双馈风力发电机组的分群方法研究

风电机组的分群对提高风电场等效建模的精度具有重要的意义。首先分析了常用分群方法的优缺点,在此基础上提出了一种基于双馈风电机组(DFIG)动态特性曲线分群的方法。将故障条件下DFIG动态特性曲线的相似度作为分群指标,运用系统聚类法对风电机组进行分群。然后又利用该分群方法,对风电机组运行状态曲线进行分区,从而得到一种快速的分群策略。最后通过仿真,对比分析了采用不同风电机组分群方法时风电场等效建模的精度,验证了所提出的风电机组分群方法的有效性和可行性。

考虑初始风速与机端故障稳态电压跌落程度的直驱风机故障响应特性分类及其判别方法研究

对电力系统进行安全分析时,往往需要对各种预想场景进行仿真。随着风电的大量接入,对风电场提出适用于预想故障分析的等值建模方法变得十分重要,而根据风机故障后的动态特性对其进行分群则是等值建模的基础。各风机在故障后的动态响应特性不仅受其控制方式的影响,还受到运行风速和故障严重程度的影响,而一般情况下风机控制的响应速度较快,可以认为在故障持续期间风机能够运行至故障稳态。基于此,提出考虑初始风速及机端故障稳态电压跌落程度的风电机组故障响应特性分类及判别方法,以此作为风机分群的依据。首先针对永磁直驱风机(direct-driven permanent magnet synchronous generator,PMSG)故障过程中无功优先控制、故障恢复期间有功斜坡恢复的控制策略,分析其在不同风速条件下故障后所有可能的响应特性,将初始风速与故障稳态电压作为分类指标,从机理上找到直驱风机进入各类响应特性的边界条件;然后,提出利用预想故障信息的风机机端故障稳态电压计算方法,在此基础上提出适用于预想故障的直驱风机分群方法;最后,通过对风电场进行详细建模仿真,验证所提分群方法的正确性。

V2G模式下基于电动汽车分群方法的配电网多目标优化运行策略

为减小电动汽车无序入网与分布式能源的波动性给配电网带来的影响,提出了一种V2G(vehicle to grid)模式下基于电动汽车分群方法的配电网运行策略。在系统宏观与具体网络拓扑结构2个层面,依据车主费用、配电网负荷均方差与网损搭建了内外嵌套模型,基于电动汽车充放电与分布式能源配合,对源网荷三方协调优化,得到配电网的最优运行工况。基于开始充电时刻和车主期望电量充电所需时间2个特征值的电动汽车分群方法,在减少变量维度的同时,也考虑到了车主的出行需求。采用GA-PSO(genetic and particle swarm optimization algorithm)算法在4种场景下的算例仿真表明,该策略在保障电动汽车车主利益的同时,可有效降低配电网负荷水平、平抑负荷波动、减小峰谷差、改善电压水平以及减小网损。

风电场集群接入系统后的聚类分析

提出了一种基于并网点暂态电压特性的聚类分群方法,即根据并网风电场受系统故障影响程度的不同,识别出动态行为相近的风电场群,进而实现同群风电场的协调控制。为验证所提方法的有效性,在高级可视化软件Powerworld建立了吉林西部电网仿真模型,并进行了聚类分析研究。仿真结果证明,该算法不仅结果相对稳定、符合电网实际情况,而且具有一定的脱网风电场动态识别功能。

基于聚类分析的双馈机组风电场动态等值模型的研究

针对大型双馈机组风电场,提出一种新的动态等效建模方法。该方法是一种基于双馈风力发电机暂态电压特性的聚类分群方法,即根据风电场内各双馈发电机受系统故障影响程度的不同,识别出电压的动态响应行为相近的风力发电机,并对分群后的双馈发电机及其电气接线系统进行等值聚合,实现了双馈机组风电场的动态等值多机表征。利用电力系统分析综合程序(PSASP 6.2),搭建了双馈机组风电场详细模型和等值模型,并与传统的单机等值模型进行了比较分析。结果表明,所建立的多机等值模型能够较准确地反映双馈机组风电场并网点的动态特性。

直驱式风电场并网动态等值研究

随着直驱式风力发电机在电网中的比例增加,研究其精确的动态等值模型越来越重要。根据直驱式风力发电机的输出特性,提出了基于风速分群的多机等值方法。在等值计算中,考虑了直驱式风电机组并网时变压器和集电线路对等值效果的影响,并计及了尾流效应对风机输入风速的影响。所提等值方法适合于直驱式风电场并网的分析计算,具有实际工程的应用价值。

风电场动态等值建模研究综述

从风电场动态等值建模(风速等值、集电网络等值、分群聚类、等值参数计算4个步骤)的角度,探讨风电场动态等值建模研究成果。结果表明,风电场动态等值建模有2种方法:一是提高分群精度,获得唯一聚类结果,进行多机等值;二是降低分群精度,转而确定强相关的待辨识参数,辨识出等值机参数。最后,预测了风电场动态等值未来可能的研究方向。

Scaling Rules for the Percolation Model of Dielectric Breakdown

Aim To study the dIelectric breakdown phenomenon in the materials with quenched disorder. Methods Renormolization group methods were used. Results The percolation limit for breakdown pc , the breakdown field Ec～(Pc-p)v, and the fractal dimension of the structure of dielectric breakdown df were obtained. Conclusion The breakdown properties of the materials with quenched disorder are characterized by universal power laws, where the exponents are universal.

Lie Group Classifications and Non-differentiable Solutions for Time-Fractional Burgers Equation

Lie group method provides an efficient tool to solve nonlinear partial differential equations. This paper suggests Lie group method for fractional partial differential equations. A time-fractional Burgers equation is used as an example to illustrate the effectiveness of the Lie group method and some classes of exact solutions are obtained.

Classification and Approximate Solutions to a Class of Perturbed Nonlinear Wave Equations

A complete approximate symmetry classification of a class of perturbed nonlinear wave equations isperformed using the method originated from Fushchich and Shtelen.Moreover,large classes of approximate invariantsolutions of the equations based on the Lie group method are constructed.

Distribution Pattern Scale of Natural Toona ciliata Population

In order to research the population distribution pattern of endangered species Toona ciliata Roem., the sampling quadrats of 5 mx5 m and 3 m×3 m in size, accurate to 1 mxl m, were established in 2 newly-found only existing T. cili- ata Roem. communities （T1 and T2） with contiguous grid quadrate method, in the Nanhe River valley, Gucheng County. By X2 test, t-test of distribution coefficient Cx method, and F test of Morisita pattern index Iδ whether the distribution patterns of the T. ciliata Roem. populations conformed to Poisson distribution were checked. The results indicated that, population T1 was in Poisson distribution under 5 m×5 m and 3 m×3 m in size by Cx and 16 methods, but in clumped distribution pattern un- der 5 m×5 m in size by the Chi-square test; however, the population was in Pois- son distribution under the dimension of 3 m×3 m. Population T2 under human dis- turbance had higher population density, indicating clumped distribution under 3 above-mentioned tests. If Chi-square test is satisfied, a distribution pattern is in Poisson distribution, and size and quantity of sampling quadrats should be given pri- ority to; and if df is greater, both theoretical values and observed values tend to- wards normal distribution more probably, and the test of distribution pattern, there- fore will be more dependable.

Gas emission source term estimation with 1-step nonlinear partial swarm optimization-Tikhonov regularization hybrid method

Source term identification is very important for the contaminant gas emission event. Thus, it is necessary to study the source parameter estimation method with high computation efficiency, high estimation accuracy and reasonable confidence interval. Tikhonov regularization method is a potential good tool to identify the source parameters. However, it is invalid for nonlinear inverse problem like gas emission process. 2-step nonlinear and linear PSO （partial swarm optimization）-Tikhonov regularization method proposed previously have estimated the emission source parameters successfully. But there are still some problems in computation efficiency and confidence interval. Hence, a new 1-step nonlinear method combined Tikhonov regularizafion and PSO algorithm with nonlinear forward dispersion model was proposed. First, the method was tested with simulation and experiment cases. The test results showed that 1-step nonlinear hybrid method is able to estimate multiple source parameters with reasonable confidence interval. Then, the estimation performances of different methods were compared with different cases. The estimation values with 1-step nonlinear method were close to that with 2-step nonlinear and linear PSO-Tikhonov regularization method, 1-step nonlinear method even performs better than other two methods in some cases, especially for source strength and downwind distance estimation. Compared with 2-step nonlinear method, 1-step method has higher computation efficiency. On the other hand, the confidence intervals with the method proposed in this paper seem more reasonable than that with other two methods. Finally, single PSO algorithm was compared with 1-step nonlinear PSO-Tikhonov hybrid regularization method. The results showed that the skill scores of 1-step nonlinear hybrid method to estimate source parameters were close to that of single PSO method and even better in some cases. One more important property of 1-step nonlinear PSO-Tikhonov regularization method is its reasonable confidence interval, which is not obtained by single PSO algorithm. Therefore, 1-step nonlinear hybrid regularization method proposed in this paper is a potential good method to estimate contaminant gas emission source term.

Particle swarm optimization for train schedule of Y-type train routing in urban rail transit

The train schedule usually includes train stop schedule,routing scheme and formation scheme.It is the basis of subway transportation.Combining the practical experience of transport organizations and the principle of the best match between transport capacity and passenger flow demand,taking the minimum value of passenger travel costs and corporation operating costs as the goal,considering the constraints of the maximum rail capacity,the minimum departure frequency and the maximum available electric multiple unit,an optimization model for city subway Y-type operation mode is constructed to determine the operation section of mainline as well as branch line and the train frequency of the Y-type operation mode.The particle swarm optimization(PSO)algorithm based on classification learning is used to solve the model,and the effectiveness of the model and algorithm is verified by a practical case.The results show that the length of branch line in Y-type operation affects the cost of waiting time of passengers significantly.

Improving cancer therapy by targeting cancer stem cells:Directions, challenges, and clinical results

Cancer stem cells （CSC） are a rare cell population withina tumor characterized by the ability to form tumorsfollowing injection into an immunocompromised host.While the role of CSC has been clearly established inanimal models, evidence of their clinical relevance hasbeen harder to demonstrate. A number of markers,or combination thereof, have been used to detect andmeasure, although non-specifically, CSC in almost allhuman tumors. Several pathways have been identifiedas crucial for, but not necessarily unique to, CSC surviva and proliferation, and novel agents have been designed to target such pathways. A number of such agents have entered early phase development. Further, drugs that have long been marketed for non-oncological indications have been redirected to oncology as they appear to affect one or more of such pathways. This article aims to review the available evidence on the clinical relevance of CSC from a drug development standpoint and the results of early phase clinical trials of agents interfering with the above pathways. It also discusses limitations of current clinical trial design and endpoints to demonstrate anti-CSC activity as well as possible strategies to overcome these limitations.

A Fast Underwater Optical Image Segmentation Algorithm Based on a Histogram Weighted Fuzzy C-means Improved by PSO

The S/N of an underwater image is low and has a fuzzy edge.If using traditional methods to process it directly,the result is not satisfying.Though the traditional fuzzy C-means algorithm can sometimes divide the image into object and background,its time-consuming computation is often an obstacle.The mission of the vision system of an autonomous underwater vehicle (AUV) is to rapidly and exactly deal with the information about the object in a complex environment for the AUV to use the obtained result to execute the next task.So,by using the statistical characteristics of the gray image histogram,a fast and effective fuzzy C-means underwater image segmentation algorithm was presented.With the weighted histogram modifying the fuzzy membership,the above algorithm can not only cut down on a large amount of data processing and storage during the computation process compared with the traditional algorithm,so as to speed up the efficiency of the segmentation,but also improve the quality of underwater image segmentation.Finally,particle swarm optimization (PSO) described by the sine function was introduced to the algorithm mentioned above.It made up for the shortcomings that the FCM algorithm can not get the global optimal solution.Thus,on the one hand,it considers the global impact and achieves the local optimal solution,and on the other hand,further greatly increases the computing speed.Experimental results indicate that the novel algorithm can reach a better segmentation quality and the processing time of each image is reduced.They enhance efficiency and satisfy the requirements of a highly effective,real-time AUV.

Tracing Changes in the Microbial Community of a Hydrocarbon-Polluted Soil by Culture-Dependent Proteomics

Hydrocarbon contamination may affect the soil microbial community, in terms of both diversity and function. A laboratory experiment was set-up, with a semi-arid control soil and the same soil but artificially contaminated with diesel oil, to follow changes in the dominant species of the microbial community in the hydrocarbon-polluted soil via proteomics. Analysis of the proteins extracted from enriched cultures growing in Luria-Bertani （LB） media showed a change in the microbial community. The majority of the proteins were related to gIycolysis pathways, structural or protein synthesis. The results showed a relative increase in the complexity of the soil microbial community with hydrocarbon contamination, especially after 15 days of incubation. Species such as Ralstonia solanacearum, Synechococcus elongatus and different Clostridium sp. were adapted to contamination, not appearing in the control soil, although Bacillus sp. dominated the growing in LB in any of the treatments. We conclude that the identification of microbial species in soil extracts by culture-dependent proteomics is able to partially explain the changes in the diversity of the soil microbial community in hydrocarbon polluted semi-arid soils, but this information is much more limited than that provided by molecular methods.

Different Criteria for the Optimal Number of Clusters and Selection of Variables with R

One of the most important problems of clustering is to define the number of classes. In fact, it is not easy to find an appropriate method to measure whether the cluster configuration is acceptable or not. In this paper we propose a possible and non-automatic solution considering different criteria of clustering and comparing their results. In this way robust structures of an analyzed dataset can be often caught （or established） and an optimal cluster configuration, which presents a meaningful association, may be defined. In particular, we also focus on the variables which may be used in cluster analysis. In fact, variables which contain little clustering information can cause misleading and not-robustness results. Therefore, three algorithms are employed in this study： K-means partitioning methods, Partitioning Around Medoids （PAM） and the Heuristic Identification of Noisy Variables （HINoV）. The results are compared with robust methods ones.

Symmetry Reductions of Two-Dimensional Variable Coefficient Burgers Equation

By use of a direct method, we discuss symmetries and reductions of the two-dimensional Burgers equation with variable coefficient (VCBurgers). Five types of symmetry-reducing VCBurgers to (1+1)-dimensional partial differential equation and three types of symmetry reducing VCBurgers to ordinary differential equation are obtained.