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Anomaly detection algorithms for vehicle-to-network interaction operator-level interactive control support systems based on machine learning

Author

Listed:
  • Wen Wang
  • Peijun Li
  • Ye Yang
  • Jian Qin
  • Guoqiang Zu
  • Ke Xu
  • Xiaoqing Zhang
  • Jiancheng Yu

Abstract

This study proposes a charging transaction model based on a consortium blockchain to establish a mutual trust network between operators and power supply companies. The model employs the Practical Byzantine Fault Tolerance algorithm for transaction validation and utilizes smart contracts to handle account transfers, evaluations, and queries. Through case analysis, it is confirmed that the proposed integration of the particle swarm optimization-K-means method can simultaneously achieve low false-positive rates and high detection rates. Experimental results demonstrate that the anomaly detection algorithm in this paper effectively screens out abnormal data from on-chain charging data.

Suggested Citation

  • Wen Wang & Peijun Li & Ye Yang & Jian Qin & Guoqiang Zu & Ke Xu & Xiaoqing Zhang & Jiancheng Yu, 2025. "Anomaly detection algorithms for vehicle-to-network interaction operator-level interactive control support systems based on machine learning," International Journal of Low-Carbon Technologies, Oxford University Press, vol. 20, pages 1-20.
    • Handle: RePEc:oup:ijlctc:v:20:y:2025:i::p:1a-20.
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    File URL: http://hdl.handle.net/10.1093/ijlct/ctae266
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