Comparative Studyof Different Optimization Algorithms for Estimation of Degradation Parameters of DC-DC Converters

Power electronic converter circuits are highly used in various fields. Regardless, the stability and reliability of the converters are the most prominent concern. Hence condition monitoring of converter is essential without any extra hardware. This work presents the identification of buck converter parameters using a digital twin with different optimization techniques. Furthermore, the comparative study of different optimization algorithms such as Black Widow Optimization Algorithm, Genetic Algorithm, Differential Evolution, Particle Swarm Optimization, and Wild Horse Optimization is applied to identify the unknown parameters of the buck converter based on data from physical counterpart and digital twin. The simulations are carried out in MATLAB/Simulink, and results demonstrate that the Wild Horse Optimization is the best optimization technique than other algorithms for the buck converter parameter identification.