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Electric Power Systems Research 103 (2013) 201 213

Contents lists available at SciVerse ScienceDirect

Electric Power Systems Research

jou rn al hom e page: www.elsev ier .co

Applica inin Perm

Yaw D. N DaCenter for Adva , Unit

a r t i c

Article history:Received 13 MReceived in reAccepted 27 MAvailable onlin

Keywords:Stator windingArticial NeurParticle SwarmExtended Kalm

ogy t in thiing. uitedticle statored to s. The

instaeuralmput

Machine. The use of the Extended Kalman Filter method, for training, ensures that the neural networkcluster used can be re-trained online to make the fault diagnosis system adapt to changing operationalconditions. Results from both computer simulation and actual machine data are presented to show theperformance of the neural network cluster and the Particle Swarm Optimization algorithm.

Published by Elsevier B.V.

1. Introdu

Permaneing increasand propulsefciency, hmance applmagnet maonline condfor these mtromotive fstress on widation leadhas conditio[4]. This mathe survivabmachines. Imachines, iHowever, re

CorresponE-mail add

nyantehyaw@edrington@cap

0378-7796/$ http://dx.doi.oction

nt Magnet Synchronous Machines (PMSM) are receiv-ing attention in robotic, automotive, electric tractionion in Shipboard Power Systems (SPS) due to their highigh energy density and their suitability for high perfor-ications made possible by advancements in permanentterials [13]. With the increased use of PMSM, efcientition monitoring and accurate machine fault diagnosis,achines, is very important. The persistence of the elec-orce, due to the permanent magnets, also puts a lot ofnding insulation and increases the possibility for degra-ing to winding faults. It has been established that SPSns that promote the degradation of winding insulationkes fault detection in these systems very important forility, availability and efcient use of energy of rotatingn the published literature on fault diagnosis of electricnduction motors have been the primary focus [57].search effort on PMSM fault diagnosis has been on the

ding author. Tel.: +1 6147073450.resses: [email protected], [email protected],gmail.com (Y.D. Nyanteh), [email protected] (S.K. Srivastava),s.fsu.edu (C.S. Edrington), [email protected] (D.A. Cartes).

rise as more PMSMs enter the market for diverse application areasas enumerated above [813].

Even though Articial Intelligence (AI) was introduced a longtime ago, it was in the early 90s that AI has achieved its great-est success, prompting their application to new elds of study. Inthis paper, a method is presented to detect the presence of statorwinding short circuits. An Articial Neural Network (ANN) and amodied version of the Particle Swarm Optimization (PSO) algo-rithm is used in the proposed method which can also provideinformation about the severity of the short circuit fault occurring ina PMSM. An ANN is a processing system consisting of a large numberof simple, highly interconnected processing elements in an archi-tecture inspired by the structure of the cerebral cortex of the brain[14]. The advantages of ANNs are their capability of arbitrary map-ping from any real input space to an output space without regard forthe underlying system dynamics; which can be difcult to modelin some situations. Once designed, most ANN architectures can beimplemented online with little computational burden. The mostimportant issue with ANN is the selection of the type of ANN,the architecture and the training method. The types of ANN tech-niques used in fault diagnosis for rotating machines include, but arenot limited to, multi-layer perceptrons, support vector machines,self-organizing maps and radial basis functions. In most casesthe selection of the type of ANN xes the training methodologyand the architecture. In the literature, fault diagnosis applications

see front matter. Published by Elsevier B.V.rg/10.1016/j.epsr.2013.05.018tion of articial intelligence to stator wanent Magnet Synchronous Machines

yanteh , Sanjeev K. Srivastava, Chris S. Edrington,nced Power Systems, Florida State University, 2000 Levy Avenue, Tallahassee, FL 32310

l e i n f o

arch 2013vised form 25 May 2013ay 2013e 2 July 2013

short circuital Network

Optimizationan Filter Method

a b s t r a c t

This paper proposes a new methodolThe fault diagnosis method presentedof a short-circuit fault in a stator windby specifying the number of short-circnetworks are combined with the Parmethod. This method is applied to the neural network, in the cluster, is traincircuited turns in the stator windingcurrent, are obtained by summing theduring the diagnosis procedure. The nmethod using fault data from both com/locate /epsr

ding fault diagnosis

vid A. Cartesed States

o solve the problem of fault diagnosis in electrical machines.s paper is, rst, able to provide information about the locationSecondly, the method enables the estimation of fault severity

turns during a fault. A cluster of Focused Time-Lagged neuralSwarm Optimization algorithm for proposed fault diagnosis

windings of a Permanent Magnet Synchronous Machine. Eachcorrelate the zero-current component to the number of short-

zero-current component, different from the zero-sequencentaneous values of current on all phases of the stator winding

networks are trained ofine with the Extended Kalman Filterer simulations and an actual Permanent Magnet Synchronous

202 Y.D. Nyanteh et al. / Electric Power Systems Research 103 (2013) 201 213

3.8

2

4x 10

-13

nt(A

)

No Short-circuit winding fault on phase-A

3.8

lt on p

3.8

lt on p

t on ph

PMSM

involving Ation machinobtain faultANN technifeatures. A dynamics onosis is prodiscussed. Tnegative sea different atransformacator. The stator currelate machinauthors preof the frequthe voltageponent of tthe voltagesystem. Thknowledge-PMSMs.

During aof the windtance. For oturns is macurrents duing and winwhich is traEKF methodmatrix to aditions of thversion of Pdetermine technique wlocusts to s

ed inue ml opt

rest drivesubstimiscusssis re3.5 3.55 3.6 3.65 3.7 3.75-4

-2

0C

urre

3.5 3.55 3.6 3.65 3.7 3.75-5

-2.5

0

2.5

5

Cur

rent

(A)

90% Short-circuit winding fau

3.5 3.55 3.6 3.65 3.7 3.75-100

-50

0

50

100

Time(s)

Cur

rent

(A)

50% Short-circuit winding fau

10% short-circuit winding faul

Fig. 1. Zero-component of three phase stator current of

NNs for electric drives have mainly involved the induc-e [1519]. The usual approach to applying ANNs is to

indicators and extract features which are learned by theque by mapping machine conditions to the extractedrecurrent, multi-layer ANN model for simulating thef an induction motor and performing online fault diag-posed in [20] even though the load uctuation is nothe case of load uctuation is addressed in [21] usingquence currents as the fault predictor. This paper usespproach using the zero current component of the DQ0

Proposareas dto loca

ThePMSMin the PSO option didiagnotion of the stator three-phase currents as the fault indi-characteristics of the zero-current component of thent is selected as the feature the ANN uses to corre-e condition to fault type and fault severity. In [22], thesent a well-developed approach based on an analysisency component of the zero-sequence component of

of PMSMs. The method presented uses the zero com-he stator current even though the zero component of

can be used in the case of current controlled invertede method presented in this paper is a time-domainbased approach to expand the use of ANNs to the

three phase fault, a number of physical parametersings change: number of turns, reluctance and resis-ur consideration the effective number of stator windingpped to the zero-current component of the three phasering training for various combinations of speed, load-ding turns. Each combination is represented by an ANNined by the Extended Kalman Filter (EKF) method. The

also enables online reconguration of the ANN weightdapt the diagnosis systems to changing operating con-e drive. During actual online fault diagnosis, an onlinearticle Swarm Optimization (PSO) is implemented tothe number of shorted turns. PSO is an optimizationhich uses the concept of ocking birds or swarming

tochastically approach the local optimum of a function.

2. PMSM d

The winthe windintion prevenmedium alings in placalso designThe insulatincreases thefciency [turn faults,the entire gresses thrstarted [7].

The weldrives can bcussed in thare neededdiscussed ifrom the zeare obtainesum of the formation evalues and 3.85 3.9 3.95 4

3.85 3.9 3.95 4

hase-A

3.85 3.9 3.95 4

hase-A

ase-A

from computer simulation.

1995, this technique has found application in severalainly to its ease of implementation and its resistance

imal traps [23]. of the paper is developed with a brief introduction to the, the stator winding short circuit problem is introducedequent section, after which we introduce the ANN andzation techniques as applied to this work. The nal sec-es the both computer simulation and experimental faultsults.rive system fault detection model development

dings of rotating machines are made up of conductors,g core and winding insulation. The winding insula-ts short circuits between conductors. The insulationso provides some additional means to hold the wind-e to reduce vibration. Winding insulation systems areed to quickly conduct heat to the cooling system [24].ion system, however, adds to the cost of the machine,e weight and size of the machine and reduces machine24]. Stator winding faults start as incipient turn-to-

which if undetected can cause the total burn-out ofphase winding since turn-to-turn faults rapidly pro-ough the stator winding and insulation system, once

l known equations that describe the performance ofe found in several texts [25,26] and would not be dis-eir entirety in this paper. The relevant equations that

for the development of the fault diagnostic system aren this section. The zero-current components (differentro-sequence components) of the stator of the PMSMd for time, t, as one-third the instantaneous value of thethree currents components as shown in the DQ0 trans-quations in (1). In (1), Idq0 is the transformed currentthe Ia is the phase current. The zero-current component

Y.D. Nyanteh et al. / Electric Power Systems Research 103 (2013) 201 213 203

3.73

1

2x 10

-13

t(A)

No Short-circuit winding fault on phase-A

3.73

on pha

3.73

on pha

rent o

is obtained currents, I0

Idq0 =23

c

s

I0 =13(Ia +

For the tions, the zethere is an the zero-cuber of shorcomponentthe A-phasturns increaincreases. Toperating uno windingwinding shtimes 3.73speed, in alrst case ofwith 90% hcase with 50reaching 10more than machine wa

During sdation thatnumber of otive numbe

s of a [27haviouctaxtra ancee whmberalthy3.7 3.705 3.71 3.715 3.72 3.725 -2

-1

0Cu

rren

3.7 3.705 3.71 3.715 3.72 3.725 -5

-2.5

0

2.5

5

Curre

nt(A

)

90% Short-circuit winding fault

3.7 3.705 3.71 3.715 3.72 3.725 -50

-25

0

25

50

Time(s)

Curre

nt(A

)

50% Short-circuit winding fault

Fig. 2. Close up of the zero-component of three phase stator cur

as the third component of the transformed three-phase, displayed in (2).

os() cos( 2

3

)cos

( + 2

3

)

in() sin( 2

3

)sin( + 2

3

)

0.5 0.5 0.5

Ia

Ib

Ic

(1)

of turn[25]. Inthe bethe indwith einductB-phasthe nuthe heIb + Ic) (2)

case of no faults and for balanced three-phase condi-ro-current component is zero. During fault conditionsimbalance in the measured current in all phases andrrent component are no longer zero. When the num-ted turns increases, it is noticed that the zero-current

increases in magnitude. If a short circuit occurs in onlye, it is noticed that as the number of short-circuitedse, the magnitude of the zero-current component alsohis is shown in Fig. 1 based on a simulation of a PMSMnder three different conditions of the stator as follows:

fault, 10% windings short circuit in the A-phase and 50%ort circuit in the A-phase. A close up of the gure from.75 s is shown in Fig. 2. The loading and commandedl cases, was the same. It would be noticed that in the

no fault the zero-current component is zero. The caseealthy stator windings has a peak of about 5 A and the% healthy windings has a peak of about 50 A with spikes0 A. For winding conditions with short-circuited turns50% of the total effective turns, speed control of thes impossible.hort circuit faults, the windings undergo physical degra-

reduces the effective number of turns, in addition to ather physical modications to the windings. The effec-r of turns is a simplication that represents the number

C-phases, r

Labc =

(1

The modof the PMSPMSM duriANN clusteconditions circuits on The approabased on PSthe term the effectivponent of t in (3) dur

2.1. ANN m

Trainingthat includemethod [33rent, multi- 3.735 3.74 3.745 3.75

3.735 3.74 3.745 3.75

se-A

3.735 3.74 3.745 3.75

se-A

f PMSM from computer simulation.

n equivalent balanced sinusoidally distributed winding31], several models have been developed to describer of windings during short circuits. In one such model,nce matrix of the three phase windings is augmentedctitious windings for each phase under fault. In (3), the

matrix is shown for the case of a winding fault in theere is the effective number of turns given as a ratio of

of the turns in the shorted windings to the windings in windings. The subscript a, b and c are for the A, B and

espectively, and f refers to the fault condition.

Laa (1 )Lab Lac Laf )Lab (1 )2Lbb (1 )Lbc (1 )LbfLac (1 )Lbc Lcc Lcf

Laf (1 )Lbf Lcf 2Lff

(3)

el shown in (3) has been used to develop an ABC modelM. This model was used to study the behavior of theng faults and was also used to obtain data to train anr for a number of machine conditions. Different faulthave been simulated to understand the effect of shortthe speed, torque, voltage and currents of the machine.ch used in [32] developed an optimization techniqueO to determine the location of a short circuit fault andin (3). The approach in this paper uses an ANN to relatee number of turns given in (3) to the zero-current com-he three-phase currents. PSO is then used to determineing online fault diagnosis.

odel training

time for a neural network depends on several factors the type of application, ANN architecture and training]. The types of architecture include feed-forward, recur-layer and ANN clusters. For this work an ANN cluster is


PMSMDrive

`

-

+

zero current component

abccurrents

Multi-layer perceptron

Known turns ratio for each phase

g trai

used for fauselected macircuit, 10%short circuiANN clustedition. By tcondition, wsenting diffcould be sparchitectureFocused TimANN architture was cothe hidden of training i

layeig. 3iagn3 shte thtraino obtnatioining

appred anFig. 3. Diagram of the ANN durin

lt diagnosis with each member ANN designed for pre-chine winding turn conditions: no turn-to-turn short

short circuit, 20% short circuit, 30% short circuit, 40%t and 50% short circuit. To extend the capabilities ofr, ANNs are designed also for machine operating con-his approach a set of ANNs, for a particular windingould be designed with different machine data repre-

erent machine operating modes. These operating modeseed of operation or loading conditions. Different ANNs were considered during design and a feed-forwarde-Lagged Neural Network (FTLN) was selected as the

hiddenlayer. Ffault din Fig. compu

To lated tcombifor traspond,modelecture since it produced the best results. The architec-mposed of 3 neurons in the input layer, 20 neurons inlayer and 3 neurons in the output layer after a numberterations. Sigmoidal activation functions are used in the

during simfor any comeach phaseThe trainin

X

PSO

X

X

ANN1 A

ANN4 A

ANN7 A

ANN C

Fig. 4. Diagram of ANN Cluster during fault ning.

r and linear activation functions are used in the output shows a diagram of each member ANN developed forosis whilst Fig. 4 shows the ANN cluster. The diagramows the ANN, input, output and the PSO algorithm toe number for each phase.

the ANN cluster, various fault conditions are simu-ain the zero-components of the stator current for eachn of winding fault and operating condition selected. The number of ANNs in the cluster should corre-oximately, to the number of stator winding conditionsd the number of machine operating conditions captured

ulation runs. The input to each ANN, during trainingbination of machine fault, is obtained by multiplying

current by the corresponding turns-ratio of the phase.g method used is online training based on the Extended

NN2 ANN3

NN5 ANN6

NN8 ANN9

luster

Cluster output

diagnosis.


PI PIir*q vr*q

v

ird

irq

*r

ANN

Kalman Filtmethod weonly shownbetter perfoand lower mtraining alloresponsive conditions t

The formKalman lteFig. 5 whichof the systement matridiscussion ato nd minat every timabove using(n), which(5) where dof d(n), givetime n = 1 acesses, the the uncorrewith the sefactor that rtions (n), Gcovariance

w(n + 1) =d(n) = C(n)

(n) = d(n)

[C(

K(n

y(n)

1|n) PIir*dr

Fig. 5. Schematic of drive system incorporating the

er (EKF) method [33]. Variants of the Back-propagationre also used for training but the results presented are

for case of the EKF method, which we found to havermance in terms of time of convergence of the weightsean square values. Online training in contrast to batchws online reconguration of the ANN to make it more

(n) =G(n) =(n) =w(n +to machine aging and changes due to other operatinghat might produce false alarms.ulations needed to congure the ANN based on ther approach can be derived with the signal ow graph in

can be represented by (4) where w(n) is the state vectorm, d(n) is the observation vector, C(n) is the measure-x and v(n) is the measurement noise [33]. Based on thebove, the Kalman ltering problem can be stated as oneimum mean-square error estimate of the state vectore step of the system whose signal ow graph is shown

the entire observation vector. The innovations process, is associated with the observation vector is dened in(n|n 1) is the minimum mean-square error estimaten all past values of the observation vector starting atnd extending up to n 1. Using the innovations pro-correlated measurement vector can be replaced withlated innovations and the Kalman lter can be derivedt of formulations in (6), where (n) is the conversionelates the ltered estimation error, e(n) to the innova-(n) is the Kalman lter gain and K(n,n 1) is the error

matrix.

w(n)

w(n) + (n) (4)

d(n|n 1) (5)

K(n + 1, n)

During ftiplying eacPSO methothe fault coput of eachzero-compoan ANN to pactual PMSthe stator wrst a faultautomaticaings turns rwinding tur

A numberate valuecould be imintelligencetechnique tber of turnsalso has othing than othimplementPSO algorithThe combintime PSO mapplicationPWM PMSM

abc

dq0

ANNCluster

Fault diagno sis

r*q

iabc

r

Encoder

fault diagnostic system.

n)K(n, n 1)CT (n) + R(n)]1

, n 1)CT (n) (n) C(n)w(n|n 1)= w(n|n 1) + G(n)(n)

(6) = K(n, n 1) G(n)C(n)K(n, n 1)

ault diagnosis, the input to the ANN is obtained by mul-h machine phase current by a number generated by thed. This number is a value between 0 and 1 representingndition on that particular phase of machine. The out-

ANN in the cluster is compared to the actual calculatednent. If the value selected by the PSO algorithm enablesroduce the same zero-current component as from the

M, the number generated is taken as the turns-ratio ofindings of the machine. This determination means that

is detected. Secondly, the location of winding fault islly determined since the method determines the wind-atio on all phases. Thirdly, by determining the actualns-ratio, the winding fault severity is also determined.er of methods could have been used to randomly gen-s that correspond to the turns-ratio. A random searchplemented as well as other stochastic computational

search techniques. PSO was selected as the intelligenceo progressively search in the solution space for the num-

of the windings during fault diagnosis. The PSO methoder features that make it computationally less demand-er stochastic methods like Genetic algorithms [34]. To

PSO in fault diagnosis by the above method, the classicalm was modied into an online optimization procedure.ation of online training by the EKF method and real-akes this approach very amenable to online diagnostics. Fig. 5 shows the complete fault diagnosis system. The


U

start

data requirstandard dr

PSO startion space c(8). In (7), Viis the inertiparticle locunder consbers that coXi(k) is the pissues with

Vi(k + 1) =

Xi(k + 1) = The mod

cally in Fig. diagnostic pprevious ttness valubest updateInialize parclesand pbest

Inializegbest

Update parcleswith PSO

Calculate Fitnessfor each parcle

Beerthan pbest

UpdatepbestBeerthan gbest

Do not updategbest

Updatepbest

Fig. 6. Flow chart of real time PSO met

ed is easily obtained with sensors that come with mostive systems.ts by randomly selecting feasible solutions in the solu-alled particles. Each particle is then adjusted by (7) and(k) is described as the velocity of particle i at time k, W(k)a weight of the system at time k, gbest is the global bestation, pbesti is the personal best location of the particleideration and rand(0.1) are randomly generated num-me from a normal distribution or uniform distribution.article i location at time k in (8). Extra implementation

developing real time PSO is discussed in [35].

W(k)Vi(k) + rand(0, 1)(gbesti(k)) + rand(0, 1)(pbesti(k))(7)

Xi(k) + Vi(k + 1) (8)ied PSO algorithm to determine is shown schemati-6. Each PSO particle is updated after each time step of therocedure. The actual PSO particle update is based on theness value compared to the present tness value. Thee that determines each particle, global best and personals is the sum of the squared deviations between the ANN

calculated componentnique is thebreakdownhealthy winlimited to a0.05 did notThe optimia discrete sphase. The search withduring a sim

3. Fault sim

In the reofine trainexperimentfaults in a Pdiscussed trithm. Therto show thePMSM drivDo this step once at thestart of fault diagnosis

Repeat this loopnl end of diagnosis

Do notupdate pbesthod.

zero current components and the actual zero currents. The solution space for the PSO optimization tech-

closed interval between 0 and 1. Zero representing total of the phase winding and one representing perfectlyding conditions. The depth of the solution space was

resolution of 0.05 since in practice a depth greater than reect measurable effects on speed, current and torque.zation problem solution space is therefore limited topace from and including 01 in steps of 0.05 for eachreal-time PSO algorithm is consequently modied toin this space for the value of the effective turns-ratioulated short circuit fault.

ulation results

st of the paper, we discuss simulation results based oning of ANN for fault detection. First we describe theal setup to obtain data to train the ANN and to simulateMSM. Secondly computer simulation results would beo show the performance of the ANN and the PSO algo-eafter, experimental results would also be presented

performance of the diagnostic system using an actuale.


Fig. 7. PMSM Drive system.

Fig. 8. Circuit diagram for stator short circuit winding.


0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5

-20

0

20

phas

eCC

urre

nt(A

)

0 0.05 0.1 0.15 0.2 0.25 0.3

-20

0

20

phas

eBC

urre

nt(A

)

0 0.05 0.1 0.15 0.2 0.25 0.3

-20

0

20

Time(s)

phas

eAC

urre

nt(A

)

with e

3.1. Descrip

The expeANN consisan 11.25 kWis mechanicacquisition sampling ocurrents ansignals per speed valuerunning thethe DC macmode. The Fthe stator wThe rst loacross a fulA9 to A9-AThese specithe stator wfor fault stu

00

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

squa

re d

evia

tion

Fig. 10.

imula

param

airs per reductan coefnt of i

mpu

t results are presented for computer based simulations car-t in MATLAB/SIMULINK environment. These were carriedth a computer simulation model in the direct machine (ABC)ce frame of a PMSM with parameters shown in Table 1.ber of machine conditions are simulated and an ANN ised for each such condition. These conditions are shown in

for changing speeds and xed loading on the machine. Foreed, six different winding conditions are simulated and theFig. 9. Computer simulated three-phase current data

tion of experimental setup

rimental setup to obtain data to validate and train thets of a 28.8 kVA variable frequency drive connected to, 480 V, 60 Hz, Y-connected 8-pole PMSM. A dc motorally coupled to the PMSM to serve as a load. The datasystem is developed utilizing dSPACE. This allows thef three phase currents, three phase voltages, fault loopd motor torque data. A speed encoder that provides 60rotation of the rotor enables the extraction of motors. Fig. 7 shows the drive system which is capable of

PMSM in various modes; for this work it is set operatehine in torque mode and the PMSM in speed controlaults can be simulated in the PMSM by short circuitingindings in two different locations as in shown in Fig. 8.cation labeled A6-A7 to A7-A8 applies a short circuitl pitch winding whilst the second location labeled A8-10 applies a short circuit across half of the windings.al connections have been made across the A phase of

Table 1PMSM s

PMSM

Pole pStatorSelf inFrictioMome

3.2. Co

Firsried ouout wireferenA numdesignTable 2each spindings and is part of a customized machine developeddies.

10 20 30 40 50 60 70 80 90 100

Training step

Training evolution for computer simulated data for one ANN.

data obtainAn ANN is ttion. The tra10,000 datathe PMSM aof the trainthree-phasfor this simon the othebecause of During faulsame input

Table 2Machine simu

Speed (Hz)

100 80 60 40 20 0.35 0.4 0.45 0.5

0.35 0.4 0.45 0.5

ffective turns-ratio of 0.9.

tion parameters.

eters Nominal values (p.u)

4sistance (Rs) 3.4 nce (L) 1.1 mHcient 0.001 Nm/(rad s)nertia 0.006 kg m2

ter simulation resultsed is used as training input for each ANN in the cluster.rained for each speed and winding condition combina-ining data for the computer simulations was made up of

points and comprised the three phase current supply tond the corresponding effective turns-ratio. The resultsing are shown in Fig. 10 based on computer simulatede current data shown in Fig. 9. The effective turns-ratioulation was 0.9 on the phase-A with no short circuitsr phases. As shown the training time was rather fastthe architecture of the ANN and the training method.t diagnosis, all the trained ANNs are presented with the

data comprising the three phase currents multiplied by

lated conditions.

Turns-ratio (ratio of healthy turns)

1, 0.9, 0.8, 0.7, 0.6, 0.51, 0.9, 0.8, 0.7, 0.6, 0.51, 0.9, 0.8, 0.7, 0.6, 0.51, 0.9, 0.8, 0.7, 0.6, 0.51, 0.9, 0.8, 0.7, 0.6, 0.5


10 20 30 40 50 60 70 80 90 1000

0.2

0.4

0.6

0.8

1

Turn

s-ra

tio P

hase

A

10 20 30 40 50 60 70 80 90 1000

0.2

0.4

0.6

0.8

1

Turn

s-ra

tio P

hase

B

10 20 30 40 50 60 70 80 90 1000

0.2

0.4

0.6

0.8

1

Iteration Number

Turn

s-ra

tio P

hase

C

PSO PerformanceError



Fig. 11. Fault diagnosis for computer simulated data (no fault case).

a value genturns-ratio would respthe machin

First thefault diagnoiterations, cnosis is alsoturns from

r of of fa. Corr

on tset anumbn 0.

t valuerated by PSO which represents a possible value for theon each phase of the PMSM. Ideally one of the ANNsond if the calculated turns-ratio from the PSO matchese condition it is trained for.

method was tested with no fault at 60 Hz. The result ofsis for this initial test is shown in Fig. 11 where after 7orrect diagnosis is obtained. The error during fault diag-

shown in Fig. 11 by subtracting the correct number of

numberesult Fig. 12cuitingspeed using betweecorrecthe calculated number of turns. In Fig. 11, the correct of the PSO

10 20 30 40 50 6000.20.40.60.8

1

Turn

s-ra

tio P

hase

A

10 20 30 40 50 600

0.20.40.60.8

1

Turn

s-ra

tio P

hase

B

10 20 30 40 50 600

0.20.40.60.8

1

Iteration Number

Turn

s-ra

tio P

hase

C

Fig. 12. Fault diagnosis for computer simulated data (10% sturns for the no-fault case is 1 for each phase. Anotherult diagnosis using the proposed method is shown inect fault diagnosis showed that there was 10% short cir-he phase-A with no fault on the other two phases andt 100 Hz. PSO particles for all phases were initializeders generated from the random number distribution5 and 1. Particles for the Phase B and Phase C assumees of 1 (indicating no error) within the 5th iteration

algorithm. By the 30th iteration of the real time PSO

70 80 90 100

70 80 90 100

70 80 90 100




horted turns on phase A).


10 20 30 40 50 60 70 80 90 10 00

0.5

1

Turn

s ra

tioP

hase

A

10 20 30 40 50 60 70 80 90 1000

0.5

1

Turn

s ra

tioP

hase

B

10 20 30 40 50 60 70 80 90 10 00

0.5

1

Iteration number

Turn

s ra

tioP

hase

C

Fig. 13. Fault diagnosis for computer simulated data (25% shorted turns on phase A).

algorithm, been obtainresult showPSO algoriton the maculations. FoANNs was tclosely matcorrect turnshown in Fwindings sh

perim

umbller hl drie as

shocenae loa

forombithe correct turns-ratio of the A-phase windings haveed. At a signal sampling rate of 0.0002 s per sample, thiss that it took approximately 0.006 s for the real-timehm to obtain the correct turns-ratio and fault locationhine. Similar results were obtained for other fault sim-r the case of machine conditions for which none of therained, the ANN which was trained for the condition thatched the simulated conditions was able to obtain thes-ratio of the windings of the faulted phase. The resultsig. 13 gave correct diagnosis with 25% of the phase-Aorted.

3.3. Ex

A ncontroimentamachincan befault sing thTable 3each c0 0.05 0.1 0.15 0.2 0.25 0.3-40-20

02040

Pha

seA

Cur

rent

(A)

0 0.05 0.1 0.15 0.2 0.25 0.3-40-20

02040

Pha

seB

Cur

rent

(A)

0 0.05 0.1 0.15 0.2 0.25 0.3-40

-20

0

20

40

Time(s)

Pha

seC

Cur

rent

(A)

Fig. 14. Current data with effective turns-ratio of 0.95ental results

er of scenarios were designed and implemented via aardware-in-the-loop simulation for the PMSM exper-ve system described in Section 3.1. The actual PMSM

shown in Fig. 8 has a xed number of windings thatrted to emulate actual machine fault condition. Therios designed for these simulations involved chang-ding on the PMSM for different speeds as shown in

the same winding conditions. An ANN is designed fornation of loading and winding condition. Fig. 14 is the0.35 0.4 0.45 0.5

0.35 0.4 0.45 0.5

0.35 0.4 0.45 0.5

from PMSM drive.


0 10 20 30 40 50 60 70 80 90 1000

0.5

1

1.5

2

Training step

squa

red

devi

atio

n

Fig. 15. Training evolution for data obtained from actual PMSM drive with 50%loading.

training result for the case of 50% loading on the PMSM experi-mental drive system described earlier. The actual input three-phasecurrent data used for the training is shown in Fig. 13. The trainingdata for the experimental fault diagnosis was made up of 10,000data points and comprised the three phase current supply to thePMSM and the corresponding effective turns-ratio. Fig. 13 showsthat when a short circuit is applied to the A-phase of the PMSM,there is an instantaneous increase in the A-phase current mag-nitude from time 0.250.35 s. A rough estimation of the effectiveturns-ratio of the PMSM during fault simulation gave a value of0.95. The training based on experimental data was more difcultand had a wfrom compnoise addethe mappincomponent

During fconditions

Table 3Machine simulated conditions.

Speed (Hz) Loading at xed turns-ratio (5% shorted winding)

10 No-load, 10%, 20%, 30%, 40%, 50% loading20 No-load, 10%, 20%, 30%, 40%, 50% loading30 No-load, 10%, 20%, 30%, 40%, 50% loading40 No-load, 10%, 20%, 30%, 40%, 50% loading50 No-load, 10%, 20%, 30%, 40%, 50% loading

ANN that was trained to catch the particular machine conditionsimulated was always able to determine the winding conditions onthe A-phase. Fault diagnosis results obtained using data from thePMSM drive took longer as shown in Fig. 15. Fig. 15 shows correctdiagnosis for the PMSM at 30% loading where at about the 50th iter-ation, the correct turns-ratio is discovered. At a signal sampling rateof 0.0002 s per sample, this result show that it took approximately0.01 s for the real-time PSO algorithm to obtain the correct turns-ratio on the machine based on the simulated condition. For the caseshown in Fig. 16, the A-phase turns-ratio when a fault is applied is0.95 and 1 for the other phases. For all cases and as already dis-cussed, the real-time PSO algorithm is randomly initialized in thediscrete solution space as described in Section 2. As was observedin the case of computer simulation, when other loading conditionswere considered for the PMSM, similar results as shown in Fig. 16were obtained. Fault diagnosis based on the PMSM data generallytook longer since as was explained earlier, the training results werenot as accurate due to noise in the data. The results, however, showthat the approach used in this paper is relatively impervious tosensor noise. For machine conditions for which none of the ANNs

raineimuled thion. F

of 416 sin

55 it

60orse total squared-error deviation compared to datauter simulation. This was due to the fact that sensord to the current data increased the nonlinearities ing from the ANN input to the calculated zero-current.ault diagnosis based on the PMSM drive, the loadingof the drive was changed for different speeds and the

were tputer smatchcondita speedin Fig. It took

0 10 20 30 40 500

0.55

0.95

Turn

s-ra

tioP

hase

A

10 10 20 30 40 50 600

0.5

Turn

s-ra

tioP

hase

B

0 10 20 30 40 50 600

0.5

1

Iteration number

Turn

s-ra

tioP

hase

C

Fig. 16. Fault diagnosis for 30% loading of the d, the results were the same as was obtained with com-ated data. The results showed that the ANN that closelye fault condition was activated to determine the faultig. 17 shows fault diagnosis for the case of 50% loading at5 Hz. The time it takes to diagnose the fault is longer thance this fault was not in the knowledge base of the ANN.erations in Fig. 17 corresponding to a time of 0.011 s.

70 80 90 100

70 80 90 10070 80 90 100

PMSM drive.


60

0.5

1

s-ra

tioas

e A

60

60ber

f the

4. Conclud

The metof AI for famethod whmation: theFTLN netwoconditions method preto all kindscomponentnent in the of PSO for ois modied PSO particlemance of thbe adequateparticles forpresented cPSO algoritalgorithm. the ability was relativcan be trainbut to redumethodologduring desiincreased nfrom only ophases havANN traine

Acknowled

The authResearch, U

nces

. Rahtors, Iano, Kt mag421Khov,MSMposiu9 (SDyante

mod0 10 20 30 40 50 0

Turn Ph

0 10 20 30 40 50 0

0.5

1

Turn

s-ra

tioPh

ase

B

0 10 20 30 40 50 0

0.5

1

Iteration num

Turn

s-ra

tioPh

ase

C

Fig. 17. Fault diagnosis for 50% loading o

ing remarks

hod presented in this paper shows a promising useult diagnosis of electrical machines in real time. Theen successful provides three important pieces of infor-

fault type, the fault location and the fault severity. Ark is used in this paper to correlate winding short faultsto the zero-current component of a PMSM. Whilst thesented is applied to a PMSM, the method can be applied

of machines. The method also uses the zero-current but the method can also use the zero-voltage compo-

Refere

[1] M.Amo

[2] Y. Knen212

[3] M. of PSym200

[4] Y. Ntioncase of a voltage controlled drive system. Whilst the useptimization has been around for some time, the methodto carry out optimization in real time by performing the

update immediately after data acquisition. The perfor-e PSO algorithm in real time has been demonstrated to

for the purposes of fault diagnosis. The number of PSO this work was xed at 20 by trial and error. The methodan also be implemented in real time using the modiedhm and the online ANN training method using the EKFAnother advantage demonstrated with this method isto tolerate noise. In particular, we showed the systemely impervious to sensor noise. In theory a single ANNed to capture all operating conditions to be consideredce training time, an ANN cluster is used. The trainingy used enables fast convergence of the ANN weights

gn of the fault diagnosis system and compensates for theumber of ANNs used for fault diagnosis. Whilst resultsne fault per phase is shown in this paper, faults on othere to be considered as different fault conditions and thed for such cases.

gement

ors acknowledge the support from the Ofce of NavalSA under Grant N00014-02-1-0623.

for estimInsulatio

[5] F. Filippetion motIndustria

[6] S.M.A. Crfor the diactions o

[7] S.M.A. CrDTC indu(2004) 13

[8] L.H. Tsouseries onand Cont

[9] B.K. Bima0130167

[10] S. Wu, T.neural ne183194

[11] S. Hua, X.of InductInformatpp. 1881

[12] R.M. Tallwork basin: Industvol. 1, 20

[13] C. Demiantics in indthe Indus

[14] A. Siddiqniques foInternatitronics an 70 80 90 100

70 80 90 100

70 80 90 100

PMSM drive.

man, P. Zhou, Analysis of brushless permanent magnet synchronousEEE Transactions on Industrial Electronics 43 (2) (1996) 256267.. Tonogi, N. Matsui, A simple non-linear analysis for axial-ux perma-net machines, IEEE Transactions on Industrial Electronics 57 (2009)33.

J. Regnier, J. Faucher, Monitoring of turn short-circuit faults in stator in closed loop by on-line parameter estimation, in: IEEE Internationalm on Diagnostics for Electric Machines, Power Electronics and Drives,EMPED 2009), August 31, 2009September 3, 2009, 2009, pp. 16.h, L. Graber, C. Edrington, S. Srivastava, D. Cartes, Overview of simula-els for partial discharge and electrical treeing to determine feasibility

ation of remaining life of machine insulation systems, in: Electricaln Conference (EIC), 58 June, 2011, pp. 327332.tti, G. Franceschini, C. Tassoni, P. Vas, Recent developments of induc-or drives fault diagnosis using AI techniques, IEEE Transactions onl Electronics 47 (5) (2000) 9941004.uz, A.J.M. Cardoso, Multiple reference frames theory: a new methodagnosis of stator faults in three-phase Induction motors, IEEE Trans-n Energy Conversion 20 (3) (2005) 611619.uz, A.J.M. Cardoso, Diagnosis of stator inter-turn short circuits inction motor drives, IEEE Transactions on Industry Applications 40 (5)491360.kalas, R.E. Uhrig, Fuzzy and neural approaches in engineering, Wiley

adaptive and learning systems for signal processing, Communicationsrol (1997), ISBN: 0-471-44506-1.l, Modern Power Electronics and AC Drives, Prentice Hall, 2002, ISBN436.W.S. Chow, Induction machine fault detection using SOM-based RBFtworks, IEEE Transactions on Industrial Electronics 51 (1) (2004 Feb).

Wang, T.C. Kil, Vibration Signal Analysis for Electrical Fault Detectionion Machine Using Neural Networks, in: International Symposium onion Technology Convergence, 2007 (ISITC), 2324 November, 2007,92.

am, T.G. Habetler, R.G. Harley, D.J. Gritter, B.H. Burton, Neural net-ed on-line stator winding turn fault detection for induction motors,ry Applications Conference, 2000, Conference Record of the 2000 IEEE,00, pp. 375380., G. Cirrincione, G.A. Capolino, A neural approach for the fault diagnos-uction machines, in: IECON 02 (IEEE 2002 28th Annual Conference oftrial Electronics Society), vol. 4, 58 November, 2002, pp. 33723376.ue, G.S. Yadava, B. Singh, Applications of articial intelligence tech-r induction machine stator fault diagnostics: review, in: 4th IEEEonal Symposium on Diagnostics for Electric Machines, Power Elec-d Drives, 2003 (SDEMPED 2003), August 2426, 2003, pp. 2934.


[15] J. Sang, C. Hao, P. Wang, Diagnosis of stator winding inter-turn circuit faultsin induction motors based on wavelet packet analysis and neural network,Advanced Materials Research 529 (2012) 3742.

[16] L. Capocchi, S. Toma, G.A. Capolino, F. Fnaiech, A. Yazidi, Wound-rotor inductiongenerator short-circuit fault classication using a new neural network basedon digital data, in: 8th IEEE Symposium on Diagnostics for Electrical Machines,Power Electronics and Drives, Art. No. 6063691 (SDEMPED 2011), 2011, pp.638644.

[17] K. Yu, F. Yang, H. Guo, J. Xu, Fault diagnosis and location of brushless DC motorsystem based on wavelet transform and articial neural network, in: 2010International Conference on Electrical Machines and Systems (ICEMS2010), Art.No. 5662831, 2010, pp. 10481052.

[18] R.N. Dash, B. Subudhi, S. Das, A comparison between MLP NN and RBF NN tech-niques for the detection of stator inter-turn fault of an induction motor, in:2010 International Conference on Industrial Electronics, Control and Robotics(IECR 2010), Art. No. 5720163, 2010, pp. 251256.

[19] M.B.K. Bouzid, G. Champenois, N.M. Bellaaj, L. Signac, K. Jelassi, An effec-tive neural approach for the automatic location of stator interturn faults ininduction motor, IEEE Transactions on Industrial Electronics 55 (12) (2008)42774289.

[20] F.A. Mohamed, H. Koivo, Modelling of induction motor using non-linear neuralnetwork system identication, in: SICE 2004 Annual Conference, vol. 2 (46),2004, pp. 977982.

[21] J. Quiroga, D.A. Cartes, C.S. Edrington, L. Liu, Neural network based fault detec-tion of PMSM stator winding short under load uctuation, in: Power Electronicsand Motion Conference, 2008, pp. 793798.

[22] J.-C. Urresty, J.-R. Riba, L. Romeral, Application of the zero-sequence voltagecomponent to detect stator winding inter-turn faults in PMSMs, Electric PowerSystems Research 89 (2012) 3844.

[23] A.P. Engelbrecht, Fundamentals of Computational Swarm Intelligence, John-Wiley and Sons, 2006, ISBN 0470091916.

[24] G.C. Stone, E.H. Boutler, I. Culbert, H. Dhirani, Electrical insulation for rotatingmachines-design, evaluation, aging, testing and repair, IEEE Press Series onPower Engineering (2004), ISBN: 0-471-44506-1.

[25] P.C. Krause, O. Wasynczuk, S.D. Sudhoff, Analysis of electrical machinery anddrive systems, IEEE Press Series on Power Engineering (2002), ISBN: 0-471-14326-X (John Wiley and Sons).

[26] R. Krishnan, Electric Motor Drives: Modeling, Analysis and Control, PrenticeHall, 2001.

[27] N. Hemati, H. Kwatny, Bifurcation of equilibria and chaos in permanent magnetmachines, in: Proceedings of IEEE the 32nd Conference on Decision and Control,1993.

[28] P. Bastard, P. Bertrand, M. Meunier, A transformer model for winding faultstudies, IEEE Transactions on Power Delivery 9 (April (2)) (1994) 690699.

[29] M.A. Awadallah, M.M. Morcos, S. Gopalakrishnan, T.W.A. Nehl, Neuro-fuzzyapproach to automatic diagnosis and location of stator interturn faults in CSI-fed PM brushless DC motors, IEEE Transactions on Energy Conversion 20 (June(2)) (2005) 253259.

[30] A. Koochaki, S.M. Kouhsari, G. Ghanavati, Transformer internal faults simula-tion, Advances in Electrical Engineering 8 (2008) 2328.

[31] G.D. Gonzalez, J.G.A. Fernandez, P.A. Arboleya, Electromagnetic model of turn-to-turn short circuits in transformers, International Journal of Computation andMathematics in Electrical Engineering 23 (2) (2004) 558571.

[32] L. Liu, Robust Fault Detection and Diagnosis for Permanent Magnet Syn-chronous Motors, 2006 (Dissertation presented to the Florida State University).

[33] S. Haykin, Neural Networks A Comprehensive Foundation, Prentice Hall, 1999,ISBN 0-13-273350-1.

[34] J. Nocedal, S.J. Wright, Numerical optimization, Springer Series in OperationsResearch (1999), ISBN 0-387-98793-2.

[35] W. Lui, L. Liu, D.A. Cartes, Efforts on real-time implementation of PSO basedPMSM parameter identication, in: IEEE Power and Energy Society GeneralMeeting, 2008, pp. 17.

Application of artificial intelligence to stator winding fault diagnosis in Permanent Magnet Synchronous Machines1 Introduction2 PMSM drive system fault detection model development2.1 ANN model training

3 Fault simulation results3.1 Description of experimental setup3.2 Computer simulation results3.3 Experimental results

4 Concluding remarksAcknowledgementReferences

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