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PSO α : A Fragmented Swarm Optimisation for Improved Load Frequency Control of a Hybrid Power System Using FOPID

Author

Listed:
  • Bhargav Appasani

    (School of Electronics Engineering, Kalinga Institute of Industrial Technology, Bhubaneswar 751024, India)

  • Amitkumar V. Jha

    (School of Electronics Engineering, Kalinga Institute of Industrial Technology, Bhubaneswar 751024, India)

  • Deepak Kumar Gupta

    (School of Electrical Engineering, Kalinga Institute of Industrial Technology, Bhubaneswar 751024, India)

  • Nicu Bizon

    (Faculty of Electronics, Communication and Computers, University of Pitesti, 110040 Pitesti, Romania
    Doctoral School, University Politehnica of Bucharest, Splaiul Independentei 313, 06004 Bucharest, Romania
    ICSI Energy, National Research and Development Institute for Cryogenic and Isotopic Technologies, 240050 Ramnicu Valcea, Romania)

  • Phatiphat Thounthong

    (Renewable Energy Research Centre (RERC), Department of Teacher Training in Electrical Engineering, Faculty of Technical Education, King Mongkut’s University of Technology North Bangkok, 1518 Pracharat 1 Road, Wongsawang, Bangsue, Bangkok 10800, Thailand
    Group of Research in Electrical Engineering of Nancy (GREEN), University of Lorraine-GREEN, F-54000 Nancy, France)

Abstract

Particle swarm optimisation (PSO) is one of the widely adopted meta-heuristic methods for solving real-life problems. Its practical utility can be further enhanced by improving its performance. In order to acheive this, academics have presented several variants of the original PSO over the past few years, including the quantum PSO (QPSO), bare-bones PSO (BB-PSO), hybrid PSO, fuzzy PSO, etc. In this paper, the performance of PSO is improved by proposing a fragmented swarm optimisation approach known as the PSO α . The PSO α is tested and compared with PSOs over 14 different benchmarking cost functions to validate its efficacy. The analysis is also carried out to see the impact of α on its performance. It is observed that the average value of the cost function over 50 simulations obtained using the fragmented swarm approach is lower than that obtained using the standard PSO in 12 out of 14 benchmark functions. Similarly, the fragmented approach outperforms the standard PSO in 13 out of 14 benchmark functions when compared with the best fitness value achieved out of 50 simulations. Finally, the proposed approach is applied to solve the well-known real-life optimisation problem of load frequency control (LFC) in power systems. A test system comprising both renewable and traditional power sources is considered to evaluate the efficacy of the proposed technique. A fractional order proportional-integral-differential (FOPID) controller is used, whose parameters are optimised using the proposed PSO for achieving the LFC. The proposed fragmentation approach can be applied with other optimisation techniques to improve their performance.

Suggested Citation

  • Bhargav Appasani & Amitkumar V. Jha & Deepak Kumar Gupta & Nicu Bizon & Phatiphat Thounthong, 2023. "PSO α : A Fragmented Swarm Optimisation for Improved Load Frequency Control of a Hybrid Power System Using FOPID," Energies, MDPI, vol. 16(5), pages 1-17, February.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:5:p:2226-:d:1080187
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    References listed on IDEAS

    as
    1. Latif, Abdul & Hussain, S.M. Suhail & Das, Dulal Chandra & Ustun, Taha Selim, 2020. "State-of-the-art of controllers and soft computing techniques for regulated load frequency management of single/multi-area traditional and renewable energy based power systems," Applied Energy, Elsevier, vol. 266(C).
    2. Li, Jiawen & Yu, Tao & Zhang, Xiaoshun, 2022. "Coordinated load frequency control of multi-area integrated energy system using multi-agent deep reinforcement learning," Applied Energy, Elsevier, vol. 306(PA).
    3. Deepak Kumar Gupta & Ankit Kumar Soni & Amitkumar V. Jha & Sunil Kumar Mishra & Bhargav Appasani & Avireni Srinivasulu & Nicu Bizon & Phatiphat Thounthong, 2021. "Hybrid Gravitational–Firefly Algorithm-Based Load Frequency Control for Hydrothermal Two-Area System," Mathematics, MDPI, vol. 9(7), pages 1-15, March.
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