A Degradation Parameters Identification Method for Buck-Boost Converter in Digital Twin Based on Particle Swarm Optimization Algorithm

• Published in: IEEE Conference on Industrial Electronics and Applications (Online) pp. 822 - 827
• Main Authors: Han, Tianxia, Ma, Mingyao, Jiang, Tingzhi
• Format: Conference Proceeding
• Language: English
• Published: IEEE 16.12.2022
• Subjects: Buck-Boost converter; Data models; Degradation; degradation prediction; digital twin; Parameter estimation; parameter identification; particle swarm algorithm (PSO); Prediction algorithms; Real-time systems; Resistance; Semiconductor device modeling
• ISSN: 2158-2297
• DOI: 10.1109/ICIEA54703.2022.10006298

Aiming for the condition monitoring of the Buck-Boost converter, a degradation parameter identification method based on digital twin concept is studied, which can monitor the degradation of key components such as capacitors and MOSFETs in real time. The main idea of the method is to establish a real-time digital mirror model of the Buck-Boost converter, and achieve the virtual mapping of physical entities through the interaction between the mechanism model and real-time data. Then, the capacitance, inductance, capacitance parasitic resistance and MOSFET on-state resistance in the converter are estimated by the sampling data of physical entities based on the particle swarm optimization algorithm (PSO), and the method is verified under various working conditions. The simulation results verify the effectiveness of the method, and the degradation failure prediction model is proposed for fault prediction. The method requires no additional hardware circuits and calibration requirements.