Automatic detection and correction of pitch misalignment in wind turbine rotors

Abstract

Abstract. In this work, a new algorithm is presented to correct for pitch misalignment imbalances of wind turbine rotors. The method uses signals measured in the fixed frame of the machine, typically in the form of accelerations or loads. The amplitude of the one per revolution signal harmonic is used to quantify the imbalance, while its phase to locate the unbalanced blade(s). The near linearity of the unknown relationship between harmonic amplitude and pitch misalignment is used to derive a simple algorithm that iteratively rebalances the rotor. This operation is conducted while the machine is in operation, without the need of shutting it down. The method is not only applicable to the case of a single misaligned blade, but also to the generic case of multiple concurrent imbalances. A part from the availability of acceleration or load sensors, the method requires the ability of the rotor blades to be commanded independently from one another, which is typically possible on many modern machines. The new method is demonstrated in a realistic simulation environment, using an aeroservoelastic wind turbine model in a variety of wind and operating conditions.

Robust estimation and diagnosis of wind turbine pitch misalignments at a wind farm level

“…As we desire to obtain a spatially and temporally integrated FE and FD approach, we merge the temporal model ( 7) of all the WTs operating under similar wind conditions (i.e., all the WTs in the same row i). The discrete extended state-space model of the pitch systems of the i-th row of WTs can be written as detailed in (8) with z i ∈ R 2 n j being the extended state vector that stacks the collective pitch angles β i,j and the fault states ξ i,j of the WTs in the i-th row from j = 1 to j = n j . Similar applies to u i ∈ R n j , y i ∈ R n j , w i ∈ R n j , f i ∈ R n j and δ i ∈ R n j , which are the input, output, noise, fault and fault generator vectors.…”

“…The model (8) merges the temporal and spatial information regarding the pitch system of the WTs in the i-th row, which operate under similar wind conditions 3 . For ease of readability, let us omit hereafter the dependence of the system vectors on the number of row i (e.g.…”

“…holds for infrequent changes of subset membership 8 . Thus, taking account of the properties of the probability distribution function of a zero-mean uninform variable 9 , we have that…”

“…, n j ). Each observer b aims to estimate the auxiliary fault 8 From the definition of γ j p,q , it yields f [j] RMS ≤ γ j p,q p ∞; from (34), γ j p,q ≤ G j p,q (z) ∞; and from ( 25), p ∞ ≤ λ i 1,q . 9 If x is a zero-mean uninform variable, then |x| < √ 3 x RMS.…”

“…This misalignment can be originated by several factors, including high wind shear and manufacturing, installation or control errors [6,7]. In general, aerodynamic asymmetries represent a significant problem for WTs, as also witnessed by the fact that certification guidelines require relatively small pitch misalignments [8]. When a pitch misalignment is present, the loadings experienced by the blades are not balanced which results in vibrations that negatively affect other components of the turbine.…”