Non-intrusive load identification method based on GAF and RAN networks

被引:0
|
作者
Wang, Jianyuan [1 ]
Sun, Yibo [1 ]
机构
[1] Northeast Elect Power Univ, Key Lab Modern Power Syst Simulat & Control & Rene, Minist Educ, Jilin, Peoples R China
来源
关键词
non-intrusive load identification; gram code; one-dimensional reactive electric signal; residual attention net; bottleneck residual blocks;
D O I
10.3389/fenrg.2023.1330690
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
Non-intrusive load identification can improve the interaction efficiency between the power supply side and the user side of the grid. Applying this technology can alleviate the problem of energy shortage and is a key technique for achieving efficient management on the user side. In response to the cumbersome process of manually selecting load features and the low accuracy of identification in traditional machine learning algorithms for non-intrusive load identification, this paper proposes a method that transforms the one-dimensional reactive electric signal of the load into a two-dimensional image using Gram coding and utilizes the Residual Attention Network (RAN) for load classification and recognition. By transforming the one-dimensional electrical signal into a two-dimensional image as the input to the RAN network, this approach retains the original load information while providing richer information for the RAN network to extract load features. Furthermore, the RAN network effectively addresses the poor performance and gradient vanishing issues of deep learning networks through bottleneck residual blocks. Finally, experiments were conducted on a public dataset to verify the effectiveness of the proposed method.
引用
收藏
页数:11
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