2012 IEEE 7th International Power Electronics and Motion Control Conference - ECCE Asia June 2-5, 2012, Harbin, China
Sensor Fault detection and fault tolerant control of induction motor drivers for electric vehicles Shicai Fan
Jianxiao Zou
School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu, China
[email protected]
School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu, China
[email protected] The paper is organized as follows. Section I describes the model of a rotor field oriented induction motor, the scheme of indirect vector control and an adaptive model for speed estimation. Section III details the implementation of the proposed strategy about the sensor fault detection and fault tolerant control. Simulation results when sensor faults occur in induction motor driver are reported in section IV. Finally, section V gives some concluding remarks.
Abstract— An effective fault detection and fault tolerant control strategy in induction motor is described in this paper. Based on the algorithm of posterior reliability voting, the proposed approach could detect the healthy state of encoder, and adjust the weights between the speed from encoder and estimated speed based on MRAS. When redundant current sensors are installed in the controller, a logic decision scheme could detect and shield the faulty one, and guarantee that the current signals involved in the control are from the two healthy current sensors. Abrupt faults of both encoder and speed sensor are simulated in the induction motor driver individually, and simulation results in terms of speed response indicate the effectiveness of the proposed strategy subject to faulty conditions caused by encoder or current sensor.
II.
A. Mathematical model of induction motor An induction motor model under field orientation control could be described as below: ( Lm / Lr ) p 0 º ªisd º w1V Ls ªusd º ª Rs V Ls p «u » « w V L R V L p w1 Lm / Lr 0 »» « isq » (1) s s « sq » « 1 s « » « 0 » « Lm / Tr 0 1/ Tr p 0 » «Mr »
Keywords-induction motor driver; sensor toleranct control; vector control; MRAS; posterior reliablity;
I.
MODEL OF INDUCTION MOTOR AND VECTOR CONTROL
INTRODUCTION
Because of the comprehensive advantages of induction motor in efficiency, cost and reliability, the development of induction motor driver is becoming attractive[1]. As is known, several failures would affect the performance of motor driver significantly and many remedial approaches have been proposed [2,3]. So far, fault tolerant control systems are effective control strategies to improve the reliability and continuous operation of induction motor driver, and has been successfully used in control systems not only under nominal conditions but also when faults occur [4,5].
« » ¬0¼
Te
« ¬
0
Lm / Lr
n p ( Lm 2 / Lr )isq
1 isd Tr p 1
ws
»« » 0¼ ¬ 0 ¼
(2)
B. Vector control schemes The main idea of vector control is to control the flux magnitude and electromagnetic torque in two different control loops [6]. And it allows us to control the induction motor in similar way to a DC motor. It mainly includes two implementations: indirect field orientation control and direct orientation control. And indirect field orientation control is widely used for high performance over entire speed range. The overall block diagram of indirect field orientation control for induction motor is shown in figure 1. Generally, the speed and current values required in the vector control are measured by encoder and current sensors. In principle, two current sensors are enough for the control scheme because of the zero summation nature of the three phase currents. For the consideration of high reliability, we apply three current sensors in the block. Also some speed sensorless methods are developed to implement the speed estimation in case that there is no encoder installed.
This paper focuses on the problems of developing induction motor drivers with tolerance to some faults due to sensor failures, especially about the speed sensor (encoder) and current sensor. Both hardware and software are developed to perform the fault detection and fault tolerant strategy implementation. The velocity signals from Encoder and MRAS (Model Based Adaptive Structure) based speed sensorless control method are integrated in the vector control scheme, and the weights are calculated from the algorithm of posterior reliability voting. The adjustable weights could guarantee the ongoing working of driver when the encoder is invalid. Based on redundant current sensors, the logic decision scheme could detect the work state of each sensor in real time, and shield the faulty one without affecting the normal control performance. The simulation results indicate the effectiveness of the proposed fault detection and tolerant control strategy.
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978-1-4577-2088-8/11/$26.00 ©2012 IEEE
Z*
¦
PI
id
iq*
¦
*
Vq
PI
¦
PI
d,q
D,E
Vd
VD VE
,30
SVPWM
ª eˆˆmD º p« » ¬eˆˆmE ¼
T
)OX[ REVHUYHU
iq
D,E
d,q
id
D,E
ia
a , b, c
w
ib
ª 1 « W « r « « wˆ r ¬
º wˆ r » ªe º ªi º » « mD » Lm p « sD » 1» e W r ¬isE ¼ » ¬ mE ¼ Wr ¼
(6)
And the motor speed could be estimated from (7).
ic
(QFRG HU
(k p
wˆ r
,0
Figure 1. Schematic diagram of the indirect orientation control system for induction motor
III.
ki )(Qm Qˆ m ) p
(7)
FAULT DETECTION AND TOLERANT CONTROL SYSTEM
A. Encoder fault detection and tolerance C. Adaptive model Speed sensorless methodology mainly includes three branches: model based, artificial intelligence based and motor anisotropy based methods [7]. Due to the easiness of application of model based method, it is widely used both in theory research and engineering. In the paper, we applied model based adaptive structure (MRAS) to estimate the motor speed. Generally, MRAS suffers from two difficulties in speed estimation: the pure integration of measured variables, and the time-variant characteristics of motor parameters. In this paper, MRAS based on instantaneous reactive power of rotor flux is applied, which avoids the pure integration and is independent of the stator resistance [8]. The overall scheme is shown in figure 2.
usDE
Reference Model
p ( w2 | s ) are the posterior reliabilities of each part given the status s . The posterior reliability p ( w j | s ) could be got based on
MRAS,
the Bayesian equation of (9), p ( w j ) is the prior reliability,
Qˆ m
on G (equation (10) and equation (11)). G is the relative speed ratio of the difference of speed and the speed of last control period , and could be derived from equation (12). When G is larger than T , the state of encoder would be regarded as fault state. p ( s | w j ) would change in real time according to the
e
defined threshold
wc
In reference model, the reactive power is defined as the cross product of the counter electromotive force (EMF) vector em and the stator current vector is . That is,
is
em
0 º ªisD º Lr ªusD º Lr ª Rs V Ls p « » « » 0 Rs V Ls p »¼ ¬isE ¼ Lm ¬usE ¼ Lm «¬
is
eˆm
(8)
p ( wi ) * p ( s | wi ) 2
¦ p( w ) * p( s | w ) j
(9)
j
j 1
(3)
p1T ® ¯ 1
p ( s | w2 )
1 p1T ® ¯ 0
(5)
Where:
G
1307
G tT G T
p ( s | w1 ) (4)
In the adjustable model,
Qˆ m
and p1 could be given based
p ( w1 | s ) * w1 p ( w2 | s ) * w2
p ( wi | s )
Where: ª emD º «e » ¬ mE ¼
T . p( w j ) , T
on expert experience.
PI
Figure 2. The structure of MRAS model
Qm
p ( w1 | s ) and
and p ( s | w j ) is the conditional reliability which is dependent
¦
wˆ r
(8), where w1 is the speed from encoder, w2 is the speed from
Qm
isDE Adjustable Model
In order to improve the reliability of motor controller, a posterior reliability voting algorithm is used to get the motor speed from the encoder and adaptive model. The speed wc used in the control scheme is calculated based on equation
w1 wcLast wcLast
G tT G T
(10)
(11)
(12)
Frequency
Rated parameters
Then the stator currents in a DE stator fixed reference frame could be calculated based on (14).
ª ID s _ TA º «I » ¬ E s _ TA ¼
1 º ªIa _ c º 2 »« » Ib » » 3»« « »¼ I c ¬ 2 »¼
ID s _ TA IˆˆD s I E s _ TA I E s
(15)
observer below.
dI E s dt
1
V Ls
VD s
1 1 1 ( ) ID s p:I E s
V Ws
Wr
1
V LsW r
MD s
1
V Ls
p:M E s
1 1 1 1 1 1 V p:ID s ( ) I E s p:MD s M V Ls E s V Ws Wr V Ls V LsW r E s d MD s dt dME s
V
Current
2.89
A
Speed
1390
rpm
Pole pair
2
Rs
4
:
Rr
5.22
:
Ls
0.037
H
Lr
0.037
H
Lm
0.1453
H
In the control system, an abrupt failure of encoder occurs at 3.4s. If the relative speed ratio calculated from the detection method is lower than threshold, the posterior reliabilities of encoder and MRAS would change according to equation (9). Then the weight of estimated speed based on MRAS will increase, and the pure sensorless vector control would be in action very soon. Speed response is shown in figure 3. One could see that when the encoder failure occurs, the output speed would be adjusted, and after about 1s, the control system turns back to the normal state.
IˆD s and IˆE s could be calculated based on the full order dID s dt
220/380
To investigate the performance of the fault tolerant strategy under faulty conditions, both speed sensor fault and current sensor fault are simulated individually.
(14)
The healthy state of current sensor for phase A could be judged from a error variable e _ TA .
e _ TA
Hz
Voltage ( ' / Y )
B. Current sensor fault detection and tolerance In the detection of current sensor fault, a logical decision scheme is applied to analyze the three currents and distinguish the faulty one from the healthy ones [9]. We assume that there is only one current sensor in invalid state once. The main idea of the decision scheme is as blow. Assuming that current sensor of phase A is faulty, then the current of phase A could be got from (13). (13) I a _ c Ib Ic
1 ª 1 « 2 2 « 3« 3 «¬0 2
50
(16)
VD s Rs ID s VE s Rs I E s
dt
This calculation and comparison could be iterated between the three phases, and e _ TA, e _ TB, e _ TC could be calculated. If
e _ TA is smaller than a threshold, and
e _ TB, e _ TC are larger than a threshold, then we could conclude that current sensor of phase A is faulty. Similarly, we could get the healthy state of other sensors. If one of them was detected to be under fault state, it would be displaced by the redundant sensor. And the vector control process would not be affected. IV.
Figure 3. The system performance in speed repsonse when an abrupt fault of encoder occurs
SIMULATION REULTS
Similarly, one current sensor fault is simulated. At 3.4s, a current sensor is suddenly invalid, and output current changes to be zero. The detection system could immediately detect this fault, and apply another two current sensors in the vector control. The results are shown in figure 4. One could see that the performance of the system is not affected at all.
To validate the performance of the proposed strategy, a series of simulations with Matlab/Simulink were conducted for a 1.1kW induction motor with 50Hz and 380V power supply. The ratings of the motor are summarized in table 1. TABLE I. Rated values
RATED DATA OF THE SIMULATED IINDUCTION MOTOR Power
1.1
kW
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the proposed strategy is effective in the fault detection and satisfying in the tolerant control. ACKNOWLEDGMENT This work is supported by “the Fundamental Research Funds for the Central Universities (A03007023801210)”. REFERENCES [1] [2]
[3]
[4]
Figure 4. The system performance in speed response when an abrupt fault of current sensor occurs
V.
[5]
CONCLUSIONS [6]
This paper has proposed a vector control based strategy for fault detection and fault tolerance. For this purpose, posterior reliability voting algorithm is applied to detect the speed sensor fault, and the weights of encoder and MRAS model in speed integration would be adjusted accordingly. A logic decision scheme is used to identify the faulty current sensor and the remaining two healthy current sensors would put into operation automatically.
[7]
[8]
[9]
The abrupt faults of encoder and current sensor are simulated individually in the experiment. Results indicate that
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