小波检测和特征图谱决策的非侵入电动自行车充电实时监测系统

Non-intrusive Real-time Monitoring System for Electric Bicycle Charging Based on Wavelet Detection and Feature Graph Decision

电动自行车违规入户充电行为具有时间随机性以及空间隐蔽性,存在较大安全隐患且难以有效管理。利用非侵入式监测系统具有实时自主执行和便捷易推广的特性,文中提出了基于小波检测和特征图谱决策的非侵入式电动自行车充电实时监测系统。考虑电动自行车负荷的物理结构和充电特性,从暂态和稳态两方面分析电动自行车负荷的典型共性特征;预先构建具有强可分性和通用性的电动自行车专有特征图谱实现电动自行车稳态共性特征的一致性结构化表征;实际监测过程中,为了降低系统的算力需求和数据传输压力,基于小波变换精确定位具有高频分量的电动自行车专有暂态现象完成类电动自行车充电事件检测。最后,提取事件波形并通过图谱训练高效分类器进行负荷认定并实时上传。通过对实际用户进行监测,验证了监测系统的有效性,可以有效解决电动自行车进楼入户充电的问题。

The illegal charging behavior of electric bicycles(EBs)in households has temporal randomness and spatial concealment,which poses significant safety hazards and is difficult to effectively manage.A non-intrusive real-time monitoring system for EB charging based on wavelet detection and feature graph decision is proposed,utilizing the characteristics of real-time autonomous execution and promotion-friendly non-intrusive monitoring systems.Considering the physical structure and charging characteristics of EB loads,the typical common characteristics of EB loads are analyzed from both transient and steady-state perspectives.The EB proprietary feature map with strong distinguishability and universality is constructed in advance to realize consistent and structured expression of EB steady-state common features.In the actual monitoring process,in order to reduce the computational power demand and data transmission pressure of the system,EB specific transient phenomena with high-frequency components are accurately located based on wavelet transform to complete EB like charging event detection.Finally,the monitoring system extracts event waveforms and trains efficient classifiers through graphs for load identification and real-time upload.By monitoring actual users,the effectiveness of the monitoring system has been verified,which can effectively solve the problem of charging EBs in buildings and households.