摘要
针对低压台区表前漏电位置隐蔽性高,传统人工排查方式依赖于运维人员经验水平、间歇性漏电定位难度大等问题,提出了一种低压台区用户表前分支线漏电定位方法。首先基于电气先验知识,构建表前漏电物理模型分析漏电前后用户最短路径虚拟阻抗变化机理特征,建立台区用户多元线性回归方程求解用户时序虚拟阻抗矩阵,将其按列展平后作为后续模型输入。其次,建立邻近和全局关联对称相对熵模型,采用分段聚合法改进模型计算输出形式,将表前漏电用户定位问题转化为时间序列异常检测问题。在传统重构损失函数中引入最小最大化对抗优化机制,提高模型对于单一关联特征的提取能力,进一步放大正常与表前漏电用户的特征差异。结合对称相对熵协同重构误差异常评分机制,将超出阈值分数的用户划归为异常用户。搭建IEEE欧洲低压馈线系统,仿真多种漏电场景获得充足训练样本后对模型进行最优调参和消融实验,结果表明所提模型检测性能较同类算法更优。最后,在考虑台区存在停电/空载用户特殊场景以及电表量测误差、电磁干扰等影响因素下,模型表现出较高的抗干扰性,并在真实台区测试中验证了所提模型的有效性和泛用性。
To address the high concealment of pre-meter leakage locations in low-voltage distribution networks-as well as the limitations of traditional manual inspection methods that rely heavily on maintenance personnel experience and struggle with intermittent leakages-this paper proposes a branch-line leakage localization method targeting users on the customer side of the meter.First,leveraging electrical prior knowledge,a physical model of pre-meter leakage is established,and the underlying mechanism of changes in users′shortest-path virtual impedance before and after leakage is analyzed.Multivariate linear regression equations are then constructed for users in a distribution area to derive temporal virtual impedance matrices,which are flattened column-wise as model input.A symmetric relative entropy model is proposed to capture both local(adjacent)and global dependencies among users.Its output accuracy is enhanced through a segment aggregation strategy,effectively transforming the leakage localization problem into a time-series anomaly detection task.To improve the model′s sensitivity to subtle feature deviations,a minimax adversarial optimization mechanism is introduced into the reconstruction loss function to amplify differences between normal and leaking users.This is further combined with a collaborative anomaly scoring method based on symmetric relative entropy,enabling robust identification of anomalous users exceeding a predefined threshold.Extensive simulations on the IEEE European low-voltage feeder system under various leakage scenarios are conducted to support hyperparameter tuning and ablation studies.Experimental results demonstrate that the proposed method outperforms existing algorithms in detection accuracy.Moreover,by addressing edge cases-such as outages,no-load users,measurement errors,and electromagnetic interference-the model exhibits strong anti-interference capability.Its effectiveness and generalization ability are further validated through deployment tests on real-world distribution networks.
作者
陈磊
苏华锋
苏盛
冯萧飞
李彬
Chen Lei;Su Huafeng;Su Sheng;Feng Xiaofei;Li Bin(School of Electrical and Information Engineering,Changsha University of Science and Technology,Changsha 410014,China;Dongguan Power Supply Bureau of Guangdong Power Grid Corporation,Dongguan 530221,China)
出处
《仪器仪表学报》
北大核心
2025年第6期276-289,共14页
Chinese Journal of Scientific Instrument
关键词
低压台区
虚拟阻抗
邻近关联
全局关联
对称相对熵
表前漏电
low voltage distribution networks
virtual impedance
adjacent dependency
global dependency
kullback-leibler
pre-meter leakage
作者简介
陈磊,2020年于长沙理工大学获得学士学位,现为长沙理工大学硕士研究生,主要研究方向为低压漏电故障定位。E-mail:516901064@qq.com;苏华锋,2007年于清华大学获得学士学位,现为广东电网公司东莞供电局高级工程师,主要研究方向为配电网运维管理。E-mail:shf03@126.com;通信作者:苏盛,2002年于武汉大学获得硕士学位,2009年于华中科技大学获博士学位,现为长沙理工大学电气与信息工程学院教授,主要研究方向为基于配用电大数据的用电异常检测。E-mail:susheng@163.com;冯萧飞,2021年于河南工业大学获得学士学位,现为长沙理工大学博士研究生,研究方向为低压漏电故障定位。E-mail:1648529229@qq.com;李彬,2019年于长沙理工大学获得硕士学位,现为长沙理工大学博士研究生,主要研究方向为配用电大数据分析和电力气象灾害分析。E-mail:libin2021666@163.com。
