A Real-Coded Genetic Algorithm with System Reduction and Restoration for Rapid and Reliable Power Flow Solution of Power Systems

  • Hassan Abdullah Kubba, Ass. Prof. Dr. College of Engineering-University of Baghdad
  • Alaa Suheib Rodhan, MSc student College of Engineering-University of Baghdad
Keywords: load flow analysis, load modeling, power system modeling, real coded genetic algorithms, simulation, voltage measurement

Abstract

The paper presents a highly accurate power flow solution, reducing the possibility of ending at local minima, by using Real-Coded Genetic Algorithm (RCGA) with system reduction and restoration. The proposed method (RCGA) is modified to reduce the total computing time by reducing the system in size to that of the generator buses, which, for any realistic system, will be smaller in number, and the load buses are eliminated. Then solving the power flow problem for the generator buses only by real-coded GA to calculate the voltage phase angles, whereas the voltage magnitudes are specified resulted in reduced computation time for the solution. Then the system is restored by calculating the voltages of the load buses in terms of the calculated voltages of the generator buses, after a derivation of equations for calculating the voltages of the load busbars. The proposed method was demonstrated on 14-bus IEEE test systems and the practical system 362-busbar IRAQI NATIONAL GRID (ING). The proposed method has reliable convergence, a highly accurate solution and less computing time for on-line applications. The method can conveniently be applied for on-line analysis and planning studies of large power systems.                                                           

 

  

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Published
2015-05-01
How to Cite
Kubba, H. and Rodhan, A. (2015) “A Real-Coded Genetic Algorithm with System Reduction and Restoration for Rapid and Reliable Power Flow Solution of Power Systems”, Journal of Engineering, 21(5), pp. 1-19. Available at: http://joe.uobaghdad.edu.iq/index.php/main/article/view/430 (Accessed: 18February2020).