TY - JFULL AU - Khaled Abduesslam. M and Mohammed Ali and Basher H Alsdai and Muhammad Nizam and Inayati PY - 2014/9/ TI - Voltage Problem Location Classification Using Performance of Least Squares Support Vector Machine LS-SVM and Learning Vector Quantization LVQ T2 - International Journal of Electrical and Computer Engineering SP - 1327 EP - 1332 VL - 8 SN - 1307-6892 UR - https://publications.waset.org/pdf/9999499 PU - World Academy of Science, Engineering and Technology NX - Open Science Index 92, 2014 N2 - This paper presents the voltage problem location classification using performance of Least Squares Support Vector Machine (LS-SVM) and Learning Vector Quantization (LVQ) in electrical power system for proper voltage problem location implemented by IEEE 39 bus New- England. The data was collected from the time domain simulation by using Power System Analysis Toolbox (PSAT). Outputs from simulation data such as voltage, phase angle, real power and reactive power were taken as input to estimate voltage stability at particular buses based on Power Transfer Stability Index (PTSI).The simulation data was carried out on the IEEE 39 bus test system by considering load bus increased on the system. To verify of the proposed LS-SVM its performance was compared to Learning Vector Quantization (LVQ). The results showed that LS-SVM is faster and better as compared to LVQ. The results also demonstrated that the LS-SVM was estimated by 0% misclassification whereas LVQ had 7.69% misclassification. ER -