Taking the three-dimensional horizontal axis tidal current turbine as the initial model, the influence of the winglet twist angle on the hydrodynamic performance and wake characteristics of the tidal current turbine is analyzed by CFD numerical calculation, and the influence mechanism of the winglet's twist angle on the hydrodynamic performance of the turbine blade is clarified. The results show that the change of the winglet twist angle has a significant impact on the hydrodynamic performance of the tidal current turbine. Compared with the initial model, when winglet twist angle is -6°, the power coefficient of the tidal current turbine increases by 5.17% and the drag coefficient increases by 7.01%. In the range of twist angle from -9° to 6°, the power coefficient with winglet first increases and then decreases with the increase of twist angle, while the drag coefficient decreases monotonously. The changes of the winglet twist angle significantly affect the radial flow state of the fluid immediate behind the blade tip of the turbine, affect the tip loss and the surface pressure distribution, and then change the hydrodynamic performance of the tidal current turbine. By comparing the power spectrum density curve of velocity fluctuation, it can be seen that the winglet's twist angle will affect the axial velocity fluctuation of blade tip vortex, and the influence will gradually decrease with the increase of axial distance, but the influence on radial velocity fluctuation is not obvious.

Accurate estimation of lithium battery capacity is the basis for its safe and reliable operation. However, the lithium battery capacity attenuation mechanism is not clear, and its capacity shows nonlinear attenuation during use, making it extremely difficult to accurately estimate the lithium battery capacity. In order to solve these problems, this paper proposes a method to estimate the capacity of lithium batteries by using constant current charging data and a one-dimensional convolutional neural network model. Firstly, two aging feature sequences are extracted from the constant current charging stage： the constant current charging capacity and the time interval of equal charging voltage rise. Secondly, in order to make full use of the measured charging information and extract the key capacity attenuation factors in aging feature sequences, a new one-dimensional convolutional neural network capacity estimation model is constructed. Finally, the proposed capacity estimation model is verified and analyzed on the aging data set of lithium batteries in Maryland. The experimental results show that the proposed method can achieve an accurate and robust estimation of battery capacity on the complete constant current charging curve and partial constant current charging curve, which is better than other capacity estimation methods. The root mean square error （RMSE） and mean absolute error （MAE） can be controlled within 0.68% and 0.50%. Furthermore, after transfer learning with a small amount of date, the proposed model can accurately estimate battery discharge capacity at other discharge rates. After transfer learning for the first 50 cycles, the root mean square error of the model at a 0.5C discharge rate is only 0.58%.

This study aims to elucidate the influence of terrain slope on wind pressure distribution and wind load shape coefficients of PV modules through computational fluid dynamics （CFD） simulations using FLUENT software. The Reynolds-Averaged Navier-Stokes （RANS） equations coupled with the shear stress transport （SST） turbulence model were employed to investigate the wind pressure distribution patterns and wind load shape coefficients under various configurations, including different PV module inclination angles, wind direction angles, and terrain slopes. The findings reveal that both wind direction angle and mounting inclination angle significantly influence the wind load magnitude on PV modules across different terrain slopes. While terrain features induce wind bypass effects, these effects gradually diminish with increasing column height. Furthermore, under various slope conditions and mounting inclinations, single-string PV modules consistently exhibit higher shape coefficients compared to PV array configurations. The investigation also demonstrates substantial shading effects within PV arrays, resulting in distinctive wind load characteristics for subsequent rows. Specifically, the shape coefficients experience maximum attenuations of 70% and 66% for the second and third rows of PV modules, respectively. However, the presence of terrain slope significantly mitigates these shading effects, reducing the maximum attenuation by up to 45%.

To address the challenges of existing deep learning prediction models, such as long training times and a tendency to fall into local optimum, this paper proposes an innovative KOA-driven VMD-CNN-BiGRU-Attention method for short-term photovoltaic （PV） output power prediction. Firstly, the Pearson correlation coefficient is employed to identify key meteorological factors. Then, the grey relational analysis （GRA） method is used to determine the historical similarity days for the predicted days. The Keplerian Optimization Algorithm （KOA） is then used to optimize the parameters of variational mode decomposition （VMD）, which decomposes the output sequences of historical similarity days to generate a high-quality training sample set. Finally, the VMD-CNN-BiGRU-Attention model, driven by KOA, is constructed to achieve accurate PV output power prediction. Practical applications at PV power stations in Yunnan and Gansu show that the model achieves RMSE values of 0.2540 MW and 2.7981 MW, and MAPE values of 0.0234 and 1.1699, respectively. Compared with other combined prediction models, the proposed KOA-driven VMD-CNN-BiGRU-Attention model demonstrates superior ability to capture spatiotemporal features, offering significant improvements in prediction accuracy and stability. These results highlight the broad application potential of the model in PV power generation prediction.