Digital generator capability curve for improving optimal power flow based on IPSO

Mat Syai'in, Kuo Lung Lian, Adi Soeprijanto

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)

Abstract

The generator capability curve (GCC) based on neural network (NN) is used as a constraint in optimal power flow based on improved particle swarm optimization (OPF-IPSO) to replace rectangular (Pmin-Pmax and Qmin-Qmax) constraint in achieving lower cost at the same security level. The security check algorithm is developed to eliminate the complicated mathematical equations in employing GCC as a constraint in OPF-IPSO. The algorithm is very simple and flexible especially for representing non-linear generation operation limits and under excitation operation areas. In effort to avoid local optimal solution and to get global optimal solution faster, chaotic parameter is used in updating weights of PSO. The data used to verify the performance of the proposed method is the Java-Bali 500 kV power systems that containt 8 generators and 23 buses.

Original languageEnglish
Pages (from-to)912-918
Number of pages7
JournalInternational Review of Electrical Engineering
Volume8
Issue number2
Publication statusPublished - 2013

Keywords

  • Chaotic parameter
  • Generator capability curve
  • Improved particle swarm optimization
  • Neural network
  • Optimal power flow

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