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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., mismatchbetween power generation and load demand). We first pass the noisy estimates ofgrid frequency through a hypothesis test which decides whether there is nodeviation, 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 misseddetections (i.e., there is a positive/negative deviation but we declare nodeviation). Therefore, to improve further upon the performance of thehypothesis test, we represent the grid frequency's fluctuations over time as adiscrete-time hidden Markov model (HMM). We note that the outcomes of thehypothesis test are actually the emitted symbols, which are related to the truestates via emission probability matrix. We then estimate the hidden Markovsequence (the true values of the grid frequency) via maximum likelihood methodby passing the observed/emitted symbols through the Viterbi decoder.Simulations results show that the mean accuracy of Viterbi algorithm is atleast $5$\% greater than that of hypothesis test.

Power Imbalance Detection in Smart Grid via Grid Frequency Deviations: A Hidden Markov Model based Approach