基于V-I轨迹与高次谐波特征的非侵入式负荷识别方法

针对传统方法无法准确识别含高次谐波家用负荷的问题，文中提出了基于V-I轨迹矩阵、功率及高次谐波多特征融合的负荷辨识方法。首先，分析了11种典型家用负荷的V-I轨迹、功率特征以及谐波特征，提出了基于像素图像转换的混合特征矩阵构建方法，将负荷的功率、高次谐波特征通过二进制编码转换与基本V-I像素轨迹相融合，丰富了样本的特征信息；然后以混合特征矩阵作为卷积神经网络的输入，实现了对家用负荷类型的准确识别。算例中，文中所提算法可准确区分功率特征相似但高次谐波含量不同的加热器与吹风机2种负荷，且其对全类型家用负荷的准确辨识率超过93%。该算法的应用可为实际中准确排查含高次谐波家用负荷的用电安全隐患提供有力的技术支撑。

Non-intrusive load identification method based on V-I trajectory and high-order harmonic feature

To address the problem that traditional methods cannot accurately identify household loads containing high-order harmonics, a non-intrusive load identification method based on the fusion of multiple features containing V-I trajectory matrix, power and high-order harmonics is proposed. Firstly, the V-I trajectory matrix, power characteristics and harmonic characteristics of 11 kinds of family load are analyzed. Secondly a hybrid feature matrix construction method based on pixel image conversion is proposed. The power and high-order harmonic characteristics of the load are combined with the basic V-I pixel trajectory through binary coding conversion,which enriches the characteristic information of samples. Thirdly,the mixed feature matrix is used as the input of the convolutional neural network to realize the accurate recognition of the household load identification. In the calculation example,the algorithm proposed in this paper can accurately distinguish two loads of heaters and hair dryers with similar power characteristics but different high-order harmonic content. It achieves an identification accuracy rate of more than 93% for all types of household loads. This algorithm provides the technical support for accurately investigating potential safety risk of household electrical loads containing high-order harmonics in engineering application.