Support vector regression based adaptive power system stabilizer
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, nam...
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2014
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th-cmuir.6653943832-14642014-08-29T09:29:20Z Support vector regression based adaptive power system stabilizer Boonprasert U. Theera-Umpon N. Rakpenthai C. 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. 2014-08-29T09:29:20Z 2014-08-29T09:29:20Z 2003 Conference Paper 02714310 61137 PICSD http://www.scopus.com/inward/record.url?eid=2-s2.0-0037744928&partnerID=40&md5=4af62a675ff598ec38614f1854e369f2 http://cmuir.cmu.ac.th/handle/6653943832/1464 English |
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Chiang Mai University Library |
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Thailand |
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English |
| description |
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. |
| format |
Conference or Workshop Item |
| author |
Boonprasert U. Theera-Umpon N. Rakpenthai C. |
| spellingShingle |
Boonprasert U. Theera-Umpon N. Rakpenthai C. Support vector regression based adaptive power system stabilizer |
| author_facet |
Boonprasert U. Theera-Umpon N. Rakpenthai C. |
| author_sort |
Boonprasert U. |
| title |
Support vector regression based adaptive power system stabilizer |
| title_short |
Support vector regression based adaptive power system stabilizer |
| title_full |
Support vector regression based adaptive power system stabilizer |
| title_fullStr |
Support vector regression based adaptive power system stabilizer |
| title_full_unstemmed |
Support vector regression based adaptive power system stabilizer |
| title_sort |
support vector regression based adaptive power system stabilizer |
| publishDate |
2014 |
| url |
http://www.scopus.com/inward/record.url?eid=2-s2.0-0037744928&partnerID=40&md5=4af62a675ff598ec38614f1854e369f2 http://cmuir.cmu.ac.th/handle/6653943832/1464 |
| _version_ |
1681419675444969472 |