Journal
APPLIED ENERGY
Volume 340, Issue -, Pages -Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.apenergy.2023.121049
Keywords
Wind turbine power; Transfer learning; Online update
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A novel system-wide update online transfer learning model is proposed in this study to accurately predict wind turbine power. It applies various methods such as time trend quantification, convolutional neural network multi-source data fusion, and Hilbert spatial feature construction to improve data accuracy and reduce dimension. The model achieved high prediction accuracy of over 92.5%.
Accurate prediction of wind turbine power is important for the safe operation of wind farms. However, most of the previous online transfer learning methods are partially updated and time-consuming. Here we propose a novel system-wide update online transfer learning model to overcome these shortcomings. To improve the multi-source data fusion accuracy, a new time trend quantification method is applied to expand the data source, a convolutional neural network multi-source data fusion method is proposed to reduce the dimension of data, and a Hilbert spatial feature construction method is used to construct spatial information of data. To achieve system-wide update and rapid prediction, we have deleted the weight unit of traditional method and added two data buffers. The results show that: (1) the proposed multi-source data processing method has the smallest mapping errors, which mean absolute error for all wind turbines is less than 32.1; (2) the proposed online transfer learning model has the highest prediction accuracy, which is higher than 92.5%.
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