Abstract
In this paper, the lifting based second generation Wavelet Transform (SGWT) is implemented along with the widely used discrete Wavelet Transform (DWT) for the detection and localization of ten different types of power quality (PQ) disturbance signals. The SGWT provides the time domain interpretation which is an opposition to the frequency domain analysis of DWT. Further, the selected features are extracted from the detail coefficient of the variants of WT and given as inputs to the classifiers in order to characterize the signals. Moreover, a comparative assessment of the PQ signal carried out with different classifiers such as Support Vector Machine (SVM), Decision Tree (DT) and Random Forest (RF) have been presented along with aforementioned detection techniques. The RF is an ensemble decision tree and used for the classification of large number of data set. Hence, various single as well as combined power quality disturbance signals have been simulated in noisy and noise free environment in order to demonstrate the efficiency of the proposed techniques. Moreover, in order to represent in realistic environment, these proposed techniques are tested with both the single phase and tree phase signals captured from different transmission panels. Further, to aid this PQ disturbance detection, different types of real time fault signals are characterized with these aforementioned approaches.
References
[1] Gaouda A, Salama M, Sultan M, Chikhani A. Power quality detection and classification using wavelet-multiresolution signal decomposition. IEEE Trans Power Delivery. 1999;14:1469–1476.10.1109/61.796242Search in Google Scholar
[2] Angrisani L, Daponte P, D’apuzzo M, Testa A. A measurement method based on the wavelet transform for power quality analysis. IEEE Trans Power Delivery. 1998;13:990–998.10.1109/61.714415Search in Google Scholar
[3] Douglas J. Solving problems of power quality. EPRI J (Electr Power Res Institute);(United States) 1993;18.Search in Google Scholar
[4] Gu YH, Bollen MH. Time-frequency and time-scale domain analysis of voltage disturbances. IEEE Trans Power Delivery. 2000;15:1279–1284.10.1109/61.891515Search in Google Scholar
[5] Heydt G, Fjeld P, Liu C, Pierce D, Tu L, Hensley G. Applications of the windowed fft to electric power quality assessment. IEEE Trans Power Delivery. 1999;14:1411–1416.10.1109/61.796235Search in Google Scholar
[6] Xie H, Lin J, Lei Y, Liao Y. Fast-varying am–fm components extraction based on an adaptive stft. Digital Signal Processing. 2012;22:664–670.10.1016/j.dsp.2012.02.007Search in Google Scholar
[7] Gabor D. Theory of communication. part 1: The analysis of information. J Instit Electr Eng-Part III Radio Commun Eng. 1946:429–441. 93.10.1049/ji-3-2.1946.0074Search in Google Scholar
[8] Santoso S, Powers EJ, Grady W. Power quality disturbance data compression using wavelet transform methods. IEEE Trans Power Delivery. 1997;12:1250–1257.10.1109/61.637001Search in Google Scholar
[9] Kim CH, Aggarwal R. Wavelet transforms in power systems. i. general introduction to the wavelet transforms. Power Eng J. 2000;14:81–87.10.1049/pe:20000210Search in Google Scholar
[10] Grossmann A, Morlet J. Decomposition of hardy functions into square integrable wavelets of constant shape. SIAM journal on mathematical analysis. 1984;15:723–736.10.1137/0515056Search in Google Scholar
[11] Santoso S, Powers EJ, Grady WM, Hofmann P. Power quality assessment via wavelet transform analysis. IEEE Trans Power Delivery. 1996;11:924–930.10.1109/61.489353Search in Google Scholar
[12] Zhou X, Zhou C, Kemp I. An improved methodology for application of wavelet transform to partial discharge measurement denoising. IEEE Trans Dielectr Electr Insul. 2005;12:586–594.10.1109/TDEI.2005.1453464Search in Google Scholar
[13] Polikar R. The engineers ultimate guide to wavelet analysis. College of Engineering, retrieved in: Rowan University, 2006.Search in Google Scholar
[14] Biswal B, Mishra S. Power signal disturbance identification and classification using a modified frequency slice wavelet transform. IET Gener Transm Distrib. 2014;8:353–362.10.1049/iet-gtd.2013.0171Search in Google Scholar
[15] Brown RA, Frayne R. A fast discrete s-transform for biomedical signal processing. In: 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 2008; EMBS 2008, 2586–2589 IEEE. 2008.10.1109/IEMBS.2008.4649729Search in Google Scholar PubMed
[16] Mishra S, Bhende C, Panigrahi K. Detection and classification of power quality disturbances using s-transform and probabilistic neural network. IEEE Trans Power Delivery. 2008;23:280–287.10.1109/TPWRD.2007.911125Search in Google Scholar
[17] Manjunath A, Ravikumar H. Comparison of discrete wavelet transform (dwt), lifting wavelet transform (lwt) stationary wavelet transform (swt) and s-transform in power quality analysis. Eur. J. Sci. Res. 2010;39:569–576.Search in Google Scholar
[18] Daubechies I. Orthonormal bases of compactly supported wavelets. Commun Pure Appl Math. 1988;41:909–996.10.1002/cpa.3160410705Search in Google Scholar
[19] Daubechies I, Sweldens W. Factoring wavelet transforms into lifting steps. J Fourier Anal Appl. 1998;4:247–269.10.1007/BF02476026Search in Google Scholar
[20] Yilmaz AS, Subasi A, Bayrak M, Karsli VM, Ercelebi E. Application of lifting based wavelet transforms to characterize power quality events. Energy Convers Manage. 2007;48:112–123.10.1016/j.enconman.2006.05.003Search in Google Scholar
[21] Upadhyaya S, Mohanty S. Power quality disturbance detection using wavelet based signal processing. In: 2013 Annual IEEE India Conference (INDICON). 1–6 IEEE. 2013.10.1109/INDCON.2013.6725992Search in Google Scholar
[22] Lee C-Y, Shen Y-X. Optimal feature selection for power-quality disturbances classification. IEEE Trans Power Delivery. 2011;26:2342–2351.10.1109/TPWRD.2011.2149547Search in Google Scholar
[23] Panigrahi B, Pandi VR. Optimal feature selection for classification of power quality disturbances using wavelet packet-based fuzzy k-nearest neighbour algorithm. IET Gener. Transm. Distrib. 2009;3:296–306.10.1049/iet-gtd:20080190Search in Google Scholar
[24] Ghosh AK, Lubkeman DL. The classification of power system disturbance waveforms using a neural network approach. IEEE Trans Power Delivery. 1995;10:109–115.10.1109/61.368408Search in Google Scholar
[25] Hasheminejad S, Esmaeili S, Jazebi S. Power quality disturbance classification using s-transform and hidden markov model. Electr Power Compon Syst. 2012;40:1160–1182.10.1080/15325008.2012.682250Search in Google Scholar
[26] Reaz MB, Choong F, Sulaiman MS, Mohd-Yasin F, Kamada M. Expert system for power quality disturbance classifier. IEEE Trans Power Delivery. 2007;22:1979–1988.10.1109/TPWRD.2007.899774Search in Google Scholar
[27] Mishra S, Bhende C, Panigrahi K. Detection and classification of power quality disturbances using s-transform and probabilistic neural network. IEEE Trans Power Delivery. 2008;23:280–287.10.1109/TPWRD.2007.911125Search in Google Scholar
[28] Cortez P. Data mining with neural networks and support vector machines using the r/rminer tool. In: Advances in data mining. Applications and theoretical aspects, 572–583. Springer, 2010.10.1007/978-3-642-14400-4_44Search in Google Scholar
[29] Erişti H, Uçar A, Demir Y. Wavelet-based feature extraction and selection for classification of power system disturbances using support vector machines. Electr Power Syst Res. 2010;80:743–752.10.1016/j.epsr.2009.09.021Search in Google Scholar
[30] Mwitondi KS. Data mining with rattle and r. J Appl Stat. 2013;40:464–464.10.1080/02664763.2012.749050Search in Google Scholar
[31] Biswal M, Dash PK. Detection and characterization of multiple power quality disturbances with a fast s-transform and decision tree based classifier. Digital Signal Processing. 2013;23:1071–1083.10.1016/j.dsp.2013.02.012Search in Google Scholar
[32] Williams GJ. Rattle: a data mining gui for r. The R J. 2009;1:45–55.10.32614/RJ-2009-016Search in Google Scholar
[33] Breiman L. Random forests. Mach Learn. 2001;45:5–32.10.1023/A:1010933404324Search in Google Scholar
[34] Samantaray S, Kamwa I, Joos G. Ensemble decision trees for phasor measurement unit-based wide-area security assessment in the operations time frame. IET Gener Transm Distrib. 2010;4:1334–1348.10.1049/iet-gtd.2010.0201Search in Google Scholar
[35] Zhu T, Tso S, Lo K. Wavelet-based fuzzy reasoning approach to power-quality disturbance recognition. IEEE Trans Power Delivery. 2004;19:1928–1935.10.1109/TPWRD.2004.832382Search in Google Scholar
[36] Biswal M, Dash PK. Measurement and classification of simultaneous power signal patterns with an s-transform variant and fuzzy decision tree. IEEE Trans Ind Inf. 2013;9:1819–1827.10.1109/TII.2012.2210230Search in Google Scholar
[37] Mohammadi M, Gharehpetian G. Application of core vector machines for on-line voltage security assessment using a decision-tree-based feature selection algorithm. IET Gener Transm Distrib. 2009;3:701–712.10.1049/iet-gtd.2008.0374Search in Google Scholar
[38] Thirumala K, Umarikar AC, Jain T. A generalized empirical wavelet transform for classification of power quality disturbances. IEEE International Conference on Power System Technology (POWERCON). Wollongong: NSW, 2016; 1–5.10.1109/POWERCON.2016.7754026Search in Google Scholar
[39] Kumar R, Singh B, Shahani D, Chandra A, Al-Haddad K. Recognition of power-quality disturbances using s-transform-based ann classifier and rule-based decision tree. IEEE Trans Ind Appl. 2015;51:1249–1258.10.1109/TIA.2014.2356639Search in Google Scholar
[40] Jansen MH, Oonincx PJ. Second generation wavelets and applications. Springer Science & Business Media 2005.Search in Google Scholar
[41] Ebadi L, Helmi Z, Shafri M, Mansor SB, Ashurov R. A review of applying second-generation wavelets for noise removal from remote sensing data. Environ Earth Sci. 2013;70:2679.10.1007/s12665-013-2325-zSearch in Google Scholar
[42] Zhou X, Zhou C, Kemp I. An improved methodology for application of wavelet transform to partial discharge measurement denoising. IEEE Trans Dielectr Electr Insul. 2005;12:586–594.10.1109/TDEI.2005.1453464Search in Google Scholar
© 2017 Walter de Gruyter GmbH, Berlin/Boston
Articles in the same Issue
- Weak Bus-Oriented Installation of Phasor Measurement Unit for Power Network Observability
- Frequency Based Risk Assessment of a Power Producer in Indian Electricity Market
- Fast Methods for Power Quality Analysis
- Modified Teaching-Learning-Based Optimization for Combined Heat and Power Economic Dispatch
- Frequency Control Method using Automated Demand Response for Isolated Power System with Renewable Energy Sources
- Experimental İnvestigation on a Prototype Solar-Wind Hybrid System with a Pico Hydro Turbine
- Carrier Rotation Strategies for Equal Power Distributions in Cascaded H-Bridge Multilevel Inverters
- Transient Stability Improvement of a System Connected with Wind Energy Generators
- A Novel Bearing Fault Diagnosis Method Based on LMD and Wavelet Packet Energy Entropy
- Distributed Generation Units as Ancillary Services Providers in a Pre Smart Grid Environment
Articles in the same Issue
- Weak Bus-Oriented Installation of Phasor Measurement Unit for Power Network Observability
- Frequency Based Risk Assessment of a Power Producer in Indian Electricity Market
- Fast Methods for Power Quality Analysis
- Modified Teaching-Learning-Based Optimization for Combined Heat and Power Economic Dispatch
- Frequency Control Method using Automated Demand Response for Isolated Power System with Renewable Energy Sources
- Experimental İnvestigation on a Prototype Solar-Wind Hybrid System with a Pico Hydro Turbine
- Carrier Rotation Strategies for Equal Power Distributions in Cascaded H-Bridge Multilevel Inverters
- Transient Stability Improvement of a System Connected with Wind Energy Generators
- A Novel Bearing Fault Diagnosis Method Based on LMD and Wavelet Packet Energy Entropy
- Distributed Generation Units as Ancillary Services Providers in a Pre Smart Grid Environment