@article{(Open Science Index):https://publications.waset.org/pdf/9999499,
	  title     = {Voltage Problem Location Classification Using Performance of Least Squares Support Vector Machine LS-SVM and Learning Vector Quantization LVQ},
	  author    = {Khaled Abduesslam. M and  Mohammed Ali and  Basher H Alsdai and  Muhammad Nizam and  Inayati},
	  country	= {},
	  institution	= {},
	  abstract     = {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.
},
	    journal   = {International Journal of Electrical and Computer Engineering},
	  volume    = {8},
	  number    = {8},
	  year      = {2014},
	  pages     = {1328 - 1332},
	  ee        = {https://publications.waset.org/pdf/9999499},
	  url   	= {https://publications.waset.org/vol/92},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 92, 2014},
	}