이경민(Kyung-Min Lee) ; 박철원(Chul-Won Park)
Recently, for real-time monitoring of renewable energy, a wide area power system monitoring and operation technology using PMU has emerged. Through WAMS based on the PMU, time synchronization data for a wide area is acquired and a vast amount of data is accumulated. Therefore, it is a problem to be solved in the future to process this data and deliver highly usable and valuable system status information to system operators. This paper proposes a new systemic phenomenon identification algorithm using big data of PMU installed in RES and a DNN. First, the PMU installed at the RES in the Gangwon region is introduced, and then the data structure collected is explained. Next, by analyzing each system phenomenon from the PMU data, a total of 8 types of system data such as steady state, tap rise, tap fall, feed-in and out, etc. are generated. After conducting supervised learning by constructing learning data for 8 systematic phenomena using a DNN, systematic phenomena discrimination is performed on the DNN model learned through the test data. Finally, the algorithm was designed, implemented, and evaluated to identify robust systematic phenomena for new PMU based big data. The simulation results showed that the proposed new algorithm accurately discriminates all systematic phenomena.