1. A New Design Wind Speed for a Wind Turbine Generator (WTG) considering Typhoon Loads Garciano, Lessandro Estelito O. Graduate Student* Koike, Takeshi Professor* * Department of Civil Engineering Musashi Institute of Technology

2. Introduction Proposed 100 MW wind farm Proposed 30 MW wind farm Proposed 40 MW wind farm South East Asia’s first 25 MW wind farm Proposed 40 MW wind farm

3. Strong typhoons in the Philippines 1970 – 76 m/s (JOAN) 1995 – 72 m/s (ANGELA) 1995 – 72 m/s (IRMA) 1985 – 67 m/s (DOT) 1990 – 67 m/s (AMY) 1991 – 57 m/s (RUTH)

4. WTG failures in Okinawa Japan due to super typhoon Maemi Tower buckling failureBlade failure Footing-tower connection failure

5. A proposal to mitigate WTG buckling failure due to typhoons Reliability-based analysis will be used to assess the probability of failure of a WTG tower The load S will be based on distributions from a non-typhoon and typhoon prone areas The resistance is derived as a function of wind speed

6. A proposal to mitigate WTG buckling failure due to typhoons Using the relationship between load and resistance, we have We introduce so that

7. Proposed mitigation of WTG failure due to typhoons

8. Extreme Wind Load Models (a) from typhoon-prone area The Generalized Extreme Value (GEV) distribution is used to model annual extreme wind speeds Extrapolate simulated samples from the GEV distribution to WTG hub height using logarithmic law

9. Extreme Wind Load Models (b) from non typhoon-prone area The Gumbel distribution is used to model annual extreme wind speeds U 10 is simulated using the mean wind pressure equation below The annual wind speed maxima are taken from U 10 which are blocked in years

10. Buckling resistance of WTG tower Strength of tubular members from ISO recommendation & Kato et al. Introducing uncertainties in the model (Sorensen et al) Moment effect at base of WTG (Sorensen et al)

11. Buckling resistance of WTG tower Introducing uncertainties in the model Resistance in terms of wind speed

12. Numerical Simulation of GEV modeling of Distribution of

13. Numerical Simulation of One-year distribution of U 10 40-years of simulated annual maxima Distribution of

14. Numerical Simulation of VariableDistribution type Expected value c.o.v. D (m)3.0 to 4.0 t (mm) 50 and 75 Fy (MPa)LN (lognormal) 5.880.05 E (MPa)2.1e50.05 X y,ss LN10.02 X E,ss LN10.02 A (m 2 )2123 kpkp 3.3 VariableDistribution type Expected value c.o.v. c amp 1.35 X dyn LN10.05 h(m)60 X dyn LN10.10 X exp LN10.20 X st LN10.10 X str LN10.03

15. Numerical Simulation of DtD/t  R(V)  R(V) 3.0506011144 754013855 3.5507012951 754716064 4.0508014773 757318358 Simulation results for D = 3.5 and t = 75 mm Results of buckling resistance analysis

16. Results for buckling failure analysis DtP F1 11 P F2 22 3.0500.03171.85660.08321.3841 750.00852.38760.03571.8026 3.5500.01232.24720.04591.6863 750.00282.77440.01742.1110 4.0500.00482.58730.00282.7744 750.00083.14260.00033.4615

17. New buckling resistance results Dt Initial estimate of  R(V)new Final estimate of  R(V)new  R(V) new P F3  R(V) new P F3 3.050140.980.0144128.830.0317 75190.570.0010161.510.0085 3.550174.430.0023151.440.0123 75236.550.0001189.030.0028

18. New buckling resistance results  R(V) new  R(V) new P F4 44 V refnew V e50new 128.83440.15301.0246287 161.51550.05881.5656591 151.44510.07851.4156490 189.03640.02551.9526794

19. Results from the other 49 wind stations Station IDStation Name GEV Parameters  135Basco, Batanes42.459.930.18 232Aparri, Cagayan32.0412.340.22 446Virac Synop, Catanduanes36.3316.37-0.04 531San Jose, Occidental Mindoro30.497.68-0.48 The PF 1 from these stations increased when typhoons were considered

20. Results from the other 49 wind stations (D = 3.0 & t = 50 mm) Station ID  R (v’)  S(v’) P F2  R(V) new V refnew V e50new 23242.4923.230.0465121.095779 13550.3017.330.0514120.535679 53132.317.140.039191.983752 44644.6819.260.0530120.775679

21. New design wind speed map (D = 3.0 & t = 50 mm) Kriging of ArcGIS was used to interpolate results of V e50new to other areas The areas in red indicates an increase in probability of buckling failure if typhoon loads are considered Using the proposed mitigation scheme, these areas will have V e50new > 70 m/s The blue and yellow areas indicate that the P F2 < P F1 even when typhoon loads were considered

22. Concluding Remarks Strong typhoons occur in the Asian Region Based extreme wind data and the recent experience of a wind farm in typhoon prone areas, survival wind speed (V e50 ) may be exceeded during the economic life of a wind farm In view of this, the authors proposed a mitigation scheme by introducing a new buckling resistance (R(v) new ) in order to maintain the same probability of failure Based on this new resistance, a 50-year design extreme wind speed (V e50new ) and a new reference wind speed (V refnew ) can be derived

23. Concluding Remarks The authors also analyzed the probability of buckling failure of a WTG tower using typhoon data from other wind stations (using D = 3.0 m & t = 50 mm) The results showed that only the P F1 from 4 stations increased Based on the (R (v)new ) of each station, a V e50new and V refnew were also derived Using ArcGIS kriging method, a new 50-year design extreme wind speed map was developed This map will be useful for future owners of commercial size or small scale wind farm

24. Thank you