Araştırma Makalesi
BibTex RIS Kaynak Göster
Yıl 2021, Cilt: 5 Sayı: 1, 1 - 6, 31.03.2021

Öz

Kaynakça

  • [1] 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, Part 2, pp. 5962 – 5975, 2009.
  • [2] B. Biswal, H. Behera, R. Bisoi, and P. Dash, “Classification of power quality data using decision tree and chemotactic differential evolution based fuzzy clustering,” Swarm and Evolutionary Computation, vol. 4, pp. 12 – 24, 2012.
  • [3] M. Masoum, S. Jamali, and N. Ghaffarzadeh, “Detection and classification of power quality disturbances using discrete wavelet transform and wavelet networks,” Science, Measurement Technology, IET, vol. 4, no. 4, pp. 193–205, July 2010.
  • [4] O. P. Mahela, A. G. Shaik, and N. Gupta, “A critical review of detection and classification of power quality events,” Renewable and Sustainable Energy Reviews, vol. 41, pp. 495 – 505, 2015. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S1364032114007564
  • [5] O. P. Mahela, A. G. Shaik, N. Gupta, A critical review of detection and classi_cation of power quality events, Renewable and Sustainable Energy Reviews 41 (2015) 495 { 505. doi:http://dx.doi.org/10.1016/j.rser. 2014.08.070.
  • [6] D. Granados-Lieberman, R. J. Romero-Troncoso, R. A. Osornio-Rios, Garcia-Perez, E. Cabal-Yepez, Techniques and methodologies for power quality analysis and disturbances classification in power systems: a review, IET Generation, Transmission Distribution 5 (4) (2011) 519{529. doi:10.1049/iet-gtd.2010.0466.
  • [7] P. Thakur, A. K. Singh, Signal processing and ai based diagnosis of power quality disturbances: A review, in: Energy Economics and Environment (ICEEE), 2015 International Conference on, 2015, pp. 1{6. doi:10.1109/ EnergyEconomics.2015.7235071.
  • [8] S. Santoso, E. Powers, W. Grady, and P. Hofmann, “Power quality assessment via wavelet transform analysis,” Power Delivery, IEEE Transactions on, vol. 11, no. 2, pp. 924–930, Apr 1996.
  • [9] Singh, D. Shahani, A. Chandra, and K. Al-Haddad, “Recognition of power-quality disturbances using s-transform-based ANN classifier and rule-based decision tree,” Industry Applications, IEEE Transactions on, vol. 51, no. 2, pp. 1249–1258, March 2015
  • [10] R. Kumar, B. Singh, D. T. Shahani, A. Chandra, K. Al-Haddad, Recognition of power-quality disturbances using s-transform-based ANN classifier and rule-based decision tree, Industry Applications, IEEE Transactions on 51 (2) (2015) 1249{1258. doi:10.1109/TIA.2014.2356639.
  • [11] P. Kanirajan, V. S. Kumar, Power quality disturbance detection and classification using wavelet and RBFNN, Applied Soft Computing 35(2015)470-481. doi:http://dx.doi.org/10.1016/j.asoc.2015.05.048.
  • [12] H. Eristi, O. Yildirim, B. Eristi, Y. Demir, Automatic recognition system of underlying causes of power quality disturbances based on s-transform and extreme learning machine, International Journal of Electrical Power and Energy Systems 61 (2014) 553 { 562. doi:http://dx.doi.org/10. 1016/j.ijepes.2014.04.010.
  • [13] B. Panigrahi, P. 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, vol. 22, no. 3, pp. 442 – 454, 2009. [Online]. Available:http://www.sciencedirect.com/science/article/pii/S0952197608001481
  • [14] A. K. Ghosh, D. L. Lubkeman, The classi_cation of power system disturbance waveforms using a neural network approach, Power Delivery, IEEETransactions on 10 (1) (1995) 109{115. doi:10.1109/61.368408.
  • [15] M. Valtierra-Rodriguez, R. de Jesus Romero-Troncoso, R. A. Osornio- Rios, A. Garcia-Perez, Detection and classi_cation of single and combined power quality disturbances using neural networks, IEEE Transactions on Industrial Electronics 61 (5) (2014) 2473{2482. doi:10.1109/TIE.2013. 2272276.
  • [16] Y.-L. Chen, H.-W. Hu, and K. Tang, “Constructing a decision tree from data with hierarchical class labels,” Expert Systems with Applications, vol. 36, no. 3, Part 1, pp. 4838 – 4847, 2009. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S095741740800273X.
  • [17] Y.-L. Chen, H.-W. Hu, K. Tang, Constructing a decision tree from data with hierarchical class labels, Expert Systems with Applications 36 (3, Part I 2009) 4838- 4847. doi:http://dx.doi.org/10.1016/j.eswa.2008.05.044.
  • [18] A. Rodriguez, J. A. Aguado, F. Martin, J. J. Lopez, F. Munoz, J. E. Ruiz,675 Rule-based classi_cation of power quality disturbances using s-transform, Electric Power Systems Research 86 (2012) 113 { 121. doi:http://dx.doi.org/10.1016/j.epsr.2011.12.009.
  • [19] P. K. Ray, N. Kishor, S. R. Mohanty, Islanding and power quality disturbance detection in grid-connected hybrid power system using wavelet and s -transform, Smart Grid, IEEE Transactions on 3 (3) (2012) 1082{1094. doi:10.1109/TSG.2012.2197642.
  • [20] S. Ventosa, C. Simon, M. Schimmel, J. J. Danobeitia, A. Manuel, The s-transform from a wavelet point of view, Signal Processing, IEEE Transactions on 56 (7) (2008) 2771{2780. doi:10.1109/TSP.2008.917029.
  • [21] S. Ventosa, C. Simon, M. Schimmel, J. J. Danobeitia, A. Manuel, The s-transform from a wavelet point of view, Signal Processing, IEEE Transactions on 56 (7) (2008) 2771{2780. doi:10.1109/TSP.2008.917029.
  • [22] R. H. G. Tan and V. K. Ramachandaramurthy, “Numerical model framework of power quality events,” European Journal of Scientific Research, vol. 43, no. 1, pp. 30–47, June 2010.

Power Quality Disturbances Detection and Classification Rule-Based Decision Tree

Yıl 2021, Cilt: 5 Sayı: 1, 1 - 6, 31.03.2021

Öz

In this paper, the power quality (PQ) disturbances have been detected and classified using Stockwell’s transform (S-transform) and rule-based decision tree (DT) according to IEEE standards. The proposed technique based on the extracted features of the PQ events signals, which are extracted from the time-frequency analysis. Several PQ disturbances are considered with simple and complex disturbances to include spike, flicker, oscillatory transient, impulsive transient, and notch. The performance and robustness of the proposed technique for the recognition of PQ disturbances have been demonstrated through the results of the various disturbances. By comparing the performance of the proposed technique with other reported studies it was distinguished results under noiseless and noisy conditions.

Kaynakça

  • [1] 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, Part 2, pp. 5962 – 5975, 2009.
  • [2] B. Biswal, H. Behera, R. Bisoi, and P. Dash, “Classification of power quality data using decision tree and chemotactic differential evolution based fuzzy clustering,” Swarm and Evolutionary Computation, vol. 4, pp. 12 – 24, 2012.
  • [3] M. Masoum, S. Jamali, and N. Ghaffarzadeh, “Detection and classification of power quality disturbances using discrete wavelet transform and wavelet networks,” Science, Measurement Technology, IET, vol. 4, no. 4, pp. 193–205, July 2010.
  • [4] O. P. Mahela, A. G. Shaik, and N. Gupta, “A critical review of detection and classification of power quality events,” Renewable and Sustainable Energy Reviews, vol. 41, pp. 495 – 505, 2015. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S1364032114007564
  • [5] O. P. Mahela, A. G. Shaik, N. Gupta, A critical review of detection and classi_cation of power quality events, Renewable and Sustainable Energy Reviews 41 (2015) 495 { 505. doi:http://dx.doi.org/10.1016/j.rser. 2014.08.070.
  • [6] D. Granados-Lieberman, R. J. Romero-Troncoso, R. A. Osornio-Rios, Garcia-Perez, E. Cabal-Yepez, Techniques and methodologies for power quality analysis and disturbances classification in power systems: a review, IET Generation, Transmission Distribution 5 (4) (2011) 519{529. doi:10.1049/iet-gtd.2010.0466.
  • [7] P. Thakur, A. K. Singh, Signal processing and ai based diagnosis of power quality disturbances: A review, in: Energy Economics and Environment (ICEEE), 2015 International Conference on, 2015, pp. 1{6. doi:10.1109/ EnergyEconomics.2015.7235071.
  • [8] S. Santoso, E. Powers, W. Grady, and P. Hofmann, “Power quality assessment via wavelet transform analysis,” Power Delivery, IEEE Transactions on, vol. 11, no. 2, pp. 924–930, Apr 1996.
  • [9] Singh, D. Shahani, A. Chandra, and K. Al-Haddad, “Recognition of power-quality disturbances using s-transform-based ANN classifier and rule-based decision tree,” Industry Applications, IEEE Transactions on, vol. 51, no. 2, pp. 1249–1258, March 2015
  • [10] R. Kumar, B. Singh, D. T. Shahani, A. Chandra, K. Al-Haddad, Recognition of power-quality disturbances using s-transform-based ANN classifier and rule-based decision tree, Industry Applications, IEEE Transactions on 51 (2) (2015) 1249{1258. doi:10.1109/TIA.2014.2356639.
  • [11] P. Kanirajan, V. S. Kumar, Power quality disturbance detection and classification using wavelet and RBFNN, Applied Soft Computing 35(2015)470-481. doi:http://dx.doi.org/10.1016/j.asoc.2015.05.048.
  • [12] H. Eristi, O. Yildirim, B. Eristi, Y. Demir, Automatic recognition system of underlying causes of power quality disturbances based on s-transform and extreme learning machine, International Journal of Electrical Power and Energy Systems 61 (2014) 553 { 562. doi:http://dx.doi.org/10. 1016/j.ijepes.2014.04.010.
  • [13] B. Panigrahi, P. 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, vol. 22, no. 3, pp. 442 – 454, 2009. [Online]. Available:http://www.sciencedirect.com/science/article/pii/S0952197608001481
  • [14] A. K. Ghosh, D. L. Lubkeman, The classi_cation of power system disturbance waveforms using a neural network approach, Power Delivery, IEEETransactions on 10 (1) (1995) 109{115. doi:10.1109/61.368408.
  • [15] M. Valtierra-Rodriguez, R. de Jesus Romero-Troncoso, R. A. Osornio- Rios, A. Garcia-Perez, Detection and classi_cation of single and combined power quality disturbances using neural networks, IEEE Transactions on Industrial Electronics 61 (5) (2014) 2473{2482. doi:10.1109/TIE.2013. 2272276.
  • [16] Y.-L. Chen, H.-W. Hu, and K. Tang, “Constructing a decision tree from data with hierarchical class labels,” Expert Systems with Applications, vol. 36, no. 3, Part 1, pp. 4838 – 4847, 2009. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S095741740800273X.
  • [17] Y.-L. Chen, H.-W. Hu, K. Tang, Constructing a decision tree from data with hierarchical class labels, Expert Systems with Applications 36 (3, Part I 2009) 4838- 4847. doi:http://dx.doi.org/10.1016/j.eswa.2008.05.044.
  • [18] A. Rodriguez, J. A. Aguado, F. Martin, J. J. Lopez, F. Munoz, J. E. Ruiz,675 Rule-based classi_cation of power quality disturbances using s-transform, Electric Power Systems Research 86 (2012) 113 { 121. doi:http://dx.doi.org/10.1016/j.epsr.2011.12.009.
  • [19] P. K. Ray, N. Kishor, S. R. Mohanty, Islanding and power quality disturbance detection in grid-connected hybrid power system using wavelet and s -transform, Smart Grid, IEEE Transactions on 3 (3) (2012) 1082{1094. doi:10.1109/TSG.2012.2197642.
  • [20] S. Ventosa, C. Simon, M. Schimmel, J. J. Danobeitia, A. Manuel, The s-transform from a wavelet point of view, Signal Processing, IEEE Transactions on 56 (7) (2008) 2771{2780. doi:10.1109/TSP.2008.917029.
  • [21] S. Ventosa, C. Simon, M. Schimmel, J. J. Danobeitia, A. Manuel, The s-transform from a wavelet point of view, Signal Processing, IEEE Transactions on 56 (7) (2008) 2771{2780. doi:10.1109/TSP.2008.917029.
  • [22] R. H. G. Tan and V. K. Ramachandaramurthy, “Numerical model framework of power quality events,” European Journal of Scientific Research, vol. 43, no. 1, pp. 30–47, June 2010.
Toplam 22 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Mühendislik
Bölüm Articles
Yazarlar

Fouad Zaro 0000-0003-3107-0661

Yayımlanma Tarihi 31 Mart 2021
Yayımlandığı Sayı Yıl 2021 Cilt: 5 Sayı: 1

Kaynak Göster

IEEE F. Zaro, “Power Quality Disturbances Detection and Classification Rule-Based Decision Tree”, IJESA, c. 5, sy. 1, ss. 1–6, 2021.

ISSN 2548-1185
e-ISSN 2587-2176
Period: Quarterly
Founded: 2016
Publisher: Nisantasi University
e-mail:ilhcol@gmail.com