Digital twin based identification of degradation parameters of DC-DC converters using an Arithmetic Optimization Algorithm

Power electronic converters are extensively used in various fields. The reliability and stability of converters are a significant concern in practice. So, the estimation of unknown parameters of power converters without additional hardware is necessary. The digital twin is a virtual dynamical model of a physical system. In this paper, identifying unknown circuit parameters of a buck converter is proposed using digital twin with Arithmetic Optimization Algorithm (AOA). First, the state variables of buck converter such as inductor current and output voltage are derived under steady-state and transient conditions for estimating the physical entity. Then, AOA is applied to estimate the unknown parameter of the buck converter based on the data coming from the digital twin model and its counterpart. Finally, the performance of AOA is compared with particle swarm optimization (PSO), and it concludes that AOA has fast convergence and efficient global search than PSO.