Abstract
Wind data play a crucial role in the advanced planning, design, and maintenance of wind farms, scheduling, energy production, and accurate distribution. Most of the measured wind dataset comprises missing observations due to several reasons, including the failure of measuring devices, human mistakes, environmental impact, and natural calamities. Completing the missing observations in a wind dataset without losing significant information is still a challenging task currently. In the present research, a novel deterministic approach to wind power data imputation based on the wind speed-power model is proposed and validated using the 15 western wind datasets (capacity of 25–40 MW). The validation outcomes result in the minimum value of the root mean square error for the imputed value of the rated and score-lite wind power which exhibits the effectiveness of the proposed method.
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