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180 F. Böttcher et al.<br />

symmetric around V , neglecting the terms O(v 3 ), and averaging (32.1) one<br />

obtains:<br />

〈P (u)〉 = Pr(V )+ 1 ∂<br />

2<br />

2P (V )<br />

∂u2 · σ 2 , (32.2)<br />

where σ 2 = � v 2 (t) � denotes the variance of fluctuations. Equation (32.2) shows<br />

that the “real” power curve Pr(V ) (as it would be realized in laminar wind<br />

flows) has to be modified. This modification is strong for large variance of<br />

fluctuations. For negative curvatures the “real” power curve is underestimated<br />

by 〈P (u)〉 while for positive curvatures it is overestimated.<br />

Obviously (32.2) is only appropriate for small fluctuations, i.e., for small<br />

turbulence intensities ζ = σ/V . In addition to that Pr(V ) is in general not<br />

known in advance [2].<br />

We thus propose an alternative approach (based on the theory of Langevin<br />

processes) to determine the “real” power curve even for very noisy (turbulent)<br />

wind conditions. To this end a simple power curve model will be analyzed.<br />

32.1.1 Reconstruction of a Synthetic Power Curve<br />

A Langevin equation generally describes the temporal evolution of a state<br />

vector q(t) =(q1(t),q2(t), ..., qn(t)) and has the following form:<br />

˙qi = D (1)<br />

i (q)+ �<br />

��<br />

D (2) �<br />

(q) Γj(t), (32.3)<br />

j<br />

where D (1) denotes the deterministic drift coefficient, D (2) the stochastic<br />

diffusion coefficient [3], and Γ (t) Gaussian distributed white noise (with<br />

〈Γi(t)Γj(t ′ )〉 = δijδ(t − t ′ )). The coefficients are obtained via the conditional<br />

moments<br />

M (1)<br />

i (q,τ)=〈qi(t + τ) − qi(t)〉| q(t)=q,<br />

M (2)<br />

ij (q,τ)=〈[qi(t + τ) − qi(t)][qj(t + τ) − qj(t)]〉| q(t)=q (32.4)<br />

by first dividing them by τ and then calculating the limit τ → 0. For a good<br />

temporal resolution the relation between moments and coefficients can be<br />

approximated according to<br />

M (1)<br />

i (q,τ) ≈ τ · D (1)<br />

i (q); M (2)<br />

ij<br />

ij<br />

(q,τ) ≈ τ · D(2) ij (q). (32.5)<br />

In the following we will consider a simple approach identifying q(t) with<br />

(u(t),P(t)):<br />

˙u = −α [u(t) − V ]+ � β · Γ (t),<br />

˙<br />

P = κ [Pr(u(t)) − P (t)] . (32.6)<br />

Thus the only terms entering (32.6) are D (1)<br />

u which is linear in u, D (2)<br />

uu as a<br />

nonzero constant and D (1)<br />

P which is linear in P . In this case the velocities are

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