Comparison between Wavelet Packet Transform and M-band Wavelet Packet Transform for Identification of Power Quality Disturbances
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References
- Kanirajana P, Kumar VS. Power quality disturbance detection and classification using wavelet and RBFNN. Applied Soft Computing. 2015 Oct; 35:470–81. https://doi.org/10.1016/j.asoc.2015.05.048
- Azim R, Hoque A. A fuzzy logic based dynamic voltage restorer for voltage sag and swell mitigation for industrial induction motor loads. International Journal of Computer Applications. 2011 Sep; 30(8).
- Ramlee NAB. Detection of voltage disturbances in power quality using wavelet transform. UniversitiTun Hussein Onn Malaysia; 2012. PMid:23085542 PMCid:PMC3552913
- Kayode O, Obiseye O. Wavelet transform in the detection of electrical power quality disturbances. Emmanuel Alayande College of Education; 2013 Apr.
- Edward SJP, Grady WM, Parsons AC. Power quality disturbance waveform recognition using wavelet-based neural classifier- Part 1: Theoretical Foundation. IEEE Trans Power Delivery. 2000; 15:222–8. https://doi.org/10.1109/61.847255
- Wijayakulasooriya JV, Putrus GA, Minns PD. Electric power quality disturbance classification using self-adapting artificial neural networks. IEE Proceedings of Generation, Transmission and Distribution. 2002; 149(1):98–101. https://doi.org/10.1049/ip-gtd:20020014
- Polikar R. The engineers ultimate guide to wavelet analysis. The Wavelet Tutorial; 2006. PMCid:PMC1994255
- Dekhandji FZ. Detection of power quality disturbances using discrete wavelet transform. The 5th International Conference on Electrical Engineering- Boumerdes (ICEE-B); Boumerdes, Algeria. 2017. p. 29–31. https://doi.org/10.1109/ICEE-B.2017.8192080
- Daubechies, Ten lectures on wavelets. vol 61 of CBMSNSF Regional Conference Series in Applied Mathematics. Philadelphia, PA: Society for Industrial and Applied Mathematics (SIAM); 1992.
- Recioui A, Benseghier B, Khalfallah H. System fault detection, classification and location using the K-nearest neighbors. Proceedings of the IEEE 4th International Conference in Electrical Engineering; Boumerdes, Algeria. 2015. 978-1-4673-6673-1/15/$31.00 ©2015 IEEE. https://doi.org/10.1109/INTEE.2015.7416832
- Haykin S. Neural Networks. New York: IEEE Press; 1994.
- Wickerhauser MV. Adaptive Wavelet-Analysis. 1st ed. Bonn, Germany: Vieweg; 1996. https://doi.org/10.1007/978-3322-83127-9 PMid:8930516
- Brrus CS, Jay CA, Kanungo RN. Introduction to wavelets and wavelet transforms- a primer. Thousand Oaks, California: Sage Publications; 1988. p. 430–6.
- Mallat S. A theory for multiresolution signal decomposition: The wavelet representation. IEEE Trans on Pattern Anal Machine Intell. 1989; ll(7):674–93. https://doi.org/10.1109/34.192463
- Stefen P, Heller PN, Gopinath RA, Burrus CS. Theory of regular m-band wavelet bases. IEEE Trans on Signal Processing. 1993; 41:3497–510. https://doi.org/10.1109/78.258088
- Acharyya M, Kundu MK. An adaptive approach to unsupervised texture segmentation using M-Band wavelet transform. Signal Processing. 2001; 81:1337–56. https://doi.org/10.1016/S0165-1684(00)00278-4
- Gabor D. Theory of communication. Proc Inst Electr Eng. 1946; 93:429–41.
- Bhati D, Sharma M, Pachori RB, Gadrea VM. Time-frequency localized three-band biorthogonal wavelet filter bank using semidefinite relaxation and nonlinear least squares with epileptic seizure EEG signal classification. Digital Signal Processing. 2017; 62:259–73. https://doi.org/10.1016/j.dsp.2016.12.004
- Kumawat P, Zaveri N, Verma D. Analysis of power quality disturbances using M-band wavelet packet transform. International Conference on Signal Processing and Communication; 2017. https://doi.org/10.1109/CSPC.2017.8305871