Coded Quickest Classification With Applications in Bandwidth-Efficient Smart Grid Monitoring

Abstract

Cyber-physical systems, such as smart grids, have received lots of attention recently. Unfortunately, security breaches in cyber-physical systems can result in catastrophic consequences, thus needing to be carefully monitored. For example, abnormal voltage quality events, which are more likely to happen because of unstable renewable energy sources in smart grids, harm delicate electronic devices. We thus focus on the quickest classification, or multi-hypothesis quickest change detection, which jointly detects and classifies multiple abnormal events. Both the classification delay and misclassification probability need to be low. Multiple smart meters are adopted, where each meter transmits its local decision to a fusion center for making the final decision. For energy saving, the bandwidth (link capacity) between each meter and the fusion center is limited to be one bit. Moreover, some meters may be faulty and mislead the final decision. To combat these faulty meters under the limited bandwidth, a code-based framework for quickest classification is proposed. Our contribution is two-fold. First, a new local decision rule based on the stochastic ordering theory is proposed. Compared with existing matrix-cumulative-sums algorithm, the newly proposed local decision rule has lower complexity and comparable performance. Second, a new fusion method based on codebook switching and minimum Hamming distance rule is developed. Compared with existing fault-tolerant methods, the newly-developed method can significantly lower the misclassification probabilities.