Data augmentation for fault diagnosis of oil-immersed power transformer
Tài liệu tham khảo
Yang, 2009, Association rule mining-based dissolved gas analysis for fault diagnosis of power transformers, IEEE Trans Syst Man Cybern C, 39, 597, 10.1109/TSMCC.2009.2021989
Lima, 2015, A two-level framework to fault diagnosis and decision making for power transformers, IEEE Trans Power Deliv, 30, 497, 10.1109/TPWRD.2014.2355176
Ibrahim, 2018, Dgalab: an extensible software implementation for DGA, IET Gener Transm Distrib, 12, 4117, 10.1049/iet-gtd.2018.5564
Taha, 2020, Optimal ratio limits of rogers’ four-ratios and IEC 60599 code methods using particle swarm optimization fuzzy-logic approach, IEEE Trans Dielectr Electr Insul, 27, 222, 10.1109/TDEI.2019.008395
Boczar, 2014, Diagnostic expert system of transformer insulation systems using the acoustic emission method, IEEE Trans Dielectr Electr Insul, 21, 854, 10.1109/TDEI.2013.004126
Li, 2016, Optimal dissolved gas ratios selected by genetic algorithm for power transformer fault diagnosis based on support vector machine, IEEE Trans Dielectr Electr Insul, 23, 1198, 10.1109/TDEI.2015.005277
Hirose, 2011, More accurate diagnosis in electric power apparatus conditions using ensemble classification methods, IEEE Trans Dielectr Electr Insul, 18, 1584, 10.1109/TDEI.2011.6032828
Afrasiabi, 2019, Power transformers internal fault diagnosis based on deep convolutional neural networks, J Intell Fuzzy Systems, 37, 1165, 10.3233/JIFS-182615
Dai, 2017, Dissolved gas analysis of insulating oil for power transformer fault diagnosis with deep belief network, IEEE Trans Dielectr Electr Insul, 24, 2828, 10.1109/TDEI.2017.006727
Xu, 2020, Integrated decision-making method for power transformer fault diagnosis via rough set and DS evidence theories, IET Gener Transm Distrib, 14, 5774, 10.1049/iet-gtd.2020.0552
Thabtah, 2020, Data imbalance in classification: Experimental evaluation – ScienceDirect, Inform Sci, 5, 429, 10.1016/j.ins.2019.11.004
Khazeiynasab, 2022, Power plant model parameter calibration using conditional variational auto-encoder, IEEE Trans Power Syst, 37, 1642, 10.1109/TPWRS.2021.3107515
Pesteie, 2019, Adaptive augmentation of medical data using independently conditional variational auto-encoders, IEEE Trans Med Imaging, 38, 2807, 10.1109/TMI.2019.2914656
Ren, 2019, A fully data-driven method based on generative adversarial networks for power system dynamic security assessment with missing data, IEEE Trans Power Syst, 34, 5044, 10.1109/TPWRS.2019.2922671
Hong, 2020, Transformer winding fault diagnosis using vibration image and deep learning, IEEE Trans Power Deliv, 36, 676, 10.1109/TPWRD.2020.2988820
Barik, 2018, A decentralized fault detection technique for detecting single phase to ground faults in power distribution systems with resonant grounding, IEEE Trans Power Deliv, 33, 2462, 10.1109/TPWRD.2018.2799181
Tan, 2020, Ultra-short-term industrial power demand forecasting using LSTM based hybrid ensemble learning, IEEE Trans Power Syst, 35, 2937, 10.1109/TPWRS.2019.2963109