| Title |
Statistical Analysis of Big Data Using Python for Renewable Energy Sources |
| Authors |
Kyung-Min Lee ; Chul-Won Park |
| DOI |
http://doi.org/10.5207/JIEIE.2022.36.8.026 |
| Keywords |
Big data; F/R; PMU; Renewable Energy; RESs; Statistical analysis |
| Abstract |
The installed capacity of RESs (Renewable Energy Sources) in South Korea is 20.1GW in 2021. By reflecting the Renewable Energy 3020, the Hydrogen Economy Revitalization Roadmap, and the Green New Deal Policy, an additional 58GW is planned to be installed by 2034. RESs are inflexible power sources with large fluctuations, and since it is not easy to adjust the output, stable controls of voltage and frequency are difficult. A PMU (Phasor Measurement Unit) is synchronized with the GPS to record and estimated the phasor of the three-phase voltage and current, frequency and frequency deviation. By utilizing the large-capacity big data obtained from a PMU, it is possible to plan real-time monitoring and control of RESs and to effectively analyze linkage grid with RESs. In this paper, we describe statistical analysis of boxplot and kernel density by Python language using big data collected from PMUs and Fault Recorders (F/Rs) installed in substations to investigate the impact of RESs in connected power grids. |