[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127669-en":3,"doc-seo-127669-105":30,"detail-sidebar-cat-0-en-105":92},{"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},127669,962084925636,"Olivia Brown","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","Improved Fault Classification and Localization in Power Transmission Networks Using VAE-Generated Synthetic Data and Machine Learning Algorithms","Reliable operation of power transmission networks depends on timely detection and localization of faults, which is difficult due to the system’s complicated and dynamic nature. Recent machine learning and deep learning methods improve fault identification, but performance is strongly constrained by limited and low-quality training data. The study proposes a strategy using variational autoencoders (VAEs) to generate synthetic fault data that retains real-world statistical properties, then trains multiple ML models including CatBoost and feature selection-enhanced SVM, decision trees, random forests, and KNN. Results show 99% fault classification accuracy and 0.2 MAE fault localization, outperforming prior baselines.","machines   \nArticle  \nImproved Fault Classification and Localization in Power Transmission Networks Using VAE-Generated Synthetic Data and Machine Learning Algorithms  \nMuhammad Amir Khan 1, Bilal Asad 1,2, *, Toomas Vaimann 2, Ants Kallaste 2, Raimondas Pomarnacki 3 and Van Khang Hyunh 4  \nCitation: Khan, M.A.; Asad, B.; Vaimann, T.; Kallaste, A.;  \nPomarnacki, R.; Hyunh, V.K. Improved Fault Classification and Localization in Power Transmission Networks Using VAE-Generated Synthetic Data and Machine Learning Algorithms. Machines 2023, 11, 963. [https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)machines11100963  \nAcademic Editor: Ahmed Abu-Siada  \nReceived: 12 September 2023  \nRevised: 9 October 2023  \nAccepted: 13 October 2023  \nPublished: 16 October 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 Electrical Power Engineering, The Islamia University of Bahawalpur, Bahawalpur 63100, Pakistan; [amirblouch41@gmail.com](amirblouch41@gmail.com)  \n2 Department of Electrical Power Engineering and Mechatronics, Tallinn University of Technology,  \n12616 Tallinn, Estonia; [toomas.vaimann@taltech.ee](toomas.vaimann@taltech.ee) (T.V.); [ants.kallaste@taltech.ee](ants.kallaste@taltech.ee) (A.K.)  \n3 Department of Electronic Systems, Vilnius Gediminas Technical University, 10105 Vilnius, Lithuania; [raimondas.pomarnacki@vilniustech.lt](raimondas.pomarnacki@vilniustech.lt)  \n4 Department of Engineering Sciences, University of Agder, 4879 Grimstad, Norway; [hyunh.khang@uia.no](hyunh.khang@uia.no)  \n* [Correspondence: bilal.asad@taltech.ee](Correspondence: bilal.asad@taltech.ee)  \nAbstract: The reliable operation of power transmission networks depends on the timely detection and localization of faults. Fault classification and localization in electricity transmission networks can be challenging because of the complicated and dynamic nature of the system. In recent years, a variety of machine learning (ML) and deep learning algorithms (DL) have found applications in the enhancement of fault identification and classification within power transmission networks. Yet, the efficacy of these ML architectures is profoundly dependent upon the abundance and quality of the training data. This intellectual explanation introduces an innovative strategy for the classification and pinpointing of faults within power transmission networks. This is achieved through the utilization of variational autoencoders (VAEs) to generate synthetic data, which in turn is harnessed in conjunction with ML algorithms. This approach encompasses the augmentation of the available dataset by infusing it with synthetically generated instances, contributing to a more robust and proficient fault recognition and categorization system. Specifically, we train the VAE on a set of real-world power transmission data and generate synthetic fault data that capture the statistical properties of real-world data. To overcome the difficulty of fault diagnosis methodology in three-phase high voltage transmission networks, a categorical boosting (Cat-Boost) algorithm is proposed in this work. The other standard machine learning algorithms recommended for this study, including Support Vector Machine (SVM), Decision Trees (DT), Random Forest (RF), and K-Nearest Neighbors (KNN), utilizing the customized version of forward feature selection (FFS), were trained using synthetic data generated by a VAE. The results indicate exceptional performance, surpassing current state-of-the-art techniques, in the tasks of fault classification and localization. Notably, our approach achieves a remarkable 99% accuracy in fault classification and an extremely low mean a","cbCainJfpQePZnpv","https://ap.wps.com/l/cbCainJfpQePZnpv","pdf",8854094,1,22,"English","en",105,"# Introduction\n## Faults and the need for detection/localization\n## Role of machine learning and data quality","[{\"question\":\"Why is fault classification and localization difficult in power transmission networks?\",\"answer\":\"Fault diagnosis is challenging because the networks are complicated and highly dynamic, and abnormal conditions arise from many environmental, accidental, incidental, and aging factors.\"},{\"question\":\"How does the proposed method improve model performance?\",\"answer\":\"It trains a variational autoencoder on real transmission data to generate synthetic fault instances that capture real-world statistical properties, then uses these augmented data to train machine learning models.\"},{\"question\":\"Which models are evaluated for fault classification and localization?\",\"answer\":\"The work evaluates CatBoost and several standard machine learning models—SVM, decision trees, random forest, and K-nearest neighbors—trained using synthetic data and a customized forward feature selection approach.\"}]","Improved Fault Classification and Localization in Power Transmission Networks Using VAE-Generated Synthetic Data and Machine Learning Algorithms | 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is fault classification and localization difficult in power transmission networks?","Question",{"text":76,"@type":77},"Fault diagnosis is challenging because the networks are complicated and highly dynamic, and abnormal conditions arise from many environmental, accidental, incidental, and aging factors.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How does the proposed method improve model performance?",{"text":81,"@type":77},"It trains a variational autoencoder on real transmission data to generate synthetic fault instances that capture real-world statistical properties, then uses these augmented data to train machine learning models.",{"name":83,"@type":74,"acceptedAnswer":84},"Which models are evaluated for fault classification and localization?",{"text":85,"@type":77},"The work evaluates CatBoost and several standard machine learning models—SVM, decision trees, random forest, and K-nearest neighbors—trained using synthetic data and a customized forward feature selection 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