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Abnormal Data Detection and Identification Method of Distribution Internet of Things Monitoring Terminal Based on Spatiotemporal Correlation

Author

Listed:
  • Nan Shao

    (School of Electrical and Electronic Engineering, Shandong University of Technology, Zibo 255000, China)

  • Yu Chen

    (School of Electrical and Electronic Engineering, Shandong University of Technology, Zibo 255000, China)

Abstract

As an important part of the ubiquitous power Internet of Things, the distribution Internet of Things can further improve the automation and informatization level of the distribution network. The reliability of the measurement data of the low-voltage terminal unit, as the sensing unit of the sensing layer of the distribution Internet of Things, has a great impact on the fault processing and advanced applications of the distribution Internet of Things. The self-check and the equipment working status monitoring of the main station of the low-voltage terminal unit struggle to identify the abnormality of measurement data. Aiming at this problem, an abnormal data detection and identification recognition method of a distribution Internet of Things monitoring terminal is proposed on the basis of spatiotemporal correlation. First, using the temporal correlation of monitoring terminal data, the proposed composite temporal series similarity measurement criterion is used to calculate the distance matrix between data, and the abnormal data detection is realized via combination with the improved DBSCAN algorithm. Then, using the spatial correlation of the data of the terminal unit, the geometric features of the spatial cross-correlation coefficient of the terminal nodes are extracted as the input of the cascaded fuzzy logic system to identify the abnormal source. Lastly, the effectiveness of the method is verified by a practical example.

Suggested Citation

  • Nan Shao & Yu Chen, 2022. "Abnormal Data Detection and Identification Method of Distribution Internet of Things Monitoring Terminal Based on Spatiotemporal Correlation," Energies, MDPI, vol. 15(6), pages 1-19, March.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:6:p:2151-:d:771832
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    References listed on IDEAS

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    4. Marek Florkowski, 2021. "Anomaly Detection, Trend Evolution, and Feature Extraction in Partial Discharge Patterns," Energies, MDPI, vol. 14(13), pages 1-18, June.
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    Cited by:

    1. Guangyu Chen & Yijie Wu & Li Yang & Ke Xu & Gang Lin & Yangfei Zhang & Yuzhuo Zhang, 2022. "Ultra-Short-Term Load Dynamic Forecasting Method Considering Abnormal Data Reconstruction Based on Model Incremental Training," Energies, MDPI, vol. 15(19), pages 1-21, October.

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