Beschreibung
The Classification of Voltage Problem Using Support Vector Machine (SVM) of electrical power system using Least Squares Support Vector Machine (LS-SVM) algorithm and implemented on IEEE-39 bus New-England system. The data was collected from the time domain simulation by using input to the LS-SVM classification, and LS-SVM PTSI estimation on Least Squares Support Vector Machine, which is used as a predictor to determine the dynamic voltage collapse indices by increasing of the power in load buses. The Kernel function type and Kernel parameter are considered. In order to verify the effectiveness of the proposed LS-SVM classification and estimation method, its performance is compared with the Learning Vector Quantization (LVQ).
Autorenporträt
- Khaled Abduesslam M. Graduated master degree from Sebelas Maret University (UNS), Central Java, Indonesia. (2014).- Prof. Muhammad Nizam. M. T. PhD, Head of control unit UNS Ex.- Inayati, ST., MT., PhD. Engineering IN UNS, Surakarta.
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