Voltage Problem Location Classification Using Performance of Least Squares Support Vector Machine LS-SVM and Learning Vector Quantization LVQ

Authors: Khaled Abduesslam. M, Mohammed Ali, Basher H Alsdai, Muhammad Nizam, Inayati

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.

Keywords: IEEE 39 bus, Least Squares Support Vector Machine, Learning Vector Quantization, Voltage Collapse.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1096369

References:

[1] 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, Malaysia.2007.
[2] 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.
[3] 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.
[4] S.Ekici, Support Vector Machines for Classification and Locating Faults on Transmission Lines.Applied Soft Computing,Dec.2012, pp1650–1658
[5] 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 17-19.2007.
[6] 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.
[7] F.Milano, Documentation for Power System AnalysisTool box(PSAT).http://faraday1.ucd.ie/psat.html,2007.
[8] V. F. Laurene,Fundamentals of Neural networks (Book) Architectures, Algorithms and Applications. Learning Vector quantization. Prentice-Hall, Inc. Upper Saddle River, NJ, USA. 1994.