A sequential Bayesian approach to online power quality anomaly segmentation

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Increased observability on power distribution networks can reveal signs of incipient faults which can develop into costly and unexpected plant failures. While low-cost sensing and communications infrastructure is facilitating this, it is also highlighting the complex nature of fault signals, a challenge which entails precisely extracting anomalous regions from continuous data streams before classifying the underlying fault signature. Doing this incorrectly will result in capture of uninformative data. Extraction processes can be confounded by operational noise on the network including harmonics produced by embedded generation. In this paper, an online model is proposed. Our Bayesian Changepoint Power Quality anomaly Segmentation allows automated segmentation of anomalies from continuous current waveforms, irrespective of noise. Demonstration of the effectiveness of the proposed technique is carried out with operational field data as well as a challenging simulated network, highlighting the ability to accommodate noise from typical network penetration levels of power electronic devices.
Original languageEnglish
Number of pages10
JournalIEEE Transactions on Industrial Informatics
Early online date22 Jun 2020
Publication statusE-pub ahead of print - 22 Jun 2020


  • power distribution
  • high resolution power quality Monitoring
  • power system anomaly detection
  • changepoint analysis

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