Advanced
Study on Optimal Condition of Adaptive Maximum Torque Per Amp Controlled Induction Motor Drives
Study on Optimal Condition of Adaptive Maximum Torque Per Amp Controlled Induction Motor Drives
Journal of Electrical Engineering and Technology. 2014. Jan, 9(1): 231-238
Copyright © 2014, The Korean Institute of Electrical Engineers
  • Received : April 13, 2013
  • Accepted : October 28, 2013
  • Published : January 01, 2014
Download
PDF
e-PUB
PubReader
PPT
Export by style
Article
Author
Metrics
Cited by
TagCloud
About the Authors
Chun-Ki Kwon
Corresponding Author: Dept. of Medical Information Technology Engineering, Soonchunhyang University, Korea. (chunkikwon@sch.ac.kr)

Abstract
Adaptive Maximum Torque Per Amp (Adaptive MTPA) control for induction motor drives seeks to achieve a desired torque with the minimum possible stator current regardless of operating points. This is favorable in terms of inverter operation and nearly optimal in terms of motor efficiency. However, the Adaptive MTPA control was validated only from the viewpoint of tracking a desired torque and was not shown that the desired torque is achieved with minimum possible stator current. This work experimentally demonstrates that optimal condition for Adaptive Maximum Torque Per Amp Control Strategy is achieved regardless of rotor resistance variation.
Keywords
1. Introduction
Highly efficient operation of induction motor has been studied in the past years [1 - 6] . Among many efforts to obtain optimal performance, an Adaptive Maximum Torque Per Amp (Adaptive MTPA) control of induction motor drives was proposed [7] . One interesting approach to develop an Adaptive MTPA control is to employ an alternate qd induction machine model (AQDM) rather than the classical qd model (CQDM), such as in [8 - 11] . The Adaptive MTPA control strategy set forth in [7] has demonstrated the ability to achieve the commanded torque with good accuracy, regardless of temperature variation.
However, efforts have been made to develop many control algorithms for tracking a desired torque, particularly in the case of optimal controls [1 - 6 , 8] . Little study has been conducted on the optimality of the MTPA control that the desired torque was achieved with minimum possible stator current [1 - 6 , 8] .
Thus, this work demonstrates its optimality in a way that maximum torque was achieved for a certain stator current, which is also called maximum torque per amp condition. In addition, satisfaction of maximum torque per amp condition is also shown as rotor resistance is varied.
2. Alternate QD Induction Machine (AQDM)
Alternate QD Model (AQDM) included leakage saturation, magnetizing saturation, and distributed system effects in the rotor circuits which CQDM in [8 - 13] failed to represent over all possible operating conditions due to its constant parameters. Stator and rotor leakage inductance, and the absolute inverse magnetizing inductance are expressed as function of magnitude of magnetizing flux linkage, λm , which is equal to
PPT Slide
Lager Image
. Stator and rotor leakage inductance, and the absolute inverse magnetizing inductance are denoted as Lls ( λm ) , Llr ( λm ) , and Γ m ( λm ), respectively. To consider distributed system effects in the rotor circuits, the rotor impedance, Zr ( s ) in Laplace form. Zr ( s ) is separated into a real and imaginary part, which are denoted rr ( s ) and s Llrz ( s ), respectively.
The steady-state equivalent circuit representing the AQDM in [14] is shown in Fig. 1 . Additional details on the AQDM model and its nomenclature are found in [14 , 15] . In this work, the functional forms for AQDM parameters are specified as follows:
PPT Slide
Lager Image
Steady-state equivalent circuit of AQDM model with rotor impedance represented as rr ( s) + sLlrz ( s)
PPT Slide
Lager Image
PPT Slide
Lager Image
PPT Slide
Lager Image
PPT Slide
Lager Image
The parameters in (1)-(4) of AQDM were characterized by applying fitting process to the functional forms of (1)-(4) on the laboratory experimental data taken for a 4-pole, 460 V, 50 Hp, 60 Hz, delta-connected squirrel cage induction motor. The resultant parameters in (1)-(4) of AQDM for the test induction motor are listed in Table 1 and are illustrated in Fig. 2 . Detailed procedures of the parameter identification for AQDM is set forth in [14 , 16] but is omitted due to limited space.
Resultant parameters
PPT Slide
Lager Image
Resultant parameters
PPT Slide
Lager Image
AQDM parameters for the test induction motor
3. Adaptive Maximum Torque per Amp Control Strategy
- 3.1 Objective and structure
The structure of Adaptive MTPA control strategy is to express root-mean-square magnitude of the stator current
Is and slip frequency ωs , as functions of the commanded torque and rotor resistance estimate
PPT Slide
Lager Image
as shown in Fig. 3.
PPT Slide
Lager Image
Structure of adaptive maximum torque per amp control strategy
Its objective is to produce a desired torque with minimum stator current regardless of rotor resistance. To this end, two properties should be satisfied. One property is the tracking property and the other optimal condition in MTPA control strategy.
- 3.1.1 Tracking property
The tracking property is to achieve a desired torque at steady states for the current of
PPT Slide
Lager Image
, the slip frequency of
PPT Slide
Lager Image
, and the estimated rotor resistance of
PPT Slide
Lager Image
in the control strategy.
PPT Slide
Lager Image
Herein, ε is a very small number. As can be seen in Fig. 4 , torques at point B and C does not achieve torque command
PPT Slide
Lager Image
, resulting in failure of the tracking property. However, torques at points A and A’ satisfies this property.
PPT Slide
Lager Image
Optimal condition of (Adaptive) Maximum Torque Per Amp controlled induction machine drive
- 3.1.2 Optimal condition: MTPA condition
The other is optimal condition in MTPA control strategy at which condition the generated torque T (
PPT Slide
Lager Image
,
PPT Slide
Lager Image
,
PPT Slide
Lager Image
) is not only the desired torque but also maximum torque for a given stator current of
PPT Slide
Lager Image
. This is also called Maximum Torque Per Amp (MTPA) condition which the control strategy was named after. In other words, the desired torque was achieved with minimum stator current by minimizing conduction loss
PPT Slide
Lager Image
In Fig. 4 , the desired torque was achieved at point A’ with I s2 and at point A with
PPT Slide
Lager Image
. Since I s2 is larger than
PPT Slide
Lager Image
, operation with
PPT Slide
Lager Image
has higher efficiency than the one with I s2 due to higher conduction loss. Thus, MTPA condition is satisfied ONLY at A point.
- 3.2 Derivation of torque equation
To derive Adaptive MTPA control strategy, we can start by expressing the general electromagnetic torque Eq. (6) in the synchronous reference frame in terms of rms magnitude of the applied stator current Is , slip frequency ωs , and rotor resistance estimate
PPT Slide
Lager Image
as the generated torque in Fig. 3.
PPT Slide
Lager Image
The relationship between the q - axis stator current in the phasor representation, and the q - and d - axis currents in the synchronous reference frame [17] is
PPT Slide
Lager Image
By letting all the stator current in the q-axis through appropriate selection of the phase reference, (7) reduces to
PPT Slide
Lager Image
where Is is the magnitude of ĩqs . Like relations of currents in (7), the magnetizing flux linkage may be expressed as
PPT Slide
Lager Image
After the algebraic manipulation of (6)-(9), the electromagnetic torque may be rewritten in terms of stator current and magnetizing flux linkage phasor as
PPT Slide
Lager Image
where the overbar ‘ − ’ indicates complex conjugate.
From the AQDM steady-state equivalent circuit in Fig. 1 ,
PPT Slide
Lager Image
is expressed as
PPT Slide
Lager Image
where Zag is the impedance in parallel with two branches, / Γ m ( λm ) and Llr ( λm ) + Zr ( s ) / S , resulting in
PPT Slide
Lager Image
Thus, substitution of (11) into (10) yields the electromagnetic torque in terms of ωs , Is , and rr .
PPT Slide
Lager Image
where, since λm =
PPT Slide
Lager Image
, λm ( ωs , Is , rr ) can be computed as
PPT Slide
Lager Image
With these two non-linear simultaneous Eqs. (13) and (14), torque can be found when ωs , Is , and rr are given. In this work, Newton-Raphson method was utilized to calculate λm ( ωs , Is , rr ) in (14). But any useful nonlinear algebraic equation solver can be used.
- 3.3 Derivation of AQDM based MTPA Control
- 3.3.1 Setting up optimization problem
Thus, maximum torque can be obtained by finding optimal slip frequency,
PPT Slide
Lager Image
for the given stator current,
PPT Slide
Lager Image
, and rotor resistance estimate,
PPT Slide
Lager Image
, by applying any optimization technique to (15)
PPT Slide
Lager Image
Therein, ωs can be any value in real numbers but confined from 0 rad/s to 4 rad/s by our experience in order to avoid finding a local maximizer. Note that maximization of the torque in (13) for a given stator current means the minimization of the current for a given torque. The processing to solve the optimal slip frequency,
PPT Slide
Lager Image
, will be repeated for all combinations of Is and rr . It is assumed that Is ranges from nearly 0 A to a somewhat over rated current and rr is selected to vary from 0.01 Ω to 0.21Ω. The j −th point of Is and k−th point of rr will be denoted
PPT Slide
Lager Image
and rr,k , respectively.
The optimal slip frequency,
PPT Slide
Lager Image
, for a given pair of (
PPT Slide
Lager Image
, rr,k ) can be obtained by solving (15) with Is and rr replaced by
PPT Slide
Lager Image
and rr,k . The resulting optimum slip frequency and the corresponding maximum value of torque for
PPT Slide
Lager Image
and rr,k will be denoted
PPT Slide
Lager Image
and
PPT Slide
Lager Image
. These resulting data points are recorded for future data processing and are illustrated in Fig. 5.
PPT Slide
Lager Image
The control law and data points for Adaptive MTPA control strategy based on AQDM with different rr
- 3.3.2 Curve fitting
The data points {
PPT Slide
Lager Image
,
PPT Slide
Lager Image
, rr,k } in Fig. 5 (a) are used to construct a stator current control law. Note that Is is not a function of rr and so the form of stator current control law can be formulated as
PPT Slide
Lager Image
where 𝑎 1 , 𝑎 2 , 𝑎 3 , 𝑏 1 , and 𝑏 2 are selected by maximizing the objective fitness function f AMTPA defined as
PPT Slide
Lager Image
where ε is a small number (10 −3 ) added to the denominator in order to prevent singularities in the unlikely event of a perfect fit, NJ is the number of a set of the stator current command selected, and
PPT Slide
Lager Image
is given by (16) with
PPT Slide
Lager Image
.
As for the slip frequency ωs , it is a function of rotor resistance as well as the electromagnetic torque as shown in Fig. 5 (b) . The data points {
PPT Slide
Lager Image
,
PPT Slide
Lager Image
, rr,k } are fit to the functional form
PPT Slide
Lager Image
where d 0 , d 1 , n 1 , n 2 , and n 3 are parameters to be identified. These parameters are identified by maximizing the objective function defined by (19)
PPT Slide
Lager Image
where NK is the number for a set of rotor resistances selected, and
PPT Slide
Lager Image
is given by (18) with
PPT Slide
Lager Image
=
PPT Slide
Lager Image
and
PPT Slide
Lager Image
. Both (16) and (18) are composed of the AQDM based Adaptive MTPA control law. To obtain coefficients in (16) and (18), any fitting technique could be used. In this work, a genetic algorithm is employed, which was part of the Genetic Optimization System Engineering Tool (GOSET 1.02), a Matlab based toolbox. Details are set forth in [18] .
The resulting control laws for
PPT Slide
Lager Image
and
PPT Slide
Lager Image
for the test machine may be expressed as
PPT Slide
Lager Image
PPT Slide
Lager Image
and are also depicted, along with the individual data points, in Fig. 5. It can be seen that (20) and (21) fit the calculated data points {
PPT Slide
Lager Image
,
PPT Slide
Lager Image
,
PPT Slide
Lager Image
} in a good accuracy.
4. Test Set-up and Configuration for Study on Optimal Condition of Adaptive MTPA Control Strategy
The optimal condition of the proposed Adaptive MTPA control strategy was experimentally investigated. To this end, a current controlled inverter-fed drive was used to operate the test induction motor. The configuration of the motor drive used in this study is depicted in Fig. 6 . Therein, the upper part in Fig. 6 is the power converter topology. The lower part is composed of a speed control block with an anti-windup integrator, synchronous current regulator (SCR), and delta modulator, to determine the switching signal for switching devices, T 1 ~T 6 .
PPT Slide
Lager Image
The configuration of the Non-Adaptive and Adaptive MTPA control based induction machine drive
For this study, the optimal condition of the Adaptive MTPA control strategy was compared with that of the MTPA control strategy whose slip frequency,
PPT Slide
Lager Image
, control be obtained by substituting the value of 0.176 Ω for rotor resistance, rr , in (21), as in Fig. 4 . Thus, it results in
PPT Slide
Lager Image
in [7] . Herein, 0.176 Ω is the value of the rotor resistance at the temperature of 43 ℃ at which the test induction motor was characterized. For convenience purposes, MTPA control strategy is referred to as Non-Adaptive in that the commanded current and slip frequency are not adaptive to rotor resistance variation.
Additional details on the configuration for the drive and nomenclature are set forth in [16] . Fig. 7 shows the experimental set-up for this work. The induction motor being tested is enclosed in the small chamber and is exactly same as the motor used for the dynamometer.
PPT Slide
Lager Image
Experimental set-up snapshot
The experiment focuses on a single operating condition. As shown in Fig. 8 , the test induction machine was driven at a speed of 900 rpm and the torque command was set to 150 Nm in both cases. The torque estimator used in this work was shown to be highly accurate when the induction motor is rotating at moderate to high speeds [19 - 20] .
PPT Slide
Lager Image
Experimental flow chart
With the experimental setup mentioned above, due to the difficulty in directly measuring the actual rotor resistance of the test motor, the rotor resistance estimator proposed in [21] was utilized and incorporated into the proposed control strategy.
5. Review of Tracking Performance of Adaptive MTPA Control Strategy
To help readers understand the performance of the Adaptive MTPA control strategy, the results demonstrated in [7] are rewritten in this section. In [7] , the Adaptive MTPA control strategy was validated by comparing its performance to that of the MTPA control strategy with slip frequency control law of (22).
As in Fig. 9 , the performance for the two controls was recorded as the stator surface temperature varies. Therein, the red solid line with dot marks indicates the resultant torque of the Adaptive MTPA control strategy whose slip frequency command is
PPT Slide
Lager Image
in (21) and the dashed line indicates the resultant torque of the Non-Adaptive MTPA control strategy whose slip frequency command is
PPT Slide
Lager Image
in (22). As can be seen, Adaptive MTPA control strategy produced the resultant torque closer to the torque command of 150 Nm and larger than resultant torque which the Non-Adaptive MTPA control strategy
PPT Slide
Lager Image
Performances by the Adaptive MTPA control strategy and Non-Adaptive MTPA control strategy at the torque command of 150 Nm
6. Study on Optimal Condition of MTPA Control Strategy due to Rotor Temperature Variation
It would be of interest to investigate how MTPA condition is affected by rotor temperature variation.
- 6.1 MTPA condition of Non-Adaptive MTPA control strategy
Fig. 10 shows that the resultant electromagnetic torques at estimated optimal slip frequency command,
PPT Slide
Lager Image
, in (22) as well as two additional sets of resultant torques taken at 0.9 and 1.1 times of
PPT Slide
Lager Image
. In the initial part of each study as in Fig. 10 (a) , when the temperature is low and where rotor resistance is smaller than the rotor resistance used to design Non-adaptive MTPA control strategy, the largest torque and closest to the commanded torque was not produced at
PPT Slide
Lager Image
but at 0.9 times
PPT Slide
Lager Image
, resulting in failure of MTPA condition. Likewise, at the last part of the studies, the largest torque was again obtained at 1.1 times
PPT Slide
Lager Image
, which also means failure of MTPA condition. However, for some temperature region, maximum torque per ampere condition is in fact achieved at the estimate optimal slip frequency command
PPT Slide
Lager Image
. In the middle part of the study as in Fig. 10 (b) , when rotor resistance was close to the design value, it can be seen that a slip value of ωs =
PPT Slide
Lager Image
yields the most torque, thus satisfying MTPA condition.
PPT Slide
Lager Image
MTPA conditions along temperature variation with = 150 (Nm) when controlled by Non-Adaptive
- 6.2 MTPA condition of adaptive MTPA control strategy
The study on optimal condition, which is Maximum Torque Per Amp condition, of the proposed Adaptive MTPA control strategy was made by comparing its MTPA condition to that of the Non-Adaptive MTPA control strategy to show optimal control of the Adaptive MTPA control strategy. Fig. 11 illustrates the MTPA condition of the two controls as the stator surface temperature increased during the duration of the study. The red solid line with dot marks indicates the estimated torque of the Adaptive MTPA control strategy whose slip frequency command is
PPT Slide
Lager Image
given by (21). For Non-Adaptive MTPA control strategy, three sets of estimates over neighborhoods of
PPT Slide
Lager Image
in (22) have been included to investigate the satisfaction of the Maximum Torque Amp Condition. Therein, the dashed line indicates the estimated torque of the non-adaptive MTPA control strategy whose slip frequency command is
PPT Slide
Lager Image
given by (22). In the initial part of study, when the temperature is low and where rotor resistance is smaller than the rotor resistance used to design Non-Adaptive MTPA control strategy, the torque estimated at 0.9 times of
PPT Slide
Lager Image
is largest (of the non-adaptive controls). In the middle part (in time) of the study, when the rotor resistance was close to the design value, it can be seen that a slip value of ωs =
PPT Slide
Lager Image
in (22) yields the most torque (again, at the Non-Adaptive controls). Finally, as the studies proceed in time, eventually the largest torque estimate was obtained using 1.1 times of
PPT Slide
Lager Image
. On the other hand, Adaptive MTPA control strategy adjusted the slip frequency command,
PPT Slide
Lager Image
, given by (21) such that the largest torque are always achieved at
PPT Slide
Lager Image
, thus satisfying MTPA condition.
PPT Slide
Lager Image
MTPA conditions along temperature variation when controlled by Adaptive MTPA control strategy with = 150Nm
The observations from Fig. 11 indicate that the Adaptive MTPA control strategy satisfies MTPA condition regardless of rotor temperature variation as well as achieve the desired torque accurately as validated in [7] .
7. Conclusion
It was experimentally shown that the Adaptive MTPA control strategy performs optimally regardless of rotor resistance variation by showing that the torque produced at the optimal slip frequency is the largest and desirable at the same time, indicating that optimal condition (MTPA condition) of Adaptive MTPA control strategy is satisfied.
As can be seen from experimental results in the previous section, the Adaptive MTPA control strategy makes true optimal performance regardless of rotor resistance variation by reflecting rotor resistance variation in the design of optimal slip frequency control law.
Acknowledgements
This research was supported in part by Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Education, Science and Technology (Grant 20130235 ) and was supported in part by the Soonchunhyang University Research Fund
BIO
Chun-Ki Kwon received B.S. and M.S. degrees in electrical engineering from Korea University in 1992 and 1994, respectively. He is with Soonchunhyang University, Asan, Chungnam, Korea. His research interests include control and modeling of electric machines, and expands this area to medical engineering such as rehabilitation devices.
References
Uddin M. N. , Nam Sang Woo 2008 “New Online Loss-Minimization-Based Control of an Induction Motor Drive,” IEEE transactions on Power Electronics 23 926 - 933    DOI : 10.1109/TPEL.2007.915029
Gonzalez C. , Arribas J. , Prieto D. 2006 “Optimal Regulation of Electric Drives With Constant Load Torque,” IEEE transactions on Industrial Electronics 53 (6) 1762 - 1769    DOI : 10.1109/TIE.2006.885121
Mino-Aguilar G. , Moreno-Eguilaz J.M. , Pryymak B. , Peracaula J. 2008 “An induction motor drive including a self-tuning loss-model based efficiency controller,” Applied Power Electronics Conference and Exposition, APEC 2008 1119 - 1125
Ebrahim O. S. , Badr M. A. , Elgendy A. S. , Jain P. K. 2010 “ANN-Based Optimal Energy Control of Induction Motor Drive in Pumping,” IEEE transactions on Energy Conversion 25 (3) 652 - 660    DOI : 10.1109/TEC.2010.2041352
Miao J. , Li H. , Chen S. 2008 “Study on Efficiency Optimization Control of Induction Motor Drive System,” Control and Decision Conference 3244 - 3247
Mirafzal B. , Skibinski G. L. , Tallam R.M. 2007 “Determination of Parameters in the Universal Induction Motor Model,” Indusstry Applications Conference, 2007, 42nd IAS Annual Meeting, Conference Record of the 2007 IEEE 1207 - 1216
Kwon C. “Study on an Adaptive Maximum Torque Per Amp Control Strategy for Induction Motor Drives”
2013 Journal of Electrical Engineering Technology 8 (1) 110 - 117    DOI : 10.5370/JEET.2013.8.1.110
Wasynczuk O. , Sudhoff S.D. , Corzine K.A. , Tichenor J.L. , Krause P.C. , Hansen I.G. , Taylor L.M. 1998 “A Maximum Torque Per Ampere Control Strategy for Induction Motor Drives,” IEEE Transactions on Energy Conversion 13 (2) 163 - 169    DOI : 10.1109/60.678980
Sonnaillon M. O. , Bisheimer G. , De Angelo C. , Garcia G. O. 2010 “Online Sensorless Induction Motor Temperature,” IEEE transactions on Energy Conversion 25 (2) 273 - 280    DOI : 10.1109/TEC.2010.2042220
Gao Z. , Habtler T. G. , Harley R. G. 2009 “A Complex Space Vector Approach to Rotor Temperature Estimation for Line-Connected Induction Machines With Impaired Cooling,” IEEE Transaction on industrial electronics 56 239 - 247    DOI : 10.1109/TIE.2008.918490
Karanayil B. , Rahman M. F. , Grantham C. 2007 “Online Stator and Rotor Resistance Estimation Scheme Using Artificial Neural Networks for Vector Controlled Speed Sensorless Induction Motor Drive,” IEEE transactions on Industrial Electronics 54 (1) 167 - 176    DOI : 10.1109/TIE.2006.888778
Najafabadi T. A. , Salmasi F. R. , Jabehdar-Maralani P. 2011 “Detection and Isolation of Speed-, DCLink Voltage-, and Current-Sensor Faults Based on an Adaptive Observer in Induction-Motor,” IEEE Transaction on industrial electronics 58 (5) 1662 - 1672    DOI : 10.1109/TIE.2010.2055775
Orlowska-Kowalska T. , Dybkowski M. 2010 “Stator-Current-Based MRAS Estimator for a Wide Range Speed-Sensorless Induction-Motor,” IEEE Transaction on industrial electronics 57 (4) 1296 - 1308    DOI : 10.1109/TIE.2009.2031134
Sudhoff S. D. , Chapman P. L. , Aliprantis D. C. , Kuhn B. T. 2003 “Experimental Characterization Procedure for Use with of an Advanced Induction Machine Model,” IEEE Transactions on Energy Conversion 18 48 - 56    DOI : 10.1109/TEC.2002.808333
Sudhoff S. D. , Aliprantis D. C. , Kuhn B. T. , Chapman P. L. 2002 “An Induction Machine Model for Predicting Inverter-Machine Interaction,” IEEE Transactions on Energy Conversion 17 203 - 210    DOI : 10.1109/TEC.2002.1009469
Kwon C. , Sudhoff S. D. 2006 “Genetic Algorithm-Based Induction Machine Characterization Procedure With Application to Maximum Torque Per Amp Control,” IEEE Transactions on Energy Conversion 21 405 - 415    DOI : 10.1109/TEC.2006.874224
Krause P. C. , Wasynczuk O. , Sudhoff S. D. 2002 Analysis of Electric Machinery and Drive Systems IEEE Press
2003 “Energy Systems Analysis Consortium (ESAC) Genetic Optimization System Engineering Tool (GOSET) Ver 1.02,” School of Electrical and Computer Engr., Purdue Univ. West Lafayette, IN, 47907
Kim J. , Choi J. , Sul S. 2002 “Novel Rotor Flux Observer Using Observer Characteristic Function in Complex Vector Space for Field Oriented Induction Motor Drives,” IEEE transactions on Industry Applications 38 1334 - 1443    DOI : 10.1109/TIA.2002.802994
Jansen P. L. , Lorenz R. D. 1994 “A Physically Insightful Approach to the Design and Accuracy Assessment of Flux Observer for Field Oriented Induction Machine Drives,” IEEE Transactions on Industry Applications 30 (1) 101 - 110    DOI : 10.1109/28.273627
Kwon C. , Sudhoff S. D. 2005 “An On-line Rotor Resistance Estimator for Induction Machine Drives,” the 2005 International Electric Machines and Drives Conference 391 - 397