Use of adaptive linear algorithms for very short-term prediction of wind turbine power output

The paper proposes an efficient method for very short-term prediction of wind turbine power output. The method, which models the turbine as a Hammerstein system, exploits an adaptive linear filtering algorithm. The performance of the proposed method is examined by implementation of two linear adaptive algorithms, namely, least mean squares (LMS) and recursive least squares (RLS) filters. Using synthetic generation of turbine power output, it is shown that the RLS algorithm gives more accurate results with moderate computational burden as compared to the LMS algorithm and rival artificial neural networks.