Identification of Power Quality Disturbances Using S-Transform and Multi-Class Support Vector Machine
Abstract
Abstract: An essential issue in power quality disturbances is identifying and classifying power quality disturbances from anywhere and at any time. This article proposed a new approach to identify and classify power quality disturbances over the web using S-transform, Multi-Class Support vector machine (SVM), and Matlab framework. S-Transform is used as an extraction feature to obtain the temporal frequency characteristics of power quality events. The development of the multi-class SVM classifier, in which the system classifies various power quality disturbances. Finally, the Matlab framework integrated the graphical and computational processes with remote access via the web. The test result indicated the suggested method's effectiveness and robustness for identifying and classifying power quality disturbances through the web.
Abstrak: Masalah penting dalam gangguan kualitas daya adalah mengidentifikasi dan mengklasifikasikan gangguan kualitas daya dari mana saja dan kapan saja. Artikel ini mengusulkan pendekatan baru untuk mengidentifikasi dan mengklasifikasikan gangguan kualitas daya melalui web menggunakan S-transform, Multi-Class Support vector machine (SVM), dan Matlab. S-Transform digunakan sebagai fitur ekstraksi untuk mendapatkan karakteristik frekuensi temporal dari peristiwa kualitas daya. Multi class SVM classifier dikembangkan dimana sistem mengklasifikasikan berbagai gangguan kualitas daya. Akhirnya, Matlab framework mengintegrasikan proses grafis dan komputasi sehingga dapat diakses jarak jauh melalui web. Hasil pengujian menunjukkan efektivitas dan robustnes metode yang usulkan untuk mengidentifikasi dan mengklasifikasikan gangguan kualitas daya melalui web.
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Beniwal, R. K., Saini, M. K., Nayyar, A., Qureshi, B., & Aggarwal, A. (2021). A critical analysis of methodologies for detection and classification of power quality events in smart grid. IEEE Access, 9, 83507–83534. https://doi.org/10.1109/ACCESS.2021.3087016
Bennani, Y., & Benabdeslem, K. (2006). Dendogram-based SVM for multi-class classification. Journal of Computing and Information Technology, 14(4), 283–289.
Bischl, B. (2013). Model and Algorithm Selection. Technische Universität Dortmund.
Byrd, H., & Matthewman, S. (2014). Exergy and the City: The Technology and Sociology of Power (Failure). Journal of Urban Technology, 21(3), 85–102. https://doi.org/10.1080/10630732.2014.940706
Chawda, G. S., Shaik, A. G., Shaik, M., Padmanaban, S., Holm-Nielsen, J. B., Mahela, O. P., & Kaliannan, P. (2020). Comprehensive Review on Detection and Classification of Power Quality Disturbances in Utility Grid with Renewable Energy Penetration. In IEEE Access (Vol. 8). https://doi.org/10.1109/ACCESS.2020.3014732
Deokar, S. A., & Waghmare, L. M. (2014). Integrated DWT–FFT approach for detection and classification of power quality disturbances. International Journal of Electrical Power & Energy Systems, 61, 594–605.
Kächele, M. (2020). Machine learning systems for multimodal affect recognition. Springer.
Kankale, R., Paraskar, S., & Jadhao, S. (2021). Classification of Power Quality Disturbances in Emerging Power System using S-transform and Support Vector Machine. 2021 IEEE 2nd International Conference on Electrical Power and Energy Systems, ICEPES 2021. https://doi.org/10.1109/ICEPES52894.2021.9699673
Khetarpal, P., & Tripathi, M. M. (2020). A critical and comprehensive review on power quality disturbance detection and classification. In Sustainable Computing: Informatics and Systems (Vol. 28). https://doi.org/10.1016/j.suscom.2020.100417
Mahela, O. P., Shaik, A. G., & Gupta, N. (2015). A critical review of detection and classification of power quality events. Renewable and Sustainable Energy Reviews, 41, 495–505.
Mahela, O. P., Shaik, A. G., Khan, B., Mahla, R., & Alhelou, H. H. (2020). Recognition of complex power quality disturbances using S-transform based ruled decision tree. IEEE Access, 8. https://doi.org/10.1109/ACCESS.2020.3025190
Manimala, K., Selvi, K., & Ahila, R. (2008). Artificial intelligence techniques applications for power disturbances classification. World Academy of Science, Engineering and Technology, 22, 833–840.
Mishra, M. (2019). Power quality disturbance detection and classification using signal processing and soft computing techniques: A comprehensive review. In International Transactions on Electrical Energy Systems (Vol. 29, Issue 8). https://doi.org/10.1002/2050-7038.12008
Monteiro, D. D. A., Zvietcovich, W. G., & Braga, M. F. (2018). Detection and classification of power quality disturbances with wavelet transform, decision tree algorithm and support vector machines. SBSE 2018 - 7th Brazilian Electrical Systems Symposium. https://doi.org/10.1109/SBSE.2018.8395809
Nolasco, D. H. S., Palmeira, E. S., & Costa, F. B. (2019). A cascade-type hierarchical fuzzy system with additional defuzzification of layers for the automatic power quality diagnosis. Applied Soft Computing, 80, 657–671.
Parvez, I., Aghili, M., Sarwat, A. I., Rahman, S., & Alam, F. (2019). Online power quality disturbance detection by support vector machine in smart meter. Journal of Modern Power Systems and Clean Energy, 7(5). https://doi.org/10.1007/s40565-018-0488-z
Ribeiro, E. G., Mendes, T. M., Dias, G. L., Faria, E. R. S., Viana, F. M., Barbosa, B. H. G., & Ferreira, D. D. (2018). Real-time system for automatic detection and classification of single and multiple power quality disturbances. Measurement, 128, 276–283.
Rodrigues Junior, W. L., Borges, F. A. S., Veloso, A. F. da S., Rabêlo, R. de A. L., & Rodrigues, J. J. P. C. (2019). Low voltage smart meter for monitoring of power quality disturbances applied in smart grid. Measurement: Journal of the International Measurement Confederation, 147. https://doi.org/10.1016/j.measurement.2019.106890
Sahani, M., & Dash, P. K. (2019). FPGA-based online power quality disturbances monitoring using reduced-sample HHT and class-specific weighted RVFLN. IEEE Transactions on Industrial Informatics, 15(8). https://doi.org/10.1109/TII.2019.2892873
Salim, F., Nor, K. M., Said, D. M., & Rahman, A. A. A. (2014). Voltage sags cost estimation for Malaysian industries. Conference Proceeding - 2014 IEEE International Conference on Power and Energy, PECon 2014, 41–46. https://doi.org/10.1109/PECON.2014.7062411
Stockwell, R. G., Mansinha, L., & Lowe, R. P. (1996). Localization of the complex spectrum: the S transform. IEEE Transactions on Signal Processing, 44(4), 998–1001.
Tang, Q., Qiu, W., & Zhou, Y. (2020). Classification of Complex Power Quality Disturbances Using Optimized S-Transform and Kernel SVM. IEEE Transactions on Industrial Electronics, 67(11). https://doi.org/10.1109/TIE.2019.2952823
Vapnik, V. (1998). Statistical Learning Theory New York. NY: Wiley, 1, 2.
Wang, S., & Chen, H. (2019). A novel deep learning method for the classification of power quality disturbances using deep convolutional neural network. Applied Energy, 235. https://doi.org/10.1016/j.apenergy.2018.09.160
Wenda, A., Hussain, A., Hannan, M. A., Mohamed, A., & Samad, S. A. (2011). Web based automatic classification of power quality disturbances using the S-transform and a rule based expert system. Journal of Information & Computational Science, 8(12), 2375–2383.
Wenda, A., Hussain, A., Samad, S. A., Mohamed, R., & Hannan, M. A. (2010). Web-based on mobile phone for automatic classification of power quality disturbance using the S-transform and support vector machines. TENCON 2010-2010 IEEE Region 10 Conference, 1432–1437.
DOI: http://dx.doi.org/10.22373/ekw.v8i2.13026
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ELKAWNIE
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Elkawnie: Journal of Islamic Science and Technology in 2022. Published by Faculty of Science and Technology in cooperation with Center for Research and Community Service (LP2M), UIN Ar-Raniry Banda Aceh, Aceh, Indonesia.
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