As the world's power grids grow more intelligent and interconnected, they inherit new vulnerabilities alongside their capabilities — cyberattacks, equipment failures, and the slow drift of systems edging toward collapse. Researchers have answered this tension not by declaring a single solution, but by building a reproducible framework that fairly benchmarks machine learning models against the full spectrum of grid anomalies, from sudden voltage spikes to imperceptible operational decay. Their hybrid ensemble models achieved accuracy approaching near-certainty, yet the deeper contribution is a