A Data-Driven Approach to Power System Contingency Analysis Using Support Vector Machines
| International Journal of Electrical and Electronics Engineering |
| © 2025 by SSRG - IJEEE Journal |
| Volume 12 Issue 12 |
| Year of Publication : 2025 |
| Authors : Suresh Babu Daram, Sarayu Vunnam, P. Deivendran, Shridhar S M, Immanuel Anupalli, M. Anitha |
10.14445/23488379/IJEEE-V12I12P102
How to Cite?
Suresh Babu Daram, Sarayu Vunnam, P. Deivendran, Shridhar S M, Immanuel Anupalli, M. Anitha, "A Data-Driven Approach to Power System Contingency Analysis Using Support Vector Machines," SSRG International Journal of Electrical and Electronics Engineering, vol. 12, no. 12, pp. 19-27, 2025. Crossref, https://doi.org/10.14445/23488379/IJEEE-V12I12P102
Abstract:
Power system security is one of the most considerable studies to understand the vulnerability of various contingencies occurring on the system. In this paper, the contingency ranking is performed for single-line outages. Among the increments of the load variation for cases like only active power loading, only reactive power loading, and both the active and reactive power loadings are considered to create various scenarios using the Active Power Performance Index (APPI). These scenarios have been classified through a Support Vector Machine Classifier (SVMC) to observe the impacts of line outage. The data has been generated using MATLAB software for the UPSEB Indian Utility 75-bus system. Python programming is used to classify using SVMC.
Keywords:
Active Power Performance Index, Contingency analysis, Data analysis, Machine Learning, Support Vector Machine Classifier.
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