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Accurately predicting wind power is premise for the large‐scale integration of wind energy into the power grid and realizing reliable power grid scheduling and efficient wind farm operation. The spatial–temporal differences between the acquisition points of numerical weather prediction and wind farm sites should be considered because they significantly affect the accuracy of wind power output. Meanwhile, since there are different actual constraints on wind farm electrical networks and units, and the weather conditions may vary, there are no universal methods for identifying and correcting wind power output. To this end, this paper proposes a wind speed calibration model based on an Attention‐Bi‐LSTM network and a self‐adaptive recognition‐correction model based on SHAP‐CART classification for wind power output prediction. First, a spatial–temporal dataset is established by using real weather data from wind farm sites and data from the surrounding NWP points. Then, wind speed is accurately calibrated using the Attention‐Bi‐LSTM model. Next, considering the practical constraints on wind farm electrical networks and units, the self‐adaptive recognition‐correction model based on SHAP‐CART classification is employed to effectively recognize and predict the operating states and power output of the units, thus improving the accuracy and stability of wind power output prediction. Finally, the effectiveness and superiority of the proposed method is verified by using a wind farm in Eastern China as an example and analyzing the prediction results in various approaches. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Train communication network (TCN) is an important guarantee for the reliable and safe operation of high‐speed rail train. The higher reliability of the TCN is, the safer operation of high‐speed rail train will be, and the cost of overhaul and maintenance will be greatly reduced. The 250 km/h high‐speed rail TCN being developed in China is the first time that the train communication Ethernet (TCE) is used as train control network. Therefore, it is of great practical significance to analyze the reliability of TCE and improve the reliability of TCN design and vehicle design. Dynamic fault tree (DFT) and fault Petri nets (FPN) are both important methods for system reliability analysis. Among them, DFT is most widely used in the field of engineering analysis. The FPN method also has its unique characteristics and advantages, which can describe fault propagation analysis and dynamic transition process in practical application. In this paper, TCE's structure, working principle, and fault mode are analyzed to sort out the relationship between complex fault events and establish a DFT with TCE failure as the top event. DFT and FPN methods were combined to better express the dynamic propagation of TCE fault and carry out the fault probability evaluation and system reliability based on the known prior failure rate. The analysis results show that improving the reliability of specified network segments of TCE is of great help to improve the reliability of the high‐speed rail train. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]