Open AccessA non‐intrusive load state identification method considering non‐local spatiotemporal feature
Author(s) -
Zhang Zhenyu,
Li Yong,
Duan Jing,
Duan Yilong,
Guo Yixiu,
Cao Yijia,
Rehtanz Christian
Publication year - 2022
Publication title -
iet generation, transmission and distribution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.92
H-Index - 110
eISSN - 1751-8695
pISSN - 1751-8687
DOI - 10.1049/gtd2.12330
Subject(s) - computer science , convolutional neural network , feature (linguistics) , state (computer science) , feature extraction , identification (biology) , pattern recognition (psychology) , data mining , artificial intelligence , visualization , point (geometry) , algorithm , mathematics , botany , geometry , biology , philosophy , linguistics
This paper presents a non‐intrusive method for identifying the load state of a distribution network. The method focuses on continuously varying loads. By considering the load on‐off state switching points and the continuous features at on state, a deep convolutional method considering non‐local spatiotemporal features is proposed. The addition of an attention component to the convolutional network enhances the non‐local feature extraction capability of the convolutional network. Ultimately, the effectiveness of the method is demonstrated in an experimental setting. In addition, this paper demonstrates that the proposed method can effectively integrate switching point features as well as persistent features through neural network visualization techniques.