[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122609-en":3,"doc-seo-122609-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},122609,1099513958607,"Jiven","https://ap-avatar.wpscdn.com/avatar/100002390cf8733938c?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778829742770036399",8,"Research & Report","Power System Transient Stability Assessment - Based on Machine Learning Algorithms and Grid Topology - research paper","This work develops and evaluates machine learning methods for dynamic stability assessment of a power-system mathematical model, emphasizing adaptability through the proposed method’s lack of a priori parameters. The study targets emergency control under modern-grid conditions, where reduced inertia from renewables, stricter accuracy needs, heavy digitization, and large operational datasets complicate control. Ensemble models (XGBoost and Random Forest) estimate transient stability using IEEE 39 as a test system, achieving higher instability-classification accuracy when grid topology features are included.","mathematics  \nArticle  \nPower System Transient Stability Assessment Based on Machine Learning Algorithms and Grid Topology  \nMihail Senyuk 1, Murodbek Safaraliev 1, Firuz Kamalov 2 and Hana Sulieman 3, *  \nCitation: Senyuk, M.; Safaraliev, M.; Kamalov, F.; Sulieman, H. Power System Transient Stability Assessment Based on Machine Learning Algorithms and Grid Topology. Mathematics 2023, 11, 525 . [https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)math11030525  \nAcademic Editors: Dongsheng Yu, Muhammad Junaid, Samson Yu and Yihua Hu  \nReceived: 30 December 2022  \nRevised: 15 January 2023  \nAccepted: 16 January 2023  \nPublished: 18 January 2023  \nCopyright: © 2023 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://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 Department of Automated Electrical Systems, Ural Federal University, 620002 Yekaterinburg, Russia  \n2 Department of Electrical Engineering, Canadian University Dubai,  \nDubai P.O. Box 415053, United Arab Emirates  \n3 Department of Mathematics and Statistics, American University of Sharjah, Sharjah P.O. Box 26666, United Arab Emirates  \n* [Correspondence: hsulieman@aus.edu](Correspondence: hsulieman@aus.edu)  \nAbstract: This work employs machine learning methods to develop and test a technique for dynamic stability analysis of the mathematical model of a power system. A distinctive feature of the proposed method is the absence of a priori parameters of the power system model. Thus, the adaptability of the dynamic stability assessment is achieved. The selected research topic relates to the issue of changing the structure and parameters of modern power systems. The key features of modern power systems include the following: decreased total inertia caused by integration of renewable sources energy, stricter requirements for emergency control accuracy, highly digitized operation and control of power systems, and high volumes of data that describe power system operation. Arranging emergency control in these new conditions is one of the prominent problems in modern power systems. In this study, the emergency control algorithms based on ensemble machine learning algorithms (XGBoost and Random Forest) were developed for a low-inertia power system. Transient stability of a power system was analyzed as the base function. Features of transmission line maintenance were used to increase accuracy of estimation. Algorithms were tested using the test power system IEEE39 . In the case of the test sample, accuracy of instability classification for XGBoost was 91.5%, while that for Random Forest was 81.6% . The accuracy of algorithms increased by 10.9% and 1.5%, respectively, when the topology of the power system was taken into account.  \nKeywords: ensemble machine learning; extreme gradient boosting; power system modeling; random forest; transient stability  \nMSC: 68T01  \n1. Introduction  \nApplication of algorithms based on machine learning (ML) in planning, operation, and control of power systems has become possible due to industry digitization and the collection of sufficient amounts of data. The following problems are solved using ML algorithms: equipment monitoring [1], load forecasting [2], forecasting of renewable sources of energy (RES) [3], adjustment of power system control devices [4], state estimation [5], and disturbance detection [6] . However, in modern operation and control of power systems, the preference is still given to the conventional methods based on deterministic approaches. On the other hand, ML methods have evolved into effective tools in analysis and control of power systems in terms of time response, accuracy, and adaptability. In addition, the ever-growing use of phasor measurements [7] will signi","cbCaio9HjaMt22vB","https://ap.wps.com/l/cbCaio9HjaMt22vB","pdf",2367396,1,15,"English","en",105,"# Introduction\n## Motivation from digital transformation and conventional limits\n## Dynamic stability assessment as a key control task\n# Machine learning for transient stability analysis\n## Ensemble methods (XGBoost and Random Forest)\n## Feature design using grid/topology and line maintenance","[{\"question\":\"What is the main goal of the proposed approach for transient stability assessment?\",\"answer\":\"To build and test an ML-based technique for dynamic stability analysis of a power-system mathematical model for emergency control support.\"},{\"question\":\"Why does the proposed method improve adaptability?\",\"answer\":\"It avoids relying on a priori parameters of the power-system model, enabling the assessment to adapt to changing structures and parameters.\"},{\"question\":\"How much does topology information improve the instability classification accuracy?\",\"answer\":\"Including the power-system topology increases accuracy by 10.9% for XGBoost and by 1.5% for Random Forest on the test sample.\"}]","Power System Transient Stability Assessment - 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