Big Data Analytics Platform and its Application to Frequency Excursion Analysis
Song Zhang, Xiaochuan Luo, Qiang Zhang, Xinghao Fang, Eugene Litvinov · 2018
Power utilities gather various types of data from day to day operations. A huge volume of data has been collected so far yet underutilized. As power systems modernize, many utilities/ISOs are being outpaced by the sheer amount of new information the grids present. This inability to analyze the massive quantity of data has been further aggravated as the deployment of Phasor Measurement Unit (PMU) grows. The need to uncover the value behind the data in explosive growth calls for a new, powerful and efficient approach to access information much faster than the traditional data processing tools. This paper thus presents a Hadoop-based big data analytics platform which is able to process extremely large set of historical synchrophasor data. This platform is built on top of a scalable Hadoop cluster running on Amazon Web Services (AWS) and is scalable to process various sizes of data. To demonstrate the performance of this proposed platform, a study case to examine abnormal frequency excursions is presented. The test shows that the platform could help the engineers quickly identify the time of large frequency excursions occurrence when they need to evaluate if the governor response satisfies NERC's requirement.