Accurate short-term wind power prediction (WPP) is critical for power system stability but remains challenging due to the inherent non-linearity and volati

As the share of renewable energy in modern power systems continues to grow, its inherent uncertainty and variability pose severe challenges to grid stability and the accuracy of traditional thermal power dispatch. To address this issue, this study fully exploits the fast response and flexible operation of variable-speed pumped storage (VS-PS) by developing a day-ahead scheduling model for a wind–photovoltaic–thermal–VS-PS system. The optimization model aims to minimize system operating costs, carbon emissions, and thermal power output fluctuations, while maximizing the regulation flexibility of the VS-PS plant. It is assessed using the improved multi-objective jellyfish search (IMOJS) algorithm, and its effectiveness is demonstrated through comparison with a fixed-speed pumped storage (FS-PS) system. Simulation results show that the proposed model significantly outperforms the traditional FS-PS system: it increases renewable energy accommodation capacity by an average of 68.51%, reduces total operating costs by 14.13%, and lowers carbon emissions by 3.63%.

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.

The main distributing valve (MDV) plays a core role in hydraulic amplification and precise control within the hydro-turbine governing system, and its spool position dynamics directly affect the regulation quality and stability of the hydro-turbine generator unit. However, developing a high-fidelity dynamic model of the MDV remains challenging, as existing methods typically rely on linearization assumptions and fail to fully account for system uncertainties, resulting in limited accuracy and poor cross-condition stability. To address this issue, this paper proposes a mechanistic–data-driven hybrid modeling method. Firstly, a modular modeling strategy is adopted to accurately construct a nonlinear mechanism-based model (MBM) that conforms to the actual industrial system. Secondly, to address the unknown dynamics not captured by the MBM, a Bayesian optimization-enhanced Light Gradient Boosting Machine (BO-LightGBM) model is developed. Then, a cascade–additive architecture is employed to achieve effective integration of the two. Finally, verification experiments are conducted under various dynamic processes based on the actual operating data of the governor. The results show that the proposed hybrid model significantly outperforms the other five comparison models in terms of accuracy and stability while retaining physical interpretability. This provides a more practically valuable solution for the dynamic modeling and simulation of hydroturbine actuators.

Monitoring and diagnosing the operating state of hydropower units is crucial, which becomes a hot research topic in the industry. the vibration signals provide a reliable indication to detect the abnormal working conditions of hydropower units. however, the vibration signals is affected by the environment noise inevitably, making it difficult to truly reflect the operating state of hydropower units. the non-local means (NLM) algorithm is proved to be effective in denoising the vibration signals, however, whose parameters depend on the human experience, which hinders its application and development. in the present work, based on the Bayesian parameter optimization (BPO), the parameters of NLM are set adaptively, the BPO-NLM denoising algorithm is proposed. by conducting the simulation, the denoising effectiveness of BPO-NLM is improved remarkably than that of the traditional NLM. at different snr, RMSE of the signal denoised by BPO-NLM is much smaller than that of the traditional NLM, while snr of the signal denoised by BPO-NLM is much larger, namely, the effective component of the signal is enhanced, while the noise component is suppressed.

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.