基于网源协同的300MW 机组负荷快调及综合调频技术研究

大力发展新能源和大规模外电入鲁是山东实现新旧动能转换、推动经济快速平稳发展的关键之举,截至017年,新能源发电量和外电入鲁电量已占全省发电量的40.98％,到2018年风电和光电装机容量已达到千万千瓦 级别.但是,新能源受天气因素制约,一直处于随机波动状态,实时干扰电网频率,直接影响人民生活和工业生产, 甚至会产生弃风弃光事件;而特高压直流输电线路一旦出现故障,就会产生巨大供电缺口,导致电网频率突降,极有 可能引发大面积停电事故,造成重大经济损失和不良社会影响.因此,火电机组必须具备良好的一次调频和 AGC等 涉网性能,才能弥补新能源发电量波动和外电入鲁故障造成的频率波动,才能破解新能源消纳难题,为准确应对外电 故障提供数据支撑。     

Spherical fuzzy decision making method based on combined compromise solution for IIoT industry evaluation

The Industrial Internet of Things is crucial for enterprise and country to drive the strategic upgrade and raise the level of national intelligent manufacturing. When pondering the IIoT industry evaluation, the corresponding dominating issues involve numerous indeterminacies. Spherical fuzzy set, portrayed by memberships of positive, neutral and negative, is a more efficient methods of seizing indeterminacy. In this article, firstly, the fire-new spherical fuzzy score function is explored for solving some suspensive comparison issues. Moreover, the objective weight and combined weight are determined by Renyi entropy method and non-linear weighted comprehensive method, respectively. Later, the multi-criteria decision making method based on combined compromise solutionis developed under spherical fuzzy environment. Finally, the corresponding method is effectively validated by the issue of IIoT industry evaluation. The main characteristics of the presented algorithm are: (1) without counterintuitive phenomena; (2) no division or antilogarithm by zero problem; (3) no square root by negative number issue; (4) no violation of the original definition issue.

     

设计语用学的范式研究

目的确定设计语用学研究范畴,建立设计语用学的研究范式,进而通过设计语用学理论发展对设计方法论提供新的理论依据。方法从设计符号传播语境视角出发,对设计方、用户和用户的社交对象之间的设计符号传播模式进行论述。结论结合整合营销传播理论和设计事理学理论,得出了设计语用学的研究范畴、研究意义、研究核心及整合产品设计营销传播与定义设计符号使用场景两种设计语用学的研究路径,完成了设计语用学的理论框架。     

Large capacitance and fast polarization response of thin electrolyte dielectrics by spin coating for two-dimensional MoS2 devices

Nano Research - A spin-coating method was applied to obtain thinner and smoother PEO/LiClO4 polymer electrolyte films (EFs) with a lower level of crystallization than those obtained using a...     

Physics-Informed Multistage Machine Learning Strategy for the Nanomachining-Induced Plastic Deformation Behavior

The evolution of the dislocation density induced by the nanomachining process dominates the plastic deformation behaviors of materials, thus affecting the mechanical properties significantly. However, a challenging topic related to how to establish an accurate model for predicting the dislocation density based on the limited simulations and experiments arises due to the complicated thermal–mechanical coupling mechanism during the machining process. Herein, a multistage method integrating machine learning, physics, and high-throughput atomic simulation is proposed to investigate the effect of cutting speed on the dislocation behavior in polycrystal copper. Compared with the traditional one-step machine learning method, the constraint of physical features effectively improves the accuracy and generalization ability of the model. The results indicate that the dislocation behaviors depend on the competition between the cutting force and temperature. In the low-cutting speed, the predominated role of the cutting temperature leads to a rapid decline of the dislocation density. In contrast, the dislocation density tends to be stable under a high-speed cutting process due to the dynamic balance between the effects of the cutting force and temperature. Notably, the proposed strategy provides a new and universal framework to design the machining parameters to obtain high-quality products.