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The energy transfer of Nd3+/Yb3+ in silicate glass with temperature was studied. The fluorescence spectra and fluorescence lifetime were measured at a series of different temperatures, and the energy transfer efficiency at different temperatures was calculated The energy transfer of Nd3+/Yb3+ in silicate glass with temperature was studied. The fluorescence spectra and fluorescence lifetime were measured at a series of different temperatures, and the energy transfer efficiency at different temperatures was calculated

With the objective of carbon neutrality, renewable energy resources gradually become the main power supply, whose variability poses great challenges to the operation and optimization of the system, especially to the power distribution network. In order to solve the problem of reactive power caused by high penetration of renewable energy sources, a centralized optimization model is proposed, which takes reactive power optimization and “generation-network-load-storage” multi-energy integration into account. The model aims at optimizing the operating cost and minimizing network losses and carbon emissions of the system. Reactive power compensation, regulation of energy storage, and energy conversion are considered to achieve safe and low-carbon economic dispatch of the power and gas systems. An improved second-order cone relaxation method is used for the convex relaxation of non-linear equality constraints concerned with the distribution network. The switching capacity produced by discrete reactive power compensators can be exactly linearized by the application of the big M approach. The simulation results demonstrate that the proposed method could effectively compensate the reactive power required by the grid-connection point of wind turbine, coordinate the energy interaction between the power and gas, thus improving the stability and elasticity of the distribution network after integration of large-scale renewable energy sources, which helps promote the consumption of renewable energy.

The resource utilization of solid waste is an important way to achieve energy saving and emission reduction. A lauric acid (LA)/raw fly ash (RFA)-diatomite(DT)/carbon nanotubes(CNT) composite form-stable phase change materials (FSPCM) for thermal energy storage were prepared via simple direct impregnation method, in which LA, RFA-DT binary matrix and CNT were employed as phase change materials(PCMs), the supporting material, and thermal conductive additive, respectively. The loading capacity, structure and thermal properties of FSCPCM were investigated through the diffusion-oozing testing, Fourier transform infrared spectrometer (FTIR), differential scanning calorimetry (DSC), thermo gravimetry analysis (TGA) and paperless recorder, respectively. The results show that the RFA-DT binary material can effectively prevent the leakage of LA. When the mass fraction of LA in composite is 28%, the FSPCM without leakage can be obtained. What’s more, the utilization rate of RFA reaches 55%. The FTIR demonstrates that FSPCM has good compatibility between its four components, LA, RFA-DT, and CNT. The melting onset temperature of FSPCM is 45.79 ℃ measured by DSC, and the corresponding latent heat of FSPCM is determined as 51.06 J/g. TGA analysis shows LA/RFA-DT has good thermal stability. The storage/release performance curve indicates that when mass fraction of CNT is added with 5%, the melting and solidification time of LA/RFA-DT/CNT are decreased by 60% and 62.5%, respectively, indicating that the heat transfer performance is remarkably improved.

The icing of wind turbine blade will seriously affect the power output of wind turbine and endanger the safe operation of wind turbine. Computational fluid dynamics (CFD) is used to study the icing characteristics of typical wind wing. Firstly, the impact characteristics of water droplets on NACA0012 type turbine under different attack angle of 0°,4°,8°are calculated. The calculated results are in good agreement with the experimental results, which shows that the calculation method is correct, and, the ice coating on the blade surface of typical wind turbine with different wind speed and temperature was simulated, and the variation trend of water collection coefficient and ice thickness with temperature and velocity was obtained. The results show that: 1) with the increase of wind speed, the ice thickness of blade surface increases and the icing adhesion area increases; 2) with the decrease of temperature, the ice thickness of blade surface increases; and 3) the anti-ice effect of S801 airfoil is obviously greater than that of S802 airfoil. The research result helps logically select turbine under low temperature conditions, providing reference for lowering the blade icing hazard.

The paper analyzes the advantages and disadvantages of two different energy management strategies which are the management strategy based on the dynamic system running state and the management strategy based on the driving cycle. The paper focuses on the study of the strategy based on the driving cycle, and points out that though the operation process of the global optimization strategy based on the driving cycle takes long and large amount of calculation so that it can hardly be used in the real vehicle. The strategy can be taken as the reference for the ideal optimization control target. In order to achieve the real-time optimal control, there are mainly the method based on the driving cycle of recognition control strategy and the method based on model predictive control, etc. It makes the exploration to predict the real-time optimal control strategy based on the driving cycle become real, and the effects of the control results get close to the offline global optimization. The proposed strategy is a new research hotspot and research trend of energy management strategy.

The existing FFT-WT algorithm and FFT-BP algorithm have advantages only for the detection of certain specific harmonics in photovoltaic systems. In this paper, the FFT-WT algorithm is improved, and a DFFT-WT algorithm is proposed. The FFT-BP algorithm is improved and an improved FFT-BP model is proposed. On the basis of this, the two are combined, the threshold interval is introduced to limit the search scope of the neural network iteration, and a DFFT-WT-BP(Double FFT-WT-BP) detection algorithm is proposed. The simulation results show that the algorithm can detect complex harmonic signals composed of multiple harmonics in the actual photovoltaic network inversion system, and is more accurate and practical.

When the wind turbine blades are running under normal conditions, the blades will vibrate due to periodic aerodynamic forces, and the use time of blades will be reduced. In order to study the vibration characteristics of wind turbine blades, this paper selects 5 operating conditions (wind speed range from 15 m/s to 40 m/s) under different wind speed conditions. The CFD method is used to simulate the NREL PHASE VI blade, and the vibration and vibration displacement curves under different wind speeds are obtained. The results show that: 1) the main vibration modes of the blade are waving and shimmy, and the high order blade vibration modes have complex deformation of bending and torsion. 2) When the inlet velocity increases from 15 m/s to 40 m/s, the uneven distribution of pressure distribution on suction surface is increasing, and the maximum pressure difference is about 3 000 Pa when the inlet velocity is 40 m/s. 3) when the flow velocity is 15 m/s, the minimum amplitude is 0.525 4 mm, and when the flow velocity is 40 m/s, the vibration reaches the maximum of 3.628 2 mm, which is about 6.9 times of the minimum amplitude. The vibration curves of the 5 operating conditions are all attenuated trend and the blades tend to stabilize vibration, and when the wind speed is larger, the aerodynamic force generated by the wind flow has a greater force on the blade, and the amplitude of the blade tends to increase. The results of this study can provide a reference for the design of wind turbines.

Supercapacitors have become a new type of energy storage device widely used due to their high efficiency, fast speed and good cycle stability, and electrode material is a key issue restricting their development. The porous niobium nitride fibers were prepared by electrospinning combined with reduction nitride technology using pentachloride as raw material, and Nb4N5||Nb4N5 symmetrical button capacitance was prepared. In order to improve the electrochemical performance of electrode material, NaHCO3 was added into Na2SO4 electrolyte. The results show that the prepared niobium nitride fiber exhibits a tetragonal phase, continuous and porous on the surface. The porous niobium nitride electrode has two mechanisms of electric double layer and pseudocapacitive energy storage. The specific capacitance of the capacitor increases to 187 F·g-1 with the NaHCO3 addition of 15 mmol·dm-3. The buffering effect inhibits the dissolution of niobium nitride, effectively reduces the solution resistance, the solution impedance R1 and the diffusion impedance WR reduce to 1.22 Ω and 1.47 Ω respectively and the ionic conductivity improves. The carrier concentration increases to 6.58×1024 cm3, and the relaxation time of the capacitor is shortened to 0.24 s at the same time.

As the most complex component in the transmission system, the operating state of the wind turbine gearbox has a tremendous impact on the monitoring of the health status and operation control of the wind turbine equipment. Abnormalities in wind turbines that lead to downtime not only result in a loss of electrical energy, but also a significant increase in maintenance costs. Therefore, with the wind turbine gearbox as the main object of study, the following studies were carried out: For microscopic local conditions in gearbox gear systems, a method for obtaining modal data using finite element simulation analysis of single tooth faults is proposed. Using a combination of deep auto-encoder structures and BP structures for secondary training strategies, a linear and non-linear performance evaluation method is proposed, which takes into account the relationship between performance and efficiency. Hyper-parameter configuration in deep transfer structures is often arbitrary, so a hierarchical transfer network structure hyper-parameter searching method is proposed to address the gearbox planetary system fault classification problem. The algorithm is validated using the classical LeNet-5 reconfiguration transfer application on a modal dataset of the planetary system. Finally, a stability validation and results analysis of the algorithm performance is carried out. A compressed sensing-based sparse signal decomposition method is proposed, and the structure of the transfer network is redesigned to achieve deep migration learning from rolling bearing faults to gear faults. A new network architecture was designed using a plug-and-play attention module. Pre-training models were designed and produced for fault data to improve the accuracy and recognition speed of fault diagnosis model classification. Finally, the effects of the same number of samples in the source and target domains and different distributions of sample features on the performance of the transfer learning method and the effects of hyper-parameters on the final performance of the network structure are verified.