Parameter Estimation and Physics-Structured Model Compression of Grid-Connected Inverter Systems

Abstract

The growing deployment of renewable energy systems calls for accurate parameter estimation and compact modeling of grid-connected inverter systems (GCISs). This paper presents a framework for parameter estimation and physics-structured model compression using Physics-Structured-Informed Neural Networks (Ψ-NNs). A teacher physics-informed neural network (PINN) estimates LC-filter parameters by combining measurements with governing equations. A regularized student learns the teacher mapping through physics-informed distillation. Hierarchical clustering identifies shared weight magnitudes, enabling structured reconstruction and fine-tuning. The framework is evaluated on mathematical-model and MATLAB/Simulink datasets. The teacher achieves a mean relative parameter error below 5% across all cases, outperforming the extended Kalman filter, remaining competitive with least squares on mathematical-model data, and outperforming both baselines on all MATLAB/Simulink records. The reconstructed networks retain only 23–78 trainable base coefficients (0.15–0.50% of the student’s 15,600 parameters): 23–55 (0.15–0.35%) on the mathematical-model records and 62–78 (0.40–0.50%) on the MATLAB/Simulink records. These ratios count tied magnitudes only; after cluster assignments and signs are stored, the packed footprint is about 19–22% of the student model. The representation is value-tying rather than sparsity, so dense inference is not automatically faster. Reconstruction reduces the student data residual by nearly two orders of magnitude on degraded mathematical-model data, while remaining comparable on MATLAB/Simulink data except under the strongest combined artifacts. These results demonstrate the framework’s potential to combine robust parameter estimation with compact representations of GCIS dynamics, revealing a compression–accuracy trade-off under severe measurement degradation.