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.

References

References
1.
Wang
,
L.
,
Fan
,
J.
,
Wang
,
Z.
,
Zhan
,
B.
, and
Li
,
J.
,
2015
, “
Dynamic Analysis and Control of a Permanent Magnet Synchronous Motor With External Perturbation
,”
ASME J. Dyn. Syst. Meas. Control
,
138
(
1
), p.
011003
.
2.
Pellegrino
,
G.
,
Vagati
,
A.
,
Guglielmi
,
P.
, and
Boazzo
,
B.
,
2012
, “
Performance Comparison Between Surface-Mounted and Interior PM Motor Drives for Electric Vehicle Application
,”
IEEE Trans. Ind. Electron.
,
59
(
2
), pp.
803
811
.
3.
Underwood
,
S. J.
, and
Husain
,
I.
,
2010
, “
Online Parameter Estimation and Adaptive Control of Permanent-Magnet Synchronous Machines
,”
IEEE Trans. Ind. Electron
,
57
(
7
), pp.
2435
2443
.
4.
Inoue
,
Y.
,
Kawaguchi
,
Morimoto
,
Y. S.
, and
Sanada
,
M.
,
2011
, “
Performance Improvement of Sensorless IPMSM Drives in a Low-Speed Region Using Online Parameter Identification
,”
IEEE Trans. Ind. Appl.
,
47
(
2
), pp.
798
804
.
5.
Xu
,
Y.
,
Parspour
,
N.
, and
Vollmer
,
U.
,
2014
, “
Torque Ripple Minimization Using Online Estimation of the Stator Resistances With Consideration of Magnetic Saturation
,”
IEEE Trans. Ind. Electron.
,
61
(
11
), pp.
5921
5928
.
6.
Kim
,
J. W.
, and
Ha
,
J. I.
,
2015
, “
Enhancement of Parameter Estimation Accuracy Using Current Shaping in PM Machine Drive
,” 9th International Conference on Power Electronics-
ECCE
Asia, Seoul, South Korea, June 1–5, pp.
2123
2128
.
7.
Liu
,
Q.
, and
Hameyer
,
K.
,
2015
, “
A Fast Online Full Parameter Estimation of a PMSM With Sinusoidal Signal Injection
,”
IEEE Energy Conversion Congress and Exposition
(
ECCE
), Sept. 20–24, pp.
4091
4096
.
8.
Ramakrishnan
,
R.
,
Islam
,
R.
,
Islam
,
M.
, and
Sebastian
,
T.
,
2009
, “
Real Time Estimation of Parameters for Controlling and Monitoring Permanent Magnet Synchronous Motors
,”
IEEE
Int. Elect. Mach. Drives Conf.
, Miami, FL, May 3–6, pp.
1194
1199
.
9.
Liu
,
K.
,
Zhang
,
Q.
,
Chen
,
J.
,
Zhu
,
Z. Q.
, and
Zhang
,
J.
,
2011
, “
Online Multiparameter Estimation of Nonsalient-Pole PM Synchronous Machines With Temperature Variation Tracking
,”
IEEE Trans. Ind. Electron.
,
58
(
5
), pp.
1776
1788
.
10.
Liu
,
K.
,
Zhu
,
Z. Q.
, and
Stone
,
D. A.
,
2013
, “
Parameter Estimation for Condition Monitoring of PMSM Stator Winding and Rotor Permanent Magnets
,”
IEEE Trans. Ind. Electron.
,
60
(
12
), pp.
5902
5913
.
11.
Liu
,
K.
, and
Zhu
,
Z. Q.
,
2014
, “
Online Estimation of the Rotor Flux Linkage and Voltage-Source Inverter Nonlinearity in Permanent Magnet Synchronous Machine Drives
,”
IEEE Trans. Power Electron.
,
29
(
1
), pp.
418
427
.
12.
Odhano
,
S. A.
,
Bojoi
,
R.
,
Popescu
,
M.
, and
Tenconi
,
A.
,
2015
, “
Parameter Identification and Self-Commissioning of AC Permanent Magnet Machines—A Review
,”
IEEE Workshop on Electrical Machines Design, Control and Diagnosis
(
WEMDCD
), Mar. 26–27, pp.
195
203
.
13.
Xiao
,
X.
,
Chen
,
C. M.
, and
Zhang
,
M.
,
2010
, “
Dynamic Permanent Magnet Flux Estimation of Permanent Magnet Synchronous Machines
,”
IEEE Trans. Appl. Supercond.
,
20
(
3
), pp.
1085
1088
.
14.
Shi
,
Y. C.
,
Sun
,
K.
,
Huang
,
L. P.
, and
Li
,
Y.
,
2012
, “
Online Identification of Permanent Magnet Flux Based on Extended Kalman Filter for IPMSM Drive With Position Sensorless Control
,”
IEEE Trans. Ind. Electron
,
59
(
11
), pp.
4169
4178
.
15.
Rashed
,
M.
,
Macconnell
,
P. F. A.
,
Stronach
,
A. F.
, and
Acarnley
,
P.
,
2007
, “
Sensorless Indirect-Rotor-Field-Orientation Speed Control of a Permanent-Magnet Synchronous Motor With Stator-Resistance Estimation
,”
IEEE Trans. Ind. Electron.
,
54
(
3
), pp.
1664
1675
.
16.
Thierry
,
B.
,
Nicolas
,
L.
,
Babak
,
N. M.
, and
Farid
,
M. T.
,
2011
, “
Online Identification of PMSM Parameters: Parameter Identifiability and Estimator Comparative Study
,”
IEEE Trans. Ind. Appl.
,
47
(
4
), pp.
1944
1957
.
17.
Zhang
,
Y.
,
Yin
,
Z.
,
Sun
,
X.
, and
Zhong
,
Y.
,
2015
, “
On-Line Identification Methods of Parameters for Permanent Magnet Synchronous Motors Based on Cascade MRAS
,” 9th International Conference on Power Electronics-
ECCE
Asia, Seoul, South Korea, June 1–5, pp.
345
353
.
18.
Hamida
,
M. A.
,
Leon
,
J. D.
,
Glumineau
,
A.
, and
Boisliveau
,
R.
,
2013
, “
An Adaptive Interconnected Observer for Sensorless Control of PM Synchronous Motors With Online Parameter Identification
,”
IEEE Trans. Ind. Electron.
,
60
(
2
), pp.
739
748
.
19.
Odhano
,
S. A.
,
Bojoi
,
R.
,
Rosu
,
S. G.
, and
Tenconi
,
A.
,
2015
, “
Identification of the Magnetic Model of Permanent-Magnet Synchronous Machines Using DC-Biased low-Frequency AC Signal Injection
,”
IEEE Trans. Ind. Appl.
,
51
(
4
), pp.
3208
3215
.
20.
Ji
,
X.
, and
Noguchi
,
T.
,
2014
, “
Off-Line Parameter Identification of Interior Permanent Magnet Motor by Searching Minimum Point of Current Norm Characteristics
,”
International Symposium on Power Electronics, Electrical Drives, Automation and Motion
, June 18–20, pp.
2014
2019
.
21.
Eiben
,
A. E.
, and
Smith
,
J.
,
2015
, “
From Evolutionary Computation to the Evolution of Things
,”
Nature
,
521
(
7553
), pp.
476
482
.
22.
Liu
,
K.
,
Zhu
,
Z. Q.
,
Zhang
,
J.
, and
Zhang
,
Q.
,
2010
, “
Multi-Parameter Estimation of Nonsalient Pole Permanent Magnet Synchronous Machines by Using Evolutionary Algorithms
,”
IEEE
15th International Conference on Bio-Inspired Computing. Theories and Application
, Sept. 23–26, pp.
766
774
.
23.
Liu
,
K.
,
Zhu
,
Z. Q.
, and
Stone
,
D. A.
,
2015
, “
Quantum Genetic Algorithm-Based Parameter Estimation of PMSM Under Variable Speed Control Accounting for System Identifiability and VSI Nonlinearity
,”
IEEE Trans. Ind. Electron.
,
62
(
4
), pp.
2363
2371
.
24.
Liu
,
W. X.
,
Liu
,
L.
,
Chung
,
I. Y
, and
Cartes
,
D. A.
,
2011
, “
Real-Time Particle Swarm Optimization Based Parameter Identification Applied to Permanent Magnet Synchronous Machine
,”
Appl. Soft Comput.
,
11
(
2
), pp.
2556
2564
.
25.
Pérez
,
J. N. H.
,
Hernandez
,
O. S.
,
Caporal
,
R. M.
,
Magdaleno
,
J. de J. R.
, and
Barreto
,
H. P.
,
2013
, “
Parameter Identification of a Permanent Magnet Synchronous Machine Based on Current Decay Test and Particle Swarm Optimization
,”
IEEE Latin America Trans.
,
11
(
5
), pp.
1176
1181
.
26.
Liu
,
Z. H.
,
Zhang
,
J.
,
Zhou
,
S. W.
,
Li
,
X. H.
, and
Liu
,
K.
,
2013
, “
Coevolutionary Particle Swarm Optimization Using AIS and Its Application in Multiparameter Estimation of PMSM
,”
IEEE Trans. Cybern.
,
43
(
6
), pp.
1921
1935
.
27.
Liu
,
Z. H.
,
Li
,
X. H.
,
Wu
,
L. H.
,
Zhou
,
S. W.
, and
Liu
,
K.
,
2015
, “
GPU-Accelerated Parallel Coevolutionary Algorithm for Parameters Identification and Temperature Monitoring in Permanent Magnet Synchronous Machines
,”
IEEE Trans. Ind. Inf.
,
11
(
5
), pp.
1220
1230
.
28.
Sandre-Hernandez
,
O.
,
Morales-Caporal
,
R.
,
Rangel-Magdaleno
,
J.
,
Peregrina-Barreto
,
H.
, and
Hernandez-Perez
,
N.
,
2015
, “
Parameter Identification of PMSMs Using Experimental Measurements and a PSO Algorithm
,”
IEEE Trans. Instrum. Meas.
,
64
(
8
), pp.
2146
2154
.
29.
Storn
,
R.
, and
Price
,
K.
,
1997
, “
Differential Evolution—A Simple and Efficient Heuristic for Global Optimization Over Continuous Spaces
,”
J. Global Optim.
,
11
(
4
), pp.
341
359
.
30.
Das
,
S.
, and
Suganthan
,
P. N.
,
2011
, “
Differential Evolution: A Survey of the State-of-the-Art
,”
IEEE Trans. Evol. Comput.
,
15
(
1
), pp.
4
31
.
31.
Marcic
,
T.
,
Fitumberger
,
B.
, and
Fitumberger
,
G.
,
2014
, “
Differential-Evolution-Based Parameter Identification of a Line-Start IPM Synchronous Motor
,”
IEEE Trans. Ind. Electron.
,
61
(
9
), pp.
5105
5114
.
32.
Zhan
,
C.
,
Situ
,
W.
,
Yeung
,
L. F.
,
Tsang
,
P. W. M.
, and
Yang
,
G.
,
2014
, “
A Parameter Estimation Method for Biological Systems Modelled by ODE/DDE Models Using Spline Approximation and Differential Evolution Algorithm
,”
IEEE/ACM Trans. Comput. Biol. Bioinf
,
11
(
6
), pp.
1066
1075
.
33.
Qing
,
A.
,
2006
, “
Dynamic Differential Evolution Strategy and Applications in Electromagnetic Inverse Scattering Problems
,”
IEEE Trans. Geosci. Remote Sens.
,
44
(
1
), pp.
116
125
.
34.
Harno
,
H. G.
, and
Petersen
,
I. R.
,
2015
, “
Synthesis of Linear Coherent Quantum Control Systems Using a Differential Evolution Algorithm
,”
IEEE Trans. Autom. Control
,
60
(
3
), pp.
799
805
.
35.
Wu
,
L. H.
,
Wang
,
Y. N.
,
Yuan
,
X. F.
, and
Chen
,
Z. L.
,
2011
, “
Multiobjective Optimization of HEV Fuel Economy and Emissions Using the Self-Adaptive Differential Evolution Algorithm
,”
IEEE Trans. Veh. Technol.
,
60
(
6
), pp.
2458
2472
.
36.
Qin
,
A. K.
,
Huang
,
V. L.
, and
Sugannthan
,
P. N.
,
2009
, “
Differential Evolution Algorithm With Strategy Adaptation for Global Numerical Optimization
,”
IEEE Trans. Evol. Comput.
,
13
(
2
), pp.
398
417
.
You do not currently have access to this content.