A Hybrid Approach Based on Principal Component Analysis for Power Quality Event Classification Using Support Vector Machines

Akash Saxena, Ahmad M. Alshamrani, Adel Fahad Alrasheedi, Khalid Abdulaziz Alnowibet and Ali Wagdy Mohamed ()
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Akash Saxena: Swami Keshvanand Institute of Technology, Management & Gramothan, Jaipur 302017, India
Ahmad M. Alshamrani: Statistics and Operations Research Department, College of Science, King Saud University, P.O. Box 2455, Riyadh 11451, Saudi Arabia
Adel Fahad Alrasheedi: Statistics and Operations Research Department, College of Science, King Saud University, P.O. Box 2455, Riyadh 11451, Saudi Arabia
Khalid Abdulaziz Alnowibet: Statistics and Operations Research Department, College of Science, King Saud University, P.O. Box 2455, Riyadh 11451, Saudi Arabia
Ali Wagdy Mohamed: Operations Research Department, Faculty of Graduate Studies for Statistical Research, Cairo University, Giza 12613, Egypt

Mathematics, 2022, vol. 10, issue 15, 1-16

Abstract: Power quality has emerged as a sincere denominator in the planning and operation of a power system. Various events affect the quality of power at the distribution end of the system. Detection of these events has been a major thrust area in the last decade. This paper presents the application of Support Vector Machine (SVM) in classifying the power quality events. Well-known signal processing techniques, namely Hilbert transform and Wavelet transform, are employed to extract the potential features from the observation sets of voltages. Supervised architecture consisting of SVM has been constructed by tuning the parameters of SVM by various algorithms. It has been observed that Augmented Crow Search Algorithm (ACSA) yields the best accuracy compared to other contemporary optimizers. Further, Principal Component Analysis (PCA) is employed to choose the most significant features from the available features. On the basis of PCA, three different models of tuned SVMs are constructed. Comparative analysis of these three models, along with recently published approaches, is exhibited. Results are validated by the statistical one-way analysis of variance (ANOVA) method. It is observed that SVM, which contains attributes from both signal-processing techniques, gives satisfactory results.

Keywords: power quality; harmonics; Support Vector Machine (SVM); Augmented Crow Search Algorithm (ACSA) (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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