Abstract
In this paper, the key work of smart grid equipment fault prediction and early warning, dynamic operation and maintenance strategy research is studied by using large data mining analysis method. Spark, Hive, HDFS(Hadoop Distributed File System), MapReduce and other technologies are used to build a large data analysis and early warning decision-making platform for smart grid. In this paper, taking big data technology as the core, the BP neural network algorithm is optimized by using self-developed proprietary algorithm, which improves the accuracy of fault prediction model. The algorithm can realize the functions of intelligent inspection, intelligent research and judgment, intelligent early warning, intelligent decision-making and intelligent dispatch of substation equipment. Through a lot of practical verification, the accuracy of fault prediction of the platform is 93.98%, which is in the leading international level . In the field of smart grid, it has strong application value.
Modules
Algorithms
Software And Hardware
• Hardware: Processor: i3 ,i5 RAM: 4GB Hard disk: 16 GB • Software: operating System : Windws2000/XP/7/8/10 Anaconda,jupyter,spyder,flask,hadoop Frontend :-python Backend:- MYSQL