[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121924-en":3,"doc-seo-121924-105":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},121924,549758252649,"Ivy","https://ap-avatar.wpscdn.com/avatar/8000253669c5317157?_k=1778319167496531819",8,"Research & Report","Methodology for Transient Stability Enhancement of Power Systems Based on Machine Learning Algorithms and Fast Valving in a Steam Turbine - read and analyze","Development and testing focus on a methodology for selecting fast-valving characteristics in steam-turbine-based emergency control to maintain dynamic (transient) stability of power systems using machine learning. The work targets challenges created by reduced inertia and higher stochasticity after large-scale renewable integration, which can reduce reliability of traditional deterministic emergency automation. IEEE39 modeling generates benchmark valve-position change laws, and multiple classifiers evaluate cutoff-valve behavior. Extreme gradient boosting delivers the best accuracy, with 98.17% training and 97.14% testing.","mathematics  \nArticle  \nMethodology for Transient Stability Enhancement of Power Systems Based on Machine Learning Algorithms and Fast Valving in a Steam Turbine  \nMihail Senyuk 1, Svetlana Beryozkina 2, *, Murodbek Safaraliev 1, Muhammad Nadeem 2, Ismoil Odinaev 1 and Firuz Kamalov 3  \nCitation: Senyuk, M.; Beryozkina, S.; Safaraliev, M.; Nadeem, M.; Odinaev, I.; Kamalov, F. Methodology for Transient Stability Enhancement of Power Systems Based on Machine Learning Algorithms and Fast Valving in a Steam Turbine. Mathematics 2024, 12, 1644. [https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)math12111644  \nAcademic Editor: Stavros  \nD. Kaminaris  \nReceived: 25 April 2024  \nRevised: 13 May 2024  \nAccepted: 17 May 2024  \nPublished: 24 May 2024  \nCopyright: © 2024 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; [mdseniuk@urfu.ru](mdseniuk@urfu.ru) (M.S.); [murodbek_03@mail.ru](murodbek_03@mail.ru) (M.S.); [ismoil.odinaev@urfu.ru](ismoil.odinaev@urfu.ru) (I.O.)  \n2 College of Engineering and Technology, American University of the Middle East, Kuwait; [muhammad.nadeem@aum.edu.kw](muhammad.nadeem@aum.edu.kw)  \n3 Department of Electrical Engineering, Canadian University Dubai, Dubai 117781, United Arab Emirates; [firuz@cud.ac.ae](firuz@cud.ac.ae)  \n* Correspondence: [svetlana.berjozkina@aum.edu.kw](svetlana.berjozkina@aum.edu.kw)  \nAbstract: This study presents the results of the development and testing of a methodology for selecting parameters of the characteristics of fast valving in a steam turbine for emergency power system management to maintain dynamic stability based on machine learning algorithms. Modern power systems have reduced inertia and increased stochasticity due to the active integration of renewable energy sources. As a result, there is an increased likelihood of incorrect operation in traditional emergency automation devices, developed on the principles of deterministic analysis of transient processes. To date, it is possible to increase the adaptability and accuracy of emergency power system management through the application of machine learning algorithms. In this work, fast valving in a steam turbine was chosen as the considered device of emergency automation. To form the data sample, the IEEE39 mathematical model was used, for which benchmark laws of change in the position of the cutoff valve during the fast valving of a steam turbine were selected. The considered machine learning algorithms for classifying the law of change in the position of the steam turbine’s cutoff valve, k-nearest neighbors, support vector machine, decision tree, random forest, and extreme gradient boosting were used. The results show that the highest accuracy corresponds to extreme gradient boosting. For the selected eXtreme Gradient Boosting algorithm, the classification accuracy on the training set was 98.17%, and on the test set it was 97.14% . The work also proposes a methodology for forming synthetic data for the use of machine learning algorithms for emergency management of power systems and suggests directions for further research.  \nKeywords: power system; transient stability; synchronous generator; steam turbine; fast valving; machine learning  \nMSC: 68T01  \n1. Introduction  \nOne of the basic principles of electrical power systems (EPS) management is to ensure standard static stability (SSS) and dynamic stability (TS) margins under normal and postemergency operating conditions. Ensuring SSS and TS by introducing restrictions on the amount of active power flows, on the one hand, is an effective measure, and on the o","cbCainvrjAGmTwVx","https://ap.wps.com/l/cbCainvrjAGmTwVx","pdf",2188099,1,19,"English","en",105,"# Introduction\n## Stability objectives in power system management\n## Emergency control actions and fast valving\n## Impact of renewable integration on transient processes\n# Methodology (machine learning for valve-position classification)\n## Data generation using IEEE39 model\n## Candidate machine learning algorithms\n## Model performance and results","[{\"question\":\"What problem does the methodology address in emergency power system management?\",\"answer\":\"It targets maintaining dynamic stability during emergencies by selecting parameters for fast valving in a steam turbine, avoiding incorrect behavior caused by reduced inertia and increased stochasticity.\"},{\"question\":\"How is the training data sample generated for machine learning?\",\"answer\":\"The study uses the IEEE39 mathematical model and selects benchmark laws describing the cutoff valve position during fast valving.\"},{\"question\":\"Which machine learning algorithm achieves the highest classification accuracy?\",\"answer\":\"Extreme gradient boosting shows the highest accuracy, reaching 98.17% on the training set and 97.14% on the test set.\"}]","Methodology for Transient Stability Enhancement of Power Systems Based on Machine Learning Algorithms and Fast Valving in a Steam Turbine - 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