Adaptive Power System Security Enhancement Using Machine Learning and Soft Computing

Power system security is a critical aspect of ensuring grid stability and reliability under dynamic operating conditions. This study explores advanced soft computing and machine learning techniques to improve contingency analysis and stability assessment. A novel collapse prediction index (NCPI) is proposed, integrating polynomial load modeling to provide a more comprehensive evaluation of system vulnerabilities. The approach effectively classifies line outages based on their impact on system security, facilitating proactive decision-making. By leveraging artificial intelligence-driven techniques, the proposed framework enhances fault detection, voltage stability, and contingency ranking. Simulation results demonstrate the effectiveness of the model in improving load flow accuracy, adaptive protection strategies, and overall grid resilience. The findings contribute to the development of intelligent, data-driven security mechanisms for modern power systems.