به‌کارگیری ویژگی بردار هویت و ماشین بردار پشتیبان به‌منظور شناسایی و طبقه بندی اغتشاشات کیفیت توان

نوع مقاله: مقاله پژوهشی فارسی

نویسندگان

1 دانشکده مهندسی برق و کامپیوتر، دانشگاه سمنان، سمنان، ایران

2 Assistant Professor, College of Science, University of Tehran

چکیده

یکی از مسائل مهم در سیستم‌های قدرت، تشخیص صحیح اغتشاشات کیفیت توان است. در این مقاله یک روش هوشمند به منظور شناسایی اغتشاشات کیفیت توان ارائه شده است. در روش پیشنهادی که بر مبنای ویژگی بردار هویت است، برای هرسیگنال اغتشاش یک بردار با طول ثابت استخراج می‌شود. به‌این‌صورت‌که در مرحله اول، تبدیل موجک به منظور استخراج ویژگی از سیگنال اغتشاش ورودی به‌کارگرفته شده است و سپس با استفاده از دنباله ضرایب موجک استخراج شده، بردار هویت تولید می-شود. در ادامه بعد از انجام نرمال‌سازی‌های لازم، بردار هویت نرمال شده استخراج شده توسط ماشین بردار پشتیبان طبقه‌بندی می-شود. به‌منظور ارزیابی عملکرد روش پیشنهادی دوازده نوع اغتشاش اعم از تکی و ترکیبی ایجاد و کارایی سیستم در شرایط تمیز و نویزی بررسی‌شده است. نویز اعمال شده به هر سیگنال نویز سفید گاوسی با مقادیر سیگنال به نویز 30، 40 و 50 دسی‌بل است. نتایج آزمایش‌ها میانگین دقت روش پیشنهادی را 2/99 درصد نشان می‌دهد.

کلیدواژه‌ها


Mishra, S. and T. Nagwani, “A Review on Detection and Classification Methods for Power Quality Disturbances.” International Journal of Engineering Science and Computing, 6(3), 2016.

Granados-Lieberman, D, et al, “Techniques and methodologies for power quality analysis and disturbances classification in power systems: a review. ” IET Generation, Transmission & Distribution, 5(4): p. 519-529,2011.

Biswal, M. and P.K. Dash, “Measurement and classification of simultaneous power signal patterns with an S-transform variant and fuzzy decision tree. ” IEEE Transactions on Industrial Informatics, 9(4): p. 1819-1827. 2013.

Smith, J.C, G. Hensley, and L. Ray, “IEEE recommended practice for monitoring electric power quality.” IEEE Std, p. 1159-1995.1995.

Abdelsalam, A.A, A.A. Eldesouky, and A.A. Sallam, “Classification of power system disturbances using linear Kalman filter and fuzzy-expert system. ” International Journal of Electrical Power & Energy Systems, 43(1): p. 688-695. 2012.

Yang, H.-T. and C.-C. Liao, “A de-noising scheme for enhancing wavelet-based power quality monitoring system.” IEEE Transactions on Power Delivery, 16(3): p. 353-360. 2001.

Abdel-Galil, T., et al., “Disturbance classification using hidden Markov models and vector quantization.” IEEE transactions on power delivery, 20(3): p. 2129-2135. 2005.

Eristi, H. and Y. Demir, “The feature selection based power quality event classification using wavelet transform and logistic model tree. Przegląd Elektrotechniczny, 88(7a): p. 43-48. 2012.

Masoum, M., S. Jamali, and N. Ghaffarzadeh, “Detection and classification of power quality disturbances using discrete wavelet transform and wavelet networks. ” IET Science, Measurement & Technology, 4(4): p. 193-205, 2010.

Hooshmand, R. and A. Enshaee, “Detection and classification of single and combined power quality disturbances using fuzzy systems oriented by particle swarm optimization algorithm.” Electric Power Systems Research, 80(12): p. 1552-1561,2010.

Huang, J, M. Negnevitsky, and D.T. Nguyen, “A neural-fuzzy classifier for recognition of power quality disturbances.” IEEE Transactions on Power Delivery, 17(2): p. 609-616,2002.

Panigrahi, B. 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, 3(3): p. 296-306. 2009.

Zhang, M, K. Li, and Y. Hu, “Classification of power quality disturbances using wavelet packet energy entropy and LS-SVM. ” Energy and Power Engineering, 2(03): p. 154. 2010.

علی انشایی، رحمت‌اله هوشمند، "یک روش جدید برای شناسایی اغتشاشات کیفیت توان با استفاده از تبدیل S"، مجله مهندسی برق دانشگاه تبریز، جلد45، شماره4، زمستان 1394.

Huang, N., et al., Power quality disturbances classification based on S-transform and probabilistic neural network. Neurocomputing, 98: p. 12-23. 2012.

سینا نظری، سعید اسماعیلی، فرزاد کریم زاده، "شناسایی و دسته‌بندی اغتشاشات تکی و ترکیبی کیفیت توان با استفاده از روش مبتنی بر تحلیل مولفه‌های مستقل"، مجله مهندسی برق دانشگاه دانشگاه تبریز، جلد 48، شماره 1، بهار 1397.

Thirumala, K., et al., “Tunable-Q wavelet transform and dual multiclass SVM for online automatic detection of power quality disturbances.” IEEE Transactions on Smart Grid, 9(4): p. 3018-3028, 2018.

Wang, S. and H. Chen, “A novel deep learning method for the classification of power quality disturbances using deep convolutional neural network. ” Applied Energy, 235: p. 1126-1140. 2019.

Mohan, N., K. Soman, and R. Vinayakumar. "Deep power: Deep learning architectures for power quality disturbances classification." in 2017 International Conference on Technological Advancements in Power and Energy (TAP Energy) . IEEE. 2017.

Shi, X., et al., "An independent component analysis classification for complex power quality disturbances with sparse auto encoder features." IEEE Access, 7: p. 20961-20966. 2019.

Thirumala, K., et al., "A classification method for multiple power quality disturbances using EWT based adaptive filtering and multiclass SVM." Neurocomputing. 334: p. 265-274. , 2019.

Panigrahi, B.K., P.K. Dash, and J. Reddy, “Hybrid signal processing and machine intelligence techniques for detection, quantification and classification of power quality disturbances.” Engineering Applications of Artificial Intelligence,22(3): p. 442-454. 2009

Dehak, N., et al., “Front-end factor analysis for speaker verification.”  IEEE Transactions on Audio, Speech, and Language Processing, 19(4): p. 788-798. 2011

Ghahabi, O. and J. Hernando, “I-vector modeling with deep belief networks for multi-session speaker recognition. ” network, 20: p. 13. 2014.

Kinnunen, T. and H. Li, “An overview of text-independent speaker recognition: From features to supervectors.” Speech communication, 52(1): p. 12-40. 2010.

Reynolds, D.A., T.F. Quatieri, and R.B. Dunn, “Speaker verification using adapted Gaussian mixture models. ” Digital signal processing, 10(1-3): p. 19-41. 2000.

Kenny, P., et al., “A study of interspeaker variability in speaker verification.” IEEE Transactions on Audio, Speech, and Language Processing, 16(5): p. 980. 2008.

Lee, C.-Y. and Y.-X. Shen, Optimal feature selection for power-quality disturbances classification. IEEE Transactions on power delivery, 26(4): p. 2342-2351, 2011.

Zeinali, H, et al. “Telephony text-prompted speaker verification using i-vector representation.” in Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International Conference on. 2015.

Greenberg, C.S., et al. “The NIST 2014 speaker recognition i-vector machine learning challenge.” in Odyssey: The Speaker and Language Recognition Workshop. 2014.

Hatch, A.O., S. Kajarekar, and A. Stolcke. “Within-class covariance normalization for SVM-based speaker recognition.” in Ninth international conference on spoken language processing. 2006.

Solomonoff, A., W.M. Campbell, and I. Boardman. “Advances in channel compensation for SVM speaker recognition. in Acoustics, Speech, and Signal Processing,” 2005. Proceedings.(ICASSP'05). IEEE International Conference on. 2005. IEEE

Reynolds, D.A., “Automatic speaker recognition: Current approaches and future trends.” Speaker Verification: From Research to Reality, 5: p. 14-15. 2001.

Krishnan, M.H. and R. Viswanathan, “A new concept of reduction of Gaussian noise in images based on fuzzy logic.” Applied Mathematical Sciences, 7(12): p. 595-602. 2013.

Maaten, L.v.d. and G. Hinton, “Visualizing data using t-SNE. ” Journal of machine learning research, 9(Nov): p. 2579-2605. 2008.

Moravej, Z., A. Abdoos, and M. Pazoki, “Detection and classification of power quality disturbances using wavelet transform and support vector machines. ” Electric Power Components and Systems, 38(2): p. 182-196. 2009.

Uyar, M., 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, 36(3): p. 5962-5975. 2009.

Qiu, W., et al., "Power Quality Disturbances Recognition Using Modified S Transform and Parallel Stack Sparse Auto-encoder." Electric Power Systems Research, 174: p. 105876. 2019.

Jeba Singh, O., et al., "Robust detection of real-time power quality disturbances under noisy condition using FTDD features." Automatika, 60(1): p. 11-18. 2019.