Understanding electric vehicle (EV) charging on the distribution network is key to effective EV charging management and aiding decarbonization across the energy and transport sectors. Advanced metering infrastructure has allowed distribution system operators and utility companies to collect high-resolution load data from their networks. These advancements enable the non-intrusive load monitoring (NILM) technique to detect EV charging using load measurement data. While existing studies primarily focused on NILM for EV charging detection in individual households, there is a research gap on EV charging detection at the feeder level, presenting unique challenges due to the combined load measurement from multiple households. In this paper, we develop a novel and effective approach for EV detection at the feeder level, involving sliding-window feature extraction and classical machine learning techniques, specifically models like XGBoost and Random Forest. Our developed method offers a lightweight and efficient solution, capable of quick training. Moreover, our developed method is versatile, supporting both offline and online EV charging detection. Our experimental results demonstrate high-accuracy EV charging detection at the feeder level, achieving an F-Score of 98.88% in offline detection and 93.01% in online detection.

翻译：理解配电网中的电动汽车（EV）充电行为是实现高效充电管理、推动能源与交通领域脱碳的关键。高级量测基础设施使得配电系统运营商和电力公司能够从其网络中采集高分辨率负荷数据。这些技术进步使得非侵入式负荷监测（NILM）技术能够利用负荷测量数据检测EV充电。现有研究主要集中在单户家庭的EV充电NILM检测，而面向馈线级EV充电检测的研究存在空白——由于多户家庭的组合负荷测量，这一场景面临独特挑战。本文提出一种新颖且有效的馈线级EV检测方法，该方法融合滑动窗口特征提取与经典机器学习技术，具体采用XGBoost和随机森林等模型。所开发的方法轻量高效，具备快速训练能力，且支持离线与在线两种EV充电检测模式。实验结果表明，该方法在馈线级EV充电检测中实现了高精度：离线检测F值达98.88%，在线检测F值达93.01%。