Support Vector Machine-Based Fault Diagnosis of Power Transformer Using k Nearest-Neighbor Imputed DGA Dataset

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"Support Vector Machine-Based Fault Diagnosis of Power Transformer Using k Nearest-Neighbor Imputed DGA Dataset"
written by Zahriah Binti Sahri, Rubiyah Binti Yusof,
published by Journal of Computer and Communications, Vol.2 No.9, 2014

has been cited by the following article(s):

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[1]	Lightweight mechanical equipment fault diagnosis framework based on GCGAN-MDSCNN-ICA model Scientific Reports, 2025 DOI:10.1038/s41598-025-89576-y

[2]	Fault Classification in Power Transformers via Dissolved Gas Analysis and Machine Learning Algorithms: A Systematic Literature Review Applied Sciences, 2025 DOI:10.3390/app15052395

[3]	Lightweight CNN Architecture Design Based on Spatial–Temporal Tensor and Its Application in Bearing Fault Diagnosis IEEE Transactions on Instrumentation and Measurement, 2024 DOI:10.1109/TIM.2023.3336435

[4]	Detection and Analysis of Faults in Transformer using Machine Learning 2023 International Conference on Intelligent Data Communication Technologies and Internet of Things (IDCIoT), 2023 DOI:10.1109/IDCIoT56793.2023.10052786

[5]	Research on Transformer Temperature Warning Algorithm Based on Support Vector Machine 2023 International Conference on Internet of Things, Robotics and Distributed Computing (ICIRDC), 2023 DOI:10.1109/ICIRDC62824.2023.00099

[6]	Improvement of power transformer fault diagnosis by using sequential Kalman filter sensor fusion International Journal of Electrical Power & Energy Systems, 2023 DOI:10.1016/j.ijepes.2023.109038

[7]	Lightning Fault Classification for Transmission Line Using Support Vector Machine 2023 12th Asia-Pacific International Conference on Lightning (APL), 2023 DOI:10.1109/APL57308.2023.10181525

[8]	Improvement of power transformer fault diagnosis by using sequential Kalman filter sensor fusion International Journal of Electrical Power & Energy Systems, 2023 DOI:10.1016/j.ijepes.2023.109038

[9]	Research on Machine Learning Based Fault Diagnosis Methods for Power Transformers 2023 IEEE 5th International Conference on Civil Aviation Safety and Information Technology (ICCASIT), 2023 DOI:10.1109/ICCASIT58768.2023.10351584

[10]	Detection and Analysis of Faults in Transformer using Machine Learning 2023 International Conference on Intelligent Data Communication Technologies and Internet of Things (IDCIoT), 2023 DOI:10.1109/IDCIoT56793.2023.10052786

[11]	A propagation-based fault detection and discrimination method and the optimization of sensor deployment Annals of Nuclear Energy, 2022 DOI:10.1016/j.anucene.2021.108746

[12]	Unanticipated Fault Diagnosis Method of Telescope Drive System Based on Latent Variable Mining Publications of the Astronomical Society of the Pacific, 2022 DOI:10.1088/1538-3873/ac427a

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[14]	From machine learning to deep learning: A comprehensive study of alcohol and drug use disorder Healthcare Analytics, 2022 DOI:10.1016/j.health.2022.100104

[15]	Preprocessing Approach for Power Transformer Maintenance Data Mining Based on k-Nearest Neighbor Completion and Principal Component Analysis International Transactions on Electrical Energy Systems, 2022 DOI:10.1155/2022/8546588

[16]	From machine learning to deep learning: A comprehensive study of alcohol and drug use disorder Healthcare Analytics, 2022 DOI:10.1016/j.health.2022.100104

[17]	A propagation-based fault detection and discrimination method and the optimization of sensor deployment Annals of Nuclear Energy, 2022 DOI:10.1016/j.anucene.2021.108746

[18]	Comparative Dissolved Gas Analysis with Machine Learning and Traditional Methods 2021 3rd International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA), 2021 DOI:10.1109/HORA52670.2021.9461371

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[20]	Transient Fault Detection and Location in Power Distribution Network: A Review of Current Practices and Challenges in Malaysia Energies, 2021 DOI:10.3390/en14112988

[21]	Transient Fault Detection and Location in Power Distribution Network: A Review of Current Practices and Challenges in Malaysia Energies, 2021 DOI:10.3390/en14112988

[22]	Fault diagnosis of power transformer based on tree ensemble model IOP Conference Series: Materials Science and Engineering, 2020 DOI:10.1088/1757-899X/715/1/012032

[23]	Comparative Analysis of the Defect Type Recognition Reliability in High-Voltage Power Transformers Using Different Methods of DGA Results Interpretation 2020 IEEE Problems of Automated Electrodrive. Theory and Practice (PAEP), 2020 DOI:10.1109/PAEP49887.2020.9240911

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[28]	Transformer Fault Diagnosis based on Deep Brief Sparse Autoencoder 2019 Chinese Control Conference (CCC), 2019 DOI:10.23919/ChiCC.2019.8866347

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[30]	Imputation Method of Missing Values for Dissolved Gas Analysis Data Based on Iterative KNN and XGBoost Proceedings of the 2018 International Conference on Algorithms, Computing and Artificial Intelligence, 2018 DOI:10.1145/3302425.3302447

[31]	Imputation Method of Missing Values for Dissolved Gas Analysis Data Based on Iterative KNN and XGBoost Proceedings of the 2018 International Conference on Algorithms, Computing and Artificial Intelligence, 2018 DOI:10.1145/3302425.3302447

[32]	Imputation Method of Missing Values for Dissolved Gas Analysis Data Based on Iterative KNN and XGBoost Proceedings of the 2018 International Conference on Algorithms, Computing and Artificial Intelligence - ACAI 2018, 2018 DOI:10.1145/3302425.3302447

[33]	Advances in Smart Vehicular Technology, Transportation, Communication and Applications Smart Innovation, Systems and Technologies, 2018 DOI:10.1007/978-3-319-70730-3_20

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[35]	Dissolved gas analysis of transformer oil based on Deep Belief Networks 2018 12th International Conference on the Properties and Applications of Dielectric Materials (ICPADM), 2018 DOI:10.1109/ICPADM.2018.8401156

[36]	Dissolved Gas Analysis of power transformer using K-means and Support Vector Machine 2016 IEEE 1st International Conference on Power Electronics, Intelligent Control and Energy Systems (ICPEICES), 2016 DOI:10.1109/ICPEICES.2016.7853614

[37]	A Survey of Fault Diagnosis and Fault-Tolerant Techniques Part II: Fault Diagnosis with Knowledge-Based and Hybrid/Active Approaches IEEE Transactions on Industrial Electronics, 2015 DOI:10.1109/TIE.2015.2419013

[38]	A New Approach with Three Dimension Figure and ANSI/IEEE C57.104 Standard Rule Diagnoses Transformer’s Insulating Oil Engineering, 2014 DOI:10.4236/eng.2014.612078

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