Enhancing Power System Security Using Machine Learning And SPBO-Optimized Generation Allocation

International Journal of Electrical and Electronics Engineering

© 2026 by SSRG - IJEEE Journal

Volume 13 Issue 1

Year of Publication : 2026

Authors : K Kiran, T Devaraju

10.14445/23488379/IJEEE-V13I1P104

How to Cite?

K Kiran, T Devaraju, "Enhancing Power System Security Using Machine Learning And SPBO-Optimized Generation Allocation," SSRG International Journal of Electrical and Electronics Engineering, vol. 13,  no. 1, pp. 31-39, 2026. Crossref, https://doi.org/10.14445/23488379/IJEEE-V13I1P104

Abstract:

The contemporary electrical power system is a well-integrated and interconnected environment with an incessantly growing percentage of renewable energy sources infiltration and augmented energy consumption. Traditional quantitative methods are expensive to process and do not provide sufficient nonlinearity and dynamism of grid operation. The proposed approach is a multi-layered system, uniting the latest machine learning procedures to classify the adaptive state of the system and the Student Psychology-Based Optimization (SPBO) algorithm in such a way that it would decide on the best responses to contingencies. The approach compares and contrasts ensemble ML models, which are Random Forest (RF), Support Vector Machines (SVM), Gradient Boosting Machines (GBM), and Extreme Gradient Boosting Machines (XGBM), to successfully pinpoint the power system security state in real-time. At the same time, SPBO is used to decide the optimal shift in the capacity of the generation to stabilize the system as soon as the contingency occurs and avoid the possible collapse. The technique is comprehensive since it simulates and executes the Flexible AC Transmission (FACT) equipment, which is placed to enhance the power transfer capacity. The simulation results revealed that the XGBM algorithm was most accurate in the classification to identify anomalies and threats in the system, contributing to a better situational awareness. However, more significantly, the SPBO-based solutions and generation schemes and schedules apply extremely high rates of contingency planning. This research capitalizes on the use of a data-driven strategy that is not only novel but also generic and can be applied across all power systems to enhance the security, resilience, and efficiency in the future.

Keywords:

Contingency Analysis, Extreme Gradient Boosting Machine (XGBM), Machine Learning algorithm, Student Psychology-Based Optimization.

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