Michael A. Boateng

Michael A. Boateng, Gabriel Gauderman, Nathalie Uwamahoro

Current transformers are fundamental to power system protection and measurement, yet transient core saturation can severely distort the secondary current and degrade measurement accuracy. Existing dynamic state estimation methods rely mainly on numerical discretisation and iterative solvers, but their initialisation is not informed by the physical dependency structure of the estimation problem, which limits robustness under noisy conditions. This paper presents a physics-informed enhancement for current transformer dynamic state estimation using COMTRADE measurements generated in WinIGS-T. A structured benchmark of four discretisation schemes and three iterative solvers identifies Gauss-Newton with Quadratic discretisation as the strongest baseline. To address the limitation of conventional cold-start initialisation, a graph neural network is constructed from the Jacobian sparsity pattern to generate physics-informed initial state estimates. The proposed warm-start strategy improves estimator conditioning and achieves average gains of 25% in initialisation distance and 38% in initial weighted objective value across all tested SNR levels. The results demonstrate that embedding physical structure into the initialisation stage improves the robustness of CT saturation correction and supports more reliable measurement and protection performance in modern power grids.

Michael A. Boateng, Russell Bent, Sidhant Misra et al.

The DC optimal power flow is widely used in power system operations because of its computational efficiency and scalability. However, DC dispatches are not guaranteed to satisfy the nonlinear AC power-flow equations or associated operational limits. This paper develops a parameterized, differentiable AC power-flow restoration method for mapping DC dispatches to AC-consistent operating points. The method incorporates distributed slack for active-power balancing and PV/PQ switching for reactive-power regulation, both implemented using smooth differentiable surrogates with tunable parameters, including slack participation factors, voltage setpoints, and regulation steepness. These parameters are trained offline by differentiating through the AC restoration equations using the implicit function theorem. Once trained, the optimized parameters are fixed and used directly during AC power-flow recovery from DC dispatches. The approach is evaluated on IEEE, ACTIVSg, and PEGASE test systems using setpoints computed by standard DC optimal power flow. Results show that the optimized restoration method improves AC feasibility recovery across various systems relative to conventional single-slack AC power-flow recovery. On the 9,241-bus case, the optimized method improves cost difference by 80% relative to the conventional recovery baseline and improves solving time relative to ACOPF by 75%.