本論文結合離散小波轉換與人工神經網路來辨識屋內低壓線路上的串聯電弧故障，期望能有較好的辨認成功性。低壓線路上危險的串聯電弧故障必須在火災發生之前被檢測出來，並且及時關掉電源，因此，檢測技術必須有很高的辨識正確性。然而，當串聯電弧發生時，線路電流波形之特徵相當複雜。使用離散小波轉換可以取得線路電流波形的時域-頻域特性，而且一些頻段的資料透過信號能量法是反應串聯電弧故障模式的有效訊息。此外，適當的期望值與信號能量資料可以用來訓練徑向基底類神經網路。經過訓練之後，徑向基底類神經網路擁有出色的能力去辨識串聯電弧故障情況。最後，在30個電力週期之內累加徑向基底類神經網路的輸出，去最後判斷線路上串聯電弧故障發生與否。本論文比較串聯電弧故障檢測與商用電弧故障斷路器的結果，證明本論文提出方法的優點。在未來，可以將本論文所提出的檢測方法與智慧電錶或是其他技術做結合，以提升用電安全。

This dissertation combines the discrete wavelet transform (DWT) with an artificial neural network (ANN) to identify the occurrence of serial arc faults on indoor low voltage power lines and expect to have better recognition. Dangerous serial electric arc faults on low voltage power lines must be detected in order to turn off the electric power sources before fire hazards occur. The detection technology is required to have high accurate recognition. However, the characteristics of line current waveforms during serial arc faults are complicated. The DWT is utilized to obtain the time-frequency domain characteristics of line current waveforms, and the data of some sub-bands by using signal energy method is useful information to reflect the serial arc fault patterns. And then, the appropriate expected values and the data of signal energy obtained from DWT can train a radial basis function neural network (RBFNN). After the training process, the RBFNN has excellent ability to identify the serial arc-fault circumstances. At last, the accumulative outputs of the RBFNN within 30 power cycles are used to determine whether serial arc faults occur on power lines or not. This dissertation compares the results of detecting serial arc faults with a commercial arc fault circuit interrupter (AFCI) to confirm the advantage of the purposed method. In the future, the proposed detection method in this dissertation can be combined with smart meters or other technologies to improve the factor of using electricity safely.

[1] 內政部消防署，「103年全國火災統計分析」，www.nfa.gov.tw/Uploads/1/103年全國火災統計分析.pdf
[2] National Fire Protection Association (NFPA), “Home Electrical Fires Fact Sheet,” http://www.nfpa.org/~/media/Fires/Research/Fact%20sheets/HomeElectricalFiresFactSheet.pdf
[3] J. R. Hall, Jr., “Home Electrical Fires,” National Fire Protection Association, Quincy, Massachusetts, USA, April 2013.
[4] Arc-Fault Circuit Interrupters, UL Standard 1699-2008, 2008.
[5] J. C. Engel, “Combination AFCIs: What They Will and Will Not Do,” In proceedings of the 2012 IEEE IAS Electrical Safety Workshop, Daytona Beach, FL, USA, January 2012, pp. 1-18.
[6] K. J. Lippert and T. Domitrovich, “AFCIs – from a Standards Perspective,” In proceedings of the 2013 IEEE IAS Electrical Safety Workshop, Dallas, TX, USA, March 2013, pp. 57-61.
[7] W. Y. Su and C. J. Wu, “The Assessment of Introduction of the Arc Fault Circuit Interrupters,” Report prepared for Institute of Occupational Safety and Health, Taipei, Taiwan, IOSH98-S302, 2010.
[8] Y. H. Lin, C. W. Liu, and C. S. Chen, “A New PMU-based Fault Detection/location Technique for Transmission Lines with Consideration of Arcing Fault Discrimination—part I: Theory and Algorithms,” IEEE Transactions on Power Delivery, vol. 19, no. 4, pp. 1587-1593, 2004.
[9] M. Naidu, T. J. Schoepf, and S. Gopalakrishnan, “Arc Fault Detection Scheme for 42-V Automotive DC Networks Using Current Shunt,” IEEE Transactions on Power Electronics, vol. 21, no. 3, pp. 633-639, 2006.
[10] R. Zhang and Z. Song, “Arc Fault Detection Method Based on Signal Energy Distribution in Frequency Band,” In Proceedings of the 2012 Asia Pacific Power and Energy Engineering Conference (APPEEC), Shanghai, China, March 2012, pp. 1-4.
[11] C. H. Kim, H. Kim, Y. H. Ko, S. H. Byun, R. Aggarwal, and A. T. Johns, “A Novel Fault-detection Technique of High-Impedance Arcing Faults in Transmission Lines Using the Wavelet Transform,” IEEE Transactions on Power Delivery, vol. 17, no. 4, pp. 921-929, 2002.
[12] K. Koziy, B. Gou, and J. Aslakson, “A Low-cost Power-quality Meter with Series Arc-fault Detection Capability for Smart Grid,” IEEE Transactions on Power Delivery, vol. 28, no. 3, pp. 1584-1591, 2013.
[13] C. Li, Y. Li, and F. Wang, “Research of the Obstacle Avoidance Based on RBFNN for the Mobile Robot under Dynamic Environment,” In Proceedings of the 7th World Congress on Intelligent Control and Automation (WCICA), Chongqing, China, June 2008, pp. 5770-5775.
[14] G. W. Chang, C. I. Chen, and Y. F. Teng, “Radial-Basis-Function-Based Neural Network for Harmonics Detection,” IEEE Transactions on Industrial Electronics, vol. 57, no. 6, pp. 2171-2179, 2010.
[15] A. R. Sadeghian and J. D. Lavers, “Application of Radial Basis Function Networks to Model Electric Arc Furnaces,” In Proceedings of International Joint Conference on Neural Networks (IJCNN), Washington, DC, USA, July 1999, pp. 3996-4001.
[16] G. W. Chang, C. I. Chen, and Y. F. Teng, “An application of Radial Basis Function Neural Network for Harmonics Detection,” proceedings of the 13th International Conference on Harmonics and Quality of Power, Wollongong, NSW, Australia, September 2008, pp. 1-5.
[17] C. J. Wu, C. H. Hung, and Y. W. Liu, “Smart Detection Technology of Serial Arc-Fault on Low Voltage Power Lines,” published in Advanced Material Research, 734-737 (2013) 3190-3193.
[18] P. Muller, S. Tenbohlen, R. Maier, and M. Anheuser, “Characteristics of Series and Parallel Low Current Arc Faults in the Time and Frequency Domain”, proceedings of the 56th IEEE Holm Conference on Electrical Contacts, Charleston, SC, USA, October 2010, pp. 1-7.
[19] G. D. Gregory, K. Wong, and R. F. Dvorak, “More about Arc-Fault Circuit Interrupters.” IEEE Transactions on Industry Applications, vol. 40, no. 4, pp. 1006–1011, 2004.
[20] G. D. Gregory and G. W. Scott, “The Arc-Fault Circuit Interrupter: an Emerging Product,” IEEE Transactions on Industry Applications, vol. 34, no. 5, pp. 928–933, 1998.
[21] J. J. Shea, “Conditions for Series Arcing Phenomena in PVC Wiring,” IEEE Transactions on Components and Packaging Technologies, vol. 30, no. 3, pp. 532-539, 2007.
[22] H. Cheng, X. Chen, F. Liu, and C. Wang, “Series Arc Fault Detection and Implementation Based on the Short-time Fourier Transform,” In Proceedings of the 2010 Asia-Pacific Power and Energy Engineering Conference (APPEEC), Chengdu, China, March 2010, pp. 1-4.
[23] 王奕捷，「低壓線路串聯電弧故障時域及頻域分析與檢測」，碩士論文，國立台灣科技大學碩士論文，2012。
[24] 戴顯權，「資料壓縮」，紳藍出版社，2001。
[25] 繆紹綱，「數位影像處理」，全華書局，1999。
[26] 董長虹、高志、於嘯海，「小波分析工具箱原理與應用」，國防工業出版社，2004。
[27] I. Daubechies, Ten Lectures on Wavelets, SIAM, Philadelphia, 1992.
[28] K. Sayood, Introduction to Data Compression, Morgan Kaufmann, Publishers, San Francisco, 2000.
[29] R. M. Rao and A. S. Bopardikar, Wavelet Transform Introduction to Theory and Applications, Addison-Wesley, California, 1998.
[30] W. G. Morsi and M. E. El-Hawary, “Reformulating Three-Phase Power Components Definitions Contained in the IEEE Standard 1459–2000 Using Discrete Wavelet Transform,” IEEE Transactions on Power Delivery, vol. 22, no. 3, pp. 1917-1925, 2007.
[31] W. G. Morsi and M. E. El-Hawary, “Reformulating Power Components Definitions Contained in the IEEE Standard 1459–2000 Using Discrete Wavelet Transform,” IEEE Transactions on Power Delivery, vol. 22, no. 3, pp. 1910-1916, 2007.
[32] S. Mallat, A Wavelet Tour of Signal Processing, Academic Press, San Diego, third edition, 2009.

[33] X. Li and C. C. Cheah, “Adaptive Neural Network Control of Robot Based on Unified Objective Bound,” IEEE Transactions on Control System Technology, vol. 22, no. 3, pp. 1032-1043, 2014.
[34] L. Yingwei, N. Sundararajan, and P. Saratchandran, “Performance Evaluation of a Sequential Minimal Radial Basis Function (RBF) Neural Network Learning Algorithm,” IEEE Transactions on Neural Networks, vol. 9, no. 2, pp. 308-318, 1998.