서울시 서대문구 연세로 50
제3공학관 C126호
T. 02-2123-6740 ㅣ F. 02-312-4584
Research Area
YONSEI ENERGY INFORMATICS Lab.
Smart Grid Big Data Analytics
Data-Driven Synchrophasor Analysis for Wide-Area Power Systems
Objectives
- Needs for advanced data-driven monitoring techniques are rapidly increasing because power systems have become more highly nonlinear time-varying systems owing to the implementation of various new technologies such as power electronics and renewable energy sources.
- The conventional model-based monitoring techniques using supervisory control and data acquisition (SCADA) system based can cause errors because the SCADA data has low resolution and is not time-synchronized.
- Synchrophasor technology provides directly measured (not estimated) dynamic information of power system dynamics due to global positioning systems (GPS) based time-synchronization and much higher resolution (30-120 Hz) than that of SCADA (0.25-0.5 Hz).
- In this research, advanced data-driven monitoring techniques using data mining techniques and artificial intelligence (AI) are developed for real-time monitoring of wide-area power systems.
Research Highlights
- Event detection method based on wavelet analysis of sycnrophasor data
- Event localization technique by clustering power systems (zonal analysis)
- Learning of event propagation phenomena using synchrophasor data
- Synchrophasor data-driven event identification using AI techniques
▲ Wavelet based event detection method using synchrophasor data *
▲ Synchrophasor data-driven event identification using AI techniques **
* D.-I. Kim, A. White, and Y.-J. Shin, "PMU-based Event Localization Technique for Wide-Area Power System," IEEE Trans. Power Syst., vol. 33, no. 6, pp. 5875-5883, Nov. 2018.
** D.-I. Kim, A. White, and Y.-J. Shin, "Adaptive Event Location Technique via Time Difference of Event Arrival," IEEE Trans. Power Syst., vol. 35, no. 1, pp. 75-84, Jan. 2020.
** D.-I. Kim, L. Wang, and Y.-J. Shin, “Data Driven Method for Event Classification via Regional Segmentation of Power Systems,” IEEE Access, vol. 8, pp. 48195-48204, May 2020.
Data Management of Smart Grid Data
Objectives
- Due to the much higher resolution of synchrophasor data (30-120 Hz) than that of SCADA (0.25-0.5 Hz), the needs for efficient management techniques to deal with the huge volume of power system big data are increasing.
- An efficient data compression technique that are fitted to synchrophasor data is investigated because the conventional data compression algorithm cannot be directly applied.
- Moreover, an AI-based algorithm of real-time recovering missing entries is investigated to supplement the successful application of synchrophasor data-driven applications.
- In addition, an algorithm for detecting cyber-attacked synchrophasor data is investigated to secure reliable data quality.
Research Highlights
- Wavelet-based synchrophasor data compression technique
- Missing entry estimation using multi-channel LSTM
- Monitoring cyber-attacked synchrophasor data
▲ Wavelet based synchrophasor data compression *
▲ Missing entry estimation using multi-channel LSTM **
** G. Lee, S. H. Kim, D.-I. Kim, A. White, and Y.-J. Shin "Multi-Channel Recovery for Distributed Quality Management of Synchrophasor Data," IEEE Trans. on Power Syst., to be published.
Online Monitoring of Power System Dynamics Using Synchrophasor Measurements
Purpose of Research
- Because the interactions between various power facilities render it difficult to predict the dynamic behavior of power systems, the dynamics monitoring, and analysis techniques are very important to improve the stability of the power systems.
- The importance of oscillation monitoring, analysis, and damping techniques is increasing in complex power systems.
- Dynamic states estimation methods are developed to analyze and monitor the dynamics of power facilities.
- The oscillation parameters (frequency and magnitude) estimation techniques are developed to mitigate the oscillations in power systems.
Research Content
- Kalman Filter and Particle Filter based dynamic state estimation for generator system using synchrophasor data
- Sub-Synchronous Resonance (SSR) damping techniques via power electronics control
- Oscillation parameter monitoring using synchrophasor-based state estimation techniques
▲ Dynamic state estimation for generator using synchrophasor data *
▲ Oscillation parameter monitoring using synchrophasor data *