Predictive Analysis of Smart Grid Reliability Using Enhanced Generator Metrics

The growing complexity of smart grids requires robust monitoring tools and predictive mechanisms to avoid catastrophic instability. This study explores a high-resolution, augmented dataset of generator metrics to analyze the underlying causes of system instability. By performing feature correlation analysis and class-based decomposition, we identify the most impactful variables contributing to unstable states in modern grid systems. Our findings highlight the disproportionate influence of generator gains and power outputs, suggesting potential for early anomaly detection and adaptive control systems.