Data Mining Based Power Quality Disturbance Detection Using Wavelet Transform
-
2018-11-27 
https://doi.org/10.14419/ijet.v7i4.19.27956
-
Power quality, oscillatory transients, voltage swell, voltage sag, Parseval's theorem, wavelet transform and Decision Tree(DT). -
Abstract
This paper presents the identification and classification of power quality disturbances using wavelet transform and data mining models. The various power quality events like oscillatory transient, voltage swell and voltage sag are simulated for a small distribution network using MATLAB/SIMULINK. The simulated events are then processed by the wavelet transform with Daubechies Db5 mother wavelet and wavelet coefficients are generated. The energy, standard deviation, mean, skewness, kurtosis and variance are obtained for these wavelet coefficients for the purpose of distinguishing the events. A data mining based Random Forest model and Decision Tree model are then generated using these features of voltage signal and these models are used for final classification into sag, swell and transients. The test result shows that the wavelet algorithm along with decision tree is more sharp in identifying events.
Â
Â
-
References
[1] P.Kanirajan, V.Suresh Kumar, "Power quality disturbance detection and classification using wavelet and RBFNN", Applied Soft Computing, Volume 35, October 2015, Pages 470-481.
[2] SafdarRaza, HazlieMokhlis, HamzahArof, J.A.Laghari, LiWang, "Application of signal processing techniques for islanding detection of distributed generation in distribution network: A review" Energy Conversion and Management", Volume 96, 15 May 2015, Pages 613-624.
[3] Raj Kumar, Bhim Singh, D.T. Shahani, Ambrish Chandra, Kamal Al-Haddad, " Recognition of Power- Quality Disturbances Using S-Transform-Based ANN Classifier and Rule-Based Decision Tree", IEEE Transactions on Industry Applications, Volume 51(2), 2015 , pp.1249-1258.[4] Mohammad BarghiLatran, AhmetTeke, "A novel wavelet transform based voltage sag/swell detection algorithm", International Journal of Electrical Power & Energy Systems, Volume 71, October 2015, Pages 131-139
[5] Naik C, Hafiz Faizal ; Swain Akshya, Kar AK, "Classification of power quality events using wavelet packet transform and extreme learning machine", IEEE 2nd Annual Southern Power Electronics Conference (SPEC). IEEE, New York, USA, 2016. DOI:10.1109/SPEC.2016.7846169.
[6] ZhengyouHe, ShibinGao, XiaoqinChen, JunZhang, ZhiqianBo, QingquanQian, "Study of a new method for power system transients classification based on wavelet entropy and neural network", International Journal of Electrical Power & Energy Systems, Volume 33, Issue 3, March 2011, pp. 402-410.[7] Samir Avdakovi, Nejra ÄŒiÅ¡ija, "Wavelets as a tool for power system dynamic events analysis – State-of-the-art and future applications", Journal of Electrical Systems and Information Technology, Volume 2(1), May 2015, pp. 47-57.[8] Abdelazeem A. Abdelsalam,Azza A. Eldesouky, Abdelhay A. Sallam, “Characterization of power quality disturbances using hybrid technique of linear Kalman filter and fuzzy-expert systemâ€, Electric Power Systems Research, vol.83,No.1,Feb.2012,pp.41-50.
[9] Prakash K.Ray, Soumya R.Mohanty, NandKishor, " Disturbance detection in grid-connected distributed generation system using wavelet and S-transform", Electric Power Systems Research, Volume 81, Issue 3, March 2011, Pages 805-819.[10] M.Saimurugan, K.I.Ramachandran, V.Sugumaran, N.R.Sakthivel, "Multi component fault diagnosis of rotational mechanical system based on decision tree and support vector machine", Expert Systems with Applications, Volume 38, Issue 4, April 2011, Pages 3819-3826.[11] ZhangYang, LiCe, LiLian, " Electricity price forecasting by a hybrid model, combining wavelet transform, ARMA and kernel-based extreme learning machine methods", Applied Energy, Volume 190, March 2017, pp. 291-305.[12] RuqiangYan, Robert X.Gao, XuefengChen, "Wavelets for fault diagnosis of rotary machines: A review with applications", Signal Processing, Volume 96, Part A, March 2014, Pages 1-15.
[13] S.Sendilkumar, B.L.Mathur, Joseph Henry, "Differential protection of power transformer using wavelet transform and PNN", International Journal of Electrical and Electronics Engineering, vol.4, No.7, 2010, pp.468-474.
[14] M. Amarnath, V. Sugumaran, Hemantha Kumar, "Exploiting sound signals for fault diagnosis of bearings using decision tree", Measurement, 2013, Volume 46, 1250–1256.
[15] Mishra DP, Samantaray SR, Zadeh MD, "A combined wavelet and data-mining based intelligent protection scheme for microgrid", IEEE Transaction on Smart Grid, 2016, Volume 7(5), 2295-304.
[16] KavaskarSekar, Nalin KantMohanty, "Data mining-based high impedance fault detection using mathematical morphology", Computers & Electrical Engineering, July 2018, Volume 69, Pages 129-141.
-
Downloads
-
How to Cite
Hariramakrishnan, P., & Sendilkumar, S. (2018). Data Mining Based Power Quality Disturbance Detection Using Wavelet Transform. International Journal of Engineering & Technology, 7(4.19), 550-555. https://doi.org/10.14419/ijet.v7i4.19.27956Received date: 2019-02-26
Accepted date: 2019-02-26
Published date: 2018-11-27