- Nuclear & Particle Physics
- Isotope Research & Production
- RIKEN BNL Research Center
Energy Systems Division
Renewables and Grid Modernization
Advanced Grid Modeling
A Deep Learning Based Online Platform for Critical Anomaly Detection and Emergency Control to Enhance Grid Reliability and Resiliency
Develop a data-driven, deep learning-based solution to prevent the propagation of cascading failures when the grid is challenged by unexpected contingencies or combinational contingencies under uncertain environments.
An end-to-end technology will be developed that will be an online platform capable of evaluating and predicting grid conditions and selecting emergency control actions. The focus will be on load shedding strategies and determination of timing and boundaries for splitting the grid into self-sustained islands, as needed, to mitigate the propagation of cascading failures.
A machine learning method using graph-based recurrent neural network will be developed to predict grid dynamics based on measurement data. Data on generation, wind speed and direction for wind and solar generation sites will be acquired from ERCOT, along with PMU measurement data for transient events that were caused by generator trips.
Y. Zheng, C. Hu, G. Lin, M. Yue, B. Wang, J. Xin, “Glassoformer: A Query-sparse Transformer for Post-fault Power Grid Voltage Prediction,” 2022 IEEE International Conference on Acoustics, Speech and Signal Processing.
T. Zhao, Y. Zhang, and M. Yue, “Scalable Deep Reinforcement Learning-based Volt-VAR optimization in Distribution Systems: a Mean-field Approach,” IEEE PES GM 2022.
T. Zhao, M. Yue, and J. Wang, “Structure-informed Graph Learning of Networked Dependencies for Online Prediction of Power System Transient Dynamics,” in IEEE Transactions on Power Systems, doi: 10.1109/TPWRS.2022.3153328.
Y. Zhang, M. Yue, J. Wang, S. Yoo, “Off-policy Deep Reinforcement Learning with Automatic Entropy Adjustment for Adaptive Online Grid Emergency Control,” submitted to Electric Power System Research, second revision submitted.
Y. Zhang, M. Yue, J. Wang, and L. Zhu, “Cooperative Multi-Agent Deep Reinforcement Learning for Adaptive Decentralized Emergency Voltage Control,” submitted to IEEE Transactions on Circuits and Systems, under review.
Y. Zhang, M. Yue, and J. Wang, “Multi-Agent Graph-Attention Deep Reinforcement Learning and Its Application in Post-Contingency Grid Emergency Voltage Control,” submitted to IEEE Transactions on Neural Networks and Learning Systems (IEEE TNNLS) Special Issue on Reinforcement Learning Based Control: Data-Efficient and Resilient Methods, under review.
C. Moya, S. Zhang, M. Yue, and G. Lin, “A Deep Operator Graph Network (DeepGraphONet) framework for networked dynamical systems,” submitted to Neurocomputing, under review.
T. Zhao, M. Yue, and J. Wang, “Deep Inference Learning of Koopman Model for Modeling and Controlling of Nonlinear Power System Transient Dynamics,” Submitted to IEEE Transactions on Industrial Informatics, under review.
C.Moya Calderon, Y. Sun, G. Lin, and M. Yue, “DeepGraphONet: A Deep Graph Operator Network to Learn and Zero-shot Transfer the Dynamic Response of Networked Systems,” submitted to the IEEE Systems Journal, under review.