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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Akash Saxena & Ahmad M. Alshamrani & Adel Fahad Alrasheedi & Khalid Abdulaziz Alnowibet & Ali Wagdy Mohamed, 2022. "A Hybrid Approach Based on Principal Component Analysis for Power Quality Event Classification Using Support Vector Machines," Mathematics, MDPI, vol. 10(15), pages 1-16, August.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:15:p:2780-:d:881166
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    References listed on IDEAS

    as
    1. Wenlong Fu & Jiawen Tan & Xiaoyuan Zhang & Tie Chen & Kai Wang, 2019. "Blind Parameter Identification of MAR Model and Mutation Hybrid GWO-SCA Optimized SVM for Fault Diagnosis of Rotating Machinery," Complexity, Hindawi, vol. 2019, pages 1-17, April.
    2. Adel Fahad Alrasheedi & Khalid Abdulaziz Alnowibet & Akash Saxena & Karam M. Sallam & Ali Wagdy Mohamed, 2022. "Chaos Embed Marine Predator (CMPA) Algorithm for Feature Selection," Mathematics, MDPI, vol. 10(9), pages 1-18, April.
    3. Sakthivel Ganesan & Prince Winston David & Praveen Kumar Balachandran & Devakirubakaran Samithas, 2021. "Intelligent Starting Current-Based Fault Identification of an Induction Motor Operating under Various Power Quality Issues," Energies, MDPI, vol. 14(2), pages 1-13, January.
    4. Khalid Abdulaziz Alnowibet & Shalini Shekhawat & Akash Saxena & Karam M. Sallam & Ali Wagdy Mohamed, 2022. "Development and Applications of Augmented Whale Optimization Algorithm," Mathematics, MDPI, vol. 10(12), pages 1-33, June.
    5. Pavan Babu Bandla & Indragandhi Vairavasundaram & Yuvaraja Teekaraman & Ramya Kuppusamy & Srete Nikolovski, 2021. "Real Time Sustainable Power Quality Analysis of Non-Linear Load under Symmetrical Conditions," Energies, MDPI, vol. 15(1), pages 1-16, December.
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    Cited by:

    1. Shoyab Ali & Annapurna Bhargava & Akash Saxena & Pavan Kumar, 2023. "A Hybrid Marine Predator Sine Cosine Algorithm for Parameter Selection of Hybrid Active Power Filter," Mathematics, MDPI, vol. 11(3), pages 1-25, January.

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