AUTHOR=Xiao Xiaolong , Guo Jiahao , Yang Jinggang , Fang Xing , Shi Mingming , Liu Yang , Guo Maosen , Yang Zhencheng , Wu Zaijun 

TITLE=A novel switchgear state assessment framework based on improved fuzzy C-means clustering method with deep belief network

JOURNAL=Frontiers in Energy Research

VOLUME=11

YEAR=2023

URL=https://www.frontiersin.org/journals/energy-research/articles/10.3389/fenrg.2023.1335184

DOI=10.3389/fenrg.2023.1335184

ISSN=2296-598X

ABSTRACT=<p>Due to the problems such as fuzzy state assessment grading boundaries, the recognition accuracy is low when using traditional fuzzy techniques to grade the switchgear state. To address this problem, this paper proposes a switchgear state assessment and grading method based on deep belief network (DBN) and improved fuzzy C-means clustering (IFCM). Firstly, the switchgear state information data are processed by normalization method; then the feature parameters are extracted from the switchgear state information data by using DBN, and finally the extracted feature parameters are categorised according to the condition of switchgear equipment through clustering using IFCM. The experimental results show that the accuracy of the method in assessing the switchgear state under small sample conditions reaches 94, which exceeds the accuracy of other switchgear state assessment grading methods currently in use.</p>