Model Predictive Control for Cascaded H-Bridge PV Inverter with Capacitor Voltage Balance

We designed an improved direct-current capacitor voltage balancing control model predictive control(MPC)for single-phase cascaded H-bridge multilevel photovoltaic(PV)inverters.Compared with conventional voltage balanc-ing control methods,the method proposed could make the PV strings of each submodule operate at their maximum power point by independent capacitor voltage control.Besides,the predicted and reference value of the grid-connected current was obtained according to the maximum power output of the maximum power point tracking.A cost function was con-structed to achieve the high-precision grid-connected control of the CHB inverter.Finally,the effectiveness of the proposed control method was verified through a semi-physical simulation platform with three submodules.

A Cascading Fault Path Prediction Method for Integrated Energy Distribution Networks Based on the Improved OPA Model under Typhoon Disasters

In recent times,the impact of typhoon disasters on integrated energy active distribution networks(IEADNs)has received increasing attention,particularly,in terms of effective cascading fault path prediction and enhanced fault recovery performance.In this study,we propose a modified ORNL-PSerc-Alaska(OPA)model based on optimal power flow(OPF)calculation to forecast IEADN cascading fault paths.We first established the topology and operational model of the IEADNs,and the typical fault scenario was chosen according to the component fault probability and information entropy.The modified OPA model consisted of two layers:An upper-layer model to determine the cascading fault location and a lower-layer model to calculate the OPF by using Yalmip and CPLEX and provide the data to update the upper-layer model.The approach was validated via the modified IEEE 33-node distribution system and two real IEADNs.Simulation results showed that the fault trend forecasted by the novel OPA model corresponded well with the development and movement of the typhoon above the IEADN.The proposed model also increased the load recovery rate by>24%compared to the traditional OPA model.

Nucleation of Supercooled Water by Neutrons: Latitude Dependence and Implications for Cloud Modelling

It has recently been shown that incident particles, neutrons, can initiate the freezing in a supercooled water volume. This new finding may have ramifications for the interpretation of both experimental data on the nucleation of laboratory samples of supercooled water and perhaps more importantly on the interpretation of ice nucleation involved in cloud physics. For example, if some fraction of the cloud nucleation previously attributed to dust, soot, or aerosols has been caused by cosmogenic neutrons, fresh consideration is required in the context of climate models. Moreover, as cosmogenic neutrons, most being muon-induced, have much greater flux at high latitudes, estimates of ice nucleates in these regions may be larger than required to accurately model cloud and condensation properties. This discrepancy has been pointed out in IPCC reports. Our paper discusses the connection between the new concept of neutrons nucleating supercooled water and the need for a new source of nucleation in high latitude clouds, ideally causing others to review current data, or to analyse future data with this idea in mind. .

Multivariable identification of membrane fouling based on compacted cascade neural network

The membrane fouling phenomenon,reflected with various fouling characterization in the membrane bioreactor(MBR)process,is so complicated to distinguish.This paper proposes a multivariable identification model(MIM)based on a compacted cascade neural network to identify membrane fouling accurately.Firstly,a multivariable model is proposed to calculate multiple indicators of membrane fouling using a cascade neural network,which could avoid the interference of the overlap inputs.Secondly,an unsupervised pretraining algorithm was developed with periodic information of membrane fouling to obtain the compact structure of MIM.Thirdly,a hierarchical learning algorithm was proposed to update the parameters of MIM for improving the identification accuracy online.Finally,the proposed model was tested in real plants to evaluate its efficiency and effectiveness.Experimental results have verified the benefits of the proposed method.

DISOPE distributed model predictive control of cascade systems with network communication

A novel distributed model predictive control scheme based on dynamic integrated system optimization and parameter estimation （DISOPE） was proposed for nonlinear cascade systems under network environment. Under the distributed control structure, online optimization of the cascade system was composed of several cascaded agents that can cooperate and exchange information via network communication. By iterating on modified distributed linear optimal control problems on the basis of estimating parameters at every iteration the correct optimal control action of the nonlinear model predictive control problem of the cascade system could be obtained, assuming that the algorithm was convergent. This approach avoids solving the complex nonlinear optimization problem and significantly reduces the computational burden. The simulation results of the fossil fuel power unit are illustrated to verify the effectiveness and practicability of the proposed algorithm.

一种基于Si/SiC级联H桥逆变器的高性能模型预测控制方法

Si/SiC级联H桥逆变器,能够利用不同器件的开关组合保证低输出电流谐波畸变率(Total Harmonic Distortion,THD)和装置效率,但也带来了Si/SiC子模块开关分配的难题.对此,本论文设计一种变权重的模型预测控制方法(Model Predictive Control,MPC)选择总开关状态并分配子模块开关组合.该方法在选取逆变器总开关状态和Si/SiC子模块开关组合的代价函数中引入基于器件开关损耗的变权重,以改善逆变器的效率和输出电流谐波畸变率.在五电平Si/SiC级联H桥逆变装置上验证了变权重MPC的有效性,相比于固定权重MPC,输出电流THD最多降低2.05%,装置损耗最多降低4.53%.

MOSAiC现场观测期间海冰厚度季节变化模拟误差分析

北极气候研究多学科漂流观测计划(Multidisciplinary drifting Observatory for the Study of Arctic Climate, MOSAiC)于2019年10月至2020年9月开展,期间获得了变量完整的大气、海洋、海冰厚度及积雪厚度观测,为海冰模式的发展提供了新的契机。本研究利用两个完整观测时段(2019年11月1日至2020年5月7日、2020年6月26日至7月27日)的大气和海洋强迫场,驱动一维海冰柱模式ICEPACK,模拟了MOSAiC期间海冰厚度的季节演变,同海冰厚度观测进行了对比,并诊断分析了海冰厚度模拟误差的原因。结果表明,在冬春季节,模式可以再现海冰厚度增长过程,但由于模式在春季高估了积雪向海冰的转化及对海冰物质平衡的贡献,模拟的春季海冰厚度偏厚。在夏季期间,2种热力学方案及3种融池方案的组合都表明模式高估了海冰表层的消融过程,导致模拟结束阶段的海冰厚度偏薄。我们的研究表明,使用变量完整的MOSAiC大气和海洋强迫场可以诊断目前海冰模式中的问题,为海冰模式的改进奠定基础。

Numerical investigation of hydro-morphodynamic characteristics of a cascading failure of landslide dams

A cascading failure of landslide dams caused by strong earthquakes or torrential rains in mountainous river valleys can pose great threats to people’s lives,properties,and infrastructures.In this study,based on the three-dimensional Reynoldsaveraged Navier-Stokes equations(RANS),the renormalization group(RNG)k-εturbulence model,suspended and bed load transport equations,and the instability discriminant formula of dam breach side slope,and the explicit finite volume method(FVM),a detailed numerical simulation model for calculating the hydro-morphodynamic characteristics of cascading dam breach process has been developed.The developed numerical model can simulate the breach hydrograph and the dam breach morphology evolution during the cascading failure process of landslide dams.A model test of the breaches of two cascading landslide dams has been used as the validation case.The comparison of the calculated and measured results indicates that the breach hydrograph and the breach morphology evolution process of the upstream and downstream dams are generally consistent with each other,and the relative errors of the key breaching parameters,i.e.,the peak breach flow and the time to peak of each dam,are less than±5%.Further,the comparison of the breach hydrographs of the upstream and downstream dams shows that there is an amplification effect of the breach flood on the cascading landslide dam failures.Three key parameters,i.e.,the distance between the upstream and the downstream dams,the river channel slope,and the downstream dam height,have been used to study the flood amplification effect.The parameter sensitivity analyses show that the peak breach flow at the downstream dam decreases with increasing distance between the upstream and the downstream dams,and the downstream dam height.Further,the peak breach flow at the downstream dam first increases and then decreases with steepening of the river channel slope.When the flood caused by the upstream dam failure flows to the downstream dam,it can produce a surge wave that overtops and erodes the dam crest,resulting in a lowering of the dam crest elevation.This has an impact on the failure occurrence time and the peak breach flow of the downstream dam.The influence of the surge wave on the downstream dam failure process is related to the volume of water that overtops the dam crest and the erosion characteristics of dam material.Moreover,the cascading failure case of the Xiaogangjian and Lower Xiaogangjian landslide dams has also been used as the representative case for validating the model.In comparisons of the calculated and measured breach hydrographs and final breach morphologies,the relative errors of the key dam breaching parameters are all within±10%,which verify the rationality of the model is applicable to real-world cases.Overall,the numerical model developed in this study can provide important technical support for the risk assessment and emergency treatment of failures of cascading landslide dams.

Numerical Physical Mechanism and Model of Turbulent Cascades in a Barotropic Atmosphere

In a barotropic atmosphere, new Reynolds mean momentum equations including turbulent viscosity, dispersion, and instability are used not only to derive the KdV-Burgers-Kuramoto equation but also to analyze the physical mechanism of the cascades of energy and enstrophy. It shows that it is the effects of dispersion and instability that result in the inverse cascade. Then based on the conservation laws of the energy and enstrophy, a cascade model is put forward and the processes of the cascades are described.

Climate prediction of the seasonal sea-ice early melt onset in the Bering Sea

基于大尺度环流异常对海冰消融的影响过程,本文采用年际增量预测方法研制了白令海季节性海冰早期消融开始日期(EMO)的统计预测模型.预测模型选取了3个具有明确物理意义的预测因子:1月波弗特高压,前期11月东西伯利亚地区海平面气压,以及11月东欧平原积雪覆盖率。1月波弗特高压可以通过海气相互作用影响白令海地区海温异常,该海温异常能够从1月持续到3月,进而影响白令海EMO.11月东西伯利亚地区海平面气压与11月至次年2月北太平洋中纬度东部海温密切相关。伴随着北太平洋中纬度东部冷海温异常的出现,白令海地区会出现暖海温异常,进而导致白令海海冰范围减少,EMO较晚.1月北极偶极子异常是11月东欧平原积雪覆盖率影响次年白令海EMO的桥梁之一.1981-2022年的交叉检验结果表明:统计模型对白令海EMO具有较好的预测能力,预测与观测的EMO之间时间相关系数达到了0.45,超过了99%的置信水平.统计模型对白令海EMO正常年份和异常年份的预测准确率分别为60%和41%.

基于Logistic回归和神经网络的甘肃省道路结冰预警模型研究

为更好地开展公路交通道路结冰预报预警服务工作,利用甘肃省道路结冰高发区路段(甘肃武威以东)的交通气象站逐小时观测资料,分析道路结冰空间分布特征,探讨道路结冰与气象要素的相关性,采用Logistic回归法和神经网络算法构建道路结冰预警模型。结果表明:甘肃省道路结冰主要集中在冬季(12月至次年2月),其中00:00—10:00和22:00—23:00(北京时)出现道路结冰的频率较高。Logistic回归模型和神经网络模型对未发生结冰事件的预测准确率较高,分别为91.9%和96.2%;针对发生结冰事件,Logistic回归模型的预测准确率较低,为31.6%,而神经网络模型的预测准确率可达44.6%,说明2种模型对道路结冰预警有一定指示意义,神经网络模型预测效果优于Logistic回归模型。

基于Open CASCADE的拱桥建模研究

几何引擎是三维BIM建模软件中的核心组件,对软件的性能、准确性、互操作性和用户体验等方面有着重要影响。商业软件的几何引擎虽然经过了多年的发展和优化具有较好的性能和稳定性,但常受限于软件厂商提供的API接口和商业软件所需要的系统环境,无法对底层数据进行访问或修改,灵活性较差,且不具备完全自主商业版权。基于开源几何引擎的应用在很多领域已有相关的研究成果,但在桥梁工程领域较少,基于开源几何引擎Open CASCADE对桥梁工程领域的模型构建技术进行探索,实现自主可控的桥梁工程三维建模平台,以拱桥为研究对象,结合几何引擎的模型构建技术,实现拱桥模型的创建。探索开源几何引擎在桥梁工程领域的建模实现能力,为实现更多桥型的三维建模以及基于模型更进一步的衍生应用积累经验,并为打造自主可控的桥梁工程建模软件提供参考。

CIC模型下基于社区检测的谣言抑制最大化方法

随着电子设备的日益普及和信息扩散的便利性,在线社交网络为各种负面信息的传播提供了高效的媒介.谣言是社交媒体上负面信息的突出形式之一,会引发社会动荡,造成经济损失,因此,快速有效地抑制谣言传播成为当前社交网络研究领域中的一个热点.提出一种有效的谣言抑制传播方法,从网络中选取多个正种子节点来传播真相,抑制谣言的传播.首先采用竞争性独立级联(Conpetitive Independent Cascade,CIC)模型来同时传播谣言和真相;其次,提出一种基于标签传播的社区检测算法对社交网络进行分解,并为各个社区分配正种子节点预算;最后,创新地提出节点强度来衡量网络中节点的重要性,并利用节点强度在各个社区中选取抑制谣言传播的初始正种子集.实验证明,该方法能达到与贪婪算法相匹配的抑制效果,且运行时间比贪婪算法快三个数量级.

一种支持MDD的ICES-Modeling建模语言和建模工具

随着模型驱动体系结构的理论和技术的不断发展,模型已成为软件开发的核心元素。因此,支持模型驱动体系结构的建模语言和建模工具成为研究热点。本文提出一种支持模型驱动的软件开发的ICES-Modeling建模语言,该语言融合了现有的建模方法的一些思想,可以有效地描述领域内的概念,概念与概念之间的关系。还针对该建模语言,设计并开发了建模工具,该建模工具具有支持建模语言的定义,方便模型的理解,支持多角度建模等特点。

Kinetic models of natural gas combustion in an internal combustion engine

In this study, combustion of methane was simulated using four kinetic models of methane in CHEMKIN 4.1.1 for 0-D closed internal combustion （IC） engine reactor. Two detailed （GRIMECH3.0 ＆ UBC MECH2.0） and two reduced （One step ＆ Four steps） models were examined for various IC engine designs. The detailed models （GRIMECH3.0, ＆ UBC MECH2.0） and 4-step models successfully predicted the combustion while global model was unable to predict any combustion reaction. This study illustrated that the detailed model showed good concordances in the prediction of chamber pressure, temperature and major combustion species profiles. The detailed models also exhibited the capabilities to predict the pollutants formation in an IC engine while the reduced schemes showed failure in the prediction of pollutants emissions. Although, there are discrepancies among the profiles of four considered model, the detailed models （GRIMECH3.0 ＆ UBC MECH2.0） produced the acceptable agreement in the species prediction and formation of pollutants.

Fully Automatic Segmentation of Gynaecological Abnormality Using a New Viola–Jones Model

One of the most complex tasks for computer-aided diagnosis(Intelligent decision support system)is the segmentation of lesions.Thus,this study proposes a new fully automated method for the segmentation of ovarian and breast ultrasound images.The main contributions of this research is the development of a novel Viola–James model capable of segmenting the ultrasound images of breast and ovarian cancer cases.In addition,proposed an approach that can efficiently generate region-of-interest(ROI)and new features that can be used in characterizing lesion boundaries.This study uses two databases in training and testing the proposed segmentation approach.The breast cancer database contains 250 images,while that of the ovarian tumor has 100 images obtained from several hospitals in Iraq.Results of the experiments showed that the proposed approach demonstrates better performance compared with those of other segmentation methods used for segmenting breast and ovarian ultrasound images.The segmentation result of the proposed system compared with the other existing techniques in the breast cancer data set was 78.8%.By contrast,the segmentation result of the proposed system in the ovarian tumor data set was 79.2%.In the classification results,we achieved 95.43%accuracy,92.20%sensitivity,and 97.5%specificity when we used the breast cancer data set.For the ovarian tumor data set,we achieved 94.84%accuracy,96.96%sensitivity,and 90.32%specificity.

Effects of Deposition Models on Deposition and Performance Deterioration in Axial Compressor Cascade

A new particle deposition model, namely partial deposition model, is developed in order to improve the accuracy of prediction to particle deposition. Concepts of critical velocity and critical angle are proposed and used to determine whether particles are deposited or not. The comparison of numerical results calculated by partial deposition model and existing deposition model shows that the deposition distribution obtained by partial deposition model is more reasonable. Based on the predicted deposition results, the change of total pressure loss coefficient with operating time and the distribution of pressure coefficients on blade surface after 500 hours are predicted by using partial deposition model.

Adaptive Cascade Generalized Predictive Control

Cascade control is one of the most popular structures for process control as it is a special architecture for dealing with disturbances. However, the drawbacks of cascade control are obvious that primary controller and secondary controller should be tuned together, which influences each other. In this paper, a new Adaptive Cascade Generalized Predictive Controller (ACGPC) is introduced. ACGPC is a method issued from GPC and the inner and outer controllers of a cascade system are replaced by one cascade generalized predictive controller, where both loops model are updated by Recursive Least Squares method. Compared with existing methods, the new method is simpler and yet more effective. It can be directly integrated into commercially available industrial auto-tuning systems. Some examples are given to illustrate the effectiveness and robustness of the proposed method.

A Study on Small Pest Detection Based on a CascadeR-CNN-Swin Model

This study aims to detect and prevent greening disease in citrus trees using a deep neural network.The process of collecting data on citrus greening disease is very difficult because the vector pests are too small.In this paper,since the amount of data collected for deep learning is insufficient,we intend to use the efficient feature extraction function of the neural network based on the Transformer algorithm.We want to use the Cascade Region-based Convolutional Neural Networks(Cascade R-CNN)Swin model,which is a mixture of the transformer model and Cascade R-CNN model to detect greening disease occurring in citrus.In this paper,we try to improve model safety by establishing a linear relationship between samples using Mixup and Cutmix algorithms,which are image processing-based data augmentation techniques.In addition,by using the ImageNet dataset,transfer learning,and stochastic weight averaging(SWA)methods,more accuracy can be obtained.This study compared the Faster Region-based Convolutional Neural Networks Residual Network101(Faster R-CNN ResNet101)model,Cascade Regionbased Convolutional Neural Networks Residual Network101(Cascade RCNN-ResNet101)model,and Cascade R-CNN Swin Model.As a result,the Faster R-CNN ResNet101 model came out as Average Precision(AP)(Intersection over Union(IoU)=0.5):88.2%,AP(IoU=0.75):62.8%,Recall:68.2%,and the Cascade R-CNN ResNet101 model was AP(IoU=0.5):91.5%,AP(IoU=0.75):67.2%,Recall:73.1%.Alternatively,the Cascade R-CNN Swin Model showed AP(IoU=0.5):94.9%,AP(IoU=0.75):79.8%and Recall:76.5%.Thus,the Cascade R-CNN Swin Model showed the best results for detecting citrus greening disease.

A coupled ice-ocean model for the Bohai SeaⅡ.Case study

The coupled ice- ocean model for the Bohai Sea is used for simulating the freezing, melting, and variation of ice cover and the heat bal- ance at the sea- ice, air- ice, and air- sea interfaces of the Bohai Sea during the entire winter in 1998￣1999 and 2000￣2001. The cou- pled model is forced by real time numerical weather prediction fields. The results show that the thermodynamic effects of atmosphere and ocean are very important for the evolvement of ice in the Bohai Sea, especially in the period of ice freezing and melting. Ocean heat flux plays a key role in the thermodynamic coupling. The simulation also presents the different thermodynamic features in the ice covered region and the marginal ice zone. Ice thickness, heat budget at the interface, and surface sea temperature, etc. between the two representative points are discussed.