| Title |
Comparison of State Identification of VRE Power Grid based on PMU Big Data |
| Authors |
이경민(Kyung-Min Lee) ; 박철원(Chul-Won Park) |
| DOI |
https://doi.org/10.5370/KIEE.2023.72.1.46 |
| Keywords |
Deep Neural Network; PMU Big Data; State Identification Algorithm; Support Vector Machine; Variable Renewable Energy |
| Abstract |
Because renewable energy sources are environmentally friendly, they are recognized as important to address the climate crisis and achieve 2050 carbon neutrality. South Korea continues to expand the VRE power grid. Recently, an alternative using PMU based big data is being explored. In this paper, we apply and compare the DNN and SVM techniques that can identify the state of the VRE power grid using PMU big data that can be analyzed more precisely than SCADA/EMS. First, real-time PMU data is collected from the PMU operating in the VRE power grid. In order to be applied to the state identification algorithm, data structure-based data preprocessing is performed. After designing a technique that can determine the state of the VRE power grid using DNN and SVM, respectively, it is implemented using a Python tool. Finally, we compare the performance of the two proposed algorithms for state identification of eight states of the power grid. |