Question Answering Algorithm for Grid Fault Diagnosis based on Graph Neural Network

Yahan Yu, Yun Wang, Guigang Zhang, Yi Yang, Jian Wang
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Abstract

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.
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基于图神经网络的电网故障诊断问答算法
由于电网系统自身、自然气候变化、人为因素等不确定因素的存在,电网系统仍会出现各种故障。如果故障报警不及时响应,很可能造成电网失稳甚至崩溃,造成不可估量的损失。通过构建海量电网运维信息的知识图谱,实现快速准确的故障信息推理和溯源,检索合理的故障解决措施。利用人工智能技术和大数据辅助电网系统实现更高效的运维。实现电网故障的智能诊断是当前亟待解决的问题。随着人工智能技术的快速发展和应用,如果能将人工智能和大数据技术应用到电网的故障诊断和分析中,将打破这种依赖人工分析的局面,实现对海量运维数据的高效处理。
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