A non‐intrusive load state identification method considering non‐local spatiotemporal feature. Issue 4 (25th October 2021)

• Record Type: Journal Article
• Authors: Zhang, Zhenyu; Li, Yong; Duan, Jing; Duan, Yilong; Guo, Yixiu; Cao, Yijia; Rehtanz, Christian
• Is Part Of: IET generation, transmission & distribution. Volume 16:Issue 4(2022)
• Page Start: 792
• Page End: 803
• Publication Date: 2021-10-25
• Subjects: Electric power production -- Periodicals; Electric power transmission -- Periodicals; Electric power distribution -- Periodicals; 621.3105
• DOI: 10.1049/gtd2.12330
• Languages: English
• ISSNs: 1751-8687

Abstract

Abstract: 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.