Voltage Problem Location Classification Using Performance of Least Squares Support Vector Machine LS-SVM and Learning Vector Quantization LVQ
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
 A.Izzri, W. N. A.Mohamed, and I.Yahya,A New Method of Transient
Stability Assessment in Power Systems Using LS-SVM. The 5th Student
Conference on Research and Development; 11-12 December,
 M.Nizam, A. Mohamed, and A.Hussain, Dynamic Voltage Collapse
Prediction in Power Systems Using Support Vector Regression. Expert
Systems with Applications. 37, pp3730–3736. 2010.
 M.Hasani, and M.Parniani, Method of combined static and dynamic
analysis of voltage collapses in voltage assessment. In IEEE/PES
transmission and distribution conference and exhibition Ching.2005.
 S.Ekici, Support Vector Machines for Classification and Locating Faults
on Transmission Lines.Applied Soft Computing,Dec.2012, pp1650–1658
 Nizam, M., Mohamed, A., and Hussain, A.Dynamic Voltage Collapse
Prediction in Power Systems Using Artificial Neural Network.
Proceedings of the International Conference on Electrical Engineering
and Informatics InstituteTechnology Bandung, Indonesia June
 A.Khaled, M. Mohamed, M. K. Nizam, and Inayati. Voltage Problem
area Classification using Support Vector Machine SVM. International
conference data, Civil and Mechanical Engineering (ICDMCME), Bali
(Indonesia). E0214064.Feb 4-5, 2014.
 F.Milano, Documentation for Power System AnalysisTool
 V. F. Laurene,Fundamentals of Neural networks (Book) Architectures,
Algorithms and Applications. Learning Vector quantization.
Prentice-Hall, Inc. Upper Saddle River, NJ, USA. 1994.