Publications (15)
Physics-guided Residual Learning for Probabilistic Power Flow Analysis
Kejun Chen, Yu Zhang
Probabilistic power flow (PPF) analysis is critical to power system operation and planning. PPF aims at obtaining probabilistic descriptions of the state of the system with stochas…
A robust and scalable estimation for high-dimensional volatility models
Kejun Chen, Yuchang Lin, Qianqian Zhu
This paper introduces a robust and computationally efficient estimation framework for high-dimensional volatility models in the BEKK-ARCH class. The proposed approach employs data…
Physics-Informed Gradient Estimation for Accelerating Deep Learning based AC-OPF
Kejun Chen, Shourya Bose, Yu Zhang
The optimal power flow (OPF) problem can be rapidly and reliably solved by employing responsive online solvers based on neural networks. The dynamic nature of renewable energy gene…
Private Federated Learning for High-dimensional Time Series
Kejun Chen, Qianqian Zhu
In the era of big data, leveraging information from multiple clients while preserving data privacy has emerged as a critical challenge in modern statistical modeling and forecastin…
Variation-cognizant Probabilistic Power Flow Analysis via Multi-task Learning
Kejun Chen, Yu Zhang
With an increasing high penetration of solar photovoltaic generation in electric power grids, voltage phasors and branch power flows experience more severe fluctuations. In this co…
Adversarial Multi-Agent Reinforcement Learning for Proactive False Data Injection Detection
Kejun Chen, Truc Nguyen, Abhijeet Sahu +1
Smart inverters are instrumental in the integration of distributed energy resources into the electric grid. Such inverters rely on communication layers for continuous control and m…