由於高科技產業的快速發展，對於供電品質優劣之要求日益提升，因此如何辨識異常電力訊號，維持穩定供電品質，已成為電力研究之重點。基於此，本文即提出整合近似熵與LZ演算法之規則度鑑定能力於電力訊號之分類鑑別應用，其中由於近似熵演算法具量測系統複雜度之能力，並對時域訊號有較高之靈敏度，故可藉以判別電力事件之發生；另Lempel-Ziv演算法則除了可量測系統複雜度外，亦可經由數個符號予以量化取樣之訊號，以分析電壓訊號分布範圍，並據以分類相關電力事故。此外，為驗證本文所提方法架構之可行性，本文已將此演算法予以融合應用於數種電力品質訊號之鑑別，並經由數個實際訊號予以驗證，以佐證本文所提演算法之可行性與實用價值。

Following the fast development of high-tech industry, electrical power quality has emerged as a critical concern. Therefore, how to classify the abnormal power signals and maintain a high level of power quality becomes a crucially important. In view of such importance, this thesis has proposed an approximate entropy approach combined with Lempel-Ziv method in order to classify the power signals, where the approximate entropy approach owns a capability to measure the complexity of system and Lempel-Ziv method helps justify the regularity for signals of time series. With the entropy, the regularity of signal can be better assessed. Then, by Lempel-Ziv method, it not only measures the complexity of system, but also quantifies the level of power signals such that the power events can be more accurately classified. In order to confirm the effectiveness of this integrated approach, the proposed method has been tested through several simulated signals. Meanwhile, some real signals were also verified with agreement such that the feasibility and practical value of the proposed approach is well supported.

[1] R. C. Dugan, M. F. McGranaghan, and H. W. Beaty, Electrical Power Systems Quality, Second edition, McGraw-Hill, New York, USA, 2002.
[2] D. D. Sabin and A. Sundarm, “Quality Enhances Reliability [Power Supplies],” IEEE Spectrum, Vol. 33, No. 2, pp. 34-41, February 1996.
[3] A. Domijan, G. T. Heydt, A. P. S. Maliopoulos, S. S. Venkate, and S. West, “Directions of Research on Electric Power Quality,” IEEE Transactions on Power Delivery, Vol. 8, No. 1, pp. 429-436, January 1993.
[4] S. Santoso, W. M. Gradt, E. J. Powers, J. Lamoree, and S. C. Bhatt, “Characterization of Distribution Power Quality Events with Fourier and Wavelet Transforms,” IEEE Transactions on Power Delivery, Vol. 15, No. 1, pp. 247-254, January 2000.
[5] G. T. Heydt, P. S. Fjeld, C. C. Liu, D. Pierce, L. Tu, and G. Hensely, “Applications of the Windowed FFT to Electric Power Quality Assessment,” IEEE Transactions on Power Delivery, Vol. 14, No. 4, pp. 1411-1416, October 1999.
[6] P. S. Wright, “Short-Time Fourier Transforms and Wigner-Ville Distributions Applied to the Calibration of Power Frequency Harmonic Analyzers,” IEEE Transactions on Instrumentation and Measurements, Vol. 48, No. 2, pp. 475-478, April 1999.
[7] Y. H. Gu and M. H. J. Bollen, “Time-Frequency and Time-Scale Domain Analysis of Voltage Disturbance,” IEEE Transactions on Power Delivery, Vol. 15, No. 4, pp. 1279-1284, October 2000.
[8] S. Santoso, E. J. Powers, W. M. Grady, and P. Hofmann, “Power Quality Assessment via Wavelet Transform Analysis,” IEEE Transactions on Power Delivery, Vol. 11, No. 2, pp. 924-930, April 1996.
[9] D. Borras, M. Castilla, N. Moreno, and J. C. Montano, “Wavelet and Neural Structure： A New Tool for Diagnostic of Power System Disturbances,” IEEE Transactions on Industry Applications, Vol. 37, No. 1, pp. 184-190, January-February 2001.
[10] S. J. Huang, C. T. Hsieh, and C. L. Huang, “Application of Morlet Wavelets to Supervise Power System Disturbances,” IEEE Transactions on Power Delivery, Vol. 14, No. 1, pp. 235-243, January 1999.
[11] D. G. Ece and O. N. Gerek, “Power Quality Event Detection Using Joint 2-D-Wavelet Subspaces,” IEEE Transactions on Instrumentation and Measurement, Vol. 53, No. 4, pp. 1040-1046, August 2004.
[12] J. S. Huang, M. Negnevitsky, and D. T. Nguyen, “A Neural-Fuzzy Classifier for Recognition of Power Quality Disturbances,” IEEE Transactions on Power Delivery, Vol. 17, No. 2, pp. 609-616 , April 2002.
[13] S. Mishra, C. N. Bhende, and B. K. Panigrahi, “Detection and Classification of Power Quality Disturbances Using S-Transform and Probabilistic Neural Network,” IEEE Transactions on Power Delivery, Vol. 23, No. 1, pp. 280-287 , January 2008.
[14] M. B. I. Reaz, F. Choong, M. S. Sulaiman, F. Mohd-Yasin, and M. Kamada, “Expert System for Power Quality Disturbance Classifier,” IEEE Transactions on Power Delivery, Vol. 22, No. 3, pp. 1979-1988, July 2007.
[15] S. M. Pincus, “Approximate Entropy as a Measure of System Complexity,” Proceedings of the National Academy of Sciences of the United States of America, Vol. 88, No. 6, pp. 2297-2301, March 1991.
[16] B. Hong, F. Yang, Q. Tang, and T. C. Chan, “Approximate Entropy and Its Preliminary Application in the Field of EEG and Cognition,” Proceedings of the 20th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Hong Kong, Vol. 20, No. 4, 1998
[17] M. Koskinen, T. Seppanen, T. Shanbao, S. Mustola, and N. V. Thakor, “Monotonicity of Approximate Entropy during Transition from Awareness to Unresponsiveness due to Propofol Anesthetic Induction,” IEEE Transactions on Biomedical Engineering, Vol. 53, No. 4, pp. 669-675, April 2006.
[18] R. Hornero, D. Alvarez, D. Abasolo, F. del Campo, and C. Zamarron, “Utility of Approximate Entropy From Overnight Pulse Oximetry Data in the Diagnosis of the Obstructive Sleep Apnea Syndrome,” IEEE Transactions on Biomedical Engineering, Vol. 54, No. 1, pp. 107-113, January 2007.
[19] R. Hornero, M. Aboy, D. Abasolo, J. McNames, and B. Goldstein, “Interpretation of Approximate Entropy: Analysis of Intracranial Pressure Approximate Entropy during Acute Intracranial Hypertension,” IEEE Transactions on Biomedical Engineering, Vol. 52, No. 10, pp. 1671-1680, October 2005.
[20] V. Srinivasan, C. Eswaran, and N. Sriraam, “Approximate Entropy-Based Epileptic EEG Detection Using Artificial Neural Networks,” IEEE Transactions on Information Technology in Biomedicine, Vol. 11, No. 3, pp. 288-295, May 2007.
[21] http://www.physionet.org/physiotools/ApEn
[22] A. Lempel and J. Ziv, “On the Complexity of Finite Sequences,” IEEE Transactions on Information Theory, vol. IT-22, No. 1, pp. 75-81, January 1976.
[23] R. Nagarajan, “Quantifying Physiological Data with Lempel-Ziv Complexity-Certain Issues,” IEEE Transactions on Biomedical Engineering, Vol. 49, No. 11, pp. 1371-1373, November 2002.
[24] M. Aboy, R. Hornero, D. Abasolo, and D. Alvarez, “Interpretation of the Lempel-Ziv Complexity Measure in the Context of Biomedical Signal Analysis,” IEEE Transactions on Biomedical Engineering, Vol. 53, No. 11, pp. 2282-2288, November 2006.
[25] J. Hu, J. Gao, and J. C. Principe, “Analysis of Biomedical Signals by the Lempel-Ziv Complexity: the Effect of Finite Data Size,” IEEE Transactions on Biomedical Engineering, Vol. 53, No. 12, pp. 2606-2609, December 2006.
[26] D. Abasolo, C. J. James, and R. Hornero, “Non-linear Analysis of Intracranial Electroencephalogram Recordings with Approximate Entropy and Lempel-Ziv Complexity for Epileptic Seizure Detection,” Proccedings of the 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Lyon, France, pp. 1953-1956, August 2007.
[27] R. Hornero, D. Abasolo, N. Jimeno, C. I. Sanchez, J. Poza, and M. Aboy, “Variability, Regularity, and Complexity of Time Series Generated by Schizophrenic Patients and Control Subjects,” IEEE Transactions on Biomedical Engineering, Vol. 53, No. 2, pp. 210-218, February 2006.
[28] R. Ferenets, Tarmo Lipping, A. Anier, V. Jantti, S. Melto, and S. Hovilehto, “Comparison of Entropy and Complexity Measures for the Assessment of Depth of Sedation,” IEEE Transactions on Biomedical Engineering, Vol. 53, No. 6, pp. 1067-1077, June 2006.
[29] X. -S. Zhang, R. J. Roy, and E. W. Jensen, “EEG Complexity as a Measure of Depth of Anesthesia for Patients,” IEEE Transactions on Biomedical Engineering, Vol. 48, No. 12, pp. 1424-1433, December 2001.
[30] IEEE Recommended Practice for Monitoring Electrical Power Quality, IEEE Stand 1159-1995, IEEE, New York, 1995.
[31] 江榮城，電力品質實務(一)，全華科技圖書股份有限公司，民國九十年五月。
[32] 江榮城，電力品質實務(二)，全華科技圖書股份有限公司，民國九十一年五月。
[33] 王耀諄，電力品質，高立圖書有限公司，民國九十四年九月十日。
[34] http://www.mathworks.com/product/matlab