Advanced SearchSearch Tips
An Optimized PI Controller Design for Three Phase PFC Converters Based on Multi-Objective Chaotic Particle Swarm Optimization
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
  • Journal title : Journal of Power Electronics
  • Volume 16, Issue 2,  2016, pp.610-620
  • Publisher : The Korean Institute of Power Electronics
  • DOI : 10.6113/JPE.2016.16.2.610
 Title & Authors
An Optimized PI Controller Design for Three Phase PFC Converters Based on Multi-Objective Chaotic Particle Swarm Optimization
Guo, Xin; Ren, Hai-Peng; Liu, Ding;
  PDF(new window)
The compound active clamp zero voltage soft switching (CACZVS) three-phase power factor correction (PFC) converter has many advantages, such as high efficiency, high power factor, bi-directional energy flow, and soft switching of all the switches. Triple closed-loop PI controllers are used for the three-phase power factor correction converter. The control objectives of the converter include a fast transient response, high accuracy, and unity power factor. There are six parameters of the controllers that need to be tuned in order to obtain multi-objective optimization. However, six of the parameters are mutually dependent for the objectives. This is beyond the scope of the traditional experience based PI parameters tuning method. In this paper, an improved chaotic particle swarm optimization (CPSO) method has been proposed to optimize the controller parameters. In the proposed method, multi-dimensional chaotic sequences generated by spatiotemporal chaos map are used as initial particles to get a better initial distribution and to avoid local minimums. Pareto optimal solutions are also used to avoid the weight selection difficulty of the multi-objectives. Simulation and experiment results show the effectiveness and superiority of the proposed method.
Chaotic particle swarm optimization;Pareto optimal solution;PI parameters;Spatiotemporal chaos map lattice;Three phase PFC converter;
 Cited by
Understanding Robust Adaptation Dynamics of Gene Regulatory Network, IEEE Transactions on Biomedical Circuits and Systems, 2017, 11, 4, 942  crossref(new windwow)
H. P. Ren and X. Guo, “Robust adaptive control of CACZVS three phase PFC converter for power supply of silicon growth furnace,” IEEE Trans. Ind. Electron., Vol. 63, No. 2, pp. 903-912, Feb. 2016. crossref(new window)

M. J. L. Huber and M. Kumar, “Performance comparison of PI and P compensation in DSP based average current controlled three-phase six switch boost PFC rectifier,” IEEE Trans. Power Electron., Vol. 30, No. 12, pp. 7123-7137, Dec. 2015. crossref(new window)

V. Blasko and V. Kaura. “A new mathematical model and control of a three-phase AC-DC voltage source converter,” IEEE Trans. Power Electron., vol. 12, pp. 116-123, Jan. 1997. crossref(new window)

M. Perez, R. Ortega, and J. R. Espinoza, “Passivity-based PI control of switched power converters,” IEEE Trans. Control Syst. Technol., Vol. 12, No. 11, pp. 881-890, Nov. 2004. crossref(new window)

L. Corradini, P. Mattavelli, W. Stefanutti, and S. Saggini, “Simplified model reference-based auto tuning for digitally controlled SMPs,” IEEE Trans. Power Electron., Vol. 23, No. 7, pp. 1956-1963, Jul. 2008. crossref(new window)

X. W. Bao, F. Zhuo, Y. Tian, and P. Tan, “Simplified feedback linearization control of three-phase photovoltaic inverter with an LCL filter,” IEEE Trans. Power Electron., Vol. 28, No. 6, pp. 2739-2752, Jun. 2013. crossref(new window)

X. G. Zhang, W. J. Zhang, J. M. Chen, and D. G. Xu, “Deadbeat control strategy of circulating currents in parallel connection system of three-phase PWM converter,” IEEE Trans. Energy Convers., Vol. 29, No. 12, pp. 406-417, Jun. 2014. crossref(new window)

O. S. D. Oetinger and M. E. Magana, “Centralized model predictive controller design for wave energy converter arrays,” IET Renew. Power Gener., Vol. 9, No. 2, pp. 142-153, Feb. 2015. crossref(new window)

R. Guzman, L. Garcia de Vicuna, J. Morales, M. Castilla, and J. Matas, “Sliding-mode control for a three-phase unity power factor rectifier operating at fixed switching frequency,” IEEE Trans. Power Electron., Vol. 31, No. 1, pp. 758-769, Jan. 2016. crossref(new window)

Z. Song, W. Chen, and C. Xia, “Predictive direct power control for three-phase grid-connected converters without sector information and voltage vector selection,” IEEE Trans. Power Electron., Vol. 29, No. 10, pp. 5518-5531, Oct. 2014. crossref(new window)

Z. N. He, G. G. Yen, and J. Zhang, “Fuzzy-based Pareto optimality for many-objective evolutionary algorithms,” IEEE Trans. Evolut. Comput., Vol. 18, No. 4, pp. 269-285, April 2014. crossref(new window)

H. P. Ren and T. Zheng, "Optimization design of power factor correction converter based on genetic algorithm," in Proc. ICGEC, pp. 293-296, 2010.

K.-B. Lee and J.-H. Kim, “Multi-objective particle swarm optimization with preference-based sort and its application to path following foot step optimization for humanoid robots,” IEEE Trans. Evolut. Comput., Vol. 17, No. 12, pp. 755-766, Dec. 2013. crossref(new window)

H. P. Ren and X. Guo, "Optimization controller design of CACZVS three phase PFC converter using particle swarm optimization," in Proc. IECON, pp. 1665-1671, 2014.

Y. H. Shi and R. C. Eberhart, "Empirical study of particle swarm optimization," in Proc. ECE, pp. 1945-1950, 1999.

B. Liu, L. Wang, Y. H. Jin, F. Tang, and D.-X. Huang, “Improved particle swarm optimization combined with chaos,” Chaos, Solitons and Fractals, Vol. 25, No. 3, pp. 1261-1271, Mar. 2005. crossref(new window)

L. Y. Chuang, C. H. Yang, J. H. Tsai, and C.-H. Yang, “Operon prediction using chaos embedded particle swarm optimization,” IEEE/ACM Transactions on Computational Biology and Bioinformatics, Vol. 10, No. 9, pp. 1299-1309, Sep. 2013. crossref(new window)

E. Mirzaei and H. Mojallali, "Auto tuning PID controller using chaotic PSO algorithm for a boost converter," in Proc. IFSC, pp. 1-6, 2013.

C. P. Liu and C. M. Ye, “Mutative scale chaos particle swarm optimization algorithm based on self logical mapping function,” Application Research of Computers, Vol. 28, No. 8, pp. 2825-2827, Aug. 2011.

C. A. Coello, D. A. V. Veldhuizen, and G. B. Lamont, “Evolutionary algorithms for solving multi-objective problems,” Kluwer Academic Publishers, 2002.

M. Clerc and J. Kennedy, “The particle swarm: explosion stability and convergence in a multi-dimensional complex space,” IEEE Trans. Evolut. Comput., Vol. 6, No. 1, pp. 58-73, Feb. 2002. crossref(new window)

Y. Jin, M. Olhofer, and B. Sendhoff, "Dynamic weighted aggregation for evolutionary multi-objective optimization: Why does it work and how?," in Proc. GECCO, pp. 1042-1049, 2001.

J. Zhang and H. Q. L, "A global-crowding-distance based multi-objective particle swarm optimization algorithm," in Proc. CIS, pp. 1-6, 2014.

X. Hu, R. C. Eberhart, and Y. Shi, "Particle swarm with extended memory for multi-objective optimization," in Proc. IEEE Swarm Intelligence Symposium, pp. 193-197, 2003.

J. Li, W. Wang, and Y. R. Zhong, “Joint simulation method of PSIM+MATLAB for power electronic systems,” Power Electronics, Vol. 44, No. 5, pp. 86-88, May 2010.