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Research on hybrid modified pathfinder algorithm for optimal reactive power dispatch

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Hybridization of meta-heuristic algorithms plays a major role in the optimization problem. In this paper, a new hybrid meta-heuristic algorithm called hybrid pathfinder algorithm (HPFA) is proposed to solve the optimal reactive power dispatch (ORPD) problem. The superiority of the Differential Evolution (DE) algorithm is the fast convergence speed, a mutation operator in the DE algorithm incorporates into the pathfinder algorithm (PFA). The main objective of this research is to minimize the real power losses and subject to equality and inequality constraints. The HPFA is used to find optimal control variables such as generator voltage magnitude, transformer tap settings and capacitor banks. The proposed HPFA is implemented through several simulation cases on the IEEE 118-bus system and IEEE 300-bus power system. Results show the superiority of the proposed algorithm with good quality of optimal solutions over existing optimization techniques, and hence confirm its potential to solve the ORPD problem.
Rocznik
Strony
art. no. e137733
Opis fizyczny
Bibliogr. 15 poz., rys., tab.
Twórcy
autor
  • Department of Electrical and Electronics Engineering, Government College of Engineering, Salem-11, India
  • Department of Electrical and Electronics Engineering, Government College of Engineering, Salem-11, India
Bibliografia
  • [1] M. Gwozd and L. Ciepliński, “Power supply with parallel reactive and distortion power compensation and tunable inductive filter-part 1”, Bull. Pol. Acad. Sci. Tech. Sci., vol. 68, pp. 401–408, 2020, doi: 10.24425/BPASTS.2020.133383.
  • [2] M.N. Acosta, D. Topic, and M.A. Andrade, “Optimal Microgrid–Interactive Reactive Power Management for Day–Ahead Operation”, Energies, vol. 14, no. 5, p. 1275, 2021, doi: 10.3390/en14051275.
  • [3] A.M. Tudose, I.I. Picioroaga, D.O. Sidea, and Co. Bulac, “Solving Single- and Multi-Objective Optimal Reactive Power Dispatch Problems Using an Improved Salp Swarm Algorithm”, Energies, vol. 14, no. 5, p. 1222, 2021, doi: 10.3390/en14051222.
  • [4] E. Canelas, T. Pinto-Varela, and B. Sawik, “Electricity Portfolio Optimization for Large Consumers: Iberian Electricity Market Case Study”, Energies, vol. 13, no. 9, p. 2249, 2020, doi: 10.3390/en13092249.
  • [5] V. Suresh and S.S. Kumar, “Optimal reactive power dispatch for minimization of real power loss using SBDE and DE-strategy algorithm”, J. Ambient Intell. Hum. Comput., 2020, doi: 10.1007/s12652-020-02673-w.
  • [6] H Yapici and N cetinkaya, “A new meta-heuristic optimizer: pathfinder algorithm”, Appl. Soft Comput., vol. 78, pp. 545–568, 2019, doi: 10.1016/j.asoc.2019.03.012.
  • [7] R. Storn and K. Price, “Differential evolution – A simple and efficient adaptive scheme for global optimization over continuous spaces,” J. Global Optim., vol. 11, pp. 341–359, 1997, doi: 10.1023/A:1008202821328.
  • [8] R.P Singha and S.P. Ghoshal, “Optimal reactive power dispatch by particle swarm optimization with an aging leader and challengers”, Appl. Soft Comput., vol. 29, pp. 298–309, 2015, doi: 10.1016/j.asoc.2015.01.006.
  • [9] M. Ghasemi et. al, “A new hybrid algorithm for optimal reactive power dispatch problem with discrete and continuous control variables”, Appl. Soft Comput., vol. 22, pp. 126–140, 2014, doi: 10.1016/j.asoc.2014.05.006.
  • [10] M. Ghasemi and M. Ghanbarian, “Modified teaching learning algorithm and double differential evolution algorithm for optimal reactive power dispatch problem: A comparative study”, Inf. Sci., vol. 278, pp. 231–249, 2014, doi: 10.1016/j.ins.2014.03.050.
  • [11] B. Mandal and P.K Roy, “Optimal reactive power dispatch using quasi-oppositional teaching learning based optimization”, Electr. Power Energy Syst., vol. 53, pp. 123–134, 2013, doi: 10.1016/j.ijepes.2013.04.011.
  • [12] S Mouassa and A. Salhi, “Ant lion optimizer for solving optimal reactive power dispatch problem in power systems”, Eng. Sci. Technol., vol. 20, pp 885–895, 2017, doi: 10.1016/j.jestch.2017.03.006.
  • [13] S Mugemanyi et. al., “Optimal Reactive Power Dispatch Using Chaotic Bat Algorithm”, IEEE Access, vol. 8, pp. 65830–65867, 2020, doi: 10.1109/ACCESS.2020. 2982988.
  • [14] W.M. Villa-Acevedo and J.M. Lopez-Lezama, “A novel constraint handling approach for the optimal reactive power dispatch problem”, Energies, vol. 11, p. 2352, 2018, doi: 10.3390/en11092352.
  • [15] R. Zimmerman, C.E. Murillo-Sanchez, and D. Gan, “MATPOWER 6.0, power systems engineering research center (PSERC)”, 2005, [Online]. Available: https://matpower.org/docs/MATPOWER-manual-6.0.pdf.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-cdf696c5-92ec-4523-835a-1a2560ea6b7f
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