-
Applied Soft Computing 13 (2013) 259270
Contents lists available at SciVerse ScienceDirect
Applied Soft Computing
j ourna l ho mepage: www.elsev ier .co
Wind t CAmodels
Meik Sch ichea Department o tr. 4, 2b Department o , Denmc
Department o anada
a r t i c l
Article history:Received 9 December 2011Received in revised form
3 May 2012Accepted 8 August 2012Available online 23 August 2012
Keywords:ANFIS modelsCondition monWind turbineSCADA dataNormal
behav
d turbference Systems (ANFIS). For this purpose: (1) ANFIS
normal behavior models for common SupervisoryControl And Data
Acquisition (SCADA) data are developed in order to detect abnormal
behavior of thecaptured signals and indicate component malfunctions
or faults using the prediction error. 33 differentstandard SCADA
signals are used and described, for which 45 normal behavior models
are developed.The performance of these models is evaluated in terms
of the prediction error standard deviations to
1. Introdu
Conditiotance as thnowadays mcial. Unexpeexcessive dother
speciaauxiliary eqdowntime dtors point oto monitor downtime a
CorresponE-mail add
1568-4946/$ http://dx.doi.oitoring
ior models
show the applicability of ANFIS models for monitoring wind
turbine SCADA signals. The computationaltime needed for model
training is compared to Neural Network (NN) models showing the
strength ofANFIS in training speed. (2) For automation of fault
diagnosis Fuzzy Interference Systems (FIS) are usedto analyze the
prediction errors for fault patterns. The outputs are both the
condition of the componentand a possible root cause for the
anomaly. The output is generated by the aid of rules that capture
theexisting expert knowledge linking observed prediction error
patterns to specic faults. The work is basedon continuously
measured wind turbine SCADA data from 18 turbines of the 2 MW class
covering a periodof 30 months.
The system proposed in this paper shows a novelty approach with
regard to the usage of ANFIS modelsin this context and the
application of the proposed procedure to a wide range of SCADA
signals. Theapplicability of the set up ANFIS models for anomaly
detection is proved by the achieved performance ofthe models. In
combination with the FIS the prediction errors can provide
information about the conditionof the monitored components.
In this paper the condition monitoring system is described. Part
two will entirely focus on applicationexamples and further efciency
evaluation of the system.
2012 Elsevier B.V. All rights reserved.
ction
n monitoring of wind turbines is of increasing impor-e size and
remote locations of wind turbines usedakes the technical
availability of the turbine very cru-cted faults, especially of
large components, can lead to
owntime offshore due to lack of suitable crane ships orlized
vessels. However, also smaller issues and faults ofuipment like
pumps or fans can cause expensive turbineue to restricted turbine
accessibility. From an opera-f view it is therefore worth
increasing the effort spentthe turbine condition in order to reduce
unschedulednd thus operational costs.
ding author. Tel.: +49 40533268134.ress:
[email protected] (M. Schlechtingen).
The available CMS mostly require high level knowledge aboutthe
system to be monitored. However, this knowledge is difcultto access
and does often not exist. Physical models of the systemto monitor
its condition and predict failures can thus seldom bebuilt with
high accuracy due to its complex interaction among sev-eral
dynamical subsystems. Moreover the available CMS mainlyfocus on
vibrations. Vibration analysis is by far the most prevalentmethod
for machine condition monitoring [2]. However, vibrationsensors are
not installed on all turbines and components due totheir high
costs. This causes a large number of turbines not beingcondition
monitored at all or vibration sensors being installed atthe main
components only.
On the other hand, there is a large amount of operational(SCADA)
data available, which can be used to give an indication ofthe
turbine condition. This fact is also stressed by Yang and Jiang
[3]who additionally point out that these data are the cheapest
resourcefor developing a CMS for wind turbines. The operational
data can beeither turbine status information or measurements of
signals such
see front matter 2012 Elsevier B.V. All rights
reserved.rg/10.1016/j.asoc.2012.08.033urbine condition monitoring
based on S. Part 1: System description
lechtingena,, Ilmar Ferreira Santosb, Soane Achf Technical
Operation Wind Offshore, EnBW Erneuerbare Energien GmbH,
Admiralittsf Mechanical Engineering, Section of Solid Mechanics,
Technical University of Denmarkf Mechanical Engineering, Machine
Design Section, cole Polytechnique de Montral, C
e i n f o a b s t r a c t
This paper proposes a system for winm/locate /asoc
DA data using normal behavior
c
0459 Hamburg, Germanyark
ine condition monitoring using Adaptive Neuro-Fuzzy Inter-
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260 M. Schlechtingen et al. / Applied Soft Computing 13 (2013)
259270
as temperatures, currents or pressures. Using turbine status
infor-mation Kusiak and Wenyan [4] and Kusiak and Verma [5] show
thatfaults can be predicted 560 min in advance. In order to
performpredictive maintenance this prediction period is too short,
since itdoes not leaactions. By trends of recant change[6]. Using
Net al. [7], Zahthat changesome casessuited to allbefore the
chistorical opels capable more input suited, sincsignals
meaoutput.
One advwind turbinbehavior is behavior mdecoupled [1] and
Sanoped in perhealthy (noof the compto predict acation of ch
This appbility of a sywith eXogewind turbinever, this aselection
toof signals anbe avoided.algorithms systems usi(MAS) [1,11behavior
met al. [13], wdition of a good perforresearch acgen [10] prodrive
train
Instead oa novelty, nonlinear stuning the phase.
Jangparametersfaster traindata procesthermore, aincorporatetheir
outpu
The moncan furtherfor modelinZaher and Mthe normal gen and
San
ReConstruction (FSRC) approach, since this approach
additionallyallows for monitoring the signal value magnitude. The
differentapproaches mainly concern the question of the used input
signalsto model the target signals. This approach is also followed
by the
h prnterp
exptaintert ied gecessfrbinees.
resegatioADAes me com
devrateg
mal bd in predurredrted
idens whs and
SCAiffereuoused.ectioped A datf dess forode
ses os and
predion 5eniw th
ed anorkinents are
9.
crip
CMSs ind tule orime. e comCMS he fois brve the operator with
enough time to take maintenanceapplying advanced signal analysis
methods focused onpresentative signals or combination of signals,
signi-s in turbine behavior can be detected at an early stageeural
Network (NN) model based approaches Sanz-Bobier et al. [1] as well
as Schlechtingen and Santos [8] shows in signal behavior can be
detected, days, weeks and in
months in advance. These methods are therefore betterow
operators to take measures to improve the conditionomponent nally
fails. In the model based approacheserational data is used to
develop normal behavior mod-
of predicting a certain output signal, when given one orsignals.
For wind turbine signals these attempts are welle many signals can
be found to be correlated to othersured simultaneously, e.g. the
wind speed or the power
antage of using normal behavior models to monitore signals is
that no prior knowledge about the signal
needed. Another important property is that with normalodels the
possibility of monitoring the signal is widelyfrom the operational
mode as reported by Zaher et al.z-Bobi et al. [7]. The normal
behavior models are devel-iods where the turbine components can be
consideredrmally operating), usually the period at the
beginningonent lifetime. Afterwards, the trained model is used
specic signal where the prediction error gives an indi-anges in
signal behavior and thus incipient faults.roach is of large
interest in research. In [9] the applica-stem identication approach
using an AutoRegressivenous input (ARX) model to monitor the
condition of ae generator bearing using SCADA data is shown.
How-pproach requires human intervention for parameter
nd a good performing model. Due to the large amountd turbines to
be monitored, human intervention should
Most activities take advantage of articial intelligence(learning
capabilities) and among the most advancedng this approach is SIMAP
[7] and a Multi Agent System]. Both systems use articial NN to set
up the normalodels of SCADA data. This line is also followed by
Xianghere a NN model based method to monitor the con-
wind turbine generator bearing is developed and themance of NN
in this context shown. Additionally, earliertivities by
Schlechtingen and Santos [8] and Schlechtin-posed a condition
monitoring system for wind turbine
components using NNs.f using NNs, ANFIS is used in this paper,
which presentsin this eld of application. ANFIS models can
learnignal relations by setting up a set of fuzzy rules
andMembership Function (MF) parameters in a training
[14] shows that in comparison to NN models, fewer must be
trained in ANFIS models, leading generally toing. The major
drawbacks of NNs are their black-boxsing structure and slow
convergence speed [15]. Fur-
priori knowledge about the system is difcult to bed. Here ANFIS
models have a major advantage, due tot being based on linguistic
rules and tuneable MFs.itoring systems developed by the different
researchersmore be classied according to the input signals usedg.
While Sanz-Bobi et al. [7], Zaher et al. [1] as well ascArthur [11]
use autoregressive approaches to set up
behavior models, the research presented by Schlechtin-tos [8]
showed that it is advantageous using a Full Signal
researcTo i
a fuzzythe ceran expsurveybe sucone tuturbin
Theinvestibine SCadvanc[11] ar
Thestep st
1. Noropethe
2. Occrepo
3. Thebasetern
Theteen dcontingather
In Sdevelo(SCADA briemodelused m4 focumodelfor thein Sectand a
dies hodetect(FIS) wcompoResultSection
2. Des
Thepatterning winschedudowntthat arof the
In tFig. 1) esented in this paper.ret the prediction error of
the normal behavior models,ert system that outputs a diagnosis, the
condition andy of statement based on rules that were established
bys employed by Sanz-Bobi et al. [7]. With regard to thearbox
faults in their research, this approach proved toul, since the
rules established with fault patterns from
were also applicable to predict the same faults on other
arch presented in this paper is the result of two yearsns
carried out to develop a CMS that uses wind tur-
data available to wind turbine operators. Thereby theade by
Sanz-Bobi et al. [7] as well as Zaher and McArthurbined and further
developed.
elopment of the aforementioned CMS follows the threey below:
ehavior models of the relevant SCADA data were devel-order to
monitor and detect anomalies by consideringiction error.
anomalies within the prediction error were related to
faults.tied relations were implemented in knowledge dataich are
used by FIS to automatically analyze these pat-
output a diagnosis.
DA data used in this research is obtained from eigh-nt operating
onshore turbines of the 2MW class, where
operational data from April 2009 to March 2012 were
n 2 of this article the general concept of the systemis
described giving details about how the informationa) is processed
to nally output condition statements.cription of the advantages of
ANFIS models over NN
the given application and a short description of thel setup and
structure is supplied in Section 3. Sectionn the performance of the
developed normal behavior
describes the identied inputoutput sets. An exampleiction error
analysis and the model interactions is given. In Section 6 the
concept of anomaly detection is showntion of abnormal signal
behavior is given. Section 7 clar-e prediction error together with
the information aboutomalies is processed by the Fuzzy Interference
Systemng as fuzzy expert. The output is a diagnosis about the
condition and a potential root cause of the anomaly. discussed
in Section 8 and conclusions are drawn in
tion of the general CMS concept
developed in this research aims at detecting trends and SCADA
data in order to predict possible failures, giv-rbine operators
enough time to adapt the maintenance
take further measures to prevent unexpected systemFor this
purpose 10 min averaged SCADA data are usedmonly available to
operators. The general architecture
developed is shown in Fig. 1.llowing, the function of the
different CMS modules (seeiey described.
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M. Schlechtingen et al. / Applied Soft Computing 13 (2013)
259270 261
2.1. Trainin
Within trained if a The latter ischange as apreprocesseincludes:
(1processing
In the trato allow earone month formed wheoutput of thstandard
thwind turbin
2.2. Predict
The predprocessed snormal-behlated and st
2.3. Anoma
In this mtied. This ithresholds output is anquency andanomaly
in
2.4. Fuzzy e
Here thecomponentnumber of ito be monit
ile thiagn
osisitionntial
zzy e
hin the plies.
the
del sFig. 1. CMS overview.
g module
the training module the normal behavior model ismodel is not yet
available or new training is required.
true if a component is replaced and the signal relations
consequence. Before training the model, the data ared according to
the methodology proposed in [8] which) a validity check, (2) data
range check, (3) missing dataand (4) lag removal.ining module
different training levels are implementedly monitoring. A rst model
training is performed afterof operational data collection. Further
trainings are per-n three, six and nine months of data are
available. Thee training module is the trained ANFIS model and
theresholds marking the normal operational range of thees using the
prediction error.
ion module
Whto be d
diagn cond pote
2.5. Fu
Witgiven anomato givedition.
3. Moiction module is active once a trained model of theignal is
available in the model base. The developedavior model is applied
and the prediction error calcu-ored.
ly detection module
odule the anomalies in the prediction errors are iden-s done on
the basis of the determined normal-behaviorby the training module
or expert dened values. The
anomaly matrix containing information about the fre- date of
occurrence, as well as the duration of the current
days.
xpert initialization module
FIS structures used for diagnosis of the anomalies and condition
statements are initialized with regard to thenputs and outputs as
well as their MFs. Each componentored has its own FIS
structure.
3.1. Data se
The avaicontain moters, calculset points, tvoltages, etmaximum,
ing period ifor mean vastored in thtions e.g. suthis researcthe 10
min pvariations; tative signatime series,
The remTable 1 andare illustrat
In additnon-operate inputs sorely depend on the component or
subsystemosed, each FIS structure has the following outputs:
(information about the abnormal behaving signal) (classication
in green, yellow and red color code)root cause
xpert application module
this module the initialized FIS structure is evaluated,rediction
errors and the information about presentThe output is stored in
text format and is visualizedanalyst a comprehensive summery of the
turbine con-
etup and structuret description
lable SCADA data sets from the operating wind turbinesre than
150 different signals, ranging from hour coun-ated values, digital
indicators of switch positions ando continuous measurements of
temperatures, currents,c. For some of the continuous measurements
the mean,minimum and standard deviation of the 10 min averag-s
available. In this research only normal behavior modelslues were
considered for three reasons: (1) The peakse min.max. values can be
caused by transient situa-dden changes in wind speed that are not
in the scope ofh; (2) stochastic effects in the signals are
averaged out ineriod, making the prediction less sensitive to
stochastic
(3) modeling the standard deviations requires represen-ls, i.e.
other standard deviations or higher resolution
that are not accessible.aining signals considered for the CMS
are given in
a schematic of the turbine and of the sensor locationed in Fig.
2.ion to these signals, information about service andional periods
is extracted from the corresponding hour
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262 M. Schlechtingen et al. / Applied Soft Computing 13 (2013)
259270
Table 1SCADA data signal list used for normal behavior model
development.
Name of variable Unit Sensor location (Fig. 2) Short description
Normal behavior modeled
Spinner temp. C 1 Spinner temperature (in hub housing) YesHub
controller temp. C 1 Pitch controller temperature YesPitch angle 1
Blade pitch angle (avg. over all 3 blades) YesHydraulic oil temp. C
10 Hydraulic oil temperature (used for pitching blades) YesRotor
speed rpm 2 Rotor speed (low speed shaft) YesGear bearing temp.
(HSS) C 3 High speed shaft bearing temperature YesGear oil temp C 3
Gearbox oil temperature YesGenerator speed rpm 4 Generator speed
(high speed shaft) YesGenerator bearing temp.1 C 5 Generator
bearing temperature gearbox end YesGenerator bearing temp. 2 C 5
Generator bearing temperature transformer end YesGenerator slip
ring temp. C 5 Generator slip ring temperature YesGenerator ph.1
temp. C 5 Generator stator temperature phase 1 YesGenerator ph.2
temp. C 5 Generator stator temperature phase 2 YesGenerator ph.3
temp. C 5 Generator stator temperature phase 3 YesGenerator current
ph.1 A 6 Generator current phase 1 YesGenerator current ph.2 A 6
Generator current phase 2 YesGenerator current ph.3 A 6 Generator
current phase 3 YesPower output kW 6 Turbine power output
YesReactive power kVAr 6 Turbine reactive power consumption YesGrid
inverter ph.1 temp. C 6 Inverter temperature grid end YesGrid rotor
in erature phase 1 generator end YesGrid rotor in eratuGrid rotor
in eratuConverter co ling wConverter ch ke coConverter co trolleTop
controll oller tGrid busbar ratureHV transform transfHV transform
transfHV transform transfNacelle tem in nacWind speedWind direct n
Ambient tem eratu
counters. Taccording t
3.2. Model
Several rels in the of NN are massive paduce
instanincompletewide rangeshown thatinputoutpHowever, ittapped in
lo
e nus peverter ph.1 temp. C 6 Inverter tempverter ph.2 temp. C 6
Inverter tempverter ph.3 temp. C 6 Inverter tempoling water temp. C
6 Converter coooke coil temp. C 6 Converter chontroller temp. C 6
Converter coner temp. C 6 Turbine contrtemp. C 8 Busbar tempeer
ph.1 temp. C 8 High voltage er ph.2 temp. C 8 High voltage er ph.3
temp. C 8 High voltage
p. C 7 Temperature m/s 9 Wind speed ion 9 Wind directiop. C 9
Outdoor temp
his information is used for preprocessing the datao the
methodology presented in [8] (Fig. 3).
over thneurontype
esearchers applied NN to set up normal behavior mod-eld of
condition monitoring. Some of the key featurestheir high processing
speeds which are due to theirrallelism, their proven ability to be
trained, to pro-taneous and correct responses from noisy or
partially
data, and their ability to generalize information over a [16].
In earlier publications of the authors [8,10] it was
NN are indeed capable of accurately nding an existingut mapping
for different wind turbine SCADA signals.
was found that NN have a high likelihood of becomingcal minima.
In Fig. 4 three examples of NN performances
427
9
10
8
6
53
1
Fig. 2. Wind turbine schematic: sensor positions.
The variof local migenerally uperformingruns with ashortfall.
Fisen. This leacceptable
Fuzzy syuations invwell underfast, solutiois that exist
Fig. 3. Wind turbines.re phase 2 generator end Yesre phase 3
generator end Yesater temperature Yes
il temperature Yesr temperature Yesemperature Yes
Yesormer temperature phase 1 Yesormer temperature phase 2
Yesormer temperature phase 3 Yeselle (housing) of the turbine
Yes
YesNo
re No
mber of runs are shown. For this example two hiddenr input
signal are used.
ations in performances are up to 20%, stressing the risknima.
Furthermore the number of hidden neurons isnknown. Tarrassenko [17]
and Raq et al. [18] suggest
several runs with random weight initializations and varying
number of hidden neurons to overcome thisnally the network that
performs best should be cho-ads to a large number of different
trials to obtain ansolution.stems are very useful in two general
contexts: (1) in sit-olving highly complex systems whose behaviors
are notstood and (2) in situations where an approximate, butn is
desired [19]. A further advantage of fuzzy systemsing expert
knowledge can be implemented to improve
nacelleHV
transformer
converter
meteorology
auxiliaryequipment
generatorgearbox
rotor system
turbine schematic: components/subsystems in the considered
wind
-
M. Schlechtingen et al. / Applied Soft Computing 13 (2013)
259270 263
0.7
0.75
0.8
0.85
0.9
0.95
1
No
rma
lize
d s
td(e
rro
r)
Fig. 4.
the approxifunctions an
Considering capabilisystem andnetworks hreal-world benets of
This gives thknowledge
In ANFISthe FIS is usparameters
In the fomodels are
3.2.1. FIS stThere ar
dani [22] antypes is in tion (Sugenwork the Sin the premfuzzy
equatfuzzy ifthetem under cto be more ity of the ouprediction.
3.2.2. MemMember
whose shapIn most fuzused (triangtribution mand linear
Mtribution fuwith regardFurthermorin the inpushape.
Normal FSRC model
G
Wind speed (t-lag)
Wind direction (t-lag)
A
Generator current ph.1
ompa
Num conv
famr of h thmberter pn co
Train learin [1uarese thgene4].
Input numThe cbinated antic alAPLSool ox supinpumaypoinenomatic
i2 4 6 8 10
Run
Pitch angle
Grid busbar temp.
Conv. choke coil temp.
Exemplary performances of trained NNs over different runs.
mation by tuning, removing or adding of membershipd rules.able
work has been carried out to integrate the learn-ty of NN with FIS
for deriving the initial rules of a fuzzy
tuning the membership functions [20]. Fuzzy neuralave shown to
be very advantageous in dealing withproblems [21]. These
neuro-fuzzy systems combine thethese two powerful paradigms into a
single capsule.e ability to accommodate both data and existing
expertabout the problem under consideration [20].
the advantages of NN are combined with FIS. Therebyed to set up
a set of rules whose membership functions
are tuned in a training phase.llowing sections the setup and
structure of the ANFISdescribed.
ructuree two common types of fuzzy interference: the Mam-d
Sugeno [23]. The main difference between the twothe consequent
part, which can be a nonfuzzy equa-o) instead of a fuzzy linguistic
value (Mamdani). In thisugeno type is used as it has fuzzy sets
only involvedise part. However, the consequent part can be a
non-ion. Due to the qualiers on the premise parts, eachn rule can
be viewed as a local description of the sys-
Fig. 5. Cmodel.
3.2.3. In a
who isnumberesearcthe nuthis, afbetwee
3.2.4. The
posed least sqdecreabut in time [1
3.2.5. The
tored. a commodela genesion (Glarge ptoolboto the which toring
the phautomonsideration [14]. Moreover the Sugeno type is
knowncomputationally efcient and has guaranteed continu-tput
surface, which makes it well suited for time series
bership functionsship functions can be represented by an
arbitrary curvee is dened as a function that suits the
application.
zy applications straight-line membership functions
areular/trapezoidal). In this paper generalized normal
dis-embership functions are employed for the input spaceFs for the
output space. The generalized normal dis-
nctions have the advantage of giving a broad exibility to the
function shape, depending on their parameters.e these functions
assure smoothness of the transitionst space. The free parameters
are: location, scale and
for which tinput to maccurate bulute currenthe phases
Hence, aunderstandis the prefe
Since bonal readingimportant, (Full Signalmodeled bydifferent
tycorrelated sFig. 5 for th
With thinstance difCross prediction FSRC model
ANFISGenerator current ph.1enerator current ph.2
mbient temp. (t-lag)
ANFIS
rison between a normal FSRC model and the cross prediction
FSRC
ber of membership functions/rulesentional FIS, the number of
rules is decided by an expertiliar with the system to be modeled
[14]. Generally, theMFs can be dened for each input separately. In
thise expert for this particular task is not available, and
of membership functions per input is xed to two, asreliminary
tests, was found to be a good compromisemputational efciency and
model performance.
ing methodning algorithm used is a hybrid learning rule as
pro-4], consisting of a combination of gradient decent ands
estimation. Not only can this hybrid learning approache dimension
of the search space in the gradient method,ral, it will also cut
down substantially the convergence
signalsber of input signals differs for each signal to be
moni-hoice of relevant input signals is not trivial and requiresion
of physical understanding of the system to bed advanced data
reduction techniques. In this researchgorithm combined with a
partial least squares regres-) [24] is used to detect potential
input signals from thef SCADA data. For this purpose the genetic
algorithmplied by Leardi [25] is applied. However, GAPLS pointst
signals containing most of the target signal features,
not be the best ones to choose from a condition moni-t of view
if one considers the physical understanding ofenon at hand. An
example for possible difculties withnput signal selections is the
generator phase currentshe GAPLS suggests using the generator
current ph.1 asodel generator current ph.2. A model like this is
veryt by using this inputoutput set, monitoring of the abso-t level
is impossible. Instead only differences betweencan be detected.
combination of data reduction techniques and theing of the physical
process to select suitable input setsrred strategy used in this
paper.th, the information about relative changes between sig-s of
the same type as well as their absolute level istwo types of models
are developed. The rst is a FSRC
ReConstruction) approach, where the target signal is fully
reconstructing it through correlated signals of
pes and the second is by fully reconstructing it throughignals
of the same type. The difference is illustrated ine generator phase
current.e cross prediction FSRC models, asymmetries (forferences in
generator phase currents or temperatures of
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264 M. Schlechtingen et al. / Applied Soft Computing 13 (2013)
259270
Nacelle temp .Wind speed
Ambient temp .Wind dire ction
Nacelle temp .Wind spee d
Input Outpu t
loped
the three phmal FSRC mchanges in
Howeveing input scondition mbility, as beierror. The rchoice is
diexpert knowrequiremenlow predicttion error ogenetic algonent or
subfor the 45 m
4. Model p
A good choice of inways the peof the modperformancis more of
ingiving an inphysical untion errors centered abSpinner temp .Hub
control ler temp .
Pitch angleHydraul ic oil temp .
Rotor s peed
Gear oil temp .Genera tor s peed
Generator ph .1 temp .Generator ph .2 temp .Generator ph .3 temp
.
Generator cur rent ph.1Generator cur rent ph.2Generator cur rent
ph.3
Power ou tput
Grid rotor in verter ph .1 temp .Grid rotor in verter ph .2 temp
.Grid rotor in verter ph .3 temp .
Conver ter coo ling wa ter temp .Con ver ter c hoke coil temp
.Conver ter con trol ler temp .
Top con trol ler temp .Grid bu sbar temp .
HV tran sformer ph .1 temp .HV tran sformer ph .2 temp .HV tran
sformer ph .3 temp .
Fig. 6. Inputoutput sets of the deveases) can be identied very
accurately whereas the nor-odel is used to monitor the absolute
level and relativecomparison to other signals.r, the model accuracy
is not the only criterion for choos-ignals when developing normal
behavior models foronitoring purposes. A further requisite is the
fault visi-ng the visibility of the developing fault in the
predictionelation between the fault visibility and the input
signalfcult to dene and not exactly known, which is whyledge is
required to estimate this inuence. A general
t of normal behavior models is that they should have aion error
in case of normal behavior and a high predic-therwise. For the
given problem the combination of therithm with engineering
knowledge about the compo-
system lead to the input-output sets illustrated in Fig. 6odels
developed.
erformance
model performance is the direct result of the correctput signals
and successful training. There are severalrformance can be
measured. Due to the different natureeled signals (power output,
currents, temperatures) ae comparison between them is not
meaningful. Whatterest is the prediction error variation around its
mean,dication of how good the prediction is, by keeping theit. Here
the standard deviation is used. Since the predic-after training are
close to being normal distributed andout zero this choice is
acceptable (compare Fig. 9). The
values are ctraining.
E = xm xp
P = (E)
E is the predicted valudeviation.
In ordermal operatinstance a operationaloperational
4.1. Compa
To stresperformancfor ANFIS aanalog to thonly ve ruThe numbeand
output
The train(29,513 10
The comstandard derequired fois due to thSpinner temp .Hub
contro ller temp.Pitch ang leHydraul ic oil temp.Rotor spee dGear
bear ing temp. (HSS)Gear oil temp .Generator spee dGenerator
bearing temp .Generator bearing temp. 2Generator slip ring temp
.Generator ph.1 temp.Generator ph.2 temp.Generator ph.3
temp.Generator ph.1 temp. cro ssGenerator ph.2 temp. cro
ssGenerator ph.3 temp. cro ssGenerator current ph .1Generator
current ph .2Generator current ph .3Generator current ph .1 cro
ss.Generator current ph .2 cro ss.Generator current ph .3 cro
ss.Power ou tputReactive powe rGrid inver ter ph .1 temp.Grid ro
tor inver ter ph .1 temp.Grid ro tor inver ter ph .2 temp.Grid ro
tor inver ter ph .3 temp.Grid ro tor inver ter ph .1 temp. cros
sGrid ro tor inver ter ph .2 temp. cros sGrid ro tor inver ter ph
.3 temp. cros sConver ter coo ling wa ter temp.Conver ter cho ke
coil temp.Conver ter con trol ler temp .Top con tro ller temp.Grid
bu sbar temp.HV tran sformer ph .1 temp.HV tran sformer ph .2
temp.HV tran sformer ph .3 temp.HV tran sformer ph .1 temp. cro
ssHV tran sformer ph .2 temp. cro ssHV tran sformer ph .3 temp. cro
ss
models.alculated based on test data sets that were not used
for
(1)
(2)
diction errors; xm is the measured values; xp is the pre-es; P
is the performance measure; is the standard
to assess the performance information about the nor-ional range
of the signals is essential (see Table 2). Formodel performance of
1 C is good when the normal
range is 100 to 100 C, but rather poor if the normal range is
2530 C.
rison between NN and ANFIS models
s the advances of ANFIS models the results of a briefe and
training speed comparison is shown in Table 3nd NN training. The
chosen NN training procedure ise one described in [8], but with the
difference that herens with random weight initializations are
performed.r of hidden neurons and the number of MFs, per input
signal is set to two (Table 4).ing is performed with nine month
of operational data
min average values).parison shows that the performance in terms
of theviation of both approaches is similar. However, the timer
training is generally smaller with ANFIS models, whiche necessary
trial and error procedure when training NN.
-
M. Schlechtingen et al. / Applied Soft Computing 13 (2013)
259270 265
Table 2Normal operational ranges of the SCADA data.
Signal Normal range Signal Normal range
Spinner temp. 10 to 45 C Power output 50 to 2100 kWHub
controller temp. 5 to 50 C Reactive power 25 to 300 kWArPitch angle
(avg. over all blades) 5 to 95 Grid inverter ph.1 temp. 25 to 50
CHydraulic oil temp. 10 to 65 C Grid rotor inverter ph.1 temp. 25
to 55 CRotor speed 0 to 16 rpm Grid rotor inverter ph.2 temp. 25 to
55 CGear bearing temp. (HSS) 30 to 80 C Grid rotor inverter ph.3
temp. 25 to 55 CGear oil temp. 30 to 65 C Converter cooling water
temp. 5 to 50 CGenerator speed 0 to 1700 rpm Converter choke coil
temp. 5 to 120 CGenerator bearing temp. 1 10 to 85 C Converter
controller temp. 5 to 60 CGenerator bearing temp. 2 10 to 85 C Top
controller temp. 10 to 50 CGenerator slip ring temp. 5 to 45 C Grid
busbar temp. 5 to 60 CGenerator ph.1 temp. 10 to 140 C HV
transformer ph.1 temp. 20 to 95 CGenerator ph.2 temp. 10 to 140 C
HV transformer ph.2 temp. 20 to 95 CGenerator ph.3 temp. 10 to 140
C HV transformer ph.3 temp. 20 to 95 CGenerator current ph.1 0 to
1700 A Nacelle temp. 5 to 50 CGenerator current ph.2 0 to 1700 A
Wind speed 0 to 35 m/sGenerator current ph.3 0 to 1700 A
Jang [14] showed that in ANFIS models fewer parameters must
betrained, leading generally to faster training.
The performance of ANFIS models does not vary among differ-ent
runs, which is due to the initialization of the MFs, involvinga
grid partithat with Nter performnding an other showstraining
spe
NN are gput is difcabout the shave a majoterms of rulby using
thegrid portionnMF being number of MFs is equamum of 16 to very
fastthe model cthe numbeexponentia
Table 3Performance a
SCADA signa
Hydraulic oiGenerator bePower outpuTop controll
Table 4Performance a
SCADA signa
Hydraulic oiGenerator bePower outpuTop controll
4.2. Performance of the set up ANFIS models
Schlechtingen and Santos [8] showed that averaging the
predic-tion error decreases the variance of the system and
consequentially
es the sensitivity against anomalies. In fact it was shown
averaging, upcoming faults and anomalies can be detected
and with a higher certainty (fault patterns are stronger pro-d).
Generating average values can be advantageous, becauseveraged
signals of several turbines can easily be compared
a single plot and turbine operators are most often
interestedrmat
actiouired
erro indied pr
(E)
aver (E)
0
0
0
std
(err
or)
[C
]
7
r) [
]tion algorithm [14,26]. What is important to notice isN and the
suggested number of different trials, a bet-ance can be achieved
(see Fig. 7) potentially. However,ptimal solution is computational
expensive. Fig. 7 fur-
that with ANFIS models a good compromise betweened and model
accuracy can be found.ood in identifying non-linear relations, but
their out-
ult to back-track (black box model). A priori knowledgeystem is
difcult to in cooperate. Here ANFIS modelsr advantage. Not only can
a priori knowledge be used ines or shape of MFs, the output can
also be back-tracked
information about the rule that red. When using theing method,
the number of rules is equal to: nMFni withthe number of MFs and ni
the number of inputs. Theinputs is smaller or equal to four and the
number ofl to two for the models developed, leading to a
maxi-rules. Especially for small numbers of inputs this leads
to train and simple to back-track models. However,omplexity
(number of rules) scales exponentially withr of inputs, which
causes training speed also increaselly.
nd speed of ANFIS training.
l ANFIS
increasthat byearliernouncedaily awithinin infoessaryare
reqdictionfor 144averag
Pavg =
E is theerrors;Elapsed time for training P
l temp. 125.2 s 2.00 Caring temp. 37.3 s 1.30 Ct 93.6 s 45.78
kWer temp. 14.1 s 1.24 C
nd speed of NN training.
l NN
Elapsed time for training P
l temp. 452.3 s 2.06 Caring temp. 373.2 s 1.33 Ct 284.9 s 45.99
kWer temp. 369.3 s 1.57 C
6
std
(err
o
1
1.
1.
1.
1.
std
(err
or)
[C
]
Fig. 7. Performion that can support their day-to-day planning of
nec-ns, for which not too much details (such as hourly data)
[27]. In this paper all analysis is based on averaged pre-rs.
The averaging period used is one day, i.e. the averagevidual
prediction errors is calculated, giving one singleediction error
per day.
(3)
aged prediction errors; Pavg is the performance measure is the
standard deviation of E.
.7
.8
.9 Grid busba r temp.
.5
8
Pitch angle6
.5
7
1 2 3 4 5 6 7 8 9 10.2
21
22
23
24
Run
Nacelle temp.
NN ANFIS
ance of different NN and ANFIS trainings over the number of
runs.
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266 M. Schlechtingen et al. / Applied Soft Computing 13 (2013)
259270
Table 5Example model performance in terms of standard deviation
of the prediction error after training.
Model P Pavg Unit Model P Pavg Unit
Spinner temp. 1.55 0.63 C Power output 47.00 16.34 kWHub
controller temp. 0.55 0.18 C Reactive power 0.26 0.03 kWArPitch
angle (avg. over all blades) 0.44 0.08 Grid inverter ph.1 temp.
0.45 0.13 CHydraulic oil temp. 2.59 1.35 C Grid rotor inverter ph.1
temp. 0.42 0.14 CRotor speed 0.21 0.09 rpm Grid rotor inverter ph.2
temp. 0.42 0.15 CGear bearing temp. (HSS) 0.79 0.35 C Grid rotor
inverter ph.3 temp. 0.41 0.13 CGear oil temp. 2.09 1.02 C Grid
rotor inverter ph.1 temp. cross. 0.39 0.07 CGenerator speed 23.46
9.32 rpm Grid rotor inverter ph.2 temp. cross. 0.48 0.13 CGenerator
bearing temp. 1 1.44 0.73 C Grid rotor inverter ph.3 temp. cross.
0.37 0.08 CGenerator bearing temp. 2 1.86 0.75 C Converter cooling
water temp. 0.69 0.33 CGenerator slip ring temp. 1.30 0.46 C
Converter choke coil temp. 2.95 1.26 CGenerator ph.1 temp. 4.98
1.90 C Converter controller temp. 0.54 0.22 CGenerator ph.2 temp.
4.92 1.87 C Top controller temp. 1.15 0.48 CGenerator ph.3 temp.
4.92 1.86 C Grid busbar temp. 0.57 0.19 CGenerator ph.1 temp.
cross. 0.51 0.11 C HV transformer ph.1 temp. 3.59 1.64 CGenerator
ph.2 temp. cross. 0.51 0.09 C HV transformer ph.2 temp. 2.88 1.32
CGenerator ph.3 temp. cross. 0.66 0.15 C HV transformer ph.3 temp.
3.25 1.47 CGenerator current ph.1 39.07 13.62 A HV transformer ph.1
temp. cross. 1.59 0.87 CGenerator current ph.2 38.63 13.29 A HV
transformer ph.2 temp. cross. 1.39 0.80 CGenerator current ph.3
39.05 13.57 A HV transformer ph.3 temp. cross. 1.35 0.91 CGenerator
current ph.1 cross. 2.32 1.45 A Nacelle temp. 1.21 0.60 CGenerator
cu ed Generator cu
The perfbine Genereach model
The stanwhen the p
In someachieve a gis not foundtaneously mset up. Althit should
beapproach iset al. [1] an
5. Predicti
The interequires sothiness of input signamal. Henceinto
accounmodels. Exaoutput or tthese signa
tions the n
visumpllableedicton:
) =
the
colo are sredicolortion etion s lab
8 shoat thrrent ph.2 cross. 2.37 1.72 A Wind sperrent ph.3
cross. 1.60 0.99 A
ormances are listed in Table 5 for a random Wind Tur-ator (WTG)
of the eet (the individual performance of
differs from turbine to turbine).dard deviations decrease almost
by a factor of three,redictions error is averaged.
cases it may be difcult to identify suitable inputs toood model
performance. This is for instance if a signal
to be well correlated to any of the other signals simul-easured
and thus an accurate FSRC model cannot be
ough this was not the case for the models developed, mentioned
that for those signals an alternative model
to build autoregressive models as proposed by Zaherd Sanz-Bobi
et al. [7].
on errors
rpretation of the prediction errors for fault diagnosisme
information about the validity or the trustwor-the input signals to
the models. Abnormal behavingls, consequentially cause the
prediction to be abnor-
it is of utmost importance to take this dependency
predicplot oftime iserror ais avaiThe prequati
Eper (%
Eper islimit.
Thesignalsaged pother cpredicpredicthe axi
Fig.errors t. Some input signals are used by a large number
ofmples for excessively used input signals are the powerhe nacelle
temperature. In turn that means that oncels behave abnormal, a
number of models will give bad
inuences pattern. Futake into aa powerful
Fig. 8. 2D Waterfall plot of the normalized averaged0.20 0.09
m/s
. This behavior is shown in Fig. 8, where a 2D
waterfallormalized averaged percentage prediction errors
overalized. In this plot the colors indicate the prediction
itude. White areas mark periods where no prediction, e.g. due to
missing data or non-operational periods.ion errors are normalized
according to the following
E
3(E) 100 (4)
percentage error normalized by determined anomaly
r bar in Fig. 8 is set in a way that normally behavinghown with
a light green color. Only if the one day aver-tion error exceeds
the anomaly limit (see Section 6)
s become visible. The color range indicates whether therror is
positive or negative as well as its amplitude. The
error of each model is shown in horizontal lines fromel on the
left.ws that eight further models have abnormal predictione time
the nacelle temperature increases. This effect
the analysis of the root cause of the prediction errorrthermore
it emphasizes the need of algorithms thatccount the context of the
patterns. Here fuzzy logic istool.
prediction errors.
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M. Schlechtingen et al. / Applied Soft Computing 13 (2013)
259270 267
-2000
2000
4000
6000
Num
ber
of
Valu
es i
n b
in [-]
Generator curr ent ph.1
400 0
600 0
es i
n b
in [-]
Power output
0
2000
4000
PNum
ber
of
Valu
es in
bin
[-]
6. Anomal
Anomalydata that doing patternoutliers [28the central in this
reseathat highliglimit. In 200about anomtechniques neighbor-bIn
this papbecause of ntrained moaround zerothe assumpity regions
oprobability
Due to tphase of thtistical procby a maximmay be biasselection
of
The estiof the standform the updata. Instanthe traineded as
anomcalculated a
Predictiontrained m
Predictiontrained m
The chosystem andto suppressof turbines
0 0.5 1 1.5 2 2.5
x 104
-10
-5
0
5
10
Value [ 10 min]
-10 -5 0 5 100
0.1
0.2
0.3
lower bound upper bou nd
ity [
-]
Gen erat or be aring te mp.
Predict ion err or [C]
norma l abnormalabnor mal
Probability density distribution and evolution of the 10 min
avg. predictionTG 8).
l data as abnormal. False alarms may cause extra
unsched-aintenance visits, or cause the operator to lose faith
intem and subsequently ignore indications of real
problems.gnosmainboun0 min
shoal b
d then ace anis leafurthold i
0.4
0.5
0.6 -10 0 0 100 200
Prediction error [A]
-200 -10 0 0 100 2000
200 0
Pred iction error [kW]Num
ber
of
Valu
-1 0 1
rediction err or [m/s]
Wind spe ed
-10 -5 0 5 100
200 0
400 0
600 0
Prediction error [C]Num
ber
of
Valu
es in
bin
[-]
Genera tor bearing temp.
Fig. 9. Prediction error distributions after training.
y denition and detection
detection refers to the problem of nding patterns in not conform
to expected behavior. These nonconform-s are among others often
referred to as anomalies or]. Detecting anomalies in the prediction
error is one ofissues of the condition monitoring approach
addressedrch. The task is to nd an anomaly detection algorithmhts
any unexpected patterns, once they exceed a certain9 Chandola et
al. [28] reported a comprehensive reviewaly detection algorithms. A
majority of the reportedcan be categorized into classication-based,
nearestased, clustering-based and statistical techniques [28].er
the focus lies on parametric statistical techniques,ature of the
prediction errors coming from successfully
dels that are usually normally distributed with a mean.
Statistical anomaly detection techniques are based ontion that
normal data instances occur in high probabil-f a stochastic model,
while anomalies occur in the lowregions of the stochastic model
[28].he availability of the prediction error in the traininge ANFIS
models, the parameters of the underlying sta-ess can be estimated.
In the current CMS this is doneum likelihood estimator, knowing
that the parameters
Pre
dic
tio
n e
rror
[C
]P
rob
abili
ty d
en
s
Fig. 10. error (W
normauled mthe sysMisdiational lower tion (1Fig. 10abnormtion
an
Whvarianturn th
To thresh
sity [
-]ed. Fig. 9 shows the prediction error distributions for a
models after training.mated parameters are stored and used for
estimationard thresholds for anomaly detection. These thresholdsper
and lower bounds of the normal range of the SCADAces that have a
low probability of being generated from
model, based on the applied test statistic, are classi-alies
[28]. For this purpose the standard thresholds areccording to the
following probabilistic assumptions:
errors that have a probability of being generated by theodel
larger or equal than 0.01% are considered normal
errors that have a probability of being generated by theodel
smaller than 0.01% are considered an anomaly.
ice of the probability inuences the sensitivity of the can be
adjusted if required. Here 0.01% was chosen
false anomaly classication. Due to the large amountmonitored
later, the approach taken must not classify
-5 0
0.1
0.2
0.3
Pro
ba
bili
ty d
en
a
0 -4
-2
0
2
4
Pre
dic
tion e
rror
[C
]
Fig. 11. Probaerror (WTG 8)is can result in replacement of the
wrong part and addi-tenance to correct such errors [29]. The upper
andd is calculated with regard to the original data resolu-
average) and the averaged values (one day averages).ws an
example of the boundary between normal andehavior, both in terms of
the probability density func-e prediction error in time
domain.veraged over one day, the predictions have a reducedd the
upper and lower bound move closer to zero. Inds to less alarm limit
violations as visible in Fig. 11.er reduce the risk of false
classications, an additionals set, that requires at least three
values violating the
Gene rator bea ring te mp.-4 -3 -2 -1 0 1 2 3 4 5
lower bound upper bound
Predict ion err or [C]
normal abnor malbnor mal
50 100 150 200
Value [days]
bility density distribution and evolution of the one day avg.
prediction.
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268 M. Schlechtingen et al. / Applied Soft Computing 13 (2013)
259270
probability threshold within a week, before the anomaly is
analyzedby the fuzzy expert system.
7. Fuzzy expert system
The Fuzization moanalyze pata potential In total ninwhich a
FISare (see Fig
Meteorol Rotor sys Gearbox Generato Converter Transform
Auxiliary Turbine p Nacelle
7.1. FIS inp
Only onethe predictiThe numbeof models tponent or swhen
settinedge is impwhen rulesinput signabehavior mponent or spassed
to thonly contaipresent. ThUsing the mvalues excestatements
7.2. FIS mem
Consideization modfunctions bangular andvariation
ofdependencyproposed a in inductionwith triangucombinatiopriated
for to trapezoiddened, wh
It provedify two sepFIS input ting the clashigh and veallowing
clathe expert
0
0.2
0.4
0.6
0.8
13*lb lb ub 3*ub
normalhigh very highlowvery low
sh
ip
12. M
0
0.2
0.4
0.6
0.8
1
Mem
bers
hip
13. M
ns i2 and
FIS . Thi
, ruleoftwrmapper
stanntiden
ind
master threshold table, the four bounds can be denedlly. This is
usually done after fault occurrence, or after deter-on of the
critical prediction error levels. Manual adoption is
since if for instance a model is very accurate, this
consequen-lso leads to tight standard upper and lower bounds.
Hencediction error may have a high membership in the MF verylthough
the prediction error level is still considered to becal. In this
case the thresholds can be adopted in the masterion threshold le to
match the real fault progression. Thisure is required, since the
real bounds are unknown for mostnents and signals before fault
occurrence. The thresholdsd as master thresholds are valid for all
turbines and replaceividual ones. It is therefore possible to
identify similar pat-n the whole eet, since it is expected that the
general faultteristics are similar for turbines of the same
type.
outputs of the FIS are diverse with regard to their MFs. One
ofee outputs are initialized for each FIS equally. This
concernstialization of the output condition, visualized in Fig.
14.
MF gray is reserved for periods, in which no diagnosis is pos-ue
to missing data, whereas green, yellow and red indicatedition
status.
n: component/subsystem working as expected; condition; state
okzy expert system consists of the fuzzy expert initial-dule and
the fuzzy expert application module whichterns to classify the
component conditions and outputroot cause if a matching rule is
found in the rule base.e components or subsystems were dened, for
each of
structure is developed. The components or subsystems. 3):
ogytem
r
erequipmenterformance
uts
FIS is developed per component or subsystem, henceon error of
several ANFIS models is processed by the FIS.r of inputs to the FIS
therefore depends on the numberhat exist to monitor the signals
behavior of the com-ubsystem. The relevant signals for the FIS are
denedg up the FIS structure the rst time, i.e. when the
knowl-lemented. However, the list of inputs is extendable,
implemented at a later stage require more inputs. Thels are
selected by considering the corresponding normalodel inputs and the
physical understanding of the com-ubsystem. For each input, a
modied prediction error ise input space of the FIS. The modied
prediction error
ns the true prediction error value when an anomaly ise
prediction error is set to a small number otherwise.odied
prediction error has the advantage that singleeding the anomaly
limits do not cause false condition.
bership functions
ring which inputs are used, the fuzzy expert initial-ule
initializes the FIS with regard to the membershipelonging to each
modied prediction error input. Tri-
trapezoidal MFs are used in this research. A linear the
membership values is reasonable as long as no other
is known or desired. In 2008 Rodrgues and Arkkio [30]diagnosis
method for detection of stator winding faults
motors based on fuzzy logic. The system was testedlar,
trapezoidal and Gaussian MFs. It was found that the
n of triangular and trapezoidal MFs is the most appro-fault
diagnosis in induction motors [30]. In comparisonal MFs, triangular
MFs require one less parameter to beich is why triangular MFs are
preferred in this research.
useful in expert knowledge implementation to spec-arate FIS
inputs per relevant prediction error. The rstype is with ve
membership functions (MFs) allow-sication into the categories: very
low, low, normal,ry high. The second FIS input type is with three
MFsssication into the categories: low, normal, high, givingfull
freedom to implement the rules. The membership
Me
mb
er
Fig.
Fig.
functioFigs. 1
Theformedinputsfewer slow, no the u
Thefor ideThese ues are6.
In amanuaminatiuseful,tially athe prehigh,
auncriticonditprocedcompodenethe indterns icharac
Thethe thrthe ini
Thesible dthe con
GreegoodInput variable: Anomaly error (5 MF`s)
lb = lower
boundub = upper
bound
embership functions initialized for each input: FIS input type
1.
normal highlow
Input var iable: Ano maly error (3 MF`s)
lb ub
lb = lower bou ndub = upper boun d
embership functions initialized for each input: FIS input type
2.
nitialized for each of these inputs are visualized in
13.structures are initialized each time an analysis is per-s has
the advantage that modications of the structures and membership
functions position and shape lead toare conicts. The denition of
the statements, very low,l, high and very high is given by the
standard thresholds
and lower bounds according to the 0.01% probability.dard
thresholds do only serve as a rst approximationcation and
classication of the component condition.itions are equal for all
signals and turbines, but the val-ividual, due to the anomaly
denition given in Section
-
M. Schlechtingen et al. / Applied Soft Computing 13 (2013)
259270 269
0
0.2
0.4
0.6
0.8
1grey re d yello w gree n
Output var iable: Cond itio n
Mem
bers
hip
Fig. 14. Initialization of the membership functions of the
variable condition.
Yellow: service shall be scheduled for component/subsystem
toimprove the condition or investigate the issue; condition
bad;state warning
Red: service shall be performed at component/subsystem toimprove
the condition; condition very bad; state alarm
The other two outputs contain information about the diagnosisand
the potential root cause. This information is individual to eachFIS
and is part of the knowledge that needs to be supplied by anexpert.
The setup is shown in Figs. 15 and 16.
7.3. FIS rules
The MFs are linked via rules that allow the FIS to output
thediagnosis, condition and potential root cause. The rules are
imple-mented by an expert manually. The concept of the rule base
isthat the base is constantly extended to allow the FIS to
iden-tify the various fault types accurately. The rule base is
updatedafter fault occurrence and the validity of the already
existing rulesis checked.
0
0.2
0.4
0.6
0.8
1
Mem
bers
hip
Fig. 15. S
0
0.2
0.4
0.6
0.8
1
Mem
bers
hip
Fig. 16. Schem
memberships according to their MFs and selects the rule that
ismost applicable. The consecutives are then given according to
thisrule. All rules currently implemented employ the and
method,taking the minimum value of the MF evaluation for decision
on therule applica
generic ru specic ru
The geneno specic the specicoperator thon the otheof impleme
(1) If (Anomerror (3temp. defect)
(2) If (Anom(Anoma(Diagno(Pot. ro
The ruleday average
As a basevaluating ble rule is uhigh value igiving a hig
ults
deveifferbilit
ter toces.
into turb
of ththe mfunch thg.
focuce tcreafalse peratte reor th For each set of inputs, the
FIS evaluates the input
diag . 1 diag. 2 diag. 3 .. . diag. n
Output variable: Diagnosis
chematic of the membership function of the variables
diagnosis.
root 1 root 2 root 3 ... root n
8. Res
Theety of dapplicaare fasformantakingels permodesany of or
malresearctrainin
Theto reduonly infewer bine oaccurabasis fOutput variab le: Pot.
root cause
atic of the membership function of the variables potential root
cause.
structure seexpected to
In the fudrastically vant predic1 is used forwhereas
tydifferentiatIf for instanby a speciing temperbility. The rules
can be classied into two categories:
lesles
ric rules are used to highlight present anomalies, in caserule
applies. This rule type gives no information about
condition or potential root cause, but highlights to theat an
anomaly is present in the data. The specic rulesr hand provide this
level of information. Two examplesnted specic rules are:
aly error (5 MFs) Spinner temp.==high) & (Anomaly MFs)
Nacelle temp.==ok) then (Diagnosis = Spinnerhigh) (Condition =
yellow) (Pot. root cause = Sensor
aly error (5 MFs) Generator ph.1 temp. cross==high) &ly
error (3 MFs) Generator ph.3 temp. cross==low) thensis = Generator
Ph.1 temp. to high) (Condition = yellow)ot cause = Generator phase
insulation damage)
s are implemented to automate the analysis of the one prediction
errors.
is for certainty statements of the diagnosis, the result ofthe
output values through the MFs for the best applica-sed. This value
can be seen as a matching indicator. Andicates that the rule
represents well the input pattern,h certainty in the diagnosis.
and discussion
loped ANFIS models show good performances at a vari-ent SCADA
data (see Table 5), which proves their generaly in this context.
Moreover it is shown that these models
train than NN models by achieving similar or better per- The
differences in training speed are signicant, whenaccount that the
current development counts 45 mod-ine which require training. This
is, because the failuree turbines are not known beforehand. An
anomaly inonitored SCADA signals may indicate a potential fault
tion. For the eighteen turbines accessible during thisis leads
to 810 different ANFIS models, which require
s in this work is on one day average predictions in orderhe
uctuations in the prediction error. This does notse the fault
visibility, but it also is expected to lead toalarms. False alarms
are of major concern for wind tur-ors, due to the large number of
operating systems. Anpresentation of the signal normal behavior
builds thee subsequent analysis of occurring patterns by the FISt
up. The overall performance levels achieved here are
allow early anomaly detection.zzy expert system the overall
number of rules can be
reduced by specifying two separate FIS inputs per rele-tion
error as proposed in this research. The FIS input type
the signals directly linked to the component monitored,pe 2 may
be used for all other inputs where the neion of the prediction
error magnitude is not necessary.ce the gearbox bearing condition
shall be determinedc rule, it is useful using type 1 for the
gearbox bear-ature to be able to classify the condition according
to
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270 M. Schlechtingen et al. / Applied Soft Computing 13 (2013)
259270
the prediction error magnitude (high and very high). For all
otherinputs, the ne differentiation may not be necessary, making
FIStype 2 for those inputs more useful. This reduces the number
ofpossible combinations within the rule.
9. Conclusions
In this paper a method to monitor wind turbine SCADA data,
vianormal behavior models and fuzzy logic is proposed. The
method-ology gives the possibility of mining large amount of SCADA
dataavailable to wind turbine operators in a systematic manner,
searchfor anomalies and use this information for condition
statements. Itallows not only monitoring of large components, but
also auxiliaryequipment that currently available wind turbine CMS
do not cover.
Using fuzzy logic the existing expert knowledge
inanomaly/prediction error pattern interpretation and root
causediagnosis can be implemented in an intuitive manner. This
givesthe possibility for automated fault diagnosis once specic
rules areimplemented.
However, the applicability of the proposed method and there-with
the acvariety of dcriterion is posed methlimitation cexpert knowto
highlightstatements
Future rsurements,analysis in tmenting rufor larger ddifferent
ty
In part presented bdeveloped proposed mally with rethat the
FISvalid for althe developknowledge.
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Wind turbine condition monitoring based on SCADA data using
normal behavior models. Part 1: System description1 Introduction2
Description of the general CMS concept2.1 Training module2.2
Prediction module2.3 Anomaly detection module2.4 Fuzzy expert
initialization module2.5 Fuzzy expert application module
3 Model setup and structure3.1 Data set description3.2 Model
type3.2.1 FIS structure3.2.2 Membership functions3.2.3 Number of
membership functions/rules3.2.4 Training method3.2.5 Input
signals
4 Model performance4.1 Comparison between NN and ANFIS models4.2
Performance of the set up ANFIS models
5 Prediction errors6 Anomaly definition and detection7 Fuzzy
expert system7.1 FIS inputs7.2 FIS membership functions7.3 FIS
rules
8 Results and discussion9 ConclusionsReferences