This paper presents the voltage problem location

\r\nclassification using performance of Least Squares Support Vector

\r\nMachine (LS-SVM) and Learning Vector Quantization (LVQ) in

\r\nelectrical power system for proper voltage problem location

\r\nimplemented by IEEE 39 bus New- England. The data was collected

\r\nfrom the time domain simulation by using Power System Analysis

\r\nToolbox (PSAT). Outputs from simulation data such as voltage, phase

\r\nangle, real power and reactive power were taken as input to estimate

\r\nvoltage stability at particular buses based on Power Transfer Stability

\r\nIndex (PTSI).The simulation data was carried out on the IEEE 39 bus

\r\ntest system by considering load bus increased on the system. To verify

\r\nof the proposed LS-SVM its performance was compared to Learning

\r\nVector Quantization (LVQ). The results showed that LS-SVM is faster

\r\nand better as compared to LVQ. The results also demonstrated that the

\r\nLS-SVM was estimated by 0% misclassification whereas LVQ had

\r\n7.69% misclassification.<\/p>\r\n","references":null,"publisher":"World Academy of Science, Engineering and Technology","index":"Open Science Index 92, 2014"}