Support vector regression based adaptive power system stabilizer - Boonprasert U. (2014)
The main purpose of this paper is to compare performances of our proposed support vector machine (SVM) based power system stabilizer (PSS) with the conventional PSS, artificial neural networks (ANN) and radial basis function (RBF) networks in PSS applications. We train an application of the SVM, namely the support vector regression (SVR) to approximate functions (nonlinear regression) in real-time tuning of the parameters of PSS. In addition to being a simpler model, the experimental results suggest that the SVR can be trained in much shorter time than ANN and RBF networks. Moreover, the SVR also provides the most robust among these four approaches.