2018/07/02 by Shah Hassan, Hassan, Shah, Hadia Sajjad +3
Engineering · #FOS: Computer and information sciences #FOS: Electrical engineering #Information Theory (cs.IT) #Power Quality and Harmonics #Signal Processing (eess.SP) #Smart Grid Energy Management #Smart Grid Security and Resilience #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.1807.00862
openalex publication_date 2018/07/02 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We detect the deviation of the grid frequency from the nominal value (i.e.,\n50 Hz), which itself is an indicator of the power imbalance (i.e., mismatch\nbetween power generation and load demand). We first pass the noisy estimates of\ngrid frequency through a hypothesis test which decides whether there is no\ndeviation, positive deviation, or negative deviation from the nominal value.\nThe hypothesis testing incurs miss-classification errors---false alarms (i.e.,\nthere is no deviation but we declare a positive/negative deviation), and missed\ndetections (i.e., there is a positive/negative deviation but we declare no\ndeviation). Therefore, to improve further upon the performance of the\nhypothesis test, we represent the grid frequency's fluctuations over time as a\ndiscrete-time hidden Markov model (HMM). We note that the outcomes of the\nhypothesis test are actually the emitted symbols, which are related to the true\nstates via emission probability matrix. We then estimate the hidden Markov\nsequence (the true values of the grid frequency) via maximum likelihood method\nby passing the observed/emitted symbols through the Viterbi decoder.\nSimulations results show that the mean accuracy of Viterbi algorithm is at\nleast 5 % greater than that of hypothesis test.\n