To address the non-stationary characteristics and multi-scale dynamic evolution of wind power time series, this study proposes a hybrid prediction model named VMD-FECAM-TCN-NTSformer. Firstly, Variational Mode Decomposition （VMD） is employed to adaptively decompose the original power sequence in the frequency domain, effectively separating noise interference and extracting multi-scale dynamic intrinsic mode functions. Secondly, Temporal Convolutional Networks （TCN） are used to hierarchically capture local temporal features through dilated convolutions. Meanwhile, the NTSformer utilizes a de-stationary attention mechanism to dynamically correct normalization bias, thereby enhancing the modeling of abrupt trends and periodic fluctuations. Furthermore, a Frequency Enhanced Channel Attention Module （FECAM） is introduced to extract frequency-domain features via fast Fourier transform and dynamically assign channel weights to focus on key frequency components. Experimental results show that the proposed model improves the coefficient of determination R2 by more than 17% compared to traditional CNN models and by more than 5% compared to Transformer-based models, in both 15-minute single-step forecasting and 60-step (15 h) multi-step forecasting. These results demonstrate the model′s superior prediction accuracy and robust forecasting performance.