Marine Asynchronous Propulsion Motor Parameter Identification Using Dynamic Particle Swarm Optimization

Abstract:

This paper proposes a new application of dynamic particle swarm optimization (PSO) algorithm for parameter identification of vector controlled asynchronous propulsion motor (APM) in electric propulsion ship. The dynamic PSO modifies the inertia weight, learning coefficients and two independent random sequences which affect the convergence capability and solution quality, in order to improve the performance of the standard PSO algorithm. The standard PSO and dynamic PSO algorithms use measurements of the mt-axis currents, voltages of APM as the inputs to parameter identification system. The experimental results obtained compare the identified parameters with the actual parameters. There is also a comparison of the solution quality between standard PSO and dynamic PSO algorithms. The results demonstrate that the dynamic PSO algorithm is better than standard PSO algorithm for APM parameter identification. Dynamic PSO algorithm can improve the performance of ship propulsion motor under abrupt load variation.

• Periodical: Advanced Materials Research (Volumes 860-863)
• Pages: 2211-2217
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.860-863.2211
• Online since: December 2013
• Authors: Si Yuan Liu*, Yan Cheng Liu, Chuan Wang, Jun Jie Ren
• Keywords: Asynchronous Propulsion Motor, Dynamic Particle Swarm Optimization, Parameter Identification, Vector Control (VC)

References

[1] R. J. Thome, E. Bowles, M. Reed. Integration of electromagnetic technologies into shipboard applications. IEEE Trans. Appl. Super cond. 2006, 16(2): 1074-1079.

DOI: 10.1109/tasc.2006.870790

[2] CIRRINCIONE M, PUCCIM, CIRRINCIONE G, et al. A new experimental application of least-squares techniques for the estimation of the induction motor parameters [J]. IEEE Transactions on Industry Applications, 2003, 39: 1247-1256.

DOI: 10.1109/tia.2003.816565

[3] Wang Chuan, Liu Yancheng, Zhao Youtao. Application of dynamic neighborhood small population particle swarm optimization for reconfiguration of shipboard power system[J], Engineering Applications of Artificial Intelligence, 2013, 26(4): 1255-1262.

DOI: 10.1016/j.engappai.2012.12.010

[4] HUANG K.S., WU Q.H. Effective identification of induction motor parameters based on fewer measurements, IEEE Trans. Energy Convers. 2002, 17, p.55 – 60.

DOI: 10.1109/60.986437

[5] RENJun-jie，LIUYan-cheng，ZHAOYou-tao，WANGChuan. Research on the different vector control schemes with Larger power marine PMSM[J]. Electric Machines and Control, Vol. 15 No. 5 May 2011(in Chinese).

[6] PICARDI C., ROGANO N. Parameter identification of induction motor based on particle swarm optimization. Int. Synp. Power Electronic, Electrical Drives, Automation, and Motion SPEEDAM2006, 2006, pp.32-37.

DOI: 10.1109/speedam.2006.1649908

[7] KENNEDY J. , EBERHART R. Particle swarm optimization. Proc. IEEE Int. Conf. Neural Networks, 1995, p.1942 – (1948).

[8] Jul-Ki Seok, Seung-Ki Sul. Induction Motor Parameter Tuning for High-Performance Drives. IEEE Transactions on Industry Applications. Vol. 37, No. 1, January/February, (2001).

DOI: 10.1109/28.903124