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1 Title and Contents 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

2 Rotor Faults in Scope for Pattern Classification
Rotor mass imbalance  Caused by loose material, penetrating water, icing, ...  Excites transverse nacelle oscillation at rotor frequency Aerodynamic rotor asymmetry  Caused by pitch angel adjustment failures, pitch drive failures, ...  Excites torsional nacelle oscillation at rotor frequency

3 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 nacelle oscillations transverse to rotor axis with rotational („1p“) frequency

4 Physical effects of rotor aerodynamic asymmetry
Perfectly symmetric rotor No 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

5 Test Data as Input Pattern
Experimental data: a) actual electrical power output b) 1p amplitude of transverse nacelle oscillation (band pass filtered) c) 1p amplitude of torsional oscillation at tower base (band pass filtered)

6 Training Data as Input and Output Pattern
Decreasing Probability Increasing Probability

7 Fuzzy Classifier: Fuzzy Inference System (FIS)
Fuzzyfication Rule base Inference/Defuzzyfication Inference: IVy_small = min(µsmall (x1), µmedium (x2)) = 0.2 IVy_big = min(µ medium (x1), µbig (x2)) = 0.4 Rule 1 if x1 = small and x2 = medium then y = small Defuzzyfication: Output value y is calculated as the “center of gravity” of the triangle shaped defuzzyfication functions Rule 2 if x1 = medium and x2 = big then y = big 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

8 Fuzzy Classifier: Overall Structure
Input Pattern: Transfer Function: y=(x, p) Classifier Parameter Vector p Fuzzy Classifier Measured process data from a WT Output Pattern: Output vector y=(y1, y2, y3, y4) Data processing: 1p-filtering Optimised by Evolutionary Algorithm Representation as probabilities for fault conditions Data normalisation for input pattern generation: Input vector x=(x1, x2, x3)

9  Rule Base Generation Rule Base Parameters Switching Parameters
OUT1 small OUT1 medium OUT1 big then Rule 1: If IN1 small and IN2 small and IN3 small OUT1 small OUT1 medium OUT1 big then Rule 2: If IN1 medium and IN2 small and IN3 small OUT1 small OUT1 medium OUT1 big then Rule 27: If IN1 big and IN2 big and IN3 big  Rule Base Generation

10 Shaping Parameters Membership Functions Defuzzyfication Functions
Width (bS, bM, bB) and center abscissa values (mS, mM, mB) of triangle shaped defuzzyfication functions Parameters: Abscissa values of inflection points KS1, KS2 for µsmall (x) KM1, KM2 , KM1 for µmedium (x) KB1, KB2 for µbig (x)

11 Evolutionary Optimisation
Evolutionary Algorithm Flow Diagram Random setup of 1st parameter generation Calculation of individuals fitness Evolutionary manipu- lation of individuals no Ranking of individuals (decreasing fitness) winner fitness >Thrhld STOP yes

12 Detection of a mass imbalance

13 Detection of a undefined condition

14 Detection of a aerodynamic asymmetry

15 Conclusions and Outlook
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 Outlook / Next Steps: 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, …)


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