Spectral dynamic aggregation transformer and fitted swing-door algorithm for wind power monitoring

Current research on wind energy monitoring predominantly focuses on power prediction while often overlooking the advanced warning of sudden operational anomalies. To this end, we propose a wind power monitoring model based on a spectral dynamic aggregation transformer integrated with a fitted swing gate algorithm. First, the integration of spectral and dynamic aggregation blocks within the Transformer framework yields an accurate wind power prediction model that effectively alleviates the impact of data fluctuations. On this basis, the MI method is utilized to quantify the nonlinear relationships between multi-source meteorological variables and wind power output. By integrating STL for residual analysis to extract salient features, the proposed approach not only enhances the input quality of the prediction model but also provides a physically interpretable foundation for the early warning module. This facilitates seamless integration of prediction and early warning at the feature level. Furthermore, the prediction results and key features jointly drive a two-tier early warning framework: a wind power early warning system is constructed based on the random forest algorithm and the swing door algorithm, followed by joint calibration with the predictive model. By leveraging multi-source data, the model is capable of detecting anomalous power changes and ramp events, thereby ensuring efficient anomaly identification and advanced warning. Through the case study, it is demonstrated that the proposed model can achieve wind power prediction and advanced warning functions, thereby providing robust support for flexible grid scheduling and efficient wind power integration.