基于稳态特征和IGWO-FCM模糊聚类的非侵入式负荷监测方法

杜刃刃; 杨超; 蒲阳

杜刃刃,杨超,蒲阳.基于稳态特征和IGWO-FCM模糊聚类的非侵入式负荷监测方法[J].电测与仪表,2021,58(1):152-157.
Du Renren,Yang Chao,Pu Yang.Non-intrusive load monitoring method based on steady-state characteristics and IGWO-FCM fuzzy clustering[J].Electrical Measurement & Instrumentation,2021,58(1):152-157.

基于稳态特征和IGWO-FCM模糊聚类的非侵入式负荷监测方法

Non-intrusive load monitoring method based on steady-state characteristics and IGWO-FCM fuzzy clustering

DOI：10.19753/j.issn.1001-1390.2021.01.023

中文关键词: 非侵入式负荷检测电压-电流三次谐波含量差灰狼算法模糊C均值聚类算法

英文关键词:non-intrusive load detection voltage-current third harmonic content difference gray wolf algorithm fuzzy C-means clustering algorithm

基金项目:

作者中文名	作者英文名	单位
杜刃刃	Du Renren	贵州大学电气工程学院
杨超	Yang Chao	贵州大学电气工程学院
蒲阳	Pu Yang	贵州大学电气工程学院

摘要点击次数: 964

中文摘要:

针对当前非侵入式负荷监测（NILM）方法对低功率用电设备的辨识准确率不足的问题，提出了一种改进的方法。该方法以改进FCM初始聚类中心为基础，除了采用有功功率特征外，并选取基波功率因数和电压-电流三次谐波含量差作为新特征，引入灰狼算法(GWO)和单纯形法（SF）对聚类过程进行优化，通过模糊聚类来确定负荷的种类数，实现对负荷的识别。实验结果表明，随着负荷种类的增加，该方法在不同场景下具有良好的鲁棒性和准确性。

英文摘要:

Aiming at the problem that the current non-intrusive load monitoring (NILM) method has insufficient recognition accuracy for low-power electrical equipment, an improved method is proposed. This method is based on improving the initial clustering center of FCM. In addition to using active power feature, the fundamental wave power factor and voltage-current third harmonic content difference are selected as new features. The gray wolf algorithm (GWO) and the simplex method (SF) are introduced to optimize the clustering process, and the number of load types is determined by fuzzy clustering to realize load recognition. The experimental results show that the method has good robustness and accuracy in different scenarios with increasing load types.

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