Proposal of analytics software architecture with data preparation layer for fast event identification in wide-area situational awareness

The number of PMUs (Phasor Measurement Units) has increased drastically in several countries. The collected data such as voltage, current, and frequency become over hundreds terabytes in a few years. It takes long time to retrieve specific similar patterns to a query for analysis. This paper defines data preparation layer in an analytical system. That enables enormous accumulation. The layer contains a function that creates index creation based on time-series patterns and another function that quickly retrieves user required patterns. Both functions process clusters as well as string. In the experiment, the time to retrieve similar patterns to a query is measured, and four architectures of time-series data process are compared; (1) conventional architecture with no data preparation layer (2) architecture with String (3) architecture with Cluster, and (4) architecture with String and Cluster. The experiment result shows that the best one with String and Cluster is 67.7 times faster than the conventional no-data preparation layer architecture. In conclusion, the proposed data preparation layer is effective to retrieve similar time-series data patterns to the query. It is also clarified that the layer is critical to analyze historical data for predicting wide-area power disturbance.