참고문헌 (41)

1. Secur Commun Netw YuY Xi 2022 10.1155/2022/5154649 Xi YuY, Chen L, Chen B et al (2022) Research on pattern recognition method of transformer partial discharge based on artificial neural network. Secur Commun Netw. https://doi.org/10.1155/2022/5154649 

   상세보기

2. J Electr Eng Technol Y Kim 14 825 2019 10.1007/s42835-019-00105-0 Kim Y, Park T, Kim S et al (2019) Artificial intelligent fault diagnostic method for power transformers using a new classification system of faults. J Electr Eng Technol 14:825-831. https://doi.org/10.1007/s42835-019-00105-0 

   상세보기

3. IET Gener Transm Distrib XR Zhang 2022 10.1049/gtd2.12572 Zhang XR, Wang HT, Guo RC et al (2022) Fault diagnosis technologies for power transformers during the on-site inductive oscillating switching impulse voltage withstand test. IET Gener Transm Distrib. https://doi.org/10.1049/gtd2.12572 

   상세보기

4. J Electr Eng Technol A Kang 14 1287 2019 10.1007/s42835-018-00076-8 Kang A, Tian M, Song J et al (2019) Contribution of electrical-thermal aging to slot partial discharge properties of HV motor windings. J Electr Eng Technol 14:1287-1297. https://doi.org/10.1007/s42835-018-00076-8 

   상세보기

5. J Electr Eng Technol MA Khan 14 1299 2019 10.1007/s42835-019-00115-y Khan MA, Choo J, Kim YH (2019) End-to-end partial discharge detection in power cables via time-domain convolutional neural networks. J Electr Eng Technol 14:1299-1309. https://doi.org/10.1007/s42835-019-00115-y 

   상세보기

6. Adv Technol Electr Eng Energy YX Zhou 37 6 50 2018 10.12067/ATEEE1708066 Zhou YX, Zhou ZL, Sha YC et al (2018) Assessment of stages of partial discharge process of typical oil-paper insulation defect under combined AC-DC voltage. Adv Technol Electr Eng Energy 37(6):50-57. https://doi.org/10.12067/ATEEE1708066 

   

7. IEEE Trans Dielectr Electr Insul CK Chang 29 3 1070 2022 10.1109/TDEI.2022.3168328 Chang CK, Chang HH, Boyanapalli BK (2022) Application of pulse sequence partial discharge based convolutional neural network in pattern recognition for underground cable joints. IEEE Trans Dielectr Electr Insul 29(3):1070-1078. https://doi.org/10.1109/TDEI.2022.3168328 

   상세보기

8. IET Sci Meas Technol V Basharan 12 8 1031 2018 10.1049/iet-smt.2018.5020 Basharan V, Mariasiluvairaj WI et al (2018) Recognition of multiple partial discharge patterns by multi-class SVM using fractal image processing technique. IET Sci Meas Technol 12(8):1031-1038. https://doi.org/10.1049/iet-smt.2018.5020 

   상세보기

9. Trans China Electrotec Soc XG Xiao 36 21 4418 2021 10.19595/j.cnki.1000-6753.tces.201389 Xiao XG, Li KC et al (2021) A combined de-noising method for power quality disturbances events. Trans China Electrotec Soc 36(21):4418-4428. https://doi.org/10.19595/j.cnki.1000-6753.tces.201389 

   

10. High Volt Eng JM Chen 47 01 287 2021 10.13336/j.1003-6520.hve.20200507002 Chen JM, Xu CH et al (2021) A feature fusion extraction method for partial discharge pattern in GIS based on time-frequency analysis and fractal theory. High Volt Eng 47(01):287-295. https://doi.org/10.13336/j.1003-6520.hve.20200507002 

    

11. IEEE Trans Power Deliv K Firuzi 34 2 542 2019 10.1109/TPWRD.2018.2872820 Firuzi K, Vakilian M, Phung BT et al (2019) Partial discharges pattern recognition of transformer defect model by LBP and HOG features. IEEE Trans Power Deliv 34(2):542-550. https://doi.org/10.1109/TPWRD.2018.2872820 

    상세보기

12. Neural Comput Appl G Chandrasekaran 32 5303 2020 10.1007/s00521-019-04039-6 Chandrasekaran G, Periyasamy S, Rajamanickam KP (2020) Minimization of test time in system on chip using artificial intelligence-based test scheduling techniques. Neural Comput Appl 32:5303-5312. https://doi.org/10.1007/s00521-019-04039-6 

    상세보기

13. J Intell Fuzzy Syst G Chandrasekaran 40 3 4905 2021 10.3233/JIFS-201691 Chandrasekaran G et al (2021) Test scheduling of system-on-chip using dragonfly and ant lion optimization algorithms. J Intell Fuzzy Syst 40(3):4905-4917. https://doi.org/10.3233/JIFS-201691 

    상세보기

14. SN Appl Sci G Chandrasekaran 1 1079 2019 10.1007/s42452-019-1116-x Chandrasekaran G, Periyasamy S, Karthikeyan PR (2019) Test scheduling for system on chip using modified firefly and modified ABC algorithms. SN Appl Sci 1:1079. https://doi.org/10.1007/s42452-019-1116-x 

    상세보기

15. IEEE Trans Power Deliv XS Peng 2019 10.1109/TPWRD.2019.2906086 Peng XS, Yang F, Wang GJ et al (2019) A convolutional neural network-based deep learning methodology for recognition of partial discharge patterns from high-voltage cables. IEEE Trans Power Deliv. https://doi.org/10.1109/TPWRD.2019.2906086 

    상세보기

16. J Electr Eng Technol K Zhou 17 513 2022 10.1007/s42835-021-00941-z Zhou K, Oh SK, Qiu J (2022) Design of ensemble fuzzy-RBF neural networks based on feature extraction and multi-feature fusion for GIS partial discharge recognition and classification. J Electr Eng Technol 17:513-532. https://doi.org/10.1007/s42835-021-00941-z 

    상세보기

17. J Electr Eng Technol H Wang 15 1115 2020 10.1007/s42835-020-00413-w Wang H, Qi L, Ma Y et al (2020) Method of voltage sag causes based on bidirectional LSTM and attention mechanism. J Electr Eng Technol 15:1115-1125. https://doi.org/10.1007/s42835-020-00413-w 

    상세보기

18. Trans China Electrotec Soc YF Zhu 35 3 659 2020 10.19595/j.cnki.1000-6753.tces.181954 Zhu YF, Xu YP, Chen XX et al (2020) Pattern recognition of partial discharges in DC XLPE cables based on convolutional neural network. Trans China Electrotec Soc 35(3):659-668. https://doi.org/10.19595/j.cnki.1000-6753.tces.181954 

    

19. IEEE Trans Industr Electron K Masoud 67 4 3277 2020 10.1109/TIE.2019.2908580 Masoud K, Mehrdad M, Hamed M et al (2020) A novel application of deep belief networks in learning partial discharge patterns for classifying corona, surface, and internal discharges. IEEE Trans Industr Electron 67(4):3277-3287. https://doi.org/10.1109/TIE.2019.2908580 

    상세보기

20. High Volt Eng JN Chen 2021 10.13336/j.1003-6520.hve.20210613 Chen JN, Zhou YX, Bai Z et al (2021) Pattern recognition of partial discharge in oil-paper insulation based on multi-channel convolutional neural network. High Volt Eng. https://doi.org/10.13336/j.1003-6520.hve.20210613 

    

21. Proc CSEE Y Zhang 41 18 6472 2021 10.13334/j.0258-8013.pcsee.201894 Zhang Y, Zhu YL (2021) A partial discharge pattern recognition method combining graph signal and graph convolutional network. Proc CSEE 41(18):6472-6480. https://doi.org/10.13334/j.0258-8013.pcsee.201894 

    

22. Trans China Electrotec Soc SM Song 36 10 2153 2021 10.19595/j.cnki.1000-6753.tces.200327 Song SM, Qian Y et al (2021) Improved algorithm for partial discharge pattern recognition based on histogram of oriented gradient attribute space. Trans China Electrotec Soc 36(10):2153-2160. https://doi.org/10.19595/j.cnki.1000-6753.tces.200327 

    

23. Trans China Electrotec Soc YF Zhu 35 03 659 2020 10.19595/j.cnki.1000-6753.tces.181954 Zhu YF, Xu YP et al (2020) Pattern recognition of partial discharges in DC XLPE cables based on convolutional neural network. Trans China Electrotec Soc 35(03):659-668. https://doi.org/10.19595/j.cnki.1000-6753.tces.181954 

    

24. Power Syst Technol XQ Wan 43 06 2219 2019 10.13335/j.1000-3673.pst.2018.1345 Wan XQ, Song H, Luo LG et al (2019) Application of convolutional neural networks in pattern recognition of partial discharge image. Power Syst Technol 43(06):2219-2226. https://doi.org/10.13335/j.1000-3673.pst.2018.1345 

    

25. Mech Syst Signal Process S Liu 2022 10.1016/j.ymssp.2021.108139 Liu S, Jiang H, Wu Z et al (2022) Data synthesis using deep feature enhanced generative adversarial networks for rolling bearing imbalanced fault diagnosis. Mech Syst Signal Process. https://doi.org/10.1016/j.ymssp.2021.108139 

    상세보기

26. Proc CSEE YL Zhu 41 14 5044 2021 10.13334/j.0258-8013.pcsee.201490 Zhu YL, Zhang Y, Cai WH et al (2021) Data augmentation and pattern recognition for multi-sources partial discharge based on boundary equilibrium generative adversarial network with auxiliary classifier. Proc CSEE 41(14):5044-5053. https://doi.org/10.13334/j.0258-8013.pcsee.201490 

    

27. High Volt Eng YJ Nie 46 04 1361 2020 10.13336/j.1003-6520.hve.20200430028 Nie YJ, Zhao XP, Li ST (2020) Research progress in condition monitoring and insulation diagnosis of XLPE cable. High Volt Eng 46(04):1361-1371. https://doi.org/10.13336/j.1003-6520.hve.20200430028 

    

28. J Supercomput J Li 78 7428 2022 10.1007/s11227-021-04177-6 Li J, Wu Y, Fong S et al (2022) A binary PSO-based ensemble under-sampling model for rebalancing imbalanced training data. J Supercomput 78:7428-7463. https://doi.org/10.1007/s11227-021-04177-6 

    상세보기

29. Multimed Tools Appl H Ding 79 14871 2020 10.1007/s11042-019-07856-y Ding H, Wei B, Gu Z et al (2020) KA-Ensemble: towards imbalanced image classification ensembling under-sampling and over-sampling. Multimed Tools Appl 79:14871-14888. https://doi.org/10.1007/s11042-019-07856-y 

    상세보기

30. Power Syst Technol ZY Liu 44 3 1057 2020 10.13335/j.1000-3673.pst.2019.0349 Liu ZY, Miao XR, Chen J et al (2020) Review of visible image intelligent processing for transmission line inspection. Power Syst Technol 44(3):1057-1069. https://doi.org/10.13335/j.1000-3673.pst.2019.0349 

    

31. Commun ACM I Goodfellow 63 11 139 2020 10.1145/3422622 Goodfellow I, Pouget-Abadie J, Miza M et al (2020) Generative adversarial networks. Commun ACM 63(11):139-144. https://doi.org/10.1145/3422622 

    상세보기

32. J Electr Eng Technol C Zhang 16 2201 2021 10.1007/s42835-021-00728-2 Zhang C, Sun X, Xu J et al (2021) A generative adversarial network to denoise depth maps for quality improvement of DIBR-synthesized stereoscopic images. J Electr Eng Technol 16:2201-2210. https://doi.org/10.1007/s42835-021-00728-2 

    상세보기

33. 10.1109/CMD.2018.8535718 Wang X, Huang H, Hu Y et al (2018) Partial discharge pattern recognition with data augmentation based on generative adversarial networks. In: 2018 Condition Monitoring and Diagnosis (CMD) pp 1-4, https://doi.org/10.1109/CMD.2018.8535718 

    

34. IEEE Access JA Ardila-Rey 8 24561 2020 10.1109/ACCESS.2020.2971319 Ardila-Rey JA, Ortiz JE, Creixell W et al (2020) Artificial generation of partial discharge sources through an algorithm based on deep convolutional generative adversarial networks. IEEE Access 8:24561-245751. https://doi.org/10.1109/ACCESS.2020.2971319 

    상세보기

35. Power Syst Technol Y Fu 2021 10.13335/j.1000-3673.pst.2021.1238 Fu Y, Zhou K, Zhu GY et al (2021) A method for improving the recognition accuracy of cable termination partial discharge based on improved WGAN algorithm. Power Syst Technol. https://doi.org/10.13335/j.1000-3673.pst.2021.1238 

    

36. High Voltage Y Wang 7 3 452 2022 10.1049/hve2.12135 Wang Y et al (2022) GAN and CNN for imbalanced partial discharge pattern recognition in GIS. High Voltage 7(3):452-460. https://doi.org/10.1049/hve2.12135 

    상세보기

37. Mapan L Chen 35 2 233 2020 10.1007/s12647-020-00365-6 Chen L, Li YL, Sun WJ et al (2020) Prediction of in-orbit power on time for transformer based on gas permeation analysis of the seal cavity. Mapan 35(2):233-239. https://doi.org/10.1007/s12647-020-00365-6 

    상세보기

38. Proc Inst Mech Eng J Wang 234 12 2719 2020 10.1177/0954407020923258 Wang J, Han B, Bao H et al (2020) Data augment method for machine fault diagnosis using conditional generative adversarial networks. Proc Inst Mech Eng 234(12):2719-2727. https://doi.org/10.1177/0954407020923258 

    상세보기

39. 10.48550/arXiv.1606.0365 Chen X, Duan Y, Houthooft R et al (2016) InfoGAN: interpretable representation learning by information maximizing generative adversarial nets. In: 30th Conference on Neural Information Processing Systems (NIPS) https://doi.org/10.48550/arXiv.1606.0365. 

    

40. 10.5555/3295222.3295327 Gulrajani I, Ahmed F, Arjovsky M et al (2017) Improved training of Wasserstein gans. In: Proceedings of the 31st International Conference on Neural Information Processing Systems Long Beach USA, pp 5767-5777. https://doi.org/10.5555/3295222.3295327. 

    

41. IEEE Access AK Venkataramanan 9 28872 2021 10.1109/ACCESS.2021.3056504 Venkataramanan AK, Wu C, Bovik AC et al (2021) A Hitchhiker’s guide to structural similarity. IEEE Access 9:28872-28896. https://doi.org/10.1109/ACCESS.2021.3056504 

    상세보기