State-Space Based Digital Twin of Single-Stage PV Inverter With Predictive Control for Real-Time Stability Monitoring

Real-time monitoring and predictive control are critical for ensuring stability and efficiency in grid-connected photovoltaic (PV) systems. However, conventional methods often lack dynamic system representation and stability assurance under inverter switching conditions. This article presents a digital twin (DT) framework based on discrete-time state-space modeling in the frequency domain for a single-stage three-phase grid-connected PV inverter. The proposed Digital Twin operates in parallel with the physical system to reconstruct internal states and continuously monitor stability under practical inverter switching conditions. The DT integrates a cascaded second and third-order generalized integrator–noise suppression block–compact generalized integrator (CSTOGI–NSB–CGI) filtering structure for robust voltage template extraction, a second horizon–loss minimized voltage-regulated model predictive control (SH-LMVR-MPC) strategy for dynamic current regulation, real-time internal parameter estimation using magnetic-domain alignment electromagnetic field optimization (MDA-EFO), and real-time stability monitoring. Controller and system stability are rigorously verified using discrete-time Lyapunov criteria, with real-time evaluation performed every 10 ms for control and every 20 ms for system verification. Experimental validation is conducted on an OPAL-RT (OP4512) real-time simulator and a NI sbRIO-9636 FPGA controller. Performance evaluation shows grid current THD consistently below 4.5% (IEEE-519 compliant), digital twin fidelity score (DTFS) exceeding 97% across test scenarios, and parameter estimation converging within 20 iterations, confirming high-fidelity system replication and reliable real-time operation.