یک روش جدید برای شناسایی اغتشاشات کیفیت توان با استفاده از تبدیل S

[1]M. Zhang, K. Li and Y. Hu, “A real-time classification method of power quality disturbances,” Electric Power Systems Research, vol. 81, no. 2, pp. 660-666, February 2011.

[2]T. Nguyen and Y. Liao, “Power quality disturbance classification utilizing S-transform and binary feature matrix method,” Electric Power Systems Research, vol. 79, no. 4, pp. 569-575, April 2009.

[3]Z. Moravej, A. A. Abdoos and M. Pazoki, “New combined S-transform and logistic model tree technique for recognition and classification of power quality disturbances,” Electric Power Components and Systems, vol. 39, no. 1, pp. 80-98, 2011.

[4]A. M. Gargoom, N. Ertugrul and W. L. Soong, “Automatic classification and characterization of power quality events,” IEEE Trans. on Power Delivery, vol. 23, no. 4, pp. 2417-2425, October 2008.

[5]M. Kezunovic and Y. Liao, “A novel software implementation concept for power quality study,” IEEE Trans. on Power Delivery, vol. 17, no. 2, pp. 544-549, April 2002.

[6]Y. Liao and J. B. Lee, “A fuzzy-expert system for classifying power quality disturbances,” International Journal of Electrical Power & Energy Systems, vol. 26, no. 3, pp. 199-205, March 2004.

[7]علی انشایی و رحمت‌الله هوشمند، «تشخیص و طبقه‌بندی اغتشاش‌های ساده و ترکیبی کیفیت توان با استفاده از سیستم‌های فازی راهنمایی شده با الگوریتم بهینه‌سازی گروهی ذرات»، مجله علمی-پژوهشی مهندسی برق مدرس، دوره دهم، شماره 2، صفحه 16-1، تابستان 1389.

[8]T. K. Abdel-Galil, E. F. El-Saadany, A. M.Youssef and M. M. A. Salama, “Disturbance classification using hidden Markov models and vector quantization,” IEEE Trans. on Power Delivery, vol. 20, no. 3, pp. 2129-2135, July 2005.

[9]B. K. Panigrahi and V. R. Pandi, “Optimal feature selection for classification of power quality disturbances using wavelet packet-based fuzzy k-nearest neighbour algorithm,” IET Generation, Transmission & Distribution, vol. 3, no. 3, pp. 296–306, March 2009.

[10]C. C. Liao and H. T. Yang, “Recognizing noise-influenced power quality events with integrated feature extraction and neuro-fuzzy network,” IEEE Trans. on Power Delivery, vol. 24 no. 4, pp. 2132-2141, October 2009.

[11]G. M. S. Decanini, M. S. Tonelli-Neto, F. C. V. Malange and C. R. Minussi, “Detection and classification of voltage disturbances using a fuzzy-ARTMAP-wavelet network,” Electric Power Systems Research, vol. 81, no. 12, pp. 2057-2065, December 2011.

[12]S. Kaewarsa, K. Attakitmongcol and T. Kulworawanichpong, “Recognition of power quality events by using multiwavelet-based neural networks,” International Journal of Electrical Power & Energy Systems, vol. 30, no. 4, pp. 254-260, May 2008.

[13]M. Uyar, S. Yildirima and M. T. Gencoglu, “An effective wavelet-based feature extraction method for classification of power quality disturbance signals,” Electric Power Systems Research, vol. 78, no. 10, pp. 1747-1755, October 2008.

[14]H. He and J. A. Starzyk, “A self-organizing learning array system for power quality classification based on wavelet transform,” IEEE Trans. on Power Delivery, vol. 21, no. 1, pp. 286-295, January 2006.

[15]S. Ekici, “Classification of power system disturbances using support vector machines,” Expert Systems with Applications, vol. 36, no. 6, pp. 9859-9868, August 2009.

[16]H. Eristi, A. Ucar and Y. Demir, “Wavelet-based feature extraction and selection for classification of power system disturbances using support vector machines,” Electric Power Systems Research, vol. 80, no. 7, pp. 743-752, July 2010.

[17]G. S. Hu, F. F. Zhu and Z. Ren, “Power quality disturbance identification using wavelet packet energy entropy and weighted support vector machines,” Expert Systems with Applications, vol. 35, no. 1-2, pp. 143-149, July-August 2008.

[18]T. K. Abdel-Galil, M. Kamel, A. M. Youssef, E. F. El-Saadany and M. M. A. Salama, “Power quality disturbance classification using the inductive inference approach,” IEEE Trans. on Power Delivery, vol. 19, no. 4, pp. 1812-1818, October 2004.

[19]M. V. Chilukuri and P. K. Dash, “Multiresolution S-transform-based fuzzy recognition system for power quality events,” IEEE Trans. on Power Delivery, vol. 19, no. 1, pp. 323-330, January 2004.

[20]M. E. Salem, A. Mohamed and S. A. Samad, “Rule based system for power quality disturbance classification incorporating S-transform features,” Expert Systems with Applications, vol. 37, no. 4, pp. 3229-3235, April 2010.

[21]S. Hasheminejad, S. Esmaeili and S. Jazebi, “Power quality disturbance classification using S-transform and hidden Markov model,” Electric Power Components and Systems, vol. 40, no. 10, pp. 1160-1182, 2012.

[22]M. Uyar, S. Yildirim and M. T. Gencoglu, “An expert system based on S-transform and neural network for automatic classification of power quality disturbances,” Expert Systems with Applications, vol. 36, no. 3, pp. 5962-5975, April 2009.

[23]C. N. Bhende, S. Mishra and B. K. Panigrahi, “Detection and classification of power quality disturbances using S-transform and modular neural network,” Electric Power Systems Research, vol. 78, no. 1, pp. 122-128, January 2008.

[24]S. Mishra, C. N. Bhende and B. K. Panigrahi, “Detection and classification of power quality disturbances using S-transform and probabilistic neural network,” IEEE Trans. on Power Delivery, vol. 23, no. 1, pp. 280-287, January 2008.

[25]F. Zhao and R. Yang, “Power-quality disturbance recognition using S-transform,” IEEE Trans. on Power Delivery, vol. 22, no. 2, pp. 944-950, April 2007.