  We detect the deviation of the grid frequency from the nominal value (i.e.,
50 Hz), which itself is an indicator of the power imbalance (i.e., mismatch
between power generation and load demand). We first pass the noisy estimates of
grid frequency through a hypothesis test which decides whether there is no
deviation, positive deviation, or negative deviation from the nominal value.
The hypothesis testing incurs miss-classification errors---false alarms (i.e.,
there is no deviation but we declare a positive/negative deviation), and missed
detections (i.e., there is a positive/negative deviation but we declare no
deviation). Therefore, to improve further upon the performance of the
hypothesis test, we represent the grid frequency's fluctuations over time as a
discrete-time hidden Markov model (HMM). We note that the outcomes of the
hypothesis test are actually the emitted symbols, which are related to the true
states via emission probability matrix. We then estimate the hidden Markov
sequence (the true values of the grid frequency) via maximum likelihood method
by passing the observed/emitted symbols through the Viterbi decoder.
Simulations results show that the mean accuracy of Viterbi algorithm is at
least $5$\% greater than that of hypothesis test.
