摘要
针对现有非侵入式负荷监测方法难以解决的同种类型、同种参数与同种工况下的负荷辨识与位置定位等问题,提出了一种全新的基于电力线载波阻抗特性的电力系统负荷网络主动感知方法。首先,针对居民电能表后的家用电器负荷集群引入负荷网络的概念,将传输电能的电力线视作负荷集群的一部分,组成用户侧负荷网络构架;其次,基于电力线载波的高频传输线模型下负载阻抗与输入阻抗的不同,发射载波信号得到输入阻抗频谱特性,实现相同负荷的辨识与位置定位;最后,通过多组相关试验验证了所提出方法的有效性,基于XGBoost算法实现负荷网络矩阵参数的拟合与填充。辨识信号由被动的“负荷印记”变为主动的“阻抗感知”,解决了传统方法难以处理的相同负荷辨识与位置定位等问题。
Aiming at the problems that the load identification and location under the same type,the same parameters and the same working conditions are difficult to be realized by the existing non-intrusive load monitoring methods,we propose a new active sensing method of power system load network based on power line carrier impedance.Firstly,the concept of load network is introduced into the household appliance load cluster after the residential energy meter,and the power line transmitting electric energy is regarded as a part of the load cluster to form the user side load network framework.Secondly,based on the difference between load impedance and input impedance under the high frequency transmission line model of power line carrier,the spectrum characteristics of input impedance are obtained by transmitting carrier signal,so as to realize the identification and location of the same load.Finally,the effectiveness of the proposed method is verified by several groups of related experiments,and the fitting and filling of load network matrix parameters based on XGboost algorithm are realized.The identification signal is changed from passive"load signature"to active"impedance sensing",which solves the problems of the same load identification and location that are difficult to deal with by traditional methods.
作者
卢德龙
童充
吴志坚
汪新浩
雷鸣
徐箭
LU Delong;TONG Chong;WU Zhijian;WANG Xinhao;LEI Ming;XU Jian(State Grid Jiangsu Power Company Suzhou Power Supply Bureau,Suzhou 215004,China;School of Electrical and Information Engineering,Tianjin University,Tianjin 300072,China;School of Electrical Engineering and Automation,Wuhan University,Wuhan 430072,China)
出处
《高电压技术》
EI
CAS
CSCD
北大核心
2022年第4期1296-1307,共12页
High Voltage Engineering
基金
国家重点研发计划(2016YFB0901100)。
