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

TL;DR

This paper presents a hidden Markov model-based approach to detect power imbalances in smart grids by analyzing grid frequency deviations, improving detection accuracy over traditional hypothesis testing methods.

Contribution

It introduces a novel HMM-based method combined with hypothesis testing to enhance power imbalance detection accuracy in smart grids.

Findings

Viterbi algorithm improves detection accuracy by at least 5%.

HMM approach effectively models grid frequency fluctuations.

Method reduces false alarms and missed detections.

Abstract

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.