The objective of this article is real-time detection of a change-point in the baseline distribution of the frequency signal generated by Phasor Measurement Units (PMUs) that could indicate potential for voltage collapse, false data injection, or other security threats. The alarm flags an anomaly event when the cumulative sum (CUSUM) of the log-likelihood departures of the Hurst exponent of the frequency from its baseline statistic exceeds a threshold set up as a compromise between the conflicting objectives of minimum detection delay and acceptable False Alarm Rate. As main theoretical contribution, an extra protection layer is developed that provides an estimate of the time to the alarm event, giving the Transmission System Operator (TSO) or an autonomous agent more time to deploy proactive measures to avoid large catastrophic system states. The proposed method is illustrated by a retrospective analysis of the 2012 Indian blackout that reveals that 10-12 minutes before the voltage collapse, a significant increase in the Hurst exponent of the frequency could have been detected. Conceptually, this shows that PMUs provide significant value towards ensuring autonomous cognition in the power grid by enabling the abnormal events to be fault-detected in an unsupervised fashion.