基于重要度排序的配电自动化终端优化布局方法

针对配电自动化终端优化布局问题，提出了一种基于重要度排序的终端优化布局方法。首先以等年值综合费用为目标函数，供电可靠性和投入产出比为双重约束建立了配电自动化终端优化布局模型。然后通过分析配电自动化终端对供电可靠性提升的影响，给出各个节点的“二遥”及“三遥”终端安装重要度定义及计算公式。最后采用枚举法确定最优终端安装数量，基于节点终端安装重要度排序确定终端的最优安装位置。该方法考虑了已布局节点对剩余节点终端安装重要度的影响，能够在降低计算量的同时兼顾布局合理性。运用所提方法RBTS-BUS2系统及扩充模型进行终端优化布局并与智能优化算法的布局结果进行对比，验证了本文方法的有效性及优越性。     

分布式多元随机动态场景生成及快速精准场景降维算法

高比例新能源及多源耦合是电力系统发展的重要特征,这也为系统稳定经济运行提出了新挑战。该文以园区型多能系统为对象,研究了分布式多元随机动态场景分析,从多时空角度有效量化不确定因素给系统造成的影响,可为系统灵活重构、多维度协同运行与决策提供有力模型与场景支撑。首先由预测误差驱动拟合多元功率预测误差概率分布,全面反映随机功率出力信息,提高模型泛化性;以时序相关范围参数为数据驱动关联变量,高效动态控制波动强度;最终场景生成利用逆变换映射思想保证置信度。然后针对典型场景提取,提出一种综合递归聚类思想的多段嵌套削减算法,结合改进Wasserstein距离指标,兼具准确、时效、稳定方面的优势。最后由对比实验论证该方法的前沿有效性。     

不同进水流速对泵站进水池漩涡的影响

为探究泵站进水流速大小与泵站进水池水流流态、漩涡的产生与发展变化规律,结合泵站实际运行情况,建立引渠、前池、进水池和进水管的泵站物理模型和湍流数学模型,采用VOF模型和非定常的SST k-ω湍流模型对9种不同流速的泵站进水水流特性进行数值模拟,分析不同进水流速的泵站进水池水流流场分布、漩涡涡量的变化及分布规律。研究结果表明:当进水流速为0.322 2～0.564 2 m/s时,泵站表面漩涡的强度随进水流速的增大而增强:当进水流速为0.322 2～0.401 6 m/s时,进水池出现Ⅲ、Ⅳ型漩涡;当进水流速为0.483 5 m/s时,进水池出现Ⅴ型漩涡;当进水流速为0.520 8～0.564 2 m/s时,进水池出现Ⅵ型漩涡。将数值计算结果与模型试验结果进行对比,两者基本吻合。研究结果可为泵站工程设计提供参考。     

IRS辅助的SWIPT系统中基于能效优先的波束成形设计与优化

以智能反射面(intelligent reflecting surface,IRS)辅助的无线携能通信(simultaneous wireless information and power transfer,SWIPT)系统为背景,研究了该系统中基于能效优先的多天线发送端有源波束成形与IRS无源波束成形联合设计与优化方法。以最大化接收端的最小能效为优化目标,构造在发送端功率、接收端能量阈值、IRS相移等多约束下的非线性优化问题,用交替方向乘子法(alternating direction method of multipliers,ADMM)求解。采用Dinkelbach算法转化目标函数,通过奇异值分解(singular value decomposition,SVD)和半定松弛(semi-definite relaxation,SDR)得到发送端有源波束成形向量。采用SDR得到IRS相移矩阵与反射波束成形向量。结果表明,该系统显著降低了系统能量收集(energy harvesting,EH)接收端的能量阈值。当系统总电路功耗为?15 dBm时,所提方案的用户能效为300 KB/J。当IRS反射阵源数与发送天线数均为最大值时,系统可达最大能效。     

一种用于电气设备状态监测的新式FBG传感器北大核心CSCD

为了监测绕组变压器的静态应力场和发生短路等故障时的动态应力变化,设计了一种用于电气设备状态监测的新式FBG传感器。该传感器由聚醚醚酮材料封装的FBG构成,通过内部圆锥形空腔结构实现将轴向应力集中于FBG敏感位置。通过仿真对不同压力强度下传感器结构的应力场部分及形变趋势进行了计算与分析,论证了设计的合理性。实验分别对静态载荷和动态冲击进行测试,结果显示,在静态压载测试中,当100 N相似文献   

煤矿水仓清理新工艺

通过调研现场情况以及检测煤泥特性，探寻一种实用性强、方法简单的新工艺来替代劳累的人工和繁琐的机械清淤，解决现实问题。针对实际情况开发清仓新工艺，介绍了该工艺的技术路线选择、设计依据以及实际应用效果。该新工艺具有降低清仓时间、减轻工人劳动强度、设备操作简单、实用性强等特点。     

Techno-economic-environmental evaluation of a combined tri and dry reforming of methane for methanol synthesis with a high efficiency CO2 utilization

Production of methanol, as a green energy, from syngas is coming into focus. However, natural gas based methanol plants, which are used steam reforming of methane for syngas production, have a high CO₂ emission resulting in the global warming. In this study, a novel process for methanol synthesis is proposed to reduce CO₂ emission. In this regard, natural gas and flue gas are fed to a parallel-series system with tri and dry reforming of methane for syngas production with the optimized stoichiometric number. Then, the produced syngas is converted to methanol in a reactor. Finally, the produced methanol is purified by two distillation towers. The proposed method is compared to a referenced method in the view of technological, economic and environmental metrics. The techno-economic-environmental analysis of the processes reveals that not only the proposed method, as compared to the referenced one, increases CO₂ conversion from 20.93% to 99.22%, but also it is more economical and environmentally friendly. In addition, the global warming potential of the proposed method is almost 60% lower than that for the referenced method due to the lower CO₂ emission. Therefore, the proposed method can save above MUS$ 8 a year by CO₂ capture.     

Application of artificial neural network method to predict the breakage properties of PGE bearing chromite ore

In the present investigation, systematic grinding experiments were conducted in a laboratory ball mill to determine the breakage properties of low-grade PGE bearing chromite ore. The population balance modeling technique was used to study the breakage parameters such as primary breakage distribution (B_{i, j}) and the specific rates of breakage (S_i). The breakage and selection function values were determined for six feed sizes. The results stated that the breakage follows the first-order grinding kinetics for all the feed sizes. It was observed that the coarser feed sizes exhibit higher selection function values than the finer feed size. Further, an artificial neural network was used to predict breakage characteristics of low-grade PGE bearing chromite ore. The predicted results obtained from the neural network modeling were close to the experimental results with a correlation of determination R² = 0.99 for both product size and selection function.     

Thermal properties of hierarchical YSZ and LZO ceramic microspheres with multi-scaled voids investigated by using theoretical,experimental and simulation methods

Heat transfer within ceramic feedstock powders is still unclear, which impedes optimization of the thermal and mechanical properties of the thermal sprayed coatings. The microspheres (yttria-stabilized zirconia YSZ and lanthanum zirconate LZO) were prepared via the electro-spraying assisted phase inversion method (ESP). The thermal properties of the two ESP microspheres and a commercial hollow spherical powder (HOSP) were investigated by using theoretical, experimental, and simulation methods. Thermal conductivity of the single microsphere was estimated via a novel nest model that was derived from the Maxwell-Eucken 1 and the EMT model. Thermal conductivity of a single YSZ/LZO-ESP microsphere prepared at 1100–1200 °C was within 0.36–0.75 W/m K, which was ～ 20 % lower than that of a single YSZ-HOSP microsphere with a similar porosity. Heat flux simulation showed that high tortuosity around the multi-scaled voids of the ESP microsphere led to a more efficient decrease in thermal conductivity compared with total porosity.