This paper reports a method to estimate inflow wind conditions undisturbed by wind turbine rotors while using a deep learning technique. For creating the inflow wind estimation system, datasets taken from nacelle-mounted anemometers and a vertical profiling Light Detection and Ranging (LiDAR) were employed. Our previous study showed that unreasonable errors in wind direction observations were enhanced by applying the deep learning model. However, a large amount of data was required to train the deep learning model from scratch. Therefore, long-term measurement with doppler LiDAR is required, and its cost tends to be high. In this study, we propose a transfer learning method to ensure the performance for other wind turbine with only short-term data. The proposed method was evaluated on the actual data, and the results showed that it was possible to estimate the trend of doppler LiDAR wind direction. Future work includes the evaluation of different condition sites such as the adaptation from flatland to mountainous sites.

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