Research on Condition Monitoring of Intelligent Substation Equipment Based on Hadoop and MapReduce

The mass monitoring data collected by the on-line monitoring of the substation is stored in the Hadoop Distributed File System (HDFS), and the index table structure of the online monitoring data is optimized and stored in the distributed structured database (HBase) Quick access to monitoring data. Based on Hadoop 's online monitoring data processing experiment platform, a fast fault identification and prediction model for equipment failure under large data environment is proposed for the problem that the existing fault diagnosis method is slow to analyze and the prediction accuracy is not high. Using the improved MapReduce parallel mode equipment fault classification algorithm, through the expert reasoning mechanism, the intelligent substation equipment for real-time monitoring.