基于颜色编码与谐波特征融合的非侵入式负荷识别方法

Non-intrusive load monitoring based on color coding and harmonic feature fusion

非侵入式负荷识别(NILM)作为分析用户用电行为的主要途径,对开展能耗监测、实现用电安全评估具有重要意义。针对原始电压-电流(V-I)轨迹特征辨识准确度不高的问题,本文提出一种基于V-I轨迹特征的颜色编码和负荷高次谐波特征相融合的识别方法。首先,将高频采样数据经过数据预处理,提取出负荷的V-I轨迹和高次谐波特征,并利用颜色编码技术将瞬时无功功率、功率因数与电流序列分布的数值特征分别映射到彩色图像RGB的三个通道像素矩阵中。然后,引入高次谐波特征与RGB三通道像素矩阵相融合,形成混合彩色图像。最后,运用AlexNet网络的迁移学习对负荷进行训练和分类,通过PLAID数据集与实测数据进行验证,所述负荷识别方法辨识准确率达到95%以上,模型具有良好的泛化能力,可用于家庭及类似场合的用电安全管理。

Non-intrusive load monitoring(NILM),as the main way to analyze user’s electricity consumption behavior,is of great significance to carry out energy consumption monitoring and realize electricity safety assessment.Aiming at the problem that the identification accuracy of the original voltage-current(V-I)trajectory features is not high,a recognition method based on the color coding of V-I trajectory features and the fusion of load high-order harmonic features is proposed in this paper.First,the high-frequency sampling data is preprocessed to extract the V-I trajectory and high-order harmonic characteristics of the load,and the numerical characteristics of instantaneous reactive power,power factor and current sequence distribution are mapped to the three channel pixel matrix of RGB color image by using color coding technology.Then,the high-order harmonic features are introduced and fused with three channel pixel matrix to form a mixed color image.Finally,the transfer learning of AlexNet network is used to train and classify the loads,which is verified by the PLAID data set and the measured data.The identification accuracy of the proposed load identification method is more than 95%,and the model has good generalization ability,which can be used for electricity safety management in home and similar occasions.