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Knacks of Fractional Order Swarming Intelligence for Parameter Estimation of Harmonics in Electrical Systems

Author

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  • Naveed Ahmed Malik

    (Graduate School of Engineering Science and Technology, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliou, Yunlin 64002, Taiwan)

  • Ching-Lung Chang

    (Department of Computer Science and Information Engineering, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliou, Yunlin 64002, Taiwan)

  • Naveed Ishtiaq Chaudhary

    (Future Technology Research Center, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliou, Yunlin 64002, Taiwan)

  • Muhammad Asif Zahoor Raja

    (Future Technology Research Center, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliou, Yunlin 64002, Taiwan)

  • Khalid Mehmood Cheema

    (Department of Electronic Engineering, Fatima Jinnah Women University, Rawalpindi 46000, Pakistan)

  • Chi-Min Shu

    (Department of Safety, Health, and Environmental Engineering, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliou, Yunlin 64002, Taiwan)

  • Sultan S. Alshamrani

    (Department of Information Technology, College of Computer and Information Technology, Taif University, Taif 21944, Saudi Arabia)

Abstract

The efficient parameter estimation of harmonics is required to effectively design filters to mitigate their adverse effects on the power quality of electrical systems. In this study, a fractional order swarming optimization technique is proposed for the parameter estimation of harmonics normally present in industrial loads. The proposed fractional order particle swarm optimization (FOPSO) effectively estimates the amplitude and phase parameters corresponding to the first, third, fifth, seventh and eleventh harmonics. The performance of the FOPSO was evaluated for ten fractional orders with noiseless and noisy scenarios. The robustness efficiency of the proposed FOPSO was analyzed by considering different levels of additive white Gaussian noise in the harmonic signal. Monte Carlo simulations confirmed the reliability of the FOPSO for a lower fractional order ( λ = 0.1) with a faster convergence rate and no divergent run compared to other fractional orders as well as to standard PSO ( λ = 1).

Suggested Citation

  • Naveed Ahmed Malik & Ching-Lung Chang & Naveed Ishtiaq Chaudhary & Muhammad Asif Zahoor Raja & Khalid Mehmood Cheema & Chi-Min Shu & Sultan S. Alshamrani, 2022. "Knacks of Fractional Order Swarming Intelligence for Parameter Estimation of Harmonics in Electrical Systems," Mathematics, MDPI, vol. 10(9), pages 1-20, May.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:9:p:1570-:d:809840
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    References listed on IDEAS

    as
    1. Zeshan Aslam Khan & Naveed Ishtiaq Chaudhary & Syed Zubair, 2019. "Fractional stochastic gradient descent for recommender systems," Electronic Markets, Springer;IIM University of St. Gallen, vol. 29(2), pages 275-285, June.
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    Cited by:

    1. Naveed Ahmed Malik & Naveed Ishtiaq Chaudhary & Muhammad Asif Zahoor Raja, 2023. "Firefly Optimization Heuristics for Sustainable Estimation in Power System Harmonics," Sustainability, MDPI, vol. 15(6), pages 1-20, March.
    2. Khizer Mehmood & Naveed Ishtiaq Chaudhary & Zeshan Aslam Khan & Muhammad Asif Zahoor Raja & Khalid Mehmood Cheema & Ahmad H. Milyani, 2022. "Design of Aquila Optimization Heuristic for Identification of Control Autoregressive Systems," Mathematics, MDPI, vol. 10(10), pages 1-23, May.
    3. Mukhtar, Roshana & Chang, Chuan-Yu & Raja, Muhammad Asif Zahoor & Chaudhary, Naveed Ishtiaq & Shu, Chi-Min, 2024. "Novel nonlinear fractional order Parkinson's disease model for brain electrical activity rhythms: Intelligent adaptive Bayesian networks," Chaos, Solitons & Fractals, Elsevier, vol. 180(C).

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