Multi-Input Deep Convolutional Neural Network Model for Short-Term Power Prediction of Photovoltaics.

Along with the increasing prominence of energy and environmental issues, solar energy has received more and more extensive attention from countries around the world, and the installed capacity of photovoltaic power generation, as one of the main forms of solar energy development, has developed rapidly. Solar energy is by far the largest available source of energy on Earth, the use of solar power photovoltaic system has the advantages of flexible installation, simple maintenance, environmentally friendly, etc., by the world's attention, especially the grid-connected photovoltaic power generation system has been rapid development. However, photovoltaic power generation itself is intermittent, affected by irradiance and other meteorological factors very drastically, and its own randomness and uncertainty are very large, and its grid connection affects the stability of the entire power grid. Therefore, the short-term prediction of photovoltaic power generation has important practical significance and guiding meaning. Multi-input deep convolutional neural networks belong to deep learning architectures, which use local connectivity, weight sharing, and subpolling operations, making it possible to reduce the number of weight parameters that need to be trained so that convolutional neural networks can perform well even with a large number of layers. In this paper, we propose a multi-input deep convolutional neural network model for PV short-term power prediction, which provides a short-term accurate prediction of PV power system output power, which is beneficial for the power system dispatching department to coordinate the cooperation between conventional power sources and PV power generation and reasonably adjust the dispatching plan, thus effectively mitigating the adverse effects of PV power system access on the power grid. Therefore, the accurate and reasonable prediction of PV power generation power is of great significance for the safe dispatch of power grid, maintaining the stable operation of power grid, and improving the utilization rate of PV power plants.

Fast computational depth segmentation using orthogonal fringe patterns without pattern sequence changing.

The recently proposed omnidirectional depth segmentation method (ODSM) has advantages over traditional depth segmentation in terms of robustness and computational costs. However, this method uses at least six fringe patterns and changes their sequences multiple times to perform depth segmentation, which limits its segmentation speed and increases computational complexity. This paper proposes a fast computational depth segmentation (FCDS) method in which only five patterns are used for object segmentation at different depths into isolated regions without the requirement of pattern sequence changing. Phase singularity points are fully utilized due to their significance as depth segmentation markers to extract segmenting lines used for depth determination. Meanwhile, a modified Fourier transform algorithm (MFTA) is introduced to calculate the wrapped phase sequences, which uses two groups of orthogonal phase-shifting fringe patterns and a DC component pattern (five in total). The segmenting lines along orthogonal directions can be extracted with the FCDS method without changing the fringe sequences, which not only solves the problem of phase insensitivity but reduces the calculation costs. Besides, the problem of mis-segmentation is solved with an optimization algorithm for depth segmenting lines and successfully segments objects with abrupt depth changes. The simulation results demonstrate the effectiveness and precision of the proposed method. The experimental results prove the success of the proposed method for segmenting objects of similar color with a segmentation speed that is up to a 120% increase relative to previous methods.

Passive Film Properties of Bimodal Grain Size AA7075 Aluminium Alloy Prepared by Spark Plasma Sintering.

The bimodal-grain-size 7075 aluminium alloys containing varied ratios of large and small 7075 aluminium powders were prepared by spark plasma sintering (SPS). The large powder was 100 ± 15 µm in diameter and the small one was 10 ± 5 µm in diameter. The 7075 aluminium alloys was completely densified under the 500 °C sintering temperature and 60 MPa pressure. The large powders constituted coarse grain zone, and the small powders constituted fine grain zone in sintered 7075 aluminium alloys. The microstructural and microchemical difference between the large and small powders was remained in coarse and fine grain zones in bulk alloys after SPS sintering, which allowed for us to investigate the effects of microstructure and microchemistry on passive properties of oxide film formed on sintered alloys. The average diameter of intermetallic phases was 201.3 nm in coarse grain zone, while its vale was 79.8 nm in fine grain zone. The alloying element content in intermetallic phases in coarse grain zone was 33% to 48% higher than that on fine grain zone. The alloying element depletion zone surrounding intermetallic phases in coarse grain zone showed a bigger width and a more severe element depletion. The coarse grain zone in alloys showed a bigger electrochemical heterogeneity as compared to fine grain zone. The passive film formed on coarse grain zone had a thicker thickness and a point defect density of 2.4 × 1024 m-3, and the film on fine grain zone had a thinner thickness and a point defect density of 4.0 × 1023 m-3. The film resistance was 3.25 × 105 Ωcm2 on coarse grain zone, while it was 6.46 × 105 Ωcm2 on fine grain zone. The passive potential range of sintered alloys increased from 457 mV to 678 mV, while the corrosion current density decreased from 8.59 × 10-7 A/cm2 to 6.78 × 10-7 A/cm2 as fine grain zone increasing from 0% to 100%, which implied that the corrosion resistance of alloys increased with the increasing content of fine grains. The passive film on coarse grain zone exhibited bigger corrosion cavities after pitting initiation compared to that on fine grain zone. The passive film formed on fine grain zone showed a better corrosion resistance. The protectiveness of passive film was mainly determined by defect density rather than the thickness in this work.