LC Parameters Identification for a 3-Phase AC-DC Converter through Digital Twin Modeling Technique and Optimization Algorithms

The Digital Twin (DT) technology is transforming the energy conversion industry by replicating in real-time the behavior of physical systems by means of high-fidelity digital models (DMs). Specifically, close monitoring of parameters influencing the health status of components prone to failure is essential for the application of predictive maintenance strategies. This capability is made achievable through the use of the DT concept and the advanced optimization algorithms introduced in this work. This paper presents the parameters' estimation approach based on the DT method applied to 3-phase AC-DC switching converters. First, a Particle Swarm Optimization (PSO) algorithm is employed to estimate the characteristics of the L-type AC filter and the DC capacitance. The same approach has been repeated using the Genetic Algorithm (GA) and the Simulated Annealing (SA) as optimization algorithms. Balanced and unbalanced situations have been tested to demonstrate the robustness and feasibility of the proposal and the comparison between the three proposed optimization algorithms has been carried out. The results show the potential of the procedure for application in system identification and condition monitoring.