Title and Contents

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 Energieversorgungst
echnik, 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
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
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
Output Pattern
9
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
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 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 algorithms stability

• Verification of the algorithms parameter
  sensitivity (e. g. number of individuals, gene
  manipulation rates, )