DNN-based Digital Twin Framework of a DC-DC Buck Converter using Spider Monkey Optimization Algorithm

Abstract

Component ageing is a critical concern in power electronic converter systems (PECSs). It directly impacts the reliability, performance, and operational lifespan of converters used across diverse applications, including electric vehicles (EVs), renewable energy systems (RESs) and industrial automation. Therefore, understanding and monitoring component ageing is crucial for developing robust converters and achieving long-term system reliability. This paper proposes a data-driven digital twin (DT) framework for DC-DC buck converters, integrating deep neural network (DNN) with the spider monkey optimization (SMO) algorithm to monitor and predict component degradation. Utilizing a low-power prototype testbed along with empirical and synthetic datasets, the SMO+DNN approach achieves the global optimum in 95% of trials, requires 33% fewer iterations, and results in 80% fewer parameter constraint violations compared to traditional methods. The DNN model achieves $R^2$ scores above 0.998 for all key degradation parameters and accurately forecasts time to failure ($t_{failure}$). In addition, SMO-tuned degradation profile improves the converter's performance by reducing voltage ripple by 20-25% and inductor current ripple by 15-20%.