文章基本信息

标题：Transformer Vibration Detection Based on YOLOv4 and Optical Flow in Background of High Proportion of Renewable Energy Access
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作者：Lei Su ; Hua Huang ; Lunming Qin 等
期刊名称：Frontiers in Energy Research
电子版ISSN：2296-598X
出版年度：2022
卷号：10
DOI：10.3389/fenrg.2022.764903
语种：English
出版社：Frontiers Media S.A.
摘要：In recent years, large-scale renewable energy access to substations has brought overload, harmonic, short circuit and other problems, which has led to an increase in the failure rate and shortening the service life of important power equipment such as transformers. Transformer is one of the key equipment in power system, and its operation status has an important impact on the safe and stable operation of power grid. In order to realize the real-time state evaluation of transformer, a real-time vibration signal detection method based on video is proposed in this paper. Firstly, YOLOv4 is used to detect the transformer object, and then the pyramid Lucas-Kanade optical flow method and Otsu method are used to calculate the transformer vibration vector. Experimental results show that the transformer vibration vector can be calculated in real time and accurately by using the proposed algorithm, so as to realize the real-time reliable analysis of the transformer state.