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Fractional memetic computing paradigm for reactive power management involving wind-load chaos and uncertainties

Author

Listed:
  • Muhammad, Yasir
  • Khan, Nusrat
  • Awan, Saeed Ehsan
  • Raja, Muhammad Asif Zahoor
  • Chaudhary, Naveed Ishtiaq
  • Kiani, Adiqa Kausar
  • Ullah, Farman
  • Shu, Chi-Min

Abstract

Optimal reactive power dispatch (ORPD) with integration of renewable energy resources has a growing interest in research community due to its utmost requirements during the operation, planning and design of the modern electrical power networks. The objective of ORPD is to improve the performance of power network by means of reducing the losses in transmission line, improving the voltage profile, and decreasing the overall cost of operation through optimal tuning of the operational variables such as tap position of transformers, generator output voltages and capacitor banks. However, the nonlinear, non-stationary and complex nature of power network, presence of load uncertainties, and dynamic behavior of wind generation introduces a complex optimization task which cannot be readily solved in an efficient manner. In this research work, a new fractional memetic computing paradigm, i.e., the fractional particle swarm optimization gravitational search algorithm with entropy evolution (FPSOGSA-EE), is designed to solve the ORPD problems in power system adopting wind power plants (WPPs) and load uncertainties. The proposed optimization framework FPSOGSA-EE integrates the concept of fractional calculus and Shannon entropy to strengthen the optimization characteristics of canonical algorithm. The exhaustive experimentation endorse the efficacy of FPSOGSA-EE by providing minimum gauge of fitness evaluation function, namely, the line loss and voltage deviation index minimization, in IEEE 30 and 57 bus networks. The stability, consistency and reliability of proposed FPSOGSA-EE is ascertained through statistical interpretations by means of boxplots, probability measures for cumulative distribution function, and histogram illustrations.

Suggested Citation

  • Muhammad, Yasir & Khan, Nusrat & Awan, Saeed Ehsan & Raja, Muhammad Asif Zahoor & Chaudhary, Naveed Ishtiaq & Kiani, Adiqa Kausar & Ullah, Farman & Shu, Chi-Min, 2022. "Fractional memetic computing paradigm for reactive power management involving wind-load chaos and uncertainties," Chaos, Solitons & Fractals, Elsevier, vol. 161(C).
    • Handle: RePEc:eee:chsofr:v:161:y:2022:i:c:s0960077922004957
    DOI: 10.1016/j.chaos.2022.112285
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    Cited by:

    1. Khan, Babar Sattar & Qamar, Affaq & Ullah, Farman & Bilal, Muhammad, 2023. "Ingenuity of Shannon entropy-based fractional order hybrid swarming strategy to solve optimal power flows," Chaos, Solitons & Fractals, Elsevier, vol. 170(C).
    2. Hasanien, Hany M. & Alsaleh, Ibrahim & Tostado-Véliz, Marcos & Zhang, Miao & Alateeq, Ayoob & Jurado, Francisco & Alassaf, Abdullah, 2024. "Hybrid particle swarm and sea horse optimization algorithm-based optimal reactive power dispatch of power systems comprising electric vehicles," Energy, Elsevier, vol. 286(C).
    3. 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.
    4. Mehmood, Ammara & Raja, Muhammad Asif Zahoor & Jalili, Mahdi, 2023. "Optimization of integrated load dispatch in multi-fueled renewable rich power systems using fractal firefly algorithm," Energy, Elsevier, vol. 278(PA).
    5. Yang, Zihao & Dong, Sheng, 2023. "A novel decomposition-based approach for non-stationary hub-height wind speed modelling," Energy, Elsevier, vol. 283(C).
    6. Campos, Michel W.S. & Ayres, Florindo A.C. & de Bessa, Iury Valente & de Medeiros, Renan L.P. & Martins, Paulo R.O. & Lenzi, Ervin kaminski & Filho, João E.C. & Vilchez, José R.S. & Lucena, Vicente F., 2024. "Fractional-order identification system based on Sundaresan’s technique," Chaos, Solitons & Fractals, Elsevier, vol. 185(C).

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