Power transfer characteristics in fluctuation partition algorithm for wind speed and its application to wind power forecasting

Abstract

Wind speed is the dominant meteorological factor affecting wind turbine power generation. Existing wind speed fluctuation division algorithms only focus on the wind speed changing process, but strong uncertainty exists in the wind speed–power conversion, which has been demonstrated as a multi-power transfer relationship under wind speed fluctuation. Therefore, a wind speed power transfer fluctuation partitioning (PTFP) algorithm is proposed, which fully considers the wind speed variation and power transfer characteristics to refine the modeling of wind power forecasting. The power transfer characteristics are investigated according to the wind speed–power conversion error of a wind farm. The measured wind speeds from three sources and fuzziness under different fluctuation trends are discussed based on the scatter distribution and probabilistic wind speed power curve. The model errors under different changing trends are quantitatively analyzed. Subsequently, the operation data of two wind farms in China are divided using the PTFP algorithm. The modeling error is reduced compared to that without considering the power transfer characteristics and the rationality of the proposed algorithm is demonstrated. The PTFP algorithm is also applied to wind power forecasting to verify its applicability. The results demonstrate that the proposed model can improve prediction accuracy on all time scales.