Intelligent Smart Sensor Networks for Sustainable and Predictive Power System Stability: A Machine Learning-Driven Approach

Ensuring power system stability in modern energy networks is increasingly complex due to growing demand and renewable energy integration. This study presents an innovative approach that leverages machine learning-enhanced smart sensor networks and Thyristor Controlled Series Compensators (TCSC) to optimize real-time stability prediction and control. By integrating Artificial Neural Networks (ANN) and Random Forest algorithms, our system processes multi-source sensor data to achieve 97.8% stability prediction accuracy and 18.5% improvement in TCSC efficiency, outperforming conventional methods. Additionally, the proposed private network-enabled sustainable communication model reduces energy consumption by 21.9%, enhancing system resilience while minimizing environmental impact. A comparative study demonstrates faster convergence (0.42 s) and significant reductions in latency (150 ms) for real-time control. This research lays a foundation for scalable, adaptive, and sustainable power grid management, advancing smart grid development and infrastructure sustainability.