Abstract:

To reduce fire accidents induced by illegal indoor charging of electric bicycles, non-intrusive load monitoring has become a mainstream research direction for electric bicycle monitoring. Nevertheless, practical application problems of low-cost monitoring terminals remain to be solved urgently. U-I trajectories are constructed with voltage signals and Fourier-filtered current signals, and color coding is implemented for trajectory feature processing. The ShuffleNetV2 neural network is adopted to realize switching detection and identification of illegal indoor charging behavior of electric bicycles. Meanwhile, an on-site charging identification terminal for electric bicycles is developed to resolve engineering application constraints. Experimental results demonstrate that the proposed monitoring equipment can accurately identify illegal indoor charging loads of various electric bicycle brands at the edge computing terminal, with an identification accuracy of 98.75%. It can effectively prevent fire hazards without data transmission dependence, transmission delay, privacy leakage and extra hardware cost.

Key words: non-intrusive load monitoring, U-I trajectory, color coding, ShuffleNetV2 neural network, K210 chip, edge computing