Browse > Article
http://dx.doi.org/10.5370/JEET.2016.11.2.309

Recognition and Classification of Power Quality Disturbances on the basis of Pattern Linguistic Values  

Liu, XiaoSheng (Dept. of Electrical Engineering, Harbin Institute of Technology)
Liu, Bo (Dept. of Electrical Engineering, Harbin Institute of Technology)
Xu, DianGuo (Dept. of Electrical Engineering, Harbin Institute of Technology)
Publication Information
Journal of Electrical Engineering and Technology / v.11, no.2, 2016 , pp. 309-319 More about this Journal
Abstract
This paper presents a new recognition and classification method for power quality (PQ) disturbances on the basis of pattern linguistic values. This method solves the difficulty of recognizing disturbances rapidly and accurately by using fuzzy logic. This method uses classification disturbance patterns to define the linguistic values of fuzzy input variables and used the input variables of corresponding disturbance pattern to set membership functions. This method also sets the fuzzy rules by analyzing the distribution regularities of the input variable values. One characteristic of this method is that the linguistic values of fuzzy input variables and the setting of membership functions are not only related to the input variables but also to the character of classification disturbance and the classification results. Furthermore, the number of fuzzy rules is equal to the number of disturbance patterns. By using this method for disturbance classification, the membership function and design of fuzzy rules are directly related to the objective of classification, thus effectively reducing the complexity of the design process and yielding accurate classification results. The classification results of the simulation and measured data verify the feasibility and effectiveness of this method.
Keywords
Power quality; Disturbances classification; Fuzzy logic; S-transform;
Citations & Related Records
연도 인용수 순위
  • Reference
1 F. Z. Zhao, R. G. Yang, “Voltage sag disturbance detection based on short-time Fourier transform,” Proceedings of the CSEE, vol. 27, no. 10, pp. 28-34, 109, Apr. 2007.
2 T. X. Zhu, S. K. Tso, and K. L. Lo, “Wavelet-based fuzzy reasoning approach to PQ disturbance recognition,” IEEE Trans. Power Deliv., vol. 19, no. 4, pp. 1928-1935, Oct. 2004.   DOI
3 R. G. Stockwell, L. Mansinha, and R. P. Lowe, “Localization of the complex spectrum: the S-transform,” IEEE Transactions on Signal Processing, vol. 44, no. 4, pp. 998-1001, Apr. 1996.   DOI
4 M. S. Manikandan, S. R. Samantaray, I. Kamwa, et.al, “Detection and classification of power quality disturbances using sparse signal decomposition on hybrid dictionaries,” IEEE Transactions on Instrumentation and Measurement, vol. 64, no. 1, pp. 594-605, Jan. 2015.
5 S. Kalyani and K.S. Swarup, “Classification and assessment of power system security using multiclass SVM,” IEEE Trans. Syst. Man and Cybernetics Part C-Applications and Reviews, vol. 41, no. 5, pp. 753-758, Sep. 2011.   DOI
6 K. Manimala, K. Selvi, and R. Ahila, “Optimization techniques for improving power quality data mining using wavelet packet based support vector machine,” Neurocomputing, vol. 77, no. 1, pp. 36-47, Feb. 2012.   DOI
7 H. Eristi and Y. Demir, “Automatic classification of power quality events and disturbances using wavelet transform and support vector machines,” IET Gener. Transm. Distrib., vol. 6, no. 10, pp. 986-976, 2012.   DOI
8 N. Thai and L. Yuan, “Power quality disturbance classification utilizing S-transform and binary feature matrix method,” Electric Power Systems Research, vol. 79, pp. 569-575, Apr. 2012.
9 T. K. Abdel-Galil, E. F. El-saadany, and A. M. Youssef, et.al, “Disturbance classification using hidden Markov models and vector quantization,” IEEE Trans. Power Deliv., vol. 20, no. 3, pp. 2129-2135, Jul. 2005.   DOI
10 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. Power Deliv., vol. 23, no. 1, pp. 280-287, Jan. 2008.   DOI
11 O. Mário, V. C. Denis, and D. F. Odilon, et.al, “Power quality analysis applying a hybrid methodology with wavelet transforms and neural networks,” Electrical Power and Energy Systems, vol. 31, no.5, pp. 206-212, Jun. 2009.   DOI
12 K. Raj, S. Bhim, and T. S. Dilip, “Recognition of single-stage and multiple power quality events using Hilbert-Huang transform and probabilistic neural network,” Electrical Power and Energy Systems, vol. 43, no. 6, pp. 607-619, Apr. 2015.
13 A. A. Abdelsalam, A. A. Eldesouky, and A. A. Sallam, “Classification of power system disturbances using linear Kalman filter and fuzzy-expert system,” Int. Jour. Elec. Power, vol. 43, no. 1, pp. 688-695, Dec. 2012.   DOI
14 A. N. Chirag and K. Prasanta, “Power quality disturbance classification employing S-transform and three-module artificial neural network,” Int. Trans. Electr. Energ. Syst. vol. 24, no. 9, pp. 1301-1322, Sep. 2014.   DOI
15 B.K. Panigrahi and V.R. Pandi, “Optimal feature selection for classification of power quality disturbances using wavelet packet-based fuzzy k-nearset neighbour algorithm,” IET Gener. Transm. Distrib., vol. 3, no. 3, pp. 296-306, Mar. 2009.   DOI
16 S. R. Samantaray, “Decision tree-initialised fuzzy rule-based approach for power quality events classification,” IET Gener. Transm. Distrib., vol. 4, no. 4, pp. 538-551, Apr. 2010.   DOI
17 A. Rodriguez, J. A. Aguado, and F. Martin, “Rule-based classification of power quality disturbances using S-transform,” Electric Power Systems Research, vol. 86, pp. 113-121, May 2012.   DOI
18 D. Granados-Lieberman, R.J. Romero-Troncoso, and R.A. Osornio-Rios, et.al, “Techniques and methodologies for power quality analysis and disturbances classification in power systems: a review,” IET Gener. Transm. Distrib., vol. 5, no. 4, pp. 519-529, Apr. 2011.   DOI
19 K. Rajiv and G. Rashmi, “Fuzzy lattice based technique for classification of power quality disturbances,” Eur Trans. Electr. Power, vol. 22, no. 8, pp. 1053-1064, Nov. 2012.   DOI
20 K. S. Yap, C. P. Lim, and M. T. Au, “Improved GART neural network model for pattern classification and rule extraction with application to power systems,” IEEE Trans. Neural Networ., vol.22, no.12, pp. 2310-2323, Dec. 2011.   DOI
21 M. G. Ameen, E. Nesimi, L. S. Wen, “Automatic classification and characterization of power quality events,” IEEE Trans. Power Deliv., vol. 23, no. 4, pp. 2417-2425, Oct. 2008.   DOI
22 S. K. Meher and A. K. Pradhan, “Fuzzy classifiers for power quality events analysis,” Electr. Pow. Syst. Res., vol. 80, no. 1, pp. 71-76, Jan. 2010.   DOI
23 H.S. Behera, P.K. Dashb, and B. Biswal, “Power quality time series data mining using S-transform and fuzzy expert system,” Appl. Soft Comput., vol. 10, no. 3, pp . 945-955, Jun. 2010.   DOI
24 S. A. Deokar, L. M. Waghmare, “Integrated DWT-FFT approach for detection and classification of power quality disturbances,” Electrical Power and Energy Systems, vol. 61, pp. 594-605, Oct. 2014.   DOI
25 M. Uyar, S. Yildirim, M. T. Gencoglu, “An expert system based on s-transform and neural network for automatic classification of power quality disturbances,” Expert Syst. Appl., vol. 36, no. 3, pp. 5962-5975, Apr. 2009.   DOI