4 papers
Network Interdependency-Informed Power System Dynamic Trajectory Prediction Utilizing Black-Box Modeling of Multiple Inverter-Based Resources
Sungjoo Chung, Ying Zhang, Meng Yue +1
Black-box modeling of inverter-based resources (IBRs) has attracted growing interest for real-time grid operation and control in the presence of proprietary electronic control arch…
Utilizing Adversarial Training for Robust Voltage Control: An Adaptive Deep Reinforcement Learning Method
Sungjoo Chung, Ying Zhang
Adversarial training is a defense method that trains machine learning models on intentionally perturbed attack inputs, so they learn to be robust against adversarial examples. This…
Taylor-Expansion-Based Robust Power Flow in Unbalanced Distribution Systems: A Hybrid Data-Aided Method
Sungjoo Chung, Ying Zhang, Zhaoyu Wang +1
Traditional power flow methods often adopt certain assumptions designed for passive balanced distribution systems, thus lacking practicality for unbalanced operation. Moreover, the…
Deep Reinforcement Learning-Enabled Adaptive Forecasting-Aided State Estimation in Distribution Systems with Multi-Source Multi-Rate Data
Ying Zhang, Junbo Zhao, Di Shi +1
Distribution system state estimation (DSSE) is paramount for effective state monitoring and control. However, stochastic outputs of renewables and asynchronous streaming of multi-r…