Real-Time Aggregation Approach for Power Quality Data

Abstract

Compliance verification and performance analysis of grid power quality are the main targets of state-wide power quality monitoring and analysis system in China. Real-time aggregation of power quality data is a prerequisite to achieve the targets. Since power quality data generated by over 10,000 monitors are extremely massive, data aggregations of different indicators meet great challenges for time-consuming. An aggregation framework with the incremental computing and approximate computing engine is proposed. The incremental computing methods of maximum, minimum, mean and variance functions are presented, as well as two different approximate computing methods for 95% probability value function. Performance analyses are carried out with real data.