3 papers
cs.AI2024
GLinSAT: The General Linear Satisfiability Neural Network Layer By Accelerated Gradient Descent
Hongtai Zeng, Chao Yang, Yanzhen Zhou +2
Ensuring that the outputs of neural networks satisfy specific constraints is crucial for applying neural networks to real-life decision-making problems. In this paper, we consider…
eess.SY2024
Uncertainty-Aware Transient Stability-Constrained Preventive Redispatch: A Distributional Reinforcement Learning Approach
Zhengcheng Wang, Fei Teng, Yanzhen Zhou +2
Transient stability-constrained preventive redispatch plays a crucial role in ensuring power system security and stability. Since redispatch strategies need to simultaneously satis…
eess.SY2024
Transmission Interface Power Flow Adjustment: A Deep Reinforcement Learning Approach based on Multi-task Attribution Map
Shunyu Liu, Wei Luo, Yanzhen Zhou +5
Transmission interface power flow adjustment is a critical measure to ensure the security and economy operation of power systems. However, conventional model-based adjustment schem…