Citation :

Astik Dhandhia, Jaydeepsinh Sarvaiya, Vivek Pandya, "Smart Variable Selection for Static Security Assessment: A Multi-Class Support Vector Machine Framework," International Journal of Engineering Trends and Technology (IJETT), vol. 74, no. 4, pp. 1-12, 2026. Crossref, https://doi.org/10.14445/22315381/IJETT-V74I4P101

Abstract

To maintain a secure and stable power system, operators must make informed decisions based on the current situation. Traditional power flow techniques have several drawbacks, including their high memory requirements and lengthy computation times. As a result, they are impractical options for real-time static security assessment applications. The masking problem with the performance indexes based on changes in bus voltage and power loading of transmission lines for security assessment is addressed by formulating a composite security index; as a result, the composite security index is better able to discriminate in close-range violations. Support Vector Machines are utilised within a multiclass classification framework to address the static security issues of power systems. A new smart variable selection approach is applied for static security assessment. It is demonstrated how various power system variable sets impact the efficiency of the multiclass support vector machine classifier. For optimal feature selection, sequential forward selection is employed to maximize classification accuracy while minimizing the misclassification rate. Two standard IEEE test systems validate the results of a Multi-Class Support Vector Machine Framework with smart variable selection during training and testing.

Keywords

Artificial Intelligence, Power system static security assessment, Support Vector Machine, Composite security index, Feature selection.

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