Wavelet Based Simulation and Analysis of Single and Multiple Power Quality Disturbances
Improving power quality disturbance (PQD) detection and automatic classification has been a major concern ever since the emergence of sensitive non-linear devices. The role of distributed generation in a power system is the main source of PQDs. Short-term and long-term duration single and multiple complex PQDs are difficult to monitor and need higher accuracy and time. This paper presents the analysis of different and distinctive combinations of PQDs. Variety of single and multiple PQD samples are generated using Matlab environment conferring to IEEE STD 1159-2009. Such disturbance samples are accurately detected and analyzed from waveform patterns using multi resolution analysis based discrete wavelet transform. The generation of samples and detection lies in fact that it can allow the feature extraction process for the training/testing sample features for machine learning based automatic recognition of disturbance types.
Keywords:power quality disturbances (PQDs), power quality generation, discrete wavelet transform (DWT), multi resolution analysis (MRA)
K. Thirumala, M. S. Prasad, T. Jain, A. C. Umarikar, “Tunable-Q wavelet transform and dual multiclass SVM for online automatic detection of power quality disturbances”, IEEE Transactions on Smart Grid, Vol. 9, No. 4, pp. 3018-3028, 2018 DOI: https://doi.org/10.1109/TSG.2016.2624313
H. Liu, F. Hussain, Y. Shen, S. Arif, A. Nazir, M. Abubakar, “Complex power quality disturbances classification via curvelet transform and deep learning”, Electric Power Systems Research, Vol. 163, pp. 1-9, 2018 DOI: https://doi.org/10.1016/j.epsr.2018.05.018
M. ElNozahy, R. El-Shatshat, M. Salama, “Single-phasing detection and classification in distribution systems with a high penetration of distributed generation”, Electric Power Systems Research, Vol. 131, pp. 41-48, 2016 DOI: https://doi.org/10.1016/j.epsr.2015.10.008
E. Fuchs, M. A. Masoum, Power Quality in Power Systems and Electrical Machines, Academic Press, 2011
A. A. M. Z. Suhail Khokhar, M. A. Bhayo, A. S. Mokhtar, “Automated recognition of single & hybrid power quality disturbances using wavelet transform based support vector machine”, Jurnal Teknologi, Vol. 79, No. 1, pp. 97-105, 2017 DOI: https://doi.org/10.11113/jt.v79.5693
S. Chakraborty, A. Chatterjee, S. K. Goswami, “A dual-tree complex wavelet transform-based approach for recognition of power system transients”, Expert Systems, Vol. 32, No. 1, pp. 132-140, 2015 DOI: https://doi.org/10.1111/exsy.12066
S. R. Mohanty, N. Kishor, P. K. Ray, J. P. S. Catalao, “Comparative Study of Advanced Signal Processing Techniques for Islanding Detection in a Hybrid Distributed Generation System”, IEEE Transactions on Sustainable Energy, Vol. 6, No. 1, pp. 122-131, 2015 DOI: https://doi.org/10.1109/PESGM.2015.7285854
S. Khokhar, A. A. B. M. Zin, A. S. B. Mokhtar, M. Pesaran, “A comprehensive overview on signal processing and artificial intelligence techniques applications in classification of power quality disturbances”, Renewable and Sustainable Energy Reviews, Vol. 51, pp. 1650-1663, 2015
R. Kumar, B. Singh, D. T. Shahani, “Recognition of Single-stage and Multiple Power Quality Events Using Hilbert–Huang Transform and Probabilistic Neural Network”, Electric Power Components and Systems, Vol. 43, No. 6, pp. 607-619, 2015 DOI: https://doi.org/10.1080/15325008.2014.999147
L. C. M. Andrade, M. Oleskovicz, R. A. S. Fernandes, “Adaptive threshold based on wavelet transform applied to the segmentation of single and combined power quality disturbances”, Applied Soft Computing, Vol. 38, pp. 967-977, 2015 DOI: https://doi.org/10.1016/j.asoc.2015.10.061
A. Domijan, G. T. Heydt, A. P. S. Meliopoulos, S. S. Venkata, S. West, “Directions of research on electric power quality”, IEEE Transactions on Power Delivery, Vol. 8, No. 1, pp. 429-436, 1993 DOI: https://doi.org/10.1109/61.180365
R. Kumar, B. Singh, D. Shahani, C. Jain, “Dual-Tree Complex Wavelet Transform-Based Control Algorithm for Power Quality Improvement in a Distribution System”, IEEE Transactions on Industrial Electronics, Vol. 64, No. 1, pp. 764-772, 2017 DOI: https://doi.org/10.1109/TIE.2016.2562601
S. Salcedo-Sanz, J. L. Rojo-Alvarez, M. Martinez-Ramon, G. Camps-Valls, “Support vector machines in engineering: An overview”, Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, Vol. 4, No. 3, pp. 234-267, 2014 DOI: https://doi.org/10.1002/widm.1125
F. A. Borges, R. A. Fernandes, I. N. Silva, C. B. Silva, “Feature extraction and power quality disturbances classification using smart meters signals”, IEEE Transactions on Industrial Informatics, Vol. 12, No. 2, pp. 824-833, 2016 DOI: https://doi.org/10.1109/TII.2015.2486379
Z. Liu, Q. Hu, Y. Cui, Q. Zhang, “A new detection approach of transient disturbances combining wavelet packet and Tsallis entropy”, Neurocomputing, Vol. 142, pp. 393-407, 2014 DOI: https://doi.org/10.1016/j.neucom.2014.04.020
S. Khokhar, A. A. B. Mohd Zin, A. S. B. Mokhtar, M. Pesaran, “A comprehensive overview on signal processing and artificial intelligence techniques applications in classification of power quality disturbances”, Renewable and Sustainable Energy Reviews, Vol. 51, pp. 1650-1663, 2015 DOI: https://doi.org/10.1016/j.rser.2015.07.068
M. Biswal, P. K. Dash, “Measurement and Classification of Simultaneous Power Signal Patterns With an S-Transform Variant and Fuzzy Decision Tree”, IEEE Transactions on Industrial Informatics, Vol. 9, No. 4, pp. 1819-1827, 2013 DOI: https://doi.org/10.1109/TII.2012.2210230
S. Khokhar, A. A. Mohd Zin, A. P. Memon, A. S. Mokhtar, “A new optimal feature selection algorithm for classification of power quality disturbances using discrete wavelet transform and probabilistic neural network”, Measurement, Vol. 95, pp. 246-259, 2017 DOI: https://doi.org/10.1016/j.measurement.2016.10.013
J. Barros, R. I. Diego, M. de Apraiz, “Applications of wavelets in electric power quality: Voltage events”, Electric Power Systems Research, Vol. 88, pp. 130-136, 2012 DOI: https://doi.org/10.1016/j.epsr.2012.02.009
M. Tuljapurkar, A. A. Dharme, “Wavelet based signal processing technique for classification of power quality disturbances”, 5th International Conference on Signal and Image Processing, Bangalore, India, South Korea, January 8-10, 2014 DOI: https://doi.org/10.1109/ICSIP.2014.59
A. A. Abdoos, Z. Moravej, M. Pazoki, “A hybrid method based on time frequency analysis and artificial intelligence for classification of power quality events”, Journal of Intelligent & Fuzzy Systems, Vol. 28, No. 3, pp. 1183-1193, 2015 DOI: https://doi.org/10.3233/IFS-141401
A. A. Abdoos, P. K. Mianaei, M. R. Ghadikolaei, “Combined VMD-SVM based feature selection method for classification of power quality events”, Applied Soft Computing, Vol. 38, pp. 637-646, 2016 DOI: https://doi.org/10.1016/j.asoc.2015.10.038
How to Cite
MetricsAbstract Views: 663
PDF Downloads: 296
Authors who publish with this journal agree to the following terms:
- Authors retain the copyright and grant the journal the right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) after its publication in ETASR with an acknowledgement of its initial publication in this journal.