@inproceedings{f9fc26f011e145cd842b579a07618c5c,
title = "Application of LS-SVM technique based on robust control strategy to AGC of power system",
abstract = "A nonlinear least squares support vector machine (LS-SVM) based automatic generation control (AGC) regulator is investigated in this paper. The proposed regulator is trained using a reliable data set consisting of wide operating conditions generated by robust control technique. The designed AGC regulators combine advantage of LS-SVM and robust control technique to achieve desired level of performance for all admissible uncertainties and leads to a flexible regulator with simple structure, which can be useful under diverse operating conditions. A performance comparison between proposed LS-SVM, conventional PI and multi-layer perceptron (MLP) neural network based AGC regulators is carried out in a two-area power system under various operating conditions and load changes to show the superiority of the proposed control strategy.",
keywords = "automatic generation control, LS-SVM, robust control",
author = "Gulshan Sharma and Niazi, {K. R.} and Ibraheem",
note = "Publisher Copyright: {\textcopyright} 2014 IEEE.; 2014 International Conference on Advances in Engineering and Technology Research, ICAETR 2014 ; Conference date: 01-08-2014 Through 02-08-2014",
year = "2014",
doi = "10.1109/ICAETR.2014.7012953",
language = "English",
series = "2014 International Conference on Advances in Engineering and Technology Research, ICAETR 2014",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2014 International Conference on Advances in Engineering and Technology Research, ICAETR 2014",
address = "United States",
}