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EWEC 2006 27 February - 2 March 2006 Athens, Greece

Title and Contents

Evolutionary Algorithm for Optimisation of Condition Monitoring and Fault Prediction Pattern Classification in Offshore Wind Turbines

J. Giebhardt

Institut fuer Solare Energieversorgungstechnik, ISET e.V, Kassel, Germany

Division Energy Conversion and Control Engineering

Contents:

• Rotor faults in scope

• Fuzzy classifier definition

• Input and Output Pattern

• Evolutionary Algorithm

• First results

• Conclusions and Outlook

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EWEC 2006 27 February - 2 March 2006 Athens, Greece

Rotor Faults in Scope for Pattern Classification

Caused by pitch angel adjustment failures, pitch drive failures, ...

Rotor mass imbalance

Excites torsional nacelle oscillation at rotor frequency

Aerodynamic rotor asymmetry

Excites transverse nacelle oscillation at rotor frequency

Caused by loose material, penetrating water, icing, ...

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EWEC 2006 27 February - 2 March 2006 Athens, Greece

Physical effects of rotor mass imbalance

Perfectly mass balanced rotor

Centrifugal forces of blades compensate when:

No excitation of periodic nacelle

oscillations

Mass imbalance

„Virtual“ mass mR and distance rR cause resulting centrifugal force:

Excitation of periodic nacelleoscillations transverse to

rotoraxis with rotational („1p“) frequency

332211 rmrmrm

2 RRCR rmF

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EWEC 2006 27 February - 2 March 2006 Athens, Greece

Physical effects of rotor aerodynamic asymmetry

Perfectly symmetric rotorNo excitation of periodic torsional

nacelle oscillations (with respect to the

vertical tower axis)

Aerodynamic asymmetry

Excitation of torsional periodic nacelle

Oscillations with 1p frequency caused by

different thrust forces of the individual blades

F T1 F A 1

F T2 F A 2

ro ta tio nplane

F ax1

F ax2

torsional

Pitch

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EWEC 2006 27 February - 2 March 2006 Athens, Greece

Test Data as Input Pattern

b) 1p amplitude of transverse nacelle oscillation (band pass filtered)

Experimental data:a) actual electrical power output

c) 1p amplitude of torsional oscillation at tower base (band pass filtered)

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EWEC 2006 27 February - 2 March 2006 Athens, Greece

Training Data as Input and Output Pattern

-0,4

-0,2

0

0,2

0,4

0,6

0,8

1

1,2

1,4

1 2 3 4 5 6 7 8

0

0,2

0,4

0,6

0,8

1

1,2

1 2 3 4 5 6 7 8

Decreasing Probability

0

0,2

0,4

0,6

0,8

1

1,2

1 2 3 4 5 6 7 8

Increasing Probability

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EWEC 2006 27 February - 2 March 2006 Athens, Greece

Fuzzy Classifier: Fuzzy Inference System (FIS)

Fuzzyfication

x1 = 0.4

µsmall (x1)=0.2

µmedium (x1)=0.8

µbig (x1)=0.0

x2 = 0.7

µsmall (x2)=0.0

µmedium (x2)=0.6

µbig (x2)=0.4

Rule base Inference/Defuzzyfication

Rule 1

ifx1 = small

andx2 = medium

theny = small

Rule 2

ifx1 = medium

andx2 = big

theny = big

Inference: IVy_small = min(µsmall (x1), µmedium (x2)) = 0.2 IVy_big = min(µ medium (x1), µbig (x2)) = 0.4

Defuzzyfication:Output value y is calculated as the “center of gravity” of the triangle shaped defuzzyfication functions

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EWEC 2006 27 February - 2 March 2006 Athens, Greece

Fuzzy Classifier: Overall Structure

Input Pattern:

Measured process data

from a WT

Data processing: 1p-

filtering

Data normalisation

for input pattern

generation:Input vector x=(x1, x2, x3)

Output Pattern:

Output vectory=(y1, y2, y3, y4)

Representation as probabilities

for fault conditions

Transfer Function:

y=(x, p)

Classifier Parameter Vector p

Fuzzy Classifier

Optimised by Evolutionary Algorithm

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EWEC 2006 27 February - 2 March 2006 Athens, Greece

Rule Base Parameters

Rule 1: If IN1 small and IN2 small and IN3 smallOUT1 smallOUT1 mediumOUT1 big

Switching Parameters

then

Rule 2: If IN1 medium and IN2 small and IN3 smallOUT1 smallOUT1 mediumOUT1 big

then

Rule 27: If IN1 big and IN2 big and IN3 bigOUT1 smallOUT1 mediumOUT1 big

then

Rule Base Generation

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EWEC 2006 27 February - 2 March 2006 Athens, Greece

Shaping Parameters

Membership Functions

Defuzzyfication Functions

Parameters:Abscissa values of inflection pointsKS1, KS2 for µsmall (x)

KM1, KM2 , KM1 for µmedium (x)

KB1, KB2 for µbig (x)

Parameters:Width (bS, bM, bB) and

center abscissa values

(mS, mM, mB) of triangle

shaped defuzzyfication functions

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EWEC 2006 27 February - 2 March 2006 Athens, Greece

Evolutionary Optimisation

Flow Diagram

Random setup of 1st parameter generation

Calculation ofindividuals fitness

Ranking of individuals(decreasing fitness)

winnerfitness

>ThrhldSTOP

yes

Evolutionary manipu-lation of individuals

no

Evolutionary Algorithm

Rank Individual

001 Ind021002 Ind044003 Ind098

077 Ind034078 Ind002079 Ind087

Gen

e m

anip

ulat

ion

rate

Evo

luti

on

080 Ind056081 Ind071082 Ind067

098 Ind043099 Ind007100 Ind082

FIT

NE

SS

Ran

do

mM

anip

ula

tio

n

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EWEC 2006 27 February - 2 March 2006 Athens, Greece

Detection of a mass imbalance

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EWEC 2006 27 February - 2 March 2006 Athens, Greece

Detection of a undefined condition

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EWEC 2006 27 February - 2 March 2006 Athens, Greece

Detection of a aerodynamic asymmetry

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EWEC 2006 27 February - 2 March 2006 Athens, Greece

Conclusions and Outlook

Conclusions:

Outlook / Next Steps:

• Principle concept (evolutionary optimised Fuzzy-Classifier) works• Rule base optimisation works in principle

• Calculation time of algorithm is reasonable (some minutes)

• Rule base optimisation has to be extended by shaping parameter optimisation to achieve optimum fault recognition results

• Extension of the optimisation algorithm (shaping parameters)

• Investigation of the algorithm’s stability

• Verification of the algorithm’s parameter sensitivity (e. g. number of individuals, gene manipulation rates, …)