Genetic-Algorithm-Driven Parameter Optimization of Three Representative DAB Controllers for Voltage Stability
Source abstract
Abstract
In the process of integrating renewable energy sources into DC microgrids, the isolated bidirectional bridge plays a crucial role. Under load disturbances, voltage fluctuations in the microgrid can affect system stability. This study focuses on using a Genetic Algorithm to optimize the parameters of three typical DAB controllers (PI controller based on pole placement, sliding mode controller, and model predictive controller) with the aim of improving voltage stability, especially during sudden load drops. The results demonstrate that controllers optimized using Genetic Algorithm outperform the methods of pole placement and traditional manual tuning significantly. For the PI controller, the maximum drop rate reduced from 8.00% to 4.00%. The phase margin increased from 123° to 126°. In the case of the sliding mode controller, the maximum drop rate decreased from 7.50% to 5.00%. The phase margin increased from 127° to 155°. As for the model predictive controller, the maximum drop rate reduced from 1.00% to 0.70%. The gain margin increased from 25.8 dB to 26.2 dB. These results highlight the potential of using the Genetic Algorithm in optimizing control parameters, offering the prospect of improving the performance and stability of DC–DC converters.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Microgrid Control and Optimization · Advanced DC-DC Converters · Multilevel Inverters and Converters
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Electrical Engineering, Xi’an Jiaotong University, Xi’an 710049, China
Funding
National Natural Science Foundation of China
References (31)
Show source-provided references
1. Constant power loads and their effects in DC distributed power systems: A review10.1016/j.rser.2017.01.027 ↗
2. Novel robust methodology for controller design aiming to ensure DC microgrid stability under CPL power variation10.1109/ACCESS.2019.2915027 ↗
3. Lucas, K.E., Pagano, D.J., and Medeiros, R.L.P. (2019, January 1–4). Single Phase-Shift Control of DAB Converter using Robust Parametric Approach. Proceedings of the 2019 IEEE 15th Brazilian Power Electronics Conference and 5th IEEE Southern Power Electronics Conference (COBEP/SPEC), Santos, Brazil.10.1109/COBEP/SPEC44138.2019.9065902 ↗
4. Jing, G., Zhang, A., and Zhang, H. (December, January 30). Review on DC Distribution Network Protection Technology with Distributed Power Supply. Proceedings of the 2018 Chinese Automation Congress (CAC), Xi’an, China.10.1109/CAC.2018.8623659 ↗
5. Circulating Current and ZVS-on of a Dual Active Bridge DC-DC Converter: A Review10.1109/ACCESS.2019.2911009 ↗
6. Robust LMI-LQR Control for Dual-Active-Bridge DC–DC Converters With High Parameter Uncertainties10.1109/TTE.2020.2975313 ↗
7. Li, K., Yang, Y., Tan, S.-C., and Hui, R.S.-Y. (2019, January 17–21). Sliding-mode-based direct power control of dual-active-bridge DC-DC converters. Proceedings of the 2019 IEEE Applied Power Electronics Conference and Exposition (APEC), Anaheim, CA, USA.10.1109/APEC.2019.8721849 ↗
8. Zhang, L., Wang, Y., Cheng, L., and Kang, W. (2023). A Three-Parameter Adaptive Virtual DC Motor Control Strategy for a Dual Active Bridge DC–DC Converter. Electronics, 12.10.3390/electronics12061412 ↗
9. Optimal Phase-Shift Control to Minimize Reactive Power for a Dual Active Bridge DC–DC Converter10.1109/TPEL.2018.2890292 ↗
10. Overview of Dual-Active-Bridge Isolated Bidirectional DC–DC Converter for High-Frequency-Link Power-Conversion System10.1109/TPEL.2013.2289913 ↗
11. Generic Closed-Loop Controller for Power Regulation in Dual Active Bridge DC–DC Converter With Current Stress Minimization10.1109/TIE.2018.2860535 ↗
12. Voltage and Current Regulations of Bidirectional Isolated Dual-Active-Bridge DC–DC Converters Based on a Double-Integral Sliding Mode Control10.1109/TPEL.2018.2873834 ↗
13. Model Predictive Control With Power Self-Balancing of the Output Parallel DAB DC–DC Converters in Power Electronic Traction Transformer10.1109/JESTPE.2018.2823364 ↗
14. Model predictive control for dual-active-bridge converters supplying pulsed power loads in naval DC micro-grids10.1109/TPEL.2019.2917450 ↗
15. Evolutionary algorithms for constrained parameter optimization problems10.1162/evco.1996.4.1.1 ↗
16. Genetic Algorithms in Control Systems Engineering10.1016/S1474-6670(17)49015-X ↗
17. Meng, X., and Song, B. (2007, January 18–21). Fast Genetic Algorithms Used for PID Parameter Optimization. Proceedings of the 2007 IEEE International Conference on Automation and Logistics, Jinan, China.10.1109/ICAL.2007.4338930 ↗
18. Parameter Adaptive Harmony Search Algorithm for Unimodal and Multimodal Optimization Problems
19. Richter, J.N., and Peak, D. (2002, January 28–30). Fuzzy evolutionary cellular automata. Proceedings of the International Conference on Artificial Neural Networks in Engineering, Madrid, Spain.
20. Using Genetic Algorithms to Optimize ACS-TSP10.1007/3-540-45724-0_28 ↗
21. Azab, M., and Serrano-Fontova, A. (2021). Optimal Tuning of Fractional Order Controllers for Dual Active Bridge-Based DC Microgrid Including Voltage Stability Assessment. Electronics, 10.10.3390/electronics10091109 ↗
22. Jeung, Y.-C., Choi, I.-C., and Lee, D.-C. (2016, January 22–26). Robust voltage control of dual active bridge DC-DC converters using sliding mode control. Proceedings of the 2016 IEEE 8th International Power Electronics and Motion Control Conference (IPEMC-ECCE Asia), Hefei, China.
23. Generalized average modeling of dual active bridge DC–DC converter
24. DAB-based PET in MVDC traction and shipboard applications with distribution and redundant control10.1049/joe.2018.8439 ↗
25. Tan, S.-C., Lai, Y.-M., and Tse, C.-K. (2018). Sliding Mode Control of Switching Power Converters: Techniques and Implementation, CRC Press.10.1201/9781315217796 ↗
26. Analysis of a bidirectional coupled-inductor Cuk converter operating in sliding mode10.1109/81.669058 ↗
27. Predictive control based DC microgrid stabilization with the dual active bridge converter10.1109/TIE.2020.2965460 ↗
28. Nardoto, A., Amorim, A., Santana, N., Bueno, E., Encarnação, L., and Santos, W. (2022). Adaptive Model Predictive Control for DAB Converter Switching Losses Reduction. Energies, 15.10.3390/en15186628 ↗
29. Nonlinear system identification and control using a real-coded genetic algorithm10.1016/j.apm.2005.11.024 ↗
30. Costa, L., and Oliveira, P. (2003). Metaheuristics: Computer Decision-Making. Applied Optimization, Springer.
31. A Review of Crossover Methods and Problem Representation of Genetic Algorithm in Recent Engineering Applications