With the largescale integration of distributed power sources, the shortcircuit current characteristics of large power grids become more complex and difficult to predict. Based on this, this article proposes a new energy grid shortcircuit current prediction technology based on improved convolutional neural networks. Firstly, analyze the characteristics of shortcircuit current, perform variational mode decomposition on shortcircuit current, and obtain the intrinsic mode function; Secondly, the convolutional neural network is improved by utilizing multiscale feature extraction to maximize the features of current fault data, introducing attention mechanisms to extract important information, and using skip connections during the convolutional process to prevent information loss during forward transmission, which is beneficial for improving the accuracy of prediction. A shortcircuit current prediction model based on the improved convolutional neural network is constructed; Finally, the PSCAD/EMTDC power grid model was validated, and the experimental results showed that the proposed method has high accuracy in predicting the peak shortcircuit current. Compared with common limit learning machines and support vector machines, the average relative error decreased by 0.61% and 1.09%, respectively. This verified the effectiveness of the proposed method and laid the foundation for limiting shortcircuit current in large power grids.

For the identification of wind turbine blade defect types. First, a physical models of thermal reflection coefficients of the defect were established. A new identification method of wind turbine blade defects based on the combination of thermal signal reconstruction technology and thermal reflection coefficient of defective materials was proposed. Then, the wind turbine blades specimen containing (bubble, impurity, wrinkle) was performed by the longpulse infrared thermogaphy technology. The experiments were subjected to nondestructive testing for two heating times. It is found from the experiments results that the defects of wind turbine blade specimen could identify by longpulse infrared thermal imaging technology at temperature cooling process. Experiments have proved that the physical models of the thermal reflection coefficient are feasible. The error between the test results and the prediction results is very small.

Steam methane reforming membrane reactor removes hydrogen through a hydrogen selective permeation membrane, which can promote the forward movement of the reaction, improve methane conversion rate with reduced reaction temperature, and achieve thermochemical storage under mediumtemperature of trough solar collector. However, the characteristics of multiphysical field coupling in the reactor are complex, and the influence of operating parameters on the performance of the reactor needs to be further investigated. The steam methane steam reforming reaction in the membrane reactor driven by solar at mid temperature was taken as the research object in this paper. The multiphysics coupling model of fluid flow, heat/mass transfer and chemical reactions in the reactor was established by using ANSYS FLUENT, and the effects of the key operating parameters (i.e., inlet mass flow rate, temperature, reaction pressure, water to carbon ratio and permeation pressure) on the reactor chemical and thermodynamic performances were studied. The results show that the methane conversion rate and energy efficiency are negatively correlated with the inlet flow rate. The conversion rate of methane is positively correlated with reaction temperature. The energy efficiency first increases and then decreases with the increase of temperature, existing a peak value. When the inlet flow rate is low, the methane conversion rate and energy efficiency increase with the increase of the reaction pressure, while the methane conversion rate and energy efficiency decrease with the increase of reaction pressure when the inlet flow rate is high. The increase of the water to carbon ratio can significantly improve the chemical reaction performance but reduce the energy efficiency. The lower the pressure on the permeation side, the better the reactor performance. The research results are of great significance for highgrade solar thermal utilization.

This paper intends to design a deep water spiral pile jacket foundation structure for offshore wind power, and use finite element analysis to simulate the load of wind, wave, current, wind turbine, and the bearing capacity and stability of the foundation structure under the conditions of silt and silty soil, and provide a feasible reference for the design of the finite element model of pilesoil interaction of spiral pile structure. This paper also designs a common pile control foundation model without helical blade structure by means of lateral comparison, and explores the influence of single helical blade on the bearing capacity and stability of the foundation structure. The research shows that the addition of singlelayer helical twist blade structure can significantly improve the bearing capacity and stability of offshore wind power pile foundation structure, and has little influence on the natural vibration frequency under the constraint state of the foundation structure. The relevant data can provide suggestions and references for the design of helical pile foundation in practical engineering.

The transient power angle stability and voltage stability issues of new energy grid connected systems like wind power are combined, and suffer the risk of short circuit current level. Current research mostly focuses on improving the stability based on single factor, without considering the multiple factors to develop optimization methods for wind turbine control parameters. To solve the issue, an optimization method for key parameters of wind turbine short circuit current and reactive power support is proposed in this paper considering transient power angle stability. Firstly, based on the simplified model of windthermal combined system, the mechanism of power angle stability problem and the influence of active power output of the wind turbine on power angle stability are analyzed. Then, the analytical expression for the shortcircuit current of the directdriven wind turbine is derived, and the key factors that influence the levels of active and reactive currents are analyzed. Finally, the influence of key parameters of short circuit current on the reactive power voltage support capacity of wind turbines are studied by simulation, based on which the optimization principles and method for key parameters of wind turbine short circuit current and reactive power support considering transient power angle stability are developed. The simulation analysis results based on actual power grids show that the proposed method can improve the reactive voltage support capacity of wind turbines while ensuring transient power angle stability margin and short circuit current level.

Due to the selection of solar radiation spectrum by solar cells in semi transparent photovoltaic windows, the energy consumption, indoor daylight and thermal environment of photovoltaic window buildings are different with clear glass window buildings. When semitransparent photovoltaic windows are applied to building, specific design values for thermal parameters are required for reference, but there is a lack of basis. Therefore, this paper took an office building in Taiyuan as an example, established a reference building model and a design building model, and explored the influence of the Heat Transfer Coefficient (Uvalue) and Solar Heat Gain Coefficient (SHGC) on the energy consumption of semi transparent photovoltaic window buildings. The recommended range of Uvalue and SHGC value of photovoltaic window is obtained by the method of tradeoff judgment. The results show that smaller Uvalue and larger SHGC value are more beneficial to energy saving when photovoltaic windows are used in Taiyuan. When the windowwall ratio of photovoltaic window building is greater than 0.60 and the transmittance is equal to 0.46, the maximum Uvalue limit is 1.9 times the existing energysaving standard limit. By comparing the recommended range of SHGC value with different window wall ratio, the lower limit of SHGC decreases by 25%.

With the depletion of resources in flat terrain, the site selection for wind farms is gradually shifting towards complex terrains. Complex terrain presents geographical conditions distinct from flat terrain, the undulating topography leads to intricate flow patterns, and the wind characteristics in complex terrains are also different. Therefore, studying the distribution patterns of flow fields in complex terrain is significant for micrositing of wind farms and wind power prediction. This paper, based on the opensource software OpenFOAM, establishes geometric and numerical simulation models for complex terrain. It investigates and analyzes grids, boundary conditions, and turbulence models suitable for complex terrain. The reliability of the numerical model for complex terrain is compared and analyzed using real measurement data from the Askervein mountain. The paper solves the flow field distribution for typical complex terrains such as isolated peaks, plateaus, and peak clusters, studying the impact of slope and height on the flow fields in different terrains. The research reveals that different terrains satisfy the Reynolds number independence principle. In isolated peak topography, the influence of slope becomes more pronounced in the lee zone behind the mountain as the slope increases. Plateau terrain is more affected by changes in height. For peak cluster topography, the flow field development remains consistent under varying slopes and heights, with height having a greater impact compared to slope. The provided distribution ranges of flow field characteristic values in this paper can serve as a reference for wind farm micrositing and wind power prediction in complex terrain.

The study elucidated the relationship between anaerobic digestion gas production efficiency and temperature and hydraulic retention time (HRT) using synthetic glucose wastewater as a substrate. Gas production under different temperatures (37,55 °C) and HRTs (25, 30, 50 d) was compared. The results indicated that the hydrolysis rate of glucose was higher at thermophilic temperature than at mesophilic temperature. However, volatile fatty acids, especially propionic acid, tended to accumulate at thermophilic temperature. Additionally, Methanomicrobiaes and Methanosarcinales were enriched at both moderate and high temperatures, suggesting the presence of pathways for methane production from acetic acid and acetate oxidation at both temperatures, with the acetate oxidation pathway exhibiting greater environmental resilience. The recommended optimal fermentation conditions for treating heavy glucosecontaining wastewater through anaerobic digestion are 37 °C and an HRT of 30 days.

In the security and stability analysis of largescale power grid, the wind farm models are usually simplified with the dynamic equivalent modeling method. For a largescale wind farm, due to the large number of wind turbines and the divergences in their characteristics, the wind farm is generally aggregated into multiple equivalent wind turbines. When estimating the parameters of the equivalent wind turbines, in order to avoid identification of a large number of parameters at the same time, the existing method only selects the key parameters with large sensitivity for identification, and the remaining nonkey parameters are not identified by giving the theoretical values. Therefore, the accuracy and robustness of the equivalent model are greatly affected by the accuracy of the assignment of nonkey parameters. In order to solve this problem, the paper proposes a dynamic equivalent modeling method for wind farms based on multistep parameter identification. Firstly, the clustering method is used to group the wind turbines, and the wind turbines within each subgroup are aggregated into one equivalent wind turbine to establish a simplified wind farm model. Secondly, based on the hybrid dynamic simulation technology, the external system of each equivalent wind turbine is replaced with a variable impedance to realize the independent identification of each equivalent wind turbine. Finally, the equivalent parameters of wind turbine are classified based on the trajectory sensitivity, and the classified parameters are identified with a multistep identification method. The effectiveness of the proposed method is verified in a modified IEEE 39 bus system.

In order to improve the accuracy of ultrashortterm power prediction of wind turbines, this paper proposes a CNNBiLSTM ultrashortterm power prediction method considering the health status of wind turbines and dual attention mechanism. Firstly, considering the influence of the interaction between the environmental factors and the components of the wind turbine on the output power of the wind turbine, he relative error of the normal operation of each component of the wind turbine is used as the deterioration degree of the monitoring index. Secondly, the fuzzy comprehensive evaluation method assesses the health of wind turbines, and the historical data set is categorized based on the evaluation results. Finally, the dual attention mechanism CNN BiLSTM model is used to construct an ultrashortterm power prediction model for the classified data set. The experimental results show that the RMSE and MAE considering the health status of wind turbines are reduced by 17.3% and 20.5% respectively compared with the RSME and MSE without considering the health status of wind turbines.