A wind speed vector-wind power curve modeling method based on data denoising algorithm and the improved Transformer

Abstract

• A wind power curve modeling method based on improved Transformer is proposed. • The CEEMDAN algorithm is introduced for data denoising. • Multivariate combined with DTW analysis optimizes the network input. • Convolution combined with multi-head attention enhances information mining. • A multi-dimensional evaluation mechanism is established to evaluate the model. The complex nonlinear relationship between the wind speed and the wind power, and the singularity of wind speed information leads to the lack of accuracy of current wind power curve modeling. To address the problem, this paper presents a high-precision wind power curve modeling method based on the wind speed vector, including the wind speeds and wind directions at different heights of the wind measuring tower. First, considering the stochastic fluctuation of the wind speed vectors and wind power sequences, complementary ensemble empirical modal decomposition with adaptive noise (CEEMDAN) is used to decompose and reconstruct the high-noise data. Second, based on the reconstructed data, dynamic time warping (DTW) is adopted to analyze the lagged causality between current wind power and historical wind speed series. In order to better mine the rules, the improved Transformer network is proposed with two convolutional layers and multi-head attention mechanisms to develop the wind speed vector-wind power curve model. Finally, through comparative experiments with the mainstream methods, the advancement of the proposed wind power curve modeling method is verified from the perspective of modeling error and its distribution