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Applying the Geometric Features of Cumulative Sums to the Development of Event Detection

Author

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  • Men-Shen Tsai

    (Graduate Institute of Automation Technology, Research Center of Energy Conservation for New Generation of Residential, Commercial, and Industrial Sectors, National Taipei University of Technology, Taipei 106344, Taiwan)

  • Yen-Kuang Lin

    (College of Mechanical & Electrical Engineering, National Taipei University of Technology, Taipei 106344, Taiwan)

Abstract

As a result of the severe energy shortage and the greenhouse effect, experts worldwide have been devoted to solving energy management problems. Smart grid construction is an essential technology for mastering energy allocation. Smart grids enable end users to adjust their energy consumption via incentive measures, reduce the frequency of power supply instability, and improve energy efficiency. Non-intrusive load monitoring (NILM) is a vital technology for smart grid construction. One of the fundamental steps of NILM is event detection. Proper event detection can increase the accuracy of load identification. Among traditional methods, especially the event detection method developed with the CUSUM method, although the accuracy is reasonable, the precision, recall, and f 1 score are not relatively better. Thus, there is an opportunity to improve the performance of CUSUM. Additionally, many studies focus on the step-like event, but the long-transient event is often overlooked in event detection. Therefore, in this study, it was observed that when the transient current deviates from the steady-state current, the transient current can be regarded as a key indicator for event detection. With this observation, a method is proposed to convert the root mean square (RMS) current into a cumulative sum (CUSUM) diagram method and identify turning points representing events from the CUSUM geometry. Once the slope of the turning point has been determined, event detection is achieved. Compared with traditional methods, the proposed method is easy to implement, its recognition rate can reach around 98%, and the window length is reduced from 5 s to 3 s.

Suggested Citation

  • Men-Shen Tsai & Yen-Kuang Lin, 2023. "Applying the Geometric Features of Cumulative Sums to the Development of Event Detection," Energies, MDPI, vol. 16(20), pages 1-25, October.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:20:p:7207-:d:1265505
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    References listed on IDEAS

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    1. Amitay Kligman & Arbel Yaniv & Yuval Beck, 2023. "Energy Disaggregation of Type I and II Loads by Means of Birch Clustering and Watchdog Timers," Energies, MDPI, vol. 16(7), pages 1-21, March.
    2. Tsai, Men-Shen & Lin, Yu-Hsiu, 2012. "Modern development of an Adaptive Non-Intrusive Appliance Load Monitoring system in electricity energy conservation," Applied Energy, Elsevier, vol. 96(C), pages 55-73.
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