基于LMD和模型匹配的家电负荷识别算法

家电负荷识别是智能用电的重要环节,传统侵入式负荷监测具有成本高、安装维护复杂的缺点,因此以非侵入式负荷监测为基础研究家电负荷识别算法。结合系统辨识的基本原理和方法,以稳态电流、稳态电压为特征,提出一种基于局部平均分解(LMD)和模型匹配的家电负荷识别算法。通过预先获取用电网络中各负荷的稳态数据,构建线性和非线性模型库。利用LMD算法将混合信号分解为单个负荷的用电数据,通过预筛选确定分离数据所属模型库,根据模型匹配原则进行负荷识别。仿真结果表明,所提算法可以准确识别出各负荷的运行状态,运算效率高,并能有效应对用电网络中有新负荷加入的情况。

Identification Algorithm for Appliance Load Based on LMD and Model Matching

Appliance load identification is an important part of intelligent power consumption. Traditional intrusion load monitoring has the drawbacks of high cost, complex installation and maintenance, hence the need of a load identification algorithm based on non-intrusive load monitoring. According to the principles and method of system identification, a load identification algorithm based on local mean decomposition(LMD)and model matching is proposed, which is characterized by steady-state current and voltage. In order to construct the linear and nonlinear model libraries, the steady-state data of each load in the power network is collected in advance. Then, the LMD algorithm is used to decompose the mixed signals into electricity consumption data of single load. By pre-screening, these separated data are categorized into the model library that they belong to, and finally these loads are recognized according to the model matching principles. Simulation results show that the proposed algorithm can accurately identify the operating status of each load and has high computational efficiency. Furthermore, it can effectively deal with the situation when a new load joins the power network.