TY - GEN
T1 - Cognitive Insights into Metaheuristic Digital Twin based Health Monitoring of DC-DC Converters
AU - Mirza, Abdul Basit
AU - Choksi, Kushan
AU - Vala, Sama Salehi
AU - Radha, Krishna Moorthy
AU - Chinthavali, Madhu Sudhan
AU - Luo, Fang
N1 - Publisher Copyright:
© 2022 EPE Association.
PY - 2022
Y1 - 2022
N2 - Reliability of components has always been a major concern to the performance and stability of DC-DC converters. After long-term operation, these passive components and switching devices start to degrade and become weak to withstand normal electrical and thermal stresses. An insightful digital interface to the physical layer known as Digital Twin (DT) can be a sustainable solution for ensuring reliability. This paper extends the DT concept to component level health monitoring in DC-DC converters. The proposed concept is noninvasive and does not require additional sensors. The working principle is to minimize the weighted least squared error between the digital twin output and the measured data of state variables through metaheuristic optimization. An application for Two-Phase Interleaved Boost Converter with reverse coupled inductor is considered and Hardware-in-the-loop (HIL) platform is used for sensitivity analysis for component degradation. Further, the optimization problem is solved using the following two popular metaheuristic optimization methods: Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). Further, the performance of both methods for 20 executions in terms of computational time; convergence rate and dispersion are compared. It is evident from the results that GA outperforms PSO with 50 % less execution time and better accuracy> 95 %.
AB - Reliability of components has always been a major concern to the performance and stability of DC-DC converters. After long-term operation, these passive components and switching devices start to degrade and become weak to withstand normal electrical and thermal stresses. An insightful digital interface to the physical layer known as Digital Twin (DT) can be a sustainable solution for ensuring reliability. This paper extends the DT concept to component level health monitoring in DC-DC converters. The proposed concept is noninvasive and does not require additional sensors. The working principle is to minimize the weighted least squared error between the digital twin output and the measured data of state variables through metaheuristic optimization. An application for Two-Phase Interleaved Boost Converter with reverse coupled inductor is considered and Hardware-in-the-loop (HIL) platform is used for sensitivity analysis for component degradation. Further, the optimization problem is solved using the following two popular metaheuristic optimization methods: Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). Further, the performance of both methods for 20 executions in terms of computational time; convergence rate and dispersion are compared. It is evident from the results that GA outperforms PSO with 50 % less execution time and better accuracy> 95 %.
KW - Digital Twin
KW - Genetic Algorithm (GA)
KW - Health Monitoring
KW - Metaheuristic Optimization
KW - Particle Swarm Optimization (PSO)
KW - Sensitivity Analysis
UR - https://www.scopus.com/pages/publications/85141632107
M3 - Conference contribution
AN - SCOPUS:85141632107
T3 - 24th European Conference on Power Electronics and Applications, EPE 2022 ECCE Europe
BT - 24th European Conference on Power Electronics and Applications, EPE 2022 ECCE Europe
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 24th European Conference on Power Electronics and Applications, EPE 2022 ECCE Europe
Y2 - 5 September 2022 through 9 September 2022
ER -