International Journal of Engineering
Trends and Technology

Research Article | Open Access | Download PDF
Volume 74 | Issue 5 | Year 2026 | Article Id. IJETT-V74I5P131 | DOI : https://doi.org/10.14445/22315381/IJETT-V74I5P131

Digital Twin Supported AI-Assisted Synchronization and Intelligent FRT for Grid-Connected Photovoltaic Inverters


Sanju, Kusum Lata Agarwal, Satya Sai Srikant, S Nallusamy

Received Revised Accepted Published
09 Feb 2026 16 Mar 2026 21 Apr 2026 30 May 2026

Citation :

Sanju, Kusum Lata Agarwal, Satya Sai Srikant, S Nallusamy, "Digital Twin Supported AI-Assisted Synchronization and Intelligent FRT for Grid-Connected Photovoltaic Inverters," International Journal of Engineering Trends and Technology (IJETT), vol. 74, no. 5, pp. 495-513, 2026. Crossref, https://doi.org/10.14445/22315381/IJETT-V74I5P131

Abstract

The presence of high PV in low-voltage microgrids creates operating difficulties like voltage sag, harmonics, and frequency variation. Such issues may disturb inverter operation and enhance current stress and DC-link voltage stress. The integrated method presented in this paper includes a calibrated digital twin, enhanced synchronization, and an intelligent fault ride-through strategy in a single practical workflow. A physics-informed model that consists of the dynamics of the PV array, DC-link, converter, and the grid system constructs the digital twin. The lab data used for its calibration ensures that its output matches real disturbances for controller tuning. In addition, via learning of the residual phase and frequency errors encountered during distorted voltage sags, an A2I-Phase Locked Loop (PLL) successfully enhances the performance of a regular Synchronous Reference Frame (SRF)-PLL. Ultimately, an intelligent layer of FRT decreases the active current safely and injects the reactive current for voltage support and decides tripping, which is done using clear limits on current and DC link voltage. Experimental validation, conducted on a programmable PV and grid emulation platform, demonstrates that the digital twin accurately reproduces the measured DC-link voltage and current waveforms. Furthermore, the AI-PLL exhibits superior angle tracking performance under distorted conditions, and the FRT strategy effectively reduces peak current and DC-link stress while maintaining reactive power support during deep voltage sags.

Keywords

AI-assisted PLL, Digital twin, Fault ride-through, Grid synchronization, Photovoltaic inverter, Reactive current injection, Voltage sag.

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