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Research Papers

Parameter Identification of Permanent Magnet Synchronous Machine Based on an Adaptive Mutation Dynamic Differential Evolution

[+] Author and Article Information
Lianghong Wu

School of Information and Electrical Engineering,
Hunan University of Science and Technology,
Xiangtan, Hunan 411201, China
e-mail: lhwu@hnust.edu.cn

Zhao-Hua Liu

School of Information and Electrical Engineering,
Hunan University of Science and Technology,
Xiangtan, Hunan 411201, China
e-mail: zhaohualiu2009@hotmail.com

Hua-Liang Wei

Department of Automatic Control
and Systems Engineering,
The University of Sheffield,
Sheffield S1 3JD, UK
e-mail: w.hualiang@sheffield.ac.uk

Qing-Chang Zhong

Department of Electrical and
Computer Engineering,
Illinois Institute of Technology,
Chicago, IL 60616
e-mail: zhongqc@ieee.org

Xiao-Shi Xiao

School of Information and Electrical Engineering,
Hunan University of Science and Technology,
Xiangtan, Hunan 411201, China
e-mail: xiaoxs2005@163.com

1Corresponding author.

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. Manuscript received June 10, 2016; final manuscript received November 10, 2016; published online March 23, 2017. Assoc. Editor: Davide Spinello.

J. Dyn. Sys., Meas., Control 139(6), 061006 (Mar 23, 2017) (9 pages) Paper No: DS-16-1306; doi: 10.1115/1.4035239 History: Received June 10, 2016; Revised November 10, 2016

The problem of parameter estimation of permanent-magnet synchronous machines (PMSMs) can be formulated as a nonlinear optimization problem. To obtain accurate machine parameters, it is necessary to develop easily applicable but efficient optimization algorithms to solve the parameter estimation models. This paper proposes a novel dynamic differential evolution with adaptive mutation operator (AMDDE) algorithm for the multiparameter simultaneous estimation of a nonsalient pole PMSM. The dynamic updating of population enables AMDDE to responds to any improved changes of the population immediately and thus generates better optimization solutions compared with the static mechanism used in original differential evolution. Two mutation strategies, namely DE/rand/1 and DE/best/1, are adaptively employed to balance the global exploration and local exploitation. The effectiveness of the proposed AMDDE is demonstrated on the multiparameter estimation for a nonsalient pole PMSM. Experimental results indicate that the proposed method significantly outperforms the existing peer algorithms in efficiency, accuracy, and robustness. Furthermore, the new algorithm can be potentially realized in digital microcontroller due to its simple structure and lower memory requirement. The proposed algorithm can also be applied to other parameter estimation and optimization problems.

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Figures

Grahic Jump Location
Fig. 1

Schematic diagram of vector control and multiparameter estimation

Grahic Jump Location
Fig. 2

Sampled electrical angular speed, dq-axis currents, voltages, and injected current pulse (id = −2A)

Grahic Jump Location
Fig. 3

Estimated parameters with temperature variations: (a) estimated winding resistance, (b) estimated rotor flux linkage, (c) estimated d-axis inductance (id = −2A), and (d) estimated q-axis inductance (id = 0)

Grahic Jump Location
Fig. 6

Typical statistics results by Box–Whisker plot

Grahic Jump Location
Fig. 5

Average convergence curves over 100 runs of different DE and DDE variants

Grahic Jump Location
Fig. 4

Estimated parameters of dynamic estimation: (a) estimated winding resistance, (b) estimated rotor flux linkage, (c) estimated d-axis inductance (id = −2A), and (d) estimated q-axis inductance (id = 0)

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