Multi-objective optimal allocation strategy for the energy internet in Huangpu District,Guangzhou, China

To improve the overall efficiency of the energy system, the basic structure for the energy internet of coordination and optimization of “generation-grid-load-storage” of Huangpu District, Guangzhou, China is designed, while the arrangement for the output of centralized and distributed energy module and energy storage are proposed. Taking economic benefit maximization, environmental benefit maximization and energy efficiency maximization as sub-objectives, the mathematical model of multi-objective optimal allocation and operation strategy of the energy internet is established considering supply-demand balance constraints, equipment characteristic constraints, operation mode constraints, and energy conditions constraints. The calculation results show that without considering the outsourced electricity, the balanced strategy, the economic development strategy, the environmental protection strategy, and the energy efficiency strategy are obtained by calculation, which are all superior to the traditional energy supply strategy. Moreover, considering the outsourced electricity, the proportion of outsourced electricity to total electricity is 19.8%, which is the system optimization of the energy internet under certain power demand. Compared with other strategies without outsourced electricity, the outsourced electricity strategy can have a certain emission reduction effect, but at the same time reduce the economic benefit. Furthermore, the huge difference in demand for thermal and cooling load between industrial and commercial areas results in the installed capacity of gas distributed energy stations in industrial areas being nearly twice as large as that in commercial areas. The distributed photovoltaic power generation is allocated according to the proportion of the installed roof areas of photovoltaic power generation system in residential, industrial, and commercial areas.     

基于深度学习的传感器优化布置方法

针对传感器优化布置(optimal sensor placement,简称OSP)问题,提出了一种新的使用深度神经网络的解决方案,并以简化的桥梁形状的桁架结构中的振动测试传感器优化为例进行了验证。首先,选择一种传统的传感器优化布置方法,对自动化生成的大量不同的桁架结构分别进行传感器优化布置计算,将所得优化布置结果在进行数据预处理后构建出深度学习方法所需要的训练集与验证集;其次,使用Python语言和深度学习框架TensorFlow设计实现与本研究问题适配的深度神经网络模型并训练;然后,随机生成了新的桁架结构参数;最后,将深度神经网络输出的传感器布置结果和传统方法的计算结果进行了比较,验证了本研究方法的有效性以及在速度上、可移植性与可扩展性方面的性能优势。     

基于变压器最优漏磁信息的绕组变形分类方法

为了判别电力变压器绕组变形状况,提出一种基于最优漏磁信息及多层聚类编码和最优权重译码的ECOC分类器对变压器绕组变形分类的方法,首先利用多层聚类算法构建最优ECOC编码矩阵,然后通过最优权重译码算法对分类器输出进行译码得出变压器绕组具体变形类别,建立变压器绕组二维漏磁场有限元模型,计算得出绕组可能出现的变形形式和绕组区域变形前后的磁感应强度值数据,从而得出磁感应强度测量点;最后利用所建立模型得出的绕组变形数据进行仿真判别。结果表明,所建立的分类器在绕组变形判别时具有较高的准确性,可用于变压器绕组变形类型的检测。     

枞树型核电轮槽精铣刀制造技术优化设计

以枞树型叶根精铣刀为例,阐述了可换硬质合金型线刀体和刀片精铣刀的优化设计、制造工艺、建模及优化设计,进行切削过程加工策略优化和加工过程模拟,设计制造了锥柄专用工装及检测环规、磨削刀片后角专用工装,采用NUMROTO软件编制刀片加工工步。该项目研制的可换刀片精铣刀型线精度完全能达到进口铣刀水平,能满足大部分轮槽与叶根加工对型线精度的要求,可以大幅度节约刀具费用。     

压力容器设备壁厚优化设计

为了确保压力容器应用的高效与安全,本文主要是针对压力容器设备壁厚优化设计进行简要分析。     

干湿联合冷却塔冷却节水分析软件开发及应用

为实现对干湿联合冷却塔冷却特性、节水特性的综合分析，编制干湿联合冷却塔校核计算流程，基于Visual Studio开发平台，本文开发了干湿联合冷却塔冷却节水特性分析优化软件。通过对某在运干湿联合冷却塔进行冷却节水特性计算，验证了所开发软件计算结果的准确性；在消雾节水计算结果基础上，对标干湿联合消雾冷却塔验收测试规程，生成其成雾频率曲线、耗水量曲线、塔雾指数、冷却特性曲线，综合分析其消雾特性、节水特性及冷却特性，从而确定了干湿联合冷却塔的最佳运行工况。同时运用所开发软件对比分析了某冷却塔设计干段散热面积对其消雾特性、节水特性及冷却特性的耦合影响，并分析了百叶窗开度对干湿联合冷却塔性能的影响，为干湿联合冷却塔的运行优化和设计优化提供了工具性软件支持。     

论三峡水库“蓄清排浑”运用方式及其优化

泥沙问题是三峡工程的关键技术问题之一,在三峡工程论证和初步设计阶段提出水库采取"蓄清排浑"的运用方式,可以解决泥沙问题。2003年水库蓄水运用以来的实践表明,水库基本遵循了"蓄清排浑"调度运用原则,并根据上游来水来沙减少等新情况对水库运用方式进行了优化调整。本文系统分析了三峡水库"蓄清排浑"运行方式及其优化调整的利弊,包括提前5年实施175 m试验性蓄水、汛期水位动态变化、汛末提前蓄水等对水库淤积和坝下游河道演变的影响,提出了进一步优化水库调度方式,形成"蓄清排浑"运用的新模式,保持水库长期使用的建议,试图为三峡水库科学高效安全运用提供科技支撑。     

建筑材料供应中断对承包商成本的冲击研究——基于鲁棒模型分析

为了揭示供应商与承包商为主体的二级供应链中，两者间耦合作用条件下发生的供应中断对供应商最优决策及对承包商成本的影响机制，且剖析供应中断对承包商成本的冲击效应，以供应商存在高退出风险为研究对象，在仅知产量均值与方差条件下，运用Scarf提出的“极大极小值”鲁棒决策方法，通过设定不同决策情况下的供应链系统反馈，分析了不同退出标准情况下二级供应链的博弈过程。基于理论支撑下的鲁棒模型数值仿真结果得出：在退出标准α为100时，中间变量在290~350范围内，供应商存在最优决策，但承包商最优决策无法确定；与α为0时相比，在订货量达到一定状态，即α为140时，承包商成本增加了1.5%，表明承包商成本与供应商退出标准呈正相关，退出标准越高，对承包商成本造成的冲击效应越大。此研究结论为供应链初期不稳定且信息不完整条件下发生的供应中断影响机理提供了新的视角。     

Robustness of asymptotic accelerated life test plans to small‐sample settings

Much of the statistical literature on optimal test planning for accelerated life testing utilize asymptotic methods to derive optimal test plans. While sufficient effort is made to assess the robustness of these test plans to the choice of design parameters and distribution assumptions, there is very little literature on the performance of asymptotic test plans relative to small samples (on the order of 10‐15 samples). An alternative concern is that the asymptotic test plans may not necessarily be the true “optimal” test plan for a given sample size. The purpose of this research is to present exact or “near‐exact” methods for developing test plans and compare the performance of these test plans with corresponding asymptotic test plans in small‐sample settings. The optimal location of design points and sample allocation is determined using each method for lognormal and Weibull lifetime distributions with both complete and Type 1 right‐censored data under two selected acceleration factor models. The investigations reveal that asymptotic test plans tend to corroborate quite well with exact test plans and thus are suitably robust to small‐sample settings in terms of optimal variance.