An enhanced data-driven polynomial chaos method for uncertainty propagation

As a novel type of polynomial chaos expansion (PCE), the data-driven PCE (DD-PCE) approach has been developed to have a wide range of potential applications for uncertainty propagation. While the research on DD-PCE is still ongoing, its merits compared with the existing PCE approaches have yet to be understood and explored, and its limitations also need to be addressed. In this article, the Galerkin projection technique in conjunction with the moment-matching equations is employed in DD-PCE for higher-dimensional uncertainty propagation. The enhanced DD-PCE method is then compared with current PCE methods to fully investigate its relative merits through four numerical examples considering different cases of information for random inputs. It is found that the proposed method could improve the accuracy, or in some cases leads to comparable results, demonstrating its effectiveness and advantages. Its application in dealing with a Mars entry trajectory optimization problem further verifies its effectiveness.     

数据驱动的能源互联网建模与仿真关键技术

针对能源互联网面临的多能流运行复杂性、源荷不确定性等日益突出的问题,基于能源生产、传输与消费各环节数据信息,文章设计了一种包含物理层、感知网络层、模型层、算法层、应用层的数据驱动能源互联网建模与仿真框架,并提出能源互联网数据与物理融合统一建模、分布式能源及负荷概率预测、典型运行场景生成以及多能流优化运行等关键技术,最后展望典型应用场景。以数据驱动为核心,为能源互联网建设提供技术支撑。     

Distributed data-driven observer for linear time invariant systems

This paper is concerned with distributed data-driven observer design problem. The existing data-driven observers rely on a common assumption that all the information about the system, and the calculations based upon this information are centralized. Therefore the resulting algorithms cannot be applied to the distributed systems in which each local observer receives only a part of the output signal. On the other hand, traditional model-based distributed state estimation methods generally assume that the processes are decomposed according to the known process models, while in data-driven approaches there is no such information available. The main goal of this paper is to extend the centralized data-driven observer design approach to the distributed framework. The stability of the proposed data-driven distributed observer is also proved analytically. A quadruple-tank process is simulated to demonstrate the performance of the proposed scheme.     

Modelling the propagation of social response during a disease outbreak

Epidemic trajectories and associated social responses vary widely between populations, with severe reactions sometimes observed. When confronted with fatal or novel pathogens, people exhibit a variety of behaviours from anxiety to hoarding of medical supplies, overwhelming medical infrastructure and rioting. We developed a coupled network approach to understanding and predicting social response. We couple the disease spread and panic spread processes and model them through local interactions between agents. The social contagion process depends on the prevalence of the disease, its perceived risk and a global media signal. We verify the model by analysing the spread of disease and social response during the 2009 H1N1 outbreak in Mexico City and 2003 severe acute respiratory syndrome and 2009 H1N1 outbreaks in Hong Kong, accurately predicting population-level behaviour. This kind of empirically validated model is critical to exploring strategies for public health intervention, increasing our ability to anticipate the response to infectious disease outbreaks.     

Dynamic simulation of gas turbines via feature similarity-based transfer learning

Since gas turbine plays a key role in electricity power generating, the requirements on the safety and reliability of this classical thermal system are becoming gradually strict. With a large amount of renewable energy being integrated into the power grid, the request of deep peak load regulation for satisfying the varying demand of users and maintaining the stability of the whole power grid leads to more unstable working conditions of gas turbines. The startup, shutdown, and load fluctuation are dominating the operating condition of gas turbines. Hence simulating and analyzing the dynamic behavior of the engines under such instable working conditions are important in improving their design, operation, and maintenance. However, conventional dynamic simulation methods based on the physic differential equations is unable to tackle the uncertainty and noise when faced with variant real-world operations. Although data-driven simulating methods, to some extent, can mitigate the problem, it is impossible to perform simulations with insufficient data. To tackle the issue, a novel transfer learning framework is proposed to transfer the knowledge from the physics equation domain to the real-world application domain to compensate for the lack of data. A strong dynamic operating data set with steep slope signals is created based on physics equations and then a feature similarity-based learning model with an encoder and a decoder is built and trained to achieve feature adaptive knowledge transferring. The simulation accuracy is significantly increased by 24.6% and the predicting error reduced by 63.6% compared with the baseline model. Moreover, compared with the other classical transfer learning modes, the method proposed has the best simulating performance on field testing data set. Furthermore, the effect study on the hyper parameters indicates that the method proposed is able to adaptively balance the weight of learning knowledge from the physical theory domain or from the real-world operation domain.     

[数据驱动的智能服务]数据驱动的复杂产品智能服务技术与应用

随着传感器、数据采集装置和其他具备感知能力的模块在复杂产品服务运行阶段的应用，复杂产品运维系统的数字化和智能化程度越来越高，具有实时、多源、异构、海量等特性的数据成为提高复杂产品系统可靠和低成本运行的决策依据，数字孪生技术提供了一种有效途径。介绍了数据驱动的复杂产品智能服务研究进展；分析了数据驱动的智能服务基本特征与框架模型；提出了数据驱动的复杂产品智能服务方法，主要包括面向服务的复杂产品建模与仿真方法、数据驱动的服务需求获取与精准分析预测方法、基于数字孪生的设备故障识别与动态性能预测方法、数据驱动的装备视情维修与备件库存联合多目标决策优化方法、基于数字孪生的复杂产品辅助维修技术、多要素协同的复杂装备能效精准分析预测方法、基于数据挖掘的复杂产品运行优化控制方法等；给出了智能服务系统的应用案例。所提出的框架和方法可为现代制造服务的智能化转型升级提供参考。     

飞机完好率预测仿真研究

利用飞机完好率时间序列特性,建立了NAR神经网络模型和基于不同核函数的3种支持向量机模型对平时状态下的飞机完好率变化趋势进行建模、训练和预测;运用Matlab仿真软件进行试验验证,结果表明:支持向量机模型具有较好的拟合效果,预测精度优于NAR神经网络模型,基于RBF核函数的支持向量机预测准确率相对较高.两种预测模型相比于部队现行的预测方法均具有更高的准确度和可靠度.     

真实数据驱动下的变电站三维智能仿真技术研究

随着大数据技术的发展,面向变电站三维虚拟场景,考虑变电站三维仿真模型的准确性和仿真环境的真实性,融合色彩、声音、图像等真实数据高效模拟变电站的各种操作和设备拆装。从数据层、系统设计层、功能应用层等3方面详细分析了变电站多维多媒体仿真系统架构,以变压器为例分析了变电站主设备结构建模与精准拆装设计流程,最后引入顶点纹理技术,考虑变电站不同运行状态下的动态色差,融合变电站真实数据驱动,实现动态色彩渲染与声效模拟。基于主场景漫游、GIS漏气与主设备着火等事故示例验证本文仿真渲染技术的有效性。     

基于多元大数据平台的用电行为分析构架研究

随着智能电网的发展,越来越多的测量装置向底层延伸。高级测量体系和配电网的发展不可避免地使用户用电数据量呈几何倍数增长,另一方面,电网也在积极寻求方法让需求侧可以充分地参与电网调控,增强电网可控性和经济性。在上述背景下,运用配用电数据分析用户用电行为建立相关驱动方法,可充分利用现有资源,为政府政策制定、电力公司业务拓展和用电行为引导提供新的解决思路。在配用电数据采集、聚合、处理和应用等方面提出了以大数据平台为基础的整体构架,设计了基于流处理和批处理的数据驱动方法,提出了适用于多维大数据用电行为分析的随机矩阵相关性算法,最后讨论了用电行为分析面向不同对象的应用场景。     

基于人工神经网络的电力系统精细化安全运行规则

随着大规模可再生能源不断并网,对电网的实时调控能力提出了更高的要求。传统的基于在线关键断面自动发现以及基于连续潮流的在线极限传输容量计算方法,模型复杂、计算周期长,难以做到在线运行。从数据驱动的角度出发,首先将电网实时运行状态的潮流量抽象为该时刻电网的运行特征;然后对所有特征进行聚类和分布式特征选择;最后运用人工神经网络建立所选特征与关键断面极限传输容量之间的对应关系。算例分析表明,所提基于人工神经网络的电力系统精细化安全运行规则,在保证时间效率的前提下,能够在一定程度上提高关键断面极限传输容量的预测准确度。