[
    {
        "id": "osp-13154",
        "type": "article-journal",
        "title": "Classification of Static Security Status Using Multi-Class Support Vector Machines",
        "author": [
            {
                "family": "Kalyani",
                "given": "S"
            },
            {
                "family": "Swarup",
                "given": "K. Shanti"
            }
        ],
        "URL": "https://omanscience.com/en/articles/classification-of-static-security-status-using-multi-class-support-vector-machines",
        "language": "en",
        "issued": {
            "date-parts": [
                [
                    2012
                ]
            ]
        },
        "container-title": "The Journal of Engineering Research",
        "volume": "9",
        "issue": "1",
        "page": "21",
        "DOI": "10.24200/tjer.vol9iss1pp21-30",
        "publisher": "Sultan Qaboos University",
        "ISSN": "1726-6009",
        "abstract": "This paper presents a Multi-class Support Vector Machine (SVM) based Pattern Recognition (PR) approach for static security assessment in power systems. The multi-class SVM classifier design is based on the calculation of a numeric index called the static security index. The proposed multi-class SVM based pattern recognition approach is tested on IEEE 57 Bus, 118 Bus and 300 Bus benchmark systems. The simulation results of the SVM classifier are compared to a Multilayer Perceptron (MLP) network and the Method of Least Squares (MLS). The SVM classifier was found to give high classification accuracy and a smaller misclassification rate compared to the other classifier techniques."
    }
]