Wind turbine selection is a difficult and challenging task due to conflicting nature of decision variables. Therefore, a suitable solution would be the one providing the best balancing among all decision variables. This problem has been solved through Fuzzy Logic.

1. WIND TURBINE SELECTION
USING FUZZY LOGIC
Danish Shahzad (PAF-KIET, PK)

2. INTRODUCTION
• Wind power is an excellent alternative to traditional resources of
energy.
• However, its implementation often passes through multiple
challenges, wind turbine selection is one of them.
• The goal of our project is to design a turbine selection system which
would assist the user in selection of appropriate wind turbine
amongst a given set of available turbines.

3. PROBLEM MODEL
• A multi-criteria selection problem
• Several attributes involved which are often conflicting
NEEDED:
• A solution that can find the best balance among all available
attributes

4. PREVIOUS WORK
• Multiple approaches with their limitations
• Too much simple decision model
• Use of not easily accessible parameters
• Use of inefficient and slow techniques
• Failure to account for cost effectiveness
• Failure to account for conflicts in parameters

5. OUR APPROACH
Fuzzy Logic based Multi-Criteria Turbine Selection model

6. OUR APPROACH
• The proposed system uses the fuzzy logic for selection of appropriate
turbine amongst a given set of turbines.
Fuzzy logic:
• Removes uncertainty
• Simpler to understand
• Easier to implement

7. ATTRIBUTES
1. Wind turbine rotor diameter
2. Wind turbine cut-in speed
3. Wind turbine height
4. Wind Speed at turbine hub height
5. Wind turbine rated power
6. Wind turbine rated speed

8. ALGORITHM DESIGN
• Define parameters
• Calculate upper and lower bounds for each parameter
• Calculate membership values for each parameter for each turbine
• Aggregate the membership values to calculate an overall membership
value of a turbine
• Select the turbine with the highest value

9. MEMBERSHIP FUNCTIONS
• Hub height
max
max mi
h
n
h
μ
-
= Η -Η
H Η
x
 
 
 
 
 

10. MEMBERSHIP FUNCTIONS
• Wind turbine rotor diameter
min
rd
max min
μ
-
= D -D
D D
x
 
 
 
 
 

11. MEMBERSHIP FUNCTIONS
• Wind turbine cut-in wind speed
cs
max
max min
μ
-
= C -C
C C
x
 
 
 
 
 

12. MEMBERSHIP FUNCTIONS
• Wind turbine rated power
min
rp
max min
μ
-
= P -P
P P
x
 
 
 
 
 

13. MEMBERSHIP FUNCTIONS
• Wind speed at turbine hub height
xmax
max min
μ
-
S -S
S S
=ws
 
 
 
 
 

14. MEMBERSHIP FUNCTIONS
• Wind turbine rated wind speed
xmax
max min
μ
-
R -R
R R
=rs
 
 
 
 
 

15. TOTAL MEMBERSHIP VALUE
• Overall membership value of turbine
rd cs rp rs
6
μ μ +μ +μ +μ μ
μ hh ws
tm 
 

16. EVALUATION
Fuzzy Logic based Multi-Criteria Turbine Selection model

17. WIND TURBINE SPECIFICATIONS
Turbine Model Hub Height
Wind speed at
hub height
Rotor diameter
Cut-in wind
speed
Rated wind
speed
Rated power
Enercon E40/600 44 4.67 40 2.5 13 600
Wobben E40/500 65 9.72 40 2.5 13.5 500
Senvion 2.3M130 80 10.69 130 3 11 2300
Micon M530 30 3.5 26 5 15.5 250
IMPSA IV-77-1500 62 9.4 77 3 13 1500
B44/600 42 4.5 44 3.5 16 600
NM48/750 50 5.5 48.2 3.5 16 750
G47/660 40 4.4 47 4.5 16 660
Gamesa G114/2100 80 10.69 114 1 11.5 2100
Nordex N60/1300 59 9.3 60 3.5 17 1300
Upper Bound 80.5 11.19 130.5 5.5 17.5 2300.5
Lower Bound 29.5 3 25.5 0.5 10.5 249.5

18. MEMBERSHIP VALUES OF TURBINES
Turbine Model
Membership
value
Membership
value
Membership
value
Membership
value
Membership
value
Membership
value
TOTAL MEM Result
Enercon E40/600 0.715686275 0.796092796 0.138095238 0.6 0.642857143 0.170892248 3.063623699
Wobben E40/500 0.303921569 0.179487179 0.138095238 0.6 0.571428571 0.122135544 1.915068101
Senvion 2.3M130 0.009803922 0.061050061 0.995238095 0.5 0.928571429 0.999756216 3.494419723
Micon M530 0.990196078 0.938949939 0.004761905 0.1 0.285714286 0.000243784 2.319865991
IMPSA IV-77-1500 0.362745098 0.218559219 0.49047619 0.5 0.642857143 0.609702584 2.824340234
B44/600 0.754901961 0.816849817 0.176190476 0.4 0.214285714 0.170892248 2.533120216
NM48/750 0.598039216 0.694749695 0.216190476 0.4 0.214285714 0.244027304 2.367292405
G47/660 0.794117647 0.829059829 0.204761905 0.2 0.214285714 0.20014627 2.442371365
Gamesa G114/2100 0.009803922 0.061050061 0.842857143 0.9 0.857142857 0.902242808 3.573096791
Nordex N60/1300 0.421568627 0.230769231 0.328571429 0.4 0.071428571 0.512189176 1.964527034
3.573096791

19. RESULTS
• Gamesa G114/2100: 3.573096791
• Senvion 2.3M130: 3.494419723.
Rated Power:
• Gamesa G114/2100: 2100 Kilo Watts
• Senvion 2.3M130: 2300 Kilo Watts.
Choice of selection
• Highest rated power vs Best suited.

20. CONCLUSION

21. CONCLUSION
• Wind Turbine Selection problem
• Multiple attributes (often conflicting)
• Solution:
• Fuzzy logic based solution
• Membership functions
• Membership values
• Aggregate
• Total membership
• Selection

22. THANK YOU