Main Article Content

Abstract

Weighted voltage mode control is a widely used method for regulating multiple output DC-DC converters, but inconsistent outcomes are often observed in design due to the complexity of the weighting variable optimization. This study proposes an optimization-based approach to accurately estimate the optimal weighting factors for improved output voltage regulation in multiple output forward DC-DC converters. Three optimization algorithms, the Imperialist Competitive Algorithm (ICA), Particle Swarm Optimization (PSO), and Ant Colony Optimization (ACO) are compared for their speed and accuracy in estimating the weighting factors. Additionally, a Fuzzy Logic Controller (FLC) is used to further reduce the overall steady-state error and improve transient characteristics. Simulations are performed using the MATLAB/Simulink software, and the results show that the proposed strategy significantly enhances output cross-regulation in multiple output forward DC-DC converters. The ICA-based weighting factor estimator is found to be the most effective algorithm among the three optimization algorithms tested. The main contribution of this study is to provide a more efficient and accurate method for estimating the weighting factors in multiple output forward DC-DC converters, which can lead to improved performance and reliability in various applications.

Keywords

Multiple outputs forward DC-DC converters Imperialist competitive algorithm Particle swarm optimization Ant colony optimization Weighting factor method

Article Details

How to Cite
Safarishaal, M., & Sarvi, M. (2023). Optimizing weighted voltage mode control for enhanced output cross-regulation in multi-output DC/DC converters. Future Technology, 3(1), 32–39. Retrieved from https://fupubco.com/futech/article/view/111
Bookmark and Share

References

  1. Xie, Y. and Gan, J., “Study on the Voltage Stability of Multi-Output Converters”, IPEMC. Int. Conf., Xi'an, China, Aug 2004, pp. 482-486.
  2. Khazeiynasab, S. R., & Batarseh, I. “Measurement-Based Parameter Identification of DC-DC Converters with Adaptive Approximate Bayesian Computation”. (2021). arXiv preprint arXiv:2106.15538.Matsuo
  3. Wilson, T., “Cross Regulation in an Energy-Storage DC to DC Converter with Two Regulated Outputs”, IEEE. Conf. Rec. Power Elec. Palo Alto, Calif, June 1977, pp. 190-199.
  4. Wilson, T., “Cross Regulation in a Two-Output DC-to-DC Converter with Application to Testing of Energy-Storage Transformer”, IEEE. Conf. Rec. Power Elec. June 1978, pp. 124-134.
  5. Pan, S., and Jain, PK., “A Precisely-Regulated Multiple Output Forward Converter with Automatic Master-Slave Control”, Proc. IEEE. Conf. Power Elec, Recife, Brazil, June 2005, pp. 986-992.
  6. Liu, C., Ding, K., Young, J., and Beutler, J., “A Systematic Method for the Stability Analysis of Multiple-Output Converters”, IEEE. Trans. Power Elec, Oct 1989, 2, (4), pp. 343-353.
  7. Chen. Q., Lee., F., and Jovanovic, M., “Analysis and Design of Weighted Voltage-Mode Control for a Multiple-Output Forward Converter”, Proc. IEEE. APEC’93 Conf. Blacksburg, VA, April 1993, pp. 449-455.
  8. Chen, Q., Lee, F. and Jovanovic, M., “Small Signal Analysis and Design of Weighted Voltage-Mode Control for a Multiple-Output Forward Converter”, IEEE. Trans. Power Elec, Jun 1995, 10, pp. 589-596.
  9. Atashpaz, E., and Lucas, C., “Imperialist Competitive Algorithm: An Algorithm for Optimization Inspired by Imperialistic Competition”, IEEE. Cong. Evolutionary Computation, Singapore, Sept 2007, pp. 4661-4667.
  10. M.Sarvi, M.Safari, “Fuzzy, ANFIS and ICA trained neural network modelling of Ni-Cd batteries using experimental data” - Journal of World Applied Programming., V.8 (2013). p.93-100
  11. M.Safari, M.Sarvi, “Optimal load sharing strategy for a wind/diesel/battery hybrid power system based on imperialist competitive neural network algorithm” - IET Renewable Power Generation, V.8 (2014) p. 937 – 946
  12. Kennedy, K., Eberhart, R., “Particle Swarm Optimization”, Proc. of IEEE ICNN. Perth, Australia, New Jersey, Nov 1995, pp. 1942–1948.
  13. Khazeiynasab, S. R., & Qi, J. (2021). Generator Parameter Calibration by Adaptive Approximate Bayesian Computation with Sequential Monte Carlo Sampler. IEEE Transactions on Smart Grid. Schutte,
  14. Dorigo, M., Birattari, M., and Stutzle, T., “Ant Colony Optimization”, IEEE Computational Intelligence Magazine, 2006, 1, (4), pp. 28-39.
  15. Shyu, S., Lin, B., and Yin, P., “Application of Ant Colony Optimization for No-Wait flowshop Scheduling Problem to Minimize the Total Completion Time”, J. of Computers and Industrial Engineering, 2004, 47, pp. 181–193.
  16. Abel, B., Francisco, R., Trejo, M., Felipe, M., Ruben, O., and Hugo, T., “Design and Implementation of a FLC for DC-DC Converter in a Microcontroller for PV System”, Int. J. Soft Computing and Engineering. 2013, 3 (3), pp. 26-30.
  17. M.Safari, M.Sarvi, “Estimation the Performance of a PEM Fuel Cell System at Different Operating Conditions using Neuro Fuzzy (ANFIS)-TI Journals” - World Applied Programming, V.3 (2013) p.355-360.
  18. Erickson, R.W., “Fundamentals of Power Eectronics”, (Kluwer Academic Publishers, 1997, 2nd edn 2004).
  19. Chen, Q. Lee, F.C. and Jovanovi, L., “Analysis and Design of Weighted Voltage-Mode Control for a Multiple-Output Forward Converter”, IEEE Power Electronics Specialists Conference (PESC) Rec., Seattle, WA, 1993, pp. 449-455.
  20. M.Safari, M.Sarvi, “A Fuzzy Model for Ni-Cd Batteries” International Journal of Artificial Intelligence, V.2 (2013).p.81-89