A Comprehensive Survey for Deep-Learning-Based Abnormality Detection in Smart Grids with Multimodal Image Data

Applied Sciences

Gang Wen¹  Yi Ma¹  Fangrong Zhou¹  Ran Huang¹  Hao Geng¹  Wenxian Yu²  Lei Chu²  Robert Qiu²  Ling Pei² 

[1] Electric Power Research Institute, Yunnan Power Grid Co., Ltd., Kunming 650217, China;Shanghai Key Laboratory of Navigation and Location-Based Services, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China;

关键词: abnormality detection;    smart grids;    multimodality;    image data;    intelligent monitoring;    statistical inference;   

DOI  :  10.3390/app12115336

来源: DOAJ

【 摘 要 】

In this paper, we provide a comprehensive survey of the recent advances in abnormality detection in smart grids using multimodal image data, which include visible light, infrared, and optical satellite images. The applications in visible light and infrared images, enabling abnormality detection at short range, further include several typical applications in intelligent sensors deployed in smart grids, while optical satellite image data focus on abnormality detection from a large distance. Moreover, the literature in each aspect is organized according to the considered techniques. In addition, several key methodologies and conditions for applying these techniques to abnormality detection are identified to help determine whether to use deep learning and which kind of learning techniques to use. Traditional approaches are also summarized together with their performance comparison with deep-learning-based approaches, based on which the necessity, seen in the surveyed literature, of adopting image-data-based abnormality detection is clarified. Overall, this comprehensive survey categorizes and carefully summarizes insights from representative papers in this field, which will widely benefit practitioners and academic researchers.

【 授权许可】

Unknown