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Title and Contents

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.

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Title and Contents

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  1. 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

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

  7. Defuzzyfication: Output value y is calculated as the “center of gravity” of the triangle shaped defuzzyfication functions 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 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 Transfer Function: y=(x, p) Classifier Parameter Vector p Measured process data from a WT Output vector y=(y1, y2, y3, y4) Representation as probabilities for fault conditions Data processing: 1p-filtering Data normalisation for input pattern generation: Input vector x=(x1, x2, x3) Optimised by Evolutionary Algorithm Fuzzy Classifier: Overall Structure Input Pattern: Output Pattern:

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

  10. Shaping Parameters Membership Functions Defuzzyfication Functions Parameters: 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 Algorithm Calculation of individuals fitness Ranking of individuals (decreasing fitness) yes STOP winner fitness>Thrhld no Evolutionary manipu-lation of individuals Evolutionary Optimisation Flow Diagram Random setup of 1st parameter generation

  12. Detection of a mass imbalance

  13. Detection of a undefined condition

  14. Detection of a aerodynamic asymmetry

  15. Conclusions and Outlook Conclusions: • 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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