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All Journal International Journal of Electrical and Computer Engineering Mechatronics, Electrical Power, and Vehicular Technology
Sohrab Mirsaeidi
Beijing Jiaotong University
Computer Science & IT Electrical & Electronics Engineering

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Journal : International Journal of Electrical and Computer Engineering

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Comparison analysis of different classification methods of power quality disturbances Nur Adrinna Shafiqa Zakaria; Dalila Mat Said; Norzanah Rosmin; Nasarudin Ahmad; Mohamad Shazwan Shah Jamil; Sohrab Mirsaeidi
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 6: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i6.pp5754-5764

Abstract

Good power quality delivery has always been in high demand in power system utilities where different types of power quality disturbances are the main obstacles. As these disturbances have distinct characteristics and even unique mitigation techniques, their detection and classification should be correct and effective. In this study, eight different types of power quality disturbances were synthetically generated, by using a mathematical approach. Then, continuous wavelet transform (CWT) and discrete wavelet transform with multi-resolution analysis (DWT-MRA) were applied, which eight features were then extracted from the synthesized signals. Three classifiers namely, decision tree (DT), support vector machine (SVM) and k-nearest neighbors (KNN) were trained to classify these disturbances. The accuracy of the classifiers was evaluated and analyzed. The best classifier was then integrated with the full model, which the performance of the proposed model was observed with 50 random signals, with and without noise. This study found that wavelet-transform was effective to localize the disturbances at the instant of their occurrence. On the other hand, the SVM classifier is superior to other classifiers with an overall accuracy of 94%. Still, the need for these classifiers to be further optimized is crucial in ensuring a more effective detection and classification system.
Co-Authors Ahmad Fudholi Ahmad Rajani Dalila Mat Said Dalila Mat Said Dian Andriani Haznan Abimanyu Mohamad Shazwan Shah Jamil Nasarudin Ahmad Nasarudin Ahmad Norzanah Rosmin Nur Adrinna Shafiqa Zakaria Sevia Mahdaliza Idrus Tinton Dwi Atmaja