Steady-State Stability Assessment Using Neural Network Based on Network Equivalent

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


Power systems in all over the world 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 Artificial Neural Network (ANN) 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 ANN method via online. 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 method was computationally efficient and suitable for online monitoring of steady-state stability condition in the power systems.

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