Load Feasible Region Determination by Using Adaptive Particle Swarm Optimization

Authors

  • Patchrapa Wongchai Chulalongkorn University
  • Sotdhipong Phichaisawat Chulalongkorn University

DOI:

https://doi.org/10.4186/ej.2019.23.6.239

Keywords:

particle swarm optimization, feasible region, boundary tracing method, power flow solution space

Abstract

The proposed of a method for determination a space of feasible boundary points, by using adaptive particle swarm optimization in order to solve the boundary region which represented by particle swarm points. This paper present method supports the calculation for a large-scale power system. In case of contingency will illustrate the point on the plane x-axis and y-axis dimensional power flow space. In addition, This method not only demonstrates the optimal particle swarm through the boundary tracing method of the feasible region but also present the boundary points are obtained by optimization. Moreover, receding loss function and operational constraints simultaneously are considering. The formulation points of feasible region can also determine the boundary points which is the contingencies are taken into account and the stability of load demand that system allows to execute in the normal requirements. These feasible points defined the limit of control actions and the robustness of operating points. Finally, the test systems shown the impact of system parameters on the load shedding, generator voltage control, and load level.

Downloads

Download data is not yet available.

Author Biographies

Patchrapa Wongchai

Department of Electrical Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok 10330, Thailand

Sotdhipong Phichaisawat

Department of Electrical Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok 10330, Thailand

Published

Vol 23 No 6, Nov 30, 2019

How to Cite

[1]
P. Wongchai and S. Phichaisawat, “Load Feasible Region Determination by Using Adaptive Particle Swarm Optimization”, Eng. J., vol. 23, no. 6, pp. 239-263, Nov. 2019.