[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120033-en":3,"doc-seo-120033-105":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},120033,4398048950312,"Violet","https://ap-avatar.wpscdn.com/avatar/400002538284de19e3c?_k=1778320343897328908",8,"Research & Report","Enhancing Electrical Network Vulnerability Assessment with Machine Learning and Deep Learning Techniques - Article 2","This research applies advanced machine learning to evaluate node vulnerability in power grid networks using the SciGRID and GridKit datasets with 479 nodes and 16,167 nodes, and 765 and 20,539 edges. Missing data are handled through K-nearest neighbor and median imputation, while centrality metrics are fused into a single comprehensive score to rank nodes into four vulnerability-relevant levels. Traditional learning models (XGBoost, SVM, Multilayer Perceptron) and Graph Neural Networks are benchmarked for their ability to identify critical nodes using features beyond centrality alone, addressing gaps in prior centrality-only analysis.","Northeast Journal of Complex Systems (NEJCS)  \n\n| Volume 6\u003Cbr>Number 1 Special Issue: NERCCS 2024 Papers | Article 2 |\n| --- | --- |\n| June 2024\u003Cbr>Enhancing Electrical Network Vulnerability Assessment with Machine Learning and Deep Learning Techniques\u003Cbr>M Mishkatur Rahman\u003Cbr>North Dakota State University, [mmishkatur.rahman@ndsu.edu](mmishkatur.rahman@ndsu.edu)\u003Cbr>Ayman Sajjad Akash\u003Cbr>North Dakota State University, [ayman.akash@ndsu.edu](ayman.akash@ndsu.edu)\u003Cbr>Harun Pirim\u003Cbr>North Dakota State University, [harun.pirim@ndsu.edu](harun.pirim@ndsu.edu)\u003Cbr>Chau Le\u003Cbr>North Dakota State University, [chau.le@ndsu.edu](chau.le@ndsu.edu)\u003Cbr>Trung Le\u003Cbr>University of South Florida, [tqle@usf.edu](tqle@usf.edu)\u003Cbr>See next page for additional authors\u003Cbr>Follow this and additional works at: [https://orb.binghamton.edu/nejcs](https://orb.binghamton.edu/nejcs)\u003Cbr> Part of the Operations Research, Systems Engineering and Industrial Engineering Commons |  |\n\nRecommended Citation  \nRahman, M Mishkatur; Akash, Ayman Sajjad; Pirim, Harun; Le, Chau; Le, Trung; and Yadav, Om Prakash (2024) \"Enhancing Electrical Network Vulnerability Assessment with Machine Learning and Deep Learning Techniques,\" Northeast Journal of Complex Systems (NEJCS): Vol. 6 : No. 1 , Article 2.  \nDOI: 10.22191/nejcs/vol6/iss1/2  \nAvailable at: [https://orb.binghamton.edu/nejcs/vol6/iss1/2](https://orb.binghamton.edu/nejcs/vol6/iss1/2)  \nThis Article is brought to you for free and open access by The Open Repository @ Binghamton (The ORB) . It has been accepted for inclusion in Northeast Journal of Complex Systems (NEJCS) by an authorized editor of The Open Repository @ Binghamton (The ORB) . For more information, please [contact ORB@binghamton.edu](contact ORB@binghamton.edu).  \nEnhancing Electrical Network Vulnerability Assessment with Machine Learning and Deep Learning Techniques  \nAuthors  \nM Mishkatur Rahman, Ayman Sajjad Akash, Harun Pirim, Chau Le, Trung Le, and Om Prakash Yadav  \nThis article is available in Northeast Journal of Complex Systems (NEJCS): [https://orb.binghamton.edu/nejcs/vol6/](https://orb.binghamton.edu/nejcs/vol6/)[ ](https://orb.binghamton.edu/nejcs/vol6/)iss1/2  \nEnhancing Electrical Network Vulnerability Assessment with Machine Learning and Deep Learning Techniques  \nM Mishkatur Rahman 1 , Ayman Sajjad Akash 1 , Harun Pirim 1∗ ,  \nChau Le2 , Trung (Tim) Q. Le3 and Om Prakash Yadav4  \n1Dept. of Industrial and Manufacturing Engineering, North Dakota State University, Fargo, ND, USA  \n2Dept. of Civil, Construction and Environmental Engineering, North Dakota State University, Fargo, ND, USA  \n3Dept. of Industrial and Management Systems Engineering, University of South Florida, Tampa, FL, USA  \n4Dept. of Industrial and Systems Engineering, North Carolina A&T State University, Greensboro, NC, USA  \n∗ Corresponding Author: [harun.pirim@ndsu.edu](harun.pirim@ndsu.edu)  \nAbstract  \nThis research utilizes advanced machine learning techniques to evaluate node vulnerability in power grid networks. Utilizing the SciGRID and GridKit datasets, consisting of 479, 16,167 nodes and 765, 20,539 edges respectively, the study employs K-nearest neighbor and median imputation methods to address missing data. Centrality metrics are integrated into a single comprehensive score for assessing node criticality, categorizing nodes into four centrality levels informative of vulnerability.  \nThis categorization informs the use of traditional machine learning (including XGBoost, SVM, Multilayer Perceptron) and Graph Neural Networks in the analysis.  \nThe study not only benchmarks the capabilities of these models in network analysis but also explores their potential in identifying critical nodes using features beyond centrality metrics alone, enhancing their applicability in real-world scenarios.  \nThe research addresses a significant gap in effectively assessing the vulnerability of electrical networks, marked by isolated use of traditional centrality metrics and a lack of","cbCail5pwQfWp1LO","https://ap.wps.com/l/cbCail5pwQfWp1LO","pdf",1435559,1,24,"English","en",105,"# Introduction\n## Data, preprocessing, and vulnerability scoring\n## Traditional machine learning and GNN modeling\n## Benchmarking and critical node identification\n## Motivation and research gap","[{\"question\":\"What datasets and preprocessing steps are used for evaluating electrical network vulnerability?\",\"answer\":\"The study uses the SciGRID and GridKit datasets and applies K-nearest neighbor and median imputation to address missing data.\"},{\"question\":\"How is node vulnerability quantified in the research?\",\"answer\":\"Centrality metrics are combined into a single comprehensive score, which classifies nodes into four centrality levels linked to vulnerability and criticality.\"},{\"question\":\"Which modeling approaches are compared for vulnerability assessment?\",\"answer\":\"The research benchmarks traditional machine learning models such as XGBoost, SVM, and Multilayer Perceptron against Graph Neural Networks.\"}]","Enhancing Electrical Network Vulnerability Assessment with Machine Learning and Deep Learning Techniques - 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