| Title |
A Study on Pre-processing and Fault Analysis using PMU Big Data of Substation |
| Authors |
이경민(Kyung-Min Lee) ; 박철원(Chul-Won Park) |
| DOI |
https://doi.org/10.5370/KIEE.2023.72.9.975 |
| Keywords |
Data Analysis; Fault Analysis; Instantaneous Voltage Fluctuation Rate; PMU Big Data; Pre-processing; Snapshot; S/S; WAMAC |
| Abstract |
Recently, technology development for PMU (Phasor Measurement Unit) data analysis system and Korean-style WAMAC (Wide Area Monitoring And Control) construction is being pursued for stable system operation and cost reduction. With high-speed sampling up to 256 times per cycle and the ability to collect time synchronized big data based on GPS signals, PMUs offer advantages over today SCADA/EMS (Supervisory Control and Data Acquisition/Energy Management System) and PQMS (Power Quality Monitoring System), it is possible to build an accurate and real-time condition monitoring system. By processing a vast amount of PMU big data, useful and valuable information can be delivered.
In this paper, processing and fault analysis are performed for the utilization of big data collected by 35 PMUs installed in 154kV ○○ S/S (Substation). First, we introduce the PMU installation points of the S/S and collect PMU big data to understand the data structure. After performing the pre-processing in the form of a snapshot, we analyse the voltage, and power comparisons. In addition, the analysis is performed through the IVFR (instantaneous voltage fluctuation rate), which can efficiently monitor system changes acquired from multiple measurement points. The pre-processing and fault analysis are implemented using the language of Python 3.7 version. Finally, an aim of this study is to analyze the impact of power grid faults on ○○ S/S. |