State-Space Model-Based Digital Twin With Real-Time Parameter Optimization Using MDA-EFO for Grid-Connected PV Inverter Systems

This paper proposes a Digital Twin (DT) framework for a single-stage, three-phase grid-connected photovoltaic (PV) inverter system, leveraging discrete-time state-space modelling in frequency domain for accurate dynamic representation under all switching conditions. The DT integrates a novel CSTOGI-NSB-CGI (Cascaded Second and Third-Order Generalized Integrator, Noise Suppression Block, Compact Generalized Integrator) filter for robust extraction of the fundamental grid voltage, even during voltage unbalance, distortion, and harmonic conditions. To maintain precise current regulation and consistent switching frequency, an Adaptive Hysteresis Band Current Controller (AHBCC) is implemented. For real-time tuning of inverter parameters, a Magnetic-Domain Alignment Electromagnetic Field Optimization (MDA-EFO) algorithm is introduced, offering fast convergence through dual-force interaction involving global best and peer alignment. MDA-EFO is deployed on an OPAL-RT HIL platform for real-time experiments; the algorithm is platform-agnostic and can also be implemented in MATLAB/Simulink or other real-time environments. The DT framework accurately estimates internal system parameters, such as inductances, resistances, and filter capacitances, without relying on external training data. Experimental validation using real-time hardware measurements confirms the DT’s high-fidelity performance, as supported by the Digital Twin Fidelity Score (DTFS) metric. The results demonstrate the DT’s capability for real-time monitoring, adaptive optimization, and predictive diagnostics in grid-connected PV applications.