Question Answering Algorithm for Grid Fault Diagnosis based on Graph Neural Network
Yu Yahan1,2; Wang Yun2; Zhang Guigang2; Yang Yi2; Wang Jian2
2022-12
会议名称2022 IEEE 22nd International Conference on Software Quality, Reliability, and Security Companion (QRS-C)
会议日期2022-12
会议地点Guangzhou, China
摘要

Due to the existence of uncertain factors such as the power grid system itself, natural climate change and human factors, various faults will still occur in the power grid system. If the fault alarm is not responded to in time, it is likely to cause grid instability or even collapse, resulting in inestimable losses. By building a knowledge graph for massive power grid operation and maintenance information, we can achieve fast and accurate fault information reasoning and traceability, and retrieve reasonable fault resolution measures. Use artificial intelligence technology and big data to assist power grid systems to achieve more efficient operation and maintenance. Realizing the intelligent fault diagnosis of power grid is an urgent problem to be solved at present. With the rapid development and application of artificial intelligence technology, if artificial intelligence and big data technology can be applied to the fault diagnosis and analysis of power grids, this situation of relying on manual analysis will be broken, and the efficient processing of massive operation and maintenance data will be realized.

收录类别EI
语种英语
七大方向——子方向分类人工智能+制造
国重实验室规划方向分类语音语言处理
是否有论文关联数据集需要存交
文献类型会议论文
条目标识符http://ir.ia.ac.cn/handle/173211/51870
专题数字内容技术与服务研究中心_智能技术与系统工程
通讯作者Wang Jian
作者单位1.School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
2.Institute of Automation, Chinese Academy of Sciences, Beijing, China
第一作者单位中国科学院自动化研究所
通讯作者单位中国科学院自动化研究所
推荐引用方式
GB/T 7714
Yu Yahan,Wang Yun,Zhang Guigang,et al. Question Answering Algorithm for Grid Fault Diagnosis based on Graph Neural Network[C],2022.
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