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Real-time D-PMU data compression for edge computing devices in digital distribution networks

Yang, Jiahui, Yu, Hao, Li, Peng, Ji, Haoran, Xi, Wei, Wu, Jianzhong ORCID: https://orcid.org/0000-0001-7928-3602 and Wang, Chengshan 2023. Real-time D-PMU data compression for edge computing devices in digital distribution networks. IEEE Transactions on Power Systems 10.1109/TPWRS.2023.3335282

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Abstract

The proliferation of distribution-level phasor measurement units (D-PMUs) with a high reporting rate brings a heavy transmission burden to communication systems of distribution networks, which necessitate efficient data compression on edge computing devices. This paper proposes a real-time D-PMU data compression algorithm, including three stages of prediction, quantization, and Bitpack. The current data frame is predicted by the adaptive normalized least mean square predictor based on the stochastic gradient descent algorithm. Then, the prediction errors are quantized as integers and Bitpack is established to extract significant bits and losslessly reduce the redundancy of the quantized data. For edge computing devices accessing multiple D-PMUs in distribution networks, a performance optimization mechanism is proposed. The spatial similarity of D-PMUs is explored to multiplex the predictor and reduce the computation burden. In addition, the compression performances can be adaptively adjusted to diminish the transmission delay in limited bandwidth conditions. Finally, the proposed method is validated and compared with the state-of-the-art methods using the field data and simulated data in normal and fault conditions of distribution networks. Moreover, the impacts and countermeasures of data quality are considered. The results demonstrate that the proposed method achieves accurate and efficient real-time compression in different scenarios.

Item Type: Article
Date Type: Published Online
Status: In Press
Schools: Engineering
Publisher: Institute of Electrical and Electronics Engineers
ISSN: 0885-8950
Date of First Compliant Deposit: 20 December 2023
Last Modified: 20 Dec 2023 22:39
URI: https://orca.cardiff.ac.uk/id/eprint/164767

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