An improved wind power prediction via a novel wind ramp identification algorithm
This addresses the problem of unreliable wind power forecasting for power system operators, though it appears incremental as it builds on existing techniques.
The study tackled inaccurate wind power prediction during sudden wind changes by proposing an integrated algorithm combining wind ramp identification, similar period matching, and deep learning prediction, which significantly improved accuracy compared to existing methods.
Authors: Yifan Xu Abstract: Conventional wind power prediction methods often struggle to provide accurate and reliable predictions in the presence of sudden changes in wind speed and power output. To address this challenge, this study proposes an integrated algorithm that combines a wind speed mutation identification algorithm, an optimized similar period matching algorithm and a wind power prediction algorithm. By exploiting the convergence properties of meteorological events, the method significantly improves the accuracy of wind power prediction under sudden meteorological changes. Firstly, a novel adaptive model based on variational mode decomposition, the VMD-IC model, is developed for identifying and labelling key turning points in the historical wind power data, representing abrupt meteorological environments. At the same time, this paper proposes Ramp Factor (RF) indicators and wind speed similarity coefficient to optimize the definition algorithm of the current wind power ramp event (WPRE). After innovating the definition of climbing and denoising algorithm, this paper uses the Informer deep learning algorithm to output the first two models as well as multimodal data such as NWP numerical weather forecasts to achieve accurate wind forecasts. The experimental results of the ablation study confirm the effectiveness and reliability of the proposed wind slope identification method. Compared with existing methods, the proposed model exhibits excellent performance and provides valuable guidance for the safe and cost-effective operation of power systems.