Precise short-term forecasting of wind power generation is indispensable for ensuring the security and economic efficiency of power grid operations. Nevertheless, the inherent non-stationarity and stochastic nature of wind power series present significant challenges for prediction accuracy. To address these issues, this paper proposes a dual-optimized hybrid deep learning framework combining Spearman correlation analysis, RIME-VMD, and TCN-BiGRU-SA. First, Spearman correlation analysis is employed to screen meteorological factors, eliminating redundant features and reducing model complexity. Second, an adaptive Variational Mode Decomposition (VMD) strategy, optimized by the RIME algorithm based on Minimum Envelope Entropy, decomposes the non-stationary wind power series into stable intrinsic mode functions (IMFs). Third, a hybrid predictor integrating Temporal Convolutional Network (TCN), Bidirectional Gated Recurrent Unit (BiGRU), and Self-Attention (SA) mechanisms is constructed to capture both local trends and long-term temporal dependencies. Furthermore, the RIME algorithm is utilized again to optimize the hyperparameters of the deep learning predictor to avoid local optima. The proposed framework is validated using full-year datasets from two distinct wind farms in Xinjiang and Gansu, China. Experimental results demonstrate that the proposed model achieves a Root Mean Square Error (RMSE) of 7.5340 MW on the primary dataset, significantly outperforming mainstream baseline models. The multi-dataset verification confirms the model’s superior prediction accuracy, robustness against seasonal variations, and strong generalization capability.

As a crucial component within the power industry, the hydraulic turbine speed control system significantly plays a vital role in the safe and stable operation of hydropower stations. The intelligent operation and maintenance of this system is a vital means to ensure the safety, stability, and economy of the unit. The hydropower plant has accumulated extensive fault text data related to the hydraulic turbine speed control system over the years, which has yet to be effectively mined and utilized. To address these issues, this paper proposes a novel method using BERTWWM-BiLSTM-MHA-CRF for constructing a fault knowledge graph of hydraulic turbine speed control system. Initially, the knowledge graph schema is designed, followed by an analysis of the recording characteristics of the hydraulic turbine speed control system fault text. This is accompanied by the cleaning and labeling of unstructured text. Subsequently, an entity extraction model utilizing the BERTWWM-BiLSTM-MHA-CRF framework is developed to facilitate the intelligent extraction of entities and relationships. Finally, the triples, consisting of entities and relationships, are stored in the Neo4j graph database to finalize the construction and visualization of the fault knowledge graph, along with the proposed application process for auxiliary decision-making. The data processing methodology outlined in this paper, based on the graph schema design, effectively produces high-quality datasets. Furthermore, compared to the traditional model and mainstream large language models, the BERTWWM-BiLSTM-MHA-CRF model demonstrates superior entity extraction performance. Finally, combining fault instance validation, it demonstrates that the knowledge graph provides effective support for fault diagnosis in the hydraulic turbine speed control system.