基于负荷空间划分的非侵入式辨识算法

A non-intrusive identification algorithm based on partition of the load space

针对传统的侵入式监测系统在设备投入、复杂性以及扩展性上存在的缺陷,以非侵入采集机制获取的负荷数据为基础,研究了一种基于负荷空间划分的负荷辨识方法。首先对5种典型负荷的10种特征进行降维处理,得到最佳辨识特征;利用最小平方误差算法构建判别函数,划分5种负荷的特征空间;利用负荷特征空间交叉的方法,实现负荷的辨识。利用实际采集的用电数据验证了该算法的有效性,且通过特征降维处理提高了算法的运算效率,通过负荷分离提高了辨识准确性。

Aiming at the defects of traditional intrusive monitoring system in equipment input,complexity and expansibility,this paper discusses a non-intrusive load identification algorithm based on partition of the feature space of load data.It uses K-L transform to reduce the dimension of the 10 typical features of the five typical loads,and obtains the best identification feature??It uses the least squares error algorithm to construct the discriminant function and divides the feature space into the five loads??It uses the load feature space intersection method to achieve load identification.The power consumption data acquired in the real world is used to prove that the algorithm is able to effectively achieve the load decomposition and accurately recognize the status of loads.In addition,the feature dimensionality reduction improves the efficiency of the algorithm,and the load separation improves the recognition accuracy.