数字孪生辅助的并网光伏系统健康退化分析与控制：基于UVISMC和EAAEFO算法

This paper explores the use of Digital Twin (DT) technology for real-time health monitoring of grid-integrated photovoltaic (PV) systems. The proposed DT is developed through conceptualization, computational modeling, and experimental validation, functioning as a mathematical replica of the hardware system. Operating alongside the physical setup, the DT leverages measured sensor data to enable accurate tracking and control. A novel Power Law Charge Function-based Enhanced Adaptive Artificial Electric Field Optimization (EAAEFO) algorithm is employed to continuously compare DT outputs with real system measurements, ensuring precise analysis. For control, a Unit Vector Integrated Sliding Mode Control (UVISMC) scheme based on Cascaded Generalized Integrator (CGI) is introduced. This strategy provides improved current tracking, robustness, and reduced sensitivity to parameter variations compared to conventional controllers. The DT framework is implemented on the OPAL-RT (OP4512) platform, while the hardware prototype operates on an NI sbRIO-9636 FPGA board using LabVIEW. Health degradation of interfacing inductors, converter switches, and DC-link capacitors is examined by analyzing its impact on system performance. Experimental validation demonstrates that the DT achieves over 98% accuracy in replicating hardware behavior, even under degraded conditions, establishing its effectiveness for PV system monitoring and control.