摘要
Abstract
Traditional power strip overload monitoring typically relies on current sensors,which are invasive,costly,and difficult to deploy in existing devices.To address these limitations,this paper proposes a non-intrusive overload monitoring method based on the ex-ternal thermal characteristics of power strip conductors.A dual-node electro-thermal coupling model consisting of the conductor core,wire surface,and ambient environment is established to describe the dynamic relationship between load current and external temperature.The thermal parameters are identified experimentally,and a state observer is introduced to compensate for the thermal lag inherent in sur-face temperature measurements,enabling dynamic current inversion from external temperature data.
To mitigate the strong dependence of analytical thermal models on unknown wire parameters,a lightweight one-dimensional convolu-tional neural network(1D-CNN)is further designed using temperature time-series inputs,and overload detection is formulated as a bina-ry classification problem.Experimental results obtained under multiple wire specifications,load profiles,and ambient conditions demon-strate that the proposed method achieves a fast dynamic response within a load range of 5-15 A and maintains an overload detection ac-curacy exceeding 90%for previously unseen wire configurations.The proposed approach enables effective overload monitoring without ad-ditional current sensors,providing a practical and low-cost solution for power strip safety monitoring.关键词
非侵入式监测/电热耦合模型/状态观测器/电流反演/神经网络Key words
Non-intrusive monitoring/Electro-thermal coupling model/State observer/Current inversion/Neural network分类
信息技术与安全科学
