With the construction of smart grids and the maturity of on-line monitoring technology for power transformers, the online monitoring data of power transformers are characterized by large volumes and types. Using traditional storage technology to store transformer online monitoring data can no longer meet real-time and fast requirements. To this end, a Hadoop cluster-based transformer online monitoring data storage scheme is designed. This solution utilizes the advantages of HBase (distributed columnar database) to quickly read and write data in real time, and stores the massive data collected by the transformer online monitoring system in real time and quickly. To automatically and quickly collect data in real time and avoid system crashes due to excessive data flow, Flume (log collection tool) and Kafka (distributed stream processing platform) are used to collect and cache data, respectively. Finally, the oil chromatogram data of power transformer online monitoring is taken as an example to verify the feasibility and effectiveness of the proposed storage scheme.
