Energy Society General Meeting, Chicago, IL, USA.","DOI":"10.1109\/PESGM.2017.8274126"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.apenergy.2020.114586","article-title":"Convolutional neural network-based power system transient stability assessment and instability mode prediction","volume":"263","author":"Shi","year":"2020","journal-title":"Appl. Energy"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2802","DOI":"10.1109\/TPWRS.2019.2895592","article-title":"Fast Transient Stability Batch Assessment Using Cascaded Convolutional Neural Networks","volume":"34","author":"Yan","year":"2019","journal-title":"IEEE Trans. Power Syst."},{"key":"ref_20","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":"ref_21","doi-asserted-by":"crossref","first-page":"2399","DOI":"10.1109\/TPWRS.2019.2957377","article-title":"Hierarchical Deep Learning Machine for Power System Online Transient Stability Prediction","volume":"35","author":"Zhu","year":"2020","journal-title":"IEEE Trans. Power Syst."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"93283","DOI":"10.1109\/ACCESS.2020.2991263","article-title":"Recurrent Graph Convolutional Network-Based Multi-Task Transient Stability Assessment Framework in Power System","volume":"8","author":"Huang","year":"2020","journal-title":"IEEE Access"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1016\/j.ijepes.2020.106753","article-title":"Data-driven short-term voltage stability assessment based on spatial-temporal graph convolutional network","volume":"130","author":"Luo","year":"2021","journal-title":"Int. J. Electr. Power Energy Syst."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"5044","DOI":"10.1109\/TPWRS.2019.2922671","article-title":"A Fully Data-Driven Method Based on Generative Adversarial Networks for Power System Dynamic Security Assessment With Missing Data","volume":"34","author":"Ren","year":"2019","journal-title":"IEEE Trans. Power Syst."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Li, J.M., Zhao, Y., Lee, Y.H., and Kim, S.J. (2019, January 2\u20135). Learning to Infer Voltage Stability Margin Using Transfer Learning. Proceedings of the IEEE Data Science Workshop (DSW), Minneapolis, MN, USA.","DOI":"10.1109\/DSW.2019.8755558"},{"key":"ref_26","first-page":"10","article-title":"A combinational transfer learning framework for online transient stability prediction","volume":"30","author":"Cui","year":"2022","journal-title":"Sustain. Energy Grids Netw."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Jafarzadeh, S., Moarref, N., Yaslan, Y., and Genc, V.M.I. (2019, January 28\u201330). A CNN-Based Post-Contingency Transient Stability Prediction Using Transfer Learning. Proceedings of the 11th International Conference on Electrical and Electronics Engineering (ELECO), Bursa, Turkey.","DOI":"10.23919\/ELECO47770.2019.8990506"},{"key":"ref_28","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":"ref_29","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1016\/j.ijepes.2018.11.031","article-title":"A novel data-driven approach for transient stability prediction of power systems considering the operational variability","volume":"107","author":"Zhou","year":"2019","journal-title":"Int. J. Electr. Power Energy Syst."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1016\/j.neunet.2019.01.012","article-title":"Continual lifelong learning with neural networks: A review","volume":"113","author":"Parisi","year":"2019","journal-title":"Neural Netw."},{"key":"ref_31","first-page":"3366","article-title":"A Continual Learning Survey: Defying Forgetting in Classification Tasks","volume":"44","author":"Aljundi","year":"2022","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Li, Z.Z., and Hoiem, D. (2016, January 8\u201316). Learning Without Forgetting. Proceedings of the 14th European Conference on Computer Vision (ECCV), Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46493-0_37"},{"key":"ref_33","unstructured":"Kolouri, S., Ketz, N.A., Soltoggio, A., and Pilly, P.K. (2020, January 26\u201330). Sliced Cramer Synaptic Consolidation for Preserving Deeply Learned Representations. Proceedings of the International Conference on Learning Representations, Addis Ababa, Ethiopia."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"4681","DOI":"10.1109\/TII.2019.2943898","article-title":"Deep Residual Shrinkage Networks for Fault Diagnosis","volume":"16","author":"Zhao","year":"2020","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"3271","DOI":"10.1109\/TPWRS.2020.3041774","article-title":"Definition and Classification of Power System Stability\u2014Revisited & Extended","volume":"36","author":"Hatziargyriou","year":"2021","journal-title":"IEEE Trans. Power Syst."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Pavella, M., Ernst, D., and Ruiz-Vega, D. (2000). Transient Stability of Power Systems: A Unified Approach to Assessment and Control, Springer Science & Business Media.","DOI":"10.1007\/978-1-4615-4319-0"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"He, K.M., Zhang, X.Y., Ren, S.Q., and Sun, J. (2016, January 27\u201330). Residual Learning for Image Recognition. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_38","unstructured":"Schwarz, J., Luketina, J., Czarnecki, W.M., Grabska-Barwinska, A., Teh, Y.W., Pascanu, R., and Hadsell, R. (2018, January 10\u201315). Progress & Compress: A scalable framework for continual learning. Proceedings of the 35th International Conference on Machine Learning (ICML), Stockholm, Sweden."},{"key":"ref_39","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":"ref_40","doi-asserted-by":"crossref","first-page":"1474","DOI":"10.1109\/TPWRS.2010.2082575","article-title":"Support Vector Machine-Based Algorithm for Post-Fault Transient Stability Status Prediction Using Synchronized Measurements","volume":"26","author":"Gomez","year":"2011","journal-title":"IEEE Trans. Power Syst."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"947","DOI":"10.1109\/TPWRS.2009.2036265","article-title":"Rotor Angle Instability Prediction Using Post-Disturbance Voltage Trajectories","volume":"25","author":"Rajapakse","year":"2010","journal-title":"IEEE Trans. Power Syst."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"318","DOI":"10.1109\/TPAMI.2018.2858826","article-title":"Focal Loss for Dense Object Detection","volume":"42","author":"Lin","year":"2020","journal-title":"IEEE Trans. Pattern Anal. Mach. 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