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

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较高的准确率。

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 in this paper.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 the load recognition.The experimental results show that the method has good robustness and high accuracy in different scenarios with the increasing of load types.