Determination of steady state stability margin using extreme learning machine

Indar Chaerah Gunadin*, Muhammad Abdillah, Adi Soeprijanto, Ontoseno Penangsang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)


Power systems have increased in size and complexity due to rapid growth of widespread interconnection. This situation will make power system operated closer to steady-state stability limit (SSSL) resulting in higher probability voltage instability or voltage collapse. This paper presents SSSL assessment in power system using Extreme Learning Machine (ELM) model based on REI-Dimo method. The equivalent REI-Dimo is used to determine SSSL index of the power systems. Then, the result of REI-Dimo will be taught on ELM method via online. The results of ELM will compared with Artificial Neural Network (ANN) method. Studies were carried out on a Java-Bali 500kV system. The simulation showed that the proposed method could accurately predict the proximity to SSSL in power system. The proposed method was computationally efficient and suitable for online monitoring of steady-state stability condition in the power systems.

Original languageEnglish
Pages (from-to)91-103
Number of pages13
JournalWSEAS Transactions on Power Systems
Issue number3
Publication statusPublished - Jul 2012
Externally publishedYes


  • ANN
  • Extreme Learning Machine
  • REI-Dimo Equivalent
  • SSSL
  • Voltage Collapse


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