3 papers
cs.LG2026
Towards a Practical Understanding of Lagrangian Methods in Safe Reinforcement Learning
Lindsay Spoor, Ãlvaro Serra-Gómez, Aske Plaat +1
Safe reinforcement learning addresses constrained optimization problems where maximizing performance must be balanced against safety constraints, and Lagrangian methods are a widel…
eess.SY2025
Quantum-Enhanced Reinforcement Learning for Accelerating Newton-Raphson Convergence with Ising Machines: A Case Study for Power Flow Analysis
Zeynab Kaseb, Matthias Moller, Lindsay Spoor +4
The Newton-Raphson (NR) method is widely used for solving power flow (PF) equations due to its quadratic convergence. However, its performance deteriorates under poor initializatio…
eess.SY2025
Data driven approach towards more efficient Newton-Raphson power flow calculation for distribution grids
Shengyuan Yan, Farzad Vazinram, Zeynab Kaseb +8
Power flow (PF) calculations are fundamental to power system analysis to ensure stable and reliable grid operation. The Newton-Raphson (NR) method is commonly used for PF analysis…