A Digital Twin-Based Stability Monitoring and Parameter Estimation Framework for Three-Phase Grid-Connected Photovoltaic Inverters

This paper presents a Digital Twin (DT)–based framework for real-time stability monitoring andparameter estimationof three-phase grid-connected photovoltaic (PV) inverters. The proposed system integrates a Three-Parameter Adaptive Maximum Power Point Tracking (TPA-MPPT) algorithm for rapid and oscillation-free power extraction and employs Model Predictive Control (MPC) for precise voltage source converter (VSC) regulation under dynamic grid conditions. An Improved Fifth-Order Generalized DC-Blocking Filter (IFO-GDF) enhances grid synchronization and suppresses low-frequency distortions, while Coulomb-based Electromagnetic Field Optimization (C-EFO) enables real-time estimation of internal inverter and filter parameters, improving model fidelity and reliability assessment. Stability is continuously evaluated using a Lyapunov-based energy function, providing a real-time stability index for predictive control and proactive corrective actions. Experimental validation using an OPAL-RT real-time simulator and a laboratory prototype demonstrates strong agreement between the DT and hardware responses, achieving an average Digital Twin Fidelity Score above 97% across diverse scenarios, including irradiance changes, grid voltage unbalance, and harmonic distortion. The proposed framework ensures IEEE-519 harmonic compliance, enhances converter reliability, and provides a practical solution for predictive maintenance, stability assessment, and advanced control of PV-grid interfaces.