人工神經網路已經被廣泛的應用於局部放電的領域中，本文運用深度學習架構的2D-卷積神經網路來提取特徵並作特徵分類來達成診斷。本論文主旨在於使用一套診斷系統來辨識出局部放電故障模式。基於2D-卷積神經網路進行分析。本文自IEC60034擷取馬達的四種不同局部放電模式的圖譜訊號，如定子繞組絕緣系統中的內部空隙放電、主絕緣中的內部分層放電、主絕緣中導體與絕緣之間的分層放電，以及在槽口的表面放電。 並將所有數據分為兩組，用訓練和測試二維卷積神經網絡。實驗結果證明二維卷積神經網絡可以有效的診斷局部放電的四種不同故障模式，得到最佳辨識率與最小誤差率分別為98.30%及1.41%，且預測模型也有高達分別為99%和98%的精密度和召回率。

Artificial neural networks have been widely used in the field of partial discharge. This study uses the 2D-convolution neural network of deep learning architecture to extract features and classify them to achieve diagnosis. The main purpose of this study is to identify a partial discharge failure mode using a diagnostic system. This thesis extracts the signals of four different partial discharge modes of motor from IEC60034, such as internal voids PD in stator winding insulation system, internal delamination PD in the main insulation, delamination PD between conductor and insulation in the main insulation, and surface PD in slot. Analytical classification was performed using a 2D-convolution neural network. From the experimental results the CNN can effectively diagnose four different failure modes of partial discharge. The best recognition rate and loss rate are 98.30% and1.41%, and the model has high precision and recall which about 99% and 98%.

[1] J. Tang, S. Ma, X. Li, Y. Zhang, C. Pan, and J. Su, “Impact of Velocity on Partial Discharge Characteristics of Moving Metal Particles in Transformer Oil using UHF,” IEEE Transactions on Dielectrics and Electrical Insulation, vol. 23, pp. 2207 – 2212, no. 4; August 2016
[2] L. Hao, P. L. Lewin, J. A. Hunter, D. J. Swaffield, A. Contin, C. Walton, and M. Michel, “Discrimination of Multiple PD Sources Using Wavelet Decomposition and Principal Component Analysis,” IEEE Transactions on Dielectrics and Electrical Insulation, vol.18, no.5; October 2011
[3] G. Wang, S.J. Kim, G. S. Kil and S. W. Kim, “Optimization of Wavelet and Thresholding for Partial Discharge Detection under HVDC,” IEEE Transactions on Dielectrics and Electrical Insulation, vol. 24, no.1; February 2017
[4] I. Seo, U. A. Khan, J. S. Hwang, J.G. Lee, and J. Y. Koo, “Identification of Insulation Defects Based on Chaotic Analysis of Partial Discharge in HVDC Superconducting Cable,” IEEE Transactions Ransactions on Applied Superconductivity, vol. 25, no. 3, June 2015
[5] W. Z. Lin, “On-Line Monitoring of Insulation Status of Power Equipment,” Master's thesis, Department of Electrical Engineering, National Punghu University of Science and Technology, 2015.
[6] Y. H. Cheng, “Insulation and Diagnosis of Power Equipment,” Beijing China Press, 2001.
[7] G. F. Yue, “Causes and Analysis of Partial Discharge,” Mechanical Engineering and Automation, no. 4, pp.105, August 2005.
[8] C. Yao, Y. Zhang, Y. Zhang, H. Liu , “Application of Convolutional Neural Networks in High Resolution Agricultural Remote Sensing Image Classification,” The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. XLII-2/W7, 18–22 September 2017, Wuhan, China.
[9] J. Ramirez-Nifio, S. Rivera-Castafieda, V.R. Garcia-Colon a, V.M. Casttio, “Analysis of Partial Electrical Discharges in Insulating Materials Through the Wavelet Transform,” Computational Muerials Science, pp. 379-388, 1998
[10] G.C. Stone, “Partial Discharge Measurement Evaluation Rotating Machine lnsulation Condition: A Survey,” Conference Record of the 1996 IEEE International Symposium on Electrical Insulation, Montreal, Quebec, Canada, June 16-19, 1996
[11] M. Ren, M. Dong, R. Zhong, H. D. Peng, and A. C. Qiu, “Transient Earth Voltage Measurement in PD Detection of Artificial Defect Models in SF6,” IEEE Transaction on Plasma Science, vol. 40, August 2012
[12] J. Li, X. Han, Z. Liu, X. Yao and Y. Li, “PD Characteristics of Oil-pressboard Insulation under AC and DC Mixed Voltage,” IEEE Transactions on Dielectrics and Electrical Insulation, vol. 23, no. 1; February 2016
[13] IEC/TS 60034-27-2, “ Rotating electrical machines –Part 27-2: On-line partial discharge measurements on the stator winding insulation of rotating electrical machines,” 2012.
[14] X.M. Zhang and Q.L. Han, “Global Asymptotic Stability for a Class of Generalized Neural Networks with Interval Time-Varying Delays,” IEEE Transactions on Neural Networks, vol. 22, August 2011
[15] K. Liu , G. Kang, N. Zhang, and B. B. Hou, “Breast Cancer Classification Based on Fully-Connected Layer First Convolutional Neural Networks,” IEEE Access, vol 6, 2018
[16] C. L. Philip Chen, J. Wang, C. H. Wang, and L. Chen, “A New Learning Algorithm for a Fully Connected Neuro-Fuzzy Inference System,” IEEE Transactions on Neural Networks and Learning Systems, vol. 25, no. 10, October 2014
[17] A. Dehghanian and V. Ghods, “Farsi Handwriting Digit Recognition based on Convolutional Neural Networks,” International Symposium on Computational and Business Intelligence (ISCBI), 27-29 August 2018
[18] M. M. A. Ghosh and A. Y. Maghari, “A Comparative Study on Handwriting Digit Recognition Using Neural Networks,” International Conference on Promising Electronic Technologies (ICPET), 16-17 October 2017
[19] D. Qu, Z. Huang, Z. Gao, Y. Zhao, X. Zhao, and G. Song, “An Automatic System for Smile Recognition Based on CNN and Face Detection,” IEEE International Conference on Robotics and Biomimetics (ROBIO), 12-15 December 2018
[20] S. Sharma, K. Shanmugasundaram, and S. K. Ramasamy, “FAREC — CNN based efficient face recognition technique using Dlib,” International Conference on Advanced Communication Control and Computing Technologies (ICACCCT), 25-27 May 2016
[21] H. Yanagisawa, T. Yamashita, and H. Watanabe, “A study on object detection method from manga images using CNN,” International Workshop on Advanced Image Technology (IWAIT), 7-9 Jane 2018
[22] N. Passalis and A. Tefas, “Training Lightweight Deep Convolutional Neural Networks Using Bag-of-Features Pooling,” IEEE Transactions on Neural Networks and Learning Systems, vol. 30, Issue: 6, June 2019
[23] S. Lawrence, C. L. Giles, A. C. Tsoi, and A. D. Back, “Face recognition: A convolutional neural-network approach,” IEEE transactions on neural networks, vol. 8, pp. 98-113, 1997
[24] A. Deshpande, “The 9 Deep Learning Papers You Need To Know About (Understanding CNNs Part 3),” University of California, Los Angeles (UCLA), 2016.
[25] F. Aminzadeh and P. deGroot, “A neural networks based seismic object detection technique,” in SEG Technical Program Expanded Abstracts 2005, ed: Society of Exploration Geophysicists, pp. 775-778, 2005.
[26] P. H. Pinheiro and R. Collobert, “Recurrent convolutional neural networks for scene labeling,” in 31st International Conference on Machine Learning (ICML), 2014.
[27] Q. Wu, C. Fan, Hu Chen, and D. Gu, “Construction of a Neural Network and its Application on Target Classification”, IEEE Access, vol. 7, pp. 29709–29721, 21 February 2019.
[28] G. Liang, H. Hong, W. Xie, and L. Zheng, “Combining Convolutional Neural Network With Recursive Neural Network for Blood Cell Image Classification,” IEEE Access, vol.6, pp. 36188–36197, 03 July 2018.
[29] A. Kappeler, S. Yoo, Q. Dai, and A. K. Katsaggelos, “Video Super-Resolution with Convolutional Neural Networks,” IEEE Transactions on Computational Imaging, vol. 2, pp. 109-122, June 2016.
[30] K. Gong, J. Guan, K. Kim, X. Zhang, J. Yang, and Y. Seo, “Iterative PET Image Reconstruction Using Convolutional Neural Network Representation,” IEEE Transactions on Medical Imaging, vol. 38, pp. 675- 685, March 2019.