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Open AccessArticle

Power-Based Non-Intrusive Condition Monitoring for Terminal Device in Smart Grid

School of Electrical Engineering, Zhejiang University, Hangzhou 310027, China
Author to whom correspondence should be addressed.
This paper is an extended version of our conference paper: Aidong, X.; Yixin, J.; Yang, C.; Guoming, Z.; Xiaoyu, J.; Wenyuan, X. “ADDP: Anomaly Detection for DTU Based on Power Consumption Side-Channel” Proceedings of the 2019 IEEE 3rd Conference on Energy Internet and Energy System Integration (EI2), 8–10 November 2019.
Sensors 2020, 20(13), 3635;
Received: 19 May 2020 / Revised: 18 June 2020 / Accepted: 19 June 2020 / Published: 28 June 2020
(This article belongs to the Special Issue Sensors for Smart Grids)
As a critical component in the smart grid, the Distribution Terminal Unit (DTU) dynamically adjusts the running status of the entire smart grid based on the collected electrical parameters to ensure the safe and stable operation of the smart grid. However, as a real-time embedded device, DTU has not only resource constraints but also specific requirements on real-time performance, thus, the traditional anomaly detection method cannot be deployed. To detect the tamper of the program running on DTU, we proposed a power-based non-intrusive condition monitoring method that collects and analyzes the power consumption of DTU using power sensors and machine learning (ML) techniques, the feasibility of this approach is that the power consumption is closely related to the executing code in CPUs, that is when the execution code is tampered with, the power consumption changes accordingly. To validate this idea, we set up a testbed based on DTU and simulated four types of imperceptible attacks that change the code running in ARM and DSP processors, respectively. We generate representative features and select lightweight ML algorithms to detect these attacks. We finally implemented the detection system on the windows and ubuntu platform and validated its effectiveness. The results show that the detection accuracy is up to 99.98% in a non-intrusive and lightweight way. View Full-Text
Keywords: power sensor; smart grid; condition monitoring; machine learning power sensor; smart grid; condition monitoring; machine learning
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Zhang, G.; Ji, X.; Li, Y.; Xu, W. Power-Based Non-Intrusive Condition Monitoring for Terminal Device in Smart Grid. Sensors 2020, 20, 3635.

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