Detection of Electric Bicycle Indoor Charging for Electrical Safety: A NILM Approach

• Published in: IEEE transactions on smart grid 2023-09, Vol.14 (5), p.1-1
• Main Authors: Liu, Yan, Xu, Qingshan, Yang, Yongbiao, Zhang, Weiguo
• Format: Article
• Language: English
• Subjects: Algorithms; Bicycles; Charging; electric bicycle indoor charging detection; Electric bicycles; Electrical safety; electrical safety detection; Event detection; Feature extraction; Home appliances; Monitoring; Non-intrusive load monitoring; one-class classification; Support vector machines; Training
• ISSN: 1949-3053; 1949-3061
• DOI: 10.1109/TSG.2023.3245636

The increasing fire accidents caused by electric bicycle (EB) indoor charging have raised great concerns. To ensure residential electrical safety, EB is prohibited from charging indoors by regulations, but efficient monitoring of EB indoor charging behaviour in pratice still remains as a challenging problem. From the perspective of non-intrusive load monitoring (NILM), this paper proposes a holistic approach to detect the EB indoor charging behaviour. Our approach utilizes four key elements: (i) a multi-window based cumulative sum algorithm to capture the turning-on event from aggregate power; (ii) a twostage feature selection method to determine the optimal features to form an effective appliance signature for load identification; (iii) a one-class support vector machine classifier to identify whether the turned-on appliance is EB or other appliances in a semi-supervised way; (iv) a Raspberry-Pi based edge device to implement the overall NILM algorithm. The experiment results verify the effectiveness of our NILM based approach, which can obtain a high detection performance with a lowdimensional feature space, thus providing a cost-effective and high-performance solution for EB indoor charging detection.