journal of chemical and pharmaceutical sciences issn: 0974 ... special issue...

Journal of Chemical and Pharmaceutical Sciences ISSN: 0974-2115

JCHPS Special Issue 1: February 2017 www.jchps.com Page 332

New Current Estimation Fault Tolerant of Induction Motor

Using Fuzzy LogicR. Senthil Kumar1*, I. Gerald Christopher Raj2, M.Yuvaraj1

1Dept of EEE, M. Kumarasamy College of Engineering, Karur, Tamil Nadu, India 2Dept of EEE, PSNA College of Engineering and Technology, Dindigul, Tamil Nadu, India

*Corresponding author: E-Mail: [email protected]

ABSTRACT This paper presents a three phase squirrel cage induction motor operates using indirect field control is used

and speed sensor fault is diagnosis by fuzzy logic control. Induction motors are highly reliable, they are susceptible

to many types of faults that can became catastrophic and cause production shutdowns, personal injuries, and waste

of raw material. Induction motor faults can be detected in an initial stage in order to prevent the complete failure of

the system and unexpected production costs. So, new fault tolerant algorithm is used to detect the faults and diagnosis

the fault, with the help of current estimated and speed estimated blocks. Fault tolerant algorithm was used for

diagnosis current and speed sensor faults. Here, IFOC techniques is used to control the switches of the inverter and

system performance is analyzed. Estimated current control block is used to diagnosis speed and current sensor which

is estimated by logic based decision algorithm. In this paper modeling and simulation of three phase induction motor

was developed using IFOC and fuzzy based new fault tolerant algorithm is also developed by using

MATLAB/SIMULINK software.

KEY WORDS: Current estimation, speed estimation, fault detection algorithms, induction motor (IM) drive, fuzzy

logic control, indirect field oriented control.

1. INTRODUCTION

In industry induction motors are widely used because of its inherent ruggedness and minimized cost.

Adjustable speed drives are mostly used in many industrial applications. For applications requiring a constant speed

induction motor is used because of its conventional method of speed control have either been expensive or highly

inefficient. In order to prevent sudden failure and economic damage periodic maintenance is performed (Karthikeyan

& Chenthur Pandian, 2011).

From the equation obtained from symmetrical induction machine in arbitrary reference frame the simulation

of various modes of operation is conveniently obtained (Slemon, 1989). Fig.1, (Betta & Liguori, 1998) shows entire

block diagram of induction motor drive. The mathematical calculations and algorithms are used in the control scheme

and it needs efficient and costly controllers. Here a novel theory is introduced to improve the performance of the

motor running in different points at optimum voltage and frequency for optimum motor efficiency.

Figure.1. Typical Induction Motor Drive

To achieve the timely diagnostics of incipient IM faults the on-line IM diagnostic systems are being

developed. The fault detection and analyze of rotor faults are critical so the preventive fault diagnosis is important

in industrial applications, although rotor Faults share only about20% of the overall induction machine faults.

Most of the faults are occurred in induction motor like bearing fault effect (32-52%), stator winding fault

(15-47%), rotor bar fault (5%), shaft fault (2%), external disturbances are also produced 12-15%. Common rotor

faults are break in rotor bars and the rotor to stator eccentricity. Due to short circuit of windings Stator faults are

created. By monitoring the mechanical vibrations, current, reverse sequence pole and partial charges are measured

for incipient fault detection (Karthikeyan & Pandian, 2011).

To monitor the induction machine the spectral analysis of operational process parameters like temperature,

pressure, stem flow etc. Are measured by the combination of advanced computerized data processing and acquisition

intelligent techniques. The evaluation tools used are Time domain analysis, spectrum analysis, and cestrum analysis.

To determine changes by trend setting, time domain analysis is used, to determine trends of frequencies spectrum

analysis is used, to determine amplitude and phase relations, as well as to detect periodical Components of spectra

cestrum analysis is used. In many situations, for incipient fault detection vibration monitoring methods are utilized.

However, for stator current monitoring, The Classical Fast Fourier Transform (FFT), Wavelet Analysis, Current

parks vector approach, Power spectral density analysis (Sundararaju and Nirmal Kumar, 2011).

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The control reorganization is carried out by a fuzzy decision, which assures a smooth transition from the

encoder-based (using sliding mode) to the sensorless controller (utilizing fuzzy control). All these approaches

sacrifice field orientation when fault occurs and hence offer poor dynamic performance. The implementation is

difficult and also such techniques cannot be used for complex applications inverter-fed induction motors are analyzed

under indirect field oriented control. So here fault tolerant algorithm is developed to diagnosis the faults and fuzzy

based fault algorithm is also compared to normal fault tolerant algorithm.

Dynamic model of induction motor: The dynamic model of squirrel cage induction motor [SEIM] in stationary

reference frame in α-β reference frame variables.

Figure.2. Equivalent Circuit of SCIM (a) α axis (b) β axis

Components stator and rotor voltage of the induction motor can be expressed as follows. In order to analyze

the system performance in dynamically, need to convert time domain to another form. So state variable form is

introduced in this modeling and create the variable constants.

Vsα = Rsisα + Lsd

dtisα + Lm

d

dtirα (1)

Vsβ = Rsisβ + Lsd

dtisβ + Lm

d

dtirβ (2)

0 = Rrirα + Lrd

dtirα + Lm

d

dtisα + ωrΨrβ (3)

0 = Rrirβ + Lrd

dtirβ + Lm

d

dtisβ − ωrΨrα (4)

The components of rotor flux linkage in the stationary reference can be written as

Ψrα = Lmisα + Lrirα +Ψrα0 (5)

Ψrβ = Lmisβ + Lrirβ +Ψrβ0 (6)

Where Ψrα0 and Ψrβ0 are the residual rotor flux linkages in α-β axis, respectively.

Then, with an electrical rotor speed of ωr, the components of rotating voltage in the stationary reference

frame are as the follows:

ωrΨrα = ωrLmisα + ωr Lrisα + ωrΨrα0 (7)

ωrΨrβ = ωrLmisβ + ωr Lrisβ + ωrΨrβ0 (8)

The expressions for capacitor voltages are,

Vcα = 1

C∫ iCα dt + Vcα0 (9)

Vcβ = 1

C∫ iCβ dt + Vcβ0 (10)

Using Fig.2, equations (1)-(10), for the matrix equations of SCIM at no-load in the stationary reference frame

are given by

[

0000

] [

Rs + pLs 0 pLm 0 0 Rs + pLs 0 pLm

pLm ωrLm Rr + pLr ωr Lr − ωr Lm pLm − ωr Lr Rr + pLr

] [

isαi sβi rαirβ

] +

[

Vcα

Vcβ

ωrΨrβ0

−ωrΨrα0] (11)

From (11), can be written the state equations as follows:

AIG + BIG + VG = 0 (12)

Where,

A =[

Ls 0 Lm 00 Ls 0 Lm

Lm 0 Lr 00 Lm 0 Lr

], B =⌈

Rs 0 0 00 Rs 0 0

0 ωrLm Rr ωrLr

−ωrLm 0 −ωrLr Rs

⌉ IG = [

isαi sβi rαirβ

], VG =

[

Vcα

Vcβ

ωrΨrβ0

−ωrΨrα0]

Indirect field oriented control (IFOC) of induction motor: In Fig.3, shows the block diagram of indirect field

orientation control strategy with sensor in which speed regulation is possible using a control loop.



JCHPS Special Issue 1: February 2017 www.jchps.com 34

Figure.3. Indirect Field-Oriented Control for Induction Motor Drives

There are two vector control methods, one is Direct Field Oriented control (DFOC) and another one, Indirect

Field Oriented Control (IFOC), IFOC being more commonly used because in closed-loop mode such drives more

easily operate throughout the speed range from zero speed to high-speed field-weakening in DFOC, flux magnitude

and angle feedback signals are directly calculated using so-called voltage or current models. Fig.3. explains the

fundamental principle of indirect vector control with the help of a phasor diagram. The ds- qsaxes are fixed on the

stator, but the dr-qraxes, which are fixed on the rotor, are moving at speed ωr. Synchronously rotating axes de-

qeare rotating ahead of the dr-qraxes by the positive slip angle ϴslcorresponding to slip frequency ωsl.

Since the rotor pole is directed on the deaxis and ωe = ωr + ωsl, one can write

qe = ∫ωe dt = ∫(ωr + ωsl) dt = ϴr + ϴsl (13)

The phasor diagram explain that for decoupling control, the stator flux component of current idse should be

aligned on the deaxis, and the torque component of current iqse should be on the qeaxis, as shown.

vqse = pλqs

e + ωeλdse + rsiqs

e vqr′e = pλqr

′e + (ωe − ωr)λdr′e + rr′iqr

′e (14)

If d –q axis is aligned with the rotor field, the q-component of the rotor field, λqr′e , in the chosen reference

frame would be zero.

Teme =

3

2

P

2(λqr

′e idr′e − λdr

′e iqr′e ) (15)

With λqr′e zero, the equation of thedeveloped torque, eqn.(2.3), reduces to

Tem =3

2

P

2

Lm

Lrλdr′e iqs

e (16)

Which shows that if the rotor flux linkage λqr′e is not disturbed, the torquecan be independently controlled by

adjusting the stator q component current, iqse .

For λqr′e to remain unchanged at zero, its time derivative (pλqr

′e ) mustbe zero.

λdr′e∗ =

rr, Lm

rr, +pLr

, iqse∗ (17)

When the field is properly oriented, idr′e is zero, λdr

′e∗ = Lmidse thus, the slip speed of eqn can be written as

ωsle∗ = ωe − ωr =

rr,

Lr,

iqs′e∗

ids′e∗ (18)

In IFOC, flux space angle feed forward and flux magnitude signals first measure stator currents

and rotor speed for then deriving flux space angle proper by summing the rotor angle corresponding to the rotor

speed and the calculated reference value of slip angle corresponding to the slip frequency. Indirect field orientation

is based on sensing the rotor position that is very sensitive to motor parameters (Sanchez, 2012).

Fault diagnosis Algorithm: The new current control technique is based on the concept that the angular speed and

the magnitude of the stator current can be controlled by two stator voltage components relative to the stator current.

It is assumed that the system works with two current sensors and a speed sensor. These two current sensors may be

put in any two phases. Note that the transformations from three-phase to two phase quantities required following the

standard procedure, first, it is assumed that the a-phase (of the three-phase system) and α-phase (of the two-phase

system) are along the same axes.

[isαisβ

] = [

3

20

√3

2√3

] [iaib

] (19)

However, if the b-phase sensor is defective, the corresponding current of the α-phase will remain correct,

while the current in the β-phase will be wrong. Other hand, a unique feature is extracted if the α–β phase are rotated

by 120◦.A logic-based detection mechanism in the α–β reference frame is proposed to make the drive fault tolerant

against current sensor failures (Senthil Kumar & Mahesh, 2013).

The error (E= Xr−Xs) is fed to the adaptation mechanism to generate the speed signal. This estimated value

of the rotor speed will be used to make the drive fault tolerant against speed sensor failure a phase loss, the field

orientation is lost, and hence, the q-axes flux can be checked to make a correct decision.


http://en.wikipedia.org/wiki/Rotor

http://en.wikipedia.org/wiki/Slip



[isβ_est′

isα_est′ ] = [

sin30°−ρms cos30°−ρms

cos30°−ρms sin30°−ρms] [

isd∗

isq∗ ] (20)

Therefore, depending on a fault either in b-phase or a-phase, the use of a proper transformation (either

considering that α-phase is along a-phase or α-phase is along b-phase) will provide us the true estimate of the

corresponding α-phase current. Fault detection can only be carried out if a correct estimate is available (Finley &

Hodowanec, 1999)

3. SIMULATION AND RESULTS

Some of the faults could damage the experimental setup and cannot be tested directly. Also, many

commercial motor drives have built-in protection circuitry and algorithms that take action after a fault by shutting

down or otherwise altering operation. It is not easy (or advisable) to override protection, but fault modes used here can

also integrate protection in several aspects (Kia, 2013). Even though the simulations here do not model or cover all

physical dynamics, noise, vibration, power loss, and nonlinearities of material, they provide a useful tool that can

save the cost of rebuilding a motor drive, or most other systems, after severe failure.

Figure.4. Block Diagram of Fault-Tolerant

Vector Controlled IM Drive

Figure.5. Simulation Diagram of speed sensor fault

diagnosis of three phase induction motor

Figure.6. Current Estimation of Induction Motor Figure.7. Voltage Estimation of Induction Motor

Fig.4, which shows simulation block diagram and its simulation diagram of speed sensor fault detection and

diagnosis is plotted is shown in Fig.5, and performance has shown in Figs.6 to 8.

Figure.9. Simulation Diagram for Current Sensor

Fault Detection, Isolation, and Compensation

Figure.10. Fuzzy Input and Output Block for fault

tolerant algorithm




Figure.11. Fuzzy logic rule viewer for fault tolerant

algorithm

Figure.12. Fuzzy membership function editor for

fault tolerant algorithm

Figure.13. Simulation Results For Speed And Torque After Using Fuzzy logic

A new fault tolerant logic based Current Sensor Fault Detection, Isolation, and Compensation is shown in

Fig.9, And Fuzzy logic based Fault techniques is introduced in Fig.10. Before starting fuzzy logic, rules have framed

and Defuzzification technique is developed to find the better fault tolerant. Fuzzy logic rule viewer for fault tolerant

algorithm shown in Fig.11, and Fuzzy membership function editor for fault tolerant algorithm is shown in Fig.12.

Compare to normal technique fuzzy logic based fault tolerant performance is good which is shown in Fig.13.

4. CONCLUSION

This paper has presented a complete implementation of a fault-tolerant indirect field oriented of controlled

IM drive. This system is capable to detect a fault and reconfigure itself to switch to the correct algorithm. The

controller keeps estimating different currents and speed and, in case of a fault, switches to the correct estimated value

(Finley, 1999). In this paper new fault tolerant algorithm is developed to detect the faults and diagnosis the fault,

with the help of current estimated and speed estimated blocks. By using SVPWM with vector rotator is introduced

in simulation for deciding correct estimated value. This technique works perfectly even in case of multiple sensor

failure. In this paper simulation of induction motor was created using IFOC and new fault tolerant algorithm with

current and speed estimated values and fuzzy based fault tolerant algorithm also developed by using

MATLAB/SIMULINK software.

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