Modeling and simulation of induction motor drive system to investigate and mitigate of PWM inve
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基于ANSYS的电磁感应加热系统仿真与实验
实 验 方 案 将 理 论 分 析 、数 值 仿 真 和 实 验 测 量 三 者 相 结 合 ，能 够 帮 助 学 生 更 好 地 构 建 该 课 程 系 统 全 面 的 思 维 框 架 。
关 键 词 ： 电 磁 感 应 ；涡 流 ；感 应 加 热 ；工程电磁场
中 图 分 类 号 ：TM154
文 献 标 识 码 ：A
130
实验技术与管理
1 电磁感应加热原理
1831年 ，法拉第发现电磁感应定律[7]:导体回路
中感应电动势e 的大小与穿过回路的磁通随时间的变
化率成正比。当频率为/ 的交流电流流过匝数为W 的
线 圈时，感应电动势e 为
e = - N -d <f i / d t
( 1)
感应加热技术是在法拉第电磁感应定律的基础上
基 于 ANSYS的电磁感应加热系统仿真与实验
房 紫 路 ，龚 直 ，李 玉 玲 ，姚缨英 ( 浙 江 大 学 电 气 工 程 学 院 ，浙 江 杭 州 310027 )
摘 要 ：将 电 子 工 程 专 业 基 础 课 “工 程 电 磁 场 ” 中 的 电 磁 感 应 定 律 和 涡 流 理 论 与 实 际 应 用 相 结 合 ，提 出 了 基 于 电 磁
(8 )
其 中 ：c r 为材料的电导率；~ 为 角 频 率 ， ffl = 2ir/ ，/ 为
电磁炉T .作频率。
涡流的焦耳热效应表达式为
Q = I 2R i
(9)
其中：/ 为感应电流，•/?为负载电阻值，/ 为加热时间。 1.3.2 锅 具 与 线 圈 的 距 离
电磁炉的感应线圈与锅具之间放置陶瓷玻璃板与
Z eq = ^ e q + j ^ e q
2024版Ansoft
Ansoft•Ansoft Software Overview•Ansoft Electrical SimulationTechnology目录•Ansoft's application in the fieldof microwave and radiofrequency•Ansoft application in the field ofpower electronics•Ansoft application in signalprocessing field•Ansoft software operation 目录guide and skill sharing01Ansoft SoftwareOverviewAnsoft software is a professional electrical field simulation software, which can simulate and analyze the electrical field, circuit, and thermal field of various electronic devices Ansoft software supports a variety of CAD data formats and can be seamlessly connected with other EDA software to achieve co simulation and optimization designIt has the characteristics of power simulationfunction, high simulation accuracy, easy to useand good opennessSoftware background and characteristicsApplication field and scopeAnsoft software is widely used in the design and analysis of motors, transformers, sensors,actors, inverters, and other electronic devicesIt can be used for electromagnetic interference (EMI) and electromagnetic compatibility (EMC)analysis of electronic systemsAnsoft software can also be used for the simulation and optimization design of microwavedevices, antennas, radars, and other high frequency electronic systemsAnsoft software was first developed by American school Dr. Zoltan J. Cendes in the 1980s After more than 30 years of development, it has become one of the most widely used electrical field simulation software in the world At present, Ansoft software hasbeen widely used in the fields ofelectronics, electrical appliances,aerospace, defense militaryindustry, etc., and has played animportant role in improving thedesign level and reducing thecost of electronic productsWith the continuousdevelopment of computertechnology and numericalsimulation technology, Ansoftsoftware will continue to improveits simulation accuracy andefficiency, and provide morepowerful support for the designand analysis of electronic devices010203 Development history and current situation02Ansoft ElectricalSimulationTechnology2D/3D Electrical Field Simulation2D Electrical Field Simulation01Provides fast and accurate solutions for planar electricalproblems3D Electrical Field Simulation02Offers comprehensive analysis of three dimensional electricalfields, taking into account the effects of complex geometry andmaterialsParameter Studies03Allow users to perform parameter sweeps to optimize designsand understand the impact of different variables on performanceHigh Frequency Circuit SimulationCircuit ModelingEnable the creation of accurate circuit models for high frequency components,such as transistors, diodes, and passive elementsSPICE IntegrationSupports integration with SPICE based circuit simulators for co simulation ofelectrical and circuit level effectsFrequency Domain AnalysisProvide tools for frequency domain analysis, including impact and tolerancecalculations, as well as S-Parameter extractionMotor Design and AnalysisMotor modelingOffers a range of motor modeling options, including permanent magnet,introduction, and switched relationship motorsPerformance AnalysisEnable detailed analysis of motor performance, including torque, speed,efficiency, and thermal characteristicsControl System IntegrationSupports integration with control system design tools, allowing for theevaluation of control strategies on the motor designAnsoft'sapplication in 03the field ofmicrowave andradiofrequencyAnsoft provides accurate models for a wide range of microwave and RF devices, including transistors, amplifiers, mixers, and oscillators With Ansoft's advanced circuitsimulators, engineers can designand analyze complex microwaveand RF circuits, taking intoaccount various parameters suchas frequency response, noisefigure, and linearityAnsoft enables system levelsimulation of microwave and RFsystems, allowing engineers toevaluate the performance of theentire system before prototypingDevice Modeling Circuit Simulation System LevelSimulation Modeling and Simulation of Microwave RF DevicesAntenna design and optimizationAntenna ModelingAnsoft provides powerful tools for modeling ants ofvarious types, such as wire, microstrip, and reflectorantsRadiation Pattern AnalysisEngineers can use Ansoft to analyze the radiationpatterns of antenna and optimize them for specificapplicationsAntenna Array DesignWith Ansoft, engineers can design and simulateantenna arrays, taking into account factors such asbeamforming, sidelobe levels, and grating lobesEMC AnalysisAnsoft enables engineers to perform electromagnetic compatibility (EMC) analysis to ensure that their designs comply with international EMC standards要点一要点二EMI AnalysisEngineers can use Ansoft to identify potential sources of electrical interference (EMI) in their designs and take measures to limit themSignal Integrity AnalysisAnsoft provides tools for signal integrity analysis, allowing engineers to assess the impact of EMI on signal quality and system performance要点三Electrical compatibility and interference analysis04Ansoft application inthe field ofpowerelectronicsAccurate modeling of power electronic devices: Ansoft provides a comprehensive set of tools for modeling and simulating power electronic devices, such as diodes, transformers, and thyristors These tools enable engineers to accurately report the behavior of these devices under various operating conditions Simulation of power electroniccircuits: With Ansoft, engineerscan simulate power electroniccircuits to predict theirperformance and behavior Thisincludes the ability to analyzecircuit waveforms, calculatepower losses, and assess theimpact of different componentparameters on circuitperformanceThermal analysis of powerelectronic devices: Ansoft'sthermal analysis tools allowengineers to study the heattransfer and temperaturedistribution in power electronicdevices This is critical for ensuringthe reliability and durability ofthese devices, as overeating canlead to precision failureModeling and Simulation of Power Electronic DevicesDesign and optimization of motor drive system•Motor design and analysis: Ansoft provides a range of tools for motordesign and analysis, enabling engineers to optimize motorperformance and efficiency This includes the ability to model differenttypes of motors, such as induction motors, permanent magnetsynchronous motors, and switched relationship motors•Drive system simulation: With Ansoft, engineers can simulate theentire motor drive system, including the motor, power converter, andcontrol system This allows them to assess the system's performanceunder different operating conditions and optimize the design forimproved efficiency and reliability•Control system design: Ansoft's control system design tools enableengineers to design and implement advanced control algorithms formotor drive systems This includes the ability to model and simulatevarious control strategies, such as field oriented control, direct torquecontrol, and model predictive controlHarmonic Analysis and Governance of Power System05Ansoft application insignalprocessingfieldTransmission linemodelingAnsoft provides accurate models for transmission lines, allowing for the analysis of signal promotion and reflection in complex systemsS-parameterextractionThe software can extract S-parameters, which are key tounderstanding the behavior ofhigh frequency signals incircuitsCrosstalk andcoupling analysisAnsoft enables the analysis ofcrosstalk and coupling effectsin multi layer PCBs andpackages, ensuring signalintegrity in dense designs01 02 03Power delivery network (PDN)modelingAnsoft provides tools to model the PDN, including power plans, via, and decoupling capacitorsIR drop and voltage regulationanalysisThe software can analyze IR drop and voltage regulation issues, ensuring reliable power delivery to critical componentsPower and ground bond analysisAnsoft can simulate power and ground bond effects, which are important considerations in high speed digital designs3D field solversAnsoft's 3D field solvers enable accurate simulation of electrical fields, allowing for the prediction of EMC/EMI issues Radiatedemissions analysisThe software can analyze radiatedemissions from PCBs and systems,helping to identify potential EMIproblemsSusceptibilityanalysisAnsoft can simulate thesusceptibility of a design toexternal EMI sources, providinginsights into potential interferenceissuesEMC/EMI simulation and prediction06Ansoft softwareoperationguide and skillsharingSystem requirementsIntroduce the hardware andsoftware requirements forrunning Ansoft software,including operating system,processor, memory, disk space,and necessary softwaredependenciesInstallation stepsDetail the step by step process for installing Ansoft software, including downloading the installation package, running the installer, and following the prompts to complete the installationConfiguration settingsExplain how to configure Ansoftsoftware after installation, includingTHANKS感谢观看。
柴油发电机组并联运行建模与仿真
parallel operation; modeling simulation
0引言
国内外研究人员从不同角度对发电机并联运行 问题作了分析研究. 文献[ 1] 给出小型发电机异相并 网时可接受的最大同步角; 文献[ 2] 对同步与异步发 电机的并联运行进行建模和仿真; 文献[ 3] 研究燃料 电池和风力、柴油发电机的并联运行, 分析各类发电 机与负载之间的相互作用以及对电压和频率波动的 影响; 文献[ 4] 提出采用新的修正调差率技术来控制 可调速发电系统的并联运行; 文献[ 5] 提出变频同步 电动机- 发电机组并联运行的方法; 文献[ 6] 给出发 电机电枢电流和脉动电磁力矩的计算式; 文献[ 7] 建 立变速恒频风力发电仿真系统, 并网控制和风能追 踪两大模块通过分时工作和数据转移的方式完成并 网前后过程的仿真; 文献[ 8] 在 MATLAB Simulink 中 建立高压发电机仿真模型, 并研究两种频差条件下 电机与电网的同步问题. 然而上述相关文献中的数 学模型相对简化, 特别是发电机模型, 普遍都忽略定 子暂态; 而并联的动态变化过程, 只有理想化的等效 模型及定性分析, 结论缺乏说服力且仿真精度不足; 负载转移过程也无精确数学模型支撑.
52
大连海事大学学报
第 37 卷
1 柴油发电机组并联运行过程建模
1. 1 非同期合闸时冲击电流和电磁转矩 已励磁的发电机非同期投入电网时的冲击电流
最大值( 如不考虑定子内电流非周期分量的衰减) 表 示为
I ch = 2( U + E) ( X d + X h )
( 1)
其中: U 为电网电压; E 为由励磁电流决定的发电机 空载电动势; X d 为定子回路内纵轴次暂态电抗; X h 为发电机与电网之间的电抗.
风力发电系统中双馈异步发电机的仿真研究
第２３卷 第７期
文章编号 ：１００６—９３４８（２００６）０７—０２３１—０５
计 算 机 仿 真
２００６年７月
风 力发 电 系 统 中双馈 异 步 发 电机 的仿 真 研 究
黄 凯 ，王 斌
（三峡大学 电气信息学院 ，湖北 宜昌 ４４３００２）
摘 要：该文利用坐标变换技术 ，推导 了双馈感 应风力 发 电机 在 ｄ—ｑ坐标 系下的数学 模型 ，基于变 速恒频 矢量控 制原理在 ＭＡＴＡＬＡＢ＼ＳＩＭＵＬＩＮＫ环境下分别用现成 的几种基本模块和 ｓ函数两 种方法搭建 了相应 的动态仿真模型 ，对双 ＰＷＭ 变频器 励磁 的变速恒频 双馈异步发 电机模型进行 了仿真研究。仿 真结果表 明了模 型的正确性 ，为风力发电系统 的进一步应用研究 提供 了可靠 的理论依据 。最后对两种建模方法的仿 真速度作了 比较 ，结果表 明仿 真速度取决 于模 型中模块 的数量 ，积分 函 数在仿 真过程 中最耗 时，使用 ｓ一函数能有效地提高仿真速度。 关键词 ：双馈感 应风力发电机 ；数学模型 ；仿真模型 ；仿真速度 比较 中图分 类号 ：ＴＭ３４３ 文献标识 码 ：Ａ
在考虑发电机系统方案时 ，应 结合它的运行 方式重要解决 以
下 问 题 ：
１）高质量地将不断变化的风能转换为频 率 、电压 幅度恒
定 的交 流 电 。
‘
２）高效率实现上述两种能量 的转换 ，以降低每 度电的成
本 。 ３）稳定 、可靠 地同 电网 、其他 发电装 置或者 储能设 备联
合运行 为用 户提供 稳定的电能 。 基于变速 恒频技 术 的双 馈异步 风力发 电机 的发电方案
ｌ 引言 当前 ，人类发展所 共 同面临 的两 大问题能 源储量 １３益减少 。二是生态
基于Matlab_Simulink的双馈感应风力发电机组建模和仿真研究
要控制机组的转速来实现最大风能捕获，可以
检测当前的风速并计算出最佳转速后进行转速控
制，这实际上是一种直接转速控制的方法，控制目标
明确，原理简单。但现场中风速的准确检测比较困
难，实现起来存在很多问题，风速检测的误差会降低
最大风能捕获的效果[14-15]。在实际应用中，可以通过
控制策略和控制方法的改进来避免风速的检测。这
2
2
P = 2
2 2
2
2
3 2
(ud2id2+uq2iq2)
2
2
2
P = 2
2 2
2
2
3 2
(uq2id2-ud2iq2)
（10）
清洁能源 Cle a n Ene rgy
第 26 卷 第 11 期
电网与清洁能源
97
式中，P1、Q1为定子侧向电网输出有功无功；P2、Q2为 转子侧从电网输入有功无功。
图2 风能利用系数-叶尖速比
从轮毂到发电机转子之间的机械传动部分在硬
度和阻尼系数被忽略时，可用一质量块的实用模型
来描述[6-7]，如式（4）所示。
Tgen-T'wtr=Jd
dΩgen dt
（4）
式中，Jd为等效转动惯量；T'wtr为等效风轮转矩；Tgen为 转子转矩；Ωgen为转子机械角速度。 1.2 双馈感应发电机数学模型
系：
u2 2
2 d1 2
22 2
u2 2
2 q1 2 22
= 2 2 u2 d2 2
22
u22 22
2 q2 2
-R1-L1P -ω1L1 -LmP -ωsLm
ω1L1 -R1-L1P
ωsLm -LmP
低频环境下电磁辐射对人体影响
1.文章名：Local Grid Refinement for Low-Frequency Current Computation in 3-D HumanAnatomy Models作者：Andreas Barchanski文章出处：IEEE TRANSACTIONS ON MAGNETICS, VOL. 42, NO. 4, APRIL 2006文章主要观点：●文章主要介绍人体模型方面，它是按照解剖学方式建立模型的。
低频人体解剖学模型.pdf2.文章名：Simulation of Slowly Varying Electromagnetic Fields in the Human Body Consideringthe Anisotropy of Muscle Tissues作者：Victor C. Motrescu（Germany）文章出处：IEEE TRANSACTIONS ON MAGNETICS, VOL. 42, NO. 4, APRIL 2006文章主要观点：●文章主要仿真了高压输电线下方的，在50HZ电压下人的电流分布。
●人体模型是采用的1文章中的模型，肌肉等器官的电磁参数是各向异性参数。
●为了能较好的得到仿真，仿真分为两个步骤：a.输电线下方没有人体模型。
b.取第一步的仿真数据作为人体模型的边界条件进行仿真，得到人体内部电磁场分布。
注意边界条件的施加方法与种类。
●人体各器官在各频率的介电常数与导电率是在文章：[D. Andreuccetti, R. Fossi, and C.Petrucci, An internet resource for the calculation of the dielectric properties of body tissues in the frequency range 10 Hz–100 GHz. Florence, Italy: Inst. Appl. Phys., 1997.]输电线下方人体效应.pdf3. 文章名： Modeling of Induced Current Into the Human Body by Low-Frequency MagneticField From Experimental Data作者：Riccardo Scorretti文章出处：IEEE TRANSACTIONS ON MAGNETICS, VOL. 41, NO. 5, MAY 2005文章主要观点：● 该文章主要提出了3步骤来解决50Hz 磁场对人体的影响。
无刷双馈电机的建模与仿真
无刷双馈电机的建模与仿真靳雷,陆晓强（河南质量工程职业学院,河南平顶山467001）摘要：无刷双馈电机（BDFM ）作为一种新型电机,兼有绕线式转子异步电机和同步电机的优良特性,尤其适合于变速恒频发电领域,通过分析无刷双馈电机的结构及工作原理,建立了基于转子速坐标系的d-q 轴无刷双馈电机数学模型,根据所得的数学模型,对无刷双馈电机的各种运行方式进行了仿真分析,采用M ATLAB/Simulink 进行了计算机仿真研究,得出了各种运行方式下的仿真波形,仿真结果验证了数学模型的正确性和可行性,并得到了一些有益的结论.关键词：无刷双馈电机；转子速；数学模型；仿真中图分类号：TM 301.2文献标志码：A 文章编号：1008-7516（2011）04-0083-05Modeling and simulation of brushless doubly fed machineJin Lei,Lu Xiaoqiang（Henan Quality Polytechnic,Pingdingshan 467001,China ）Abstract:As a new motor,brushless doubly-fed machine （BDFM ）has the excellent performances which include wound rotor induction motor and synchronous motor.It especially suits in the variable speed constant frequency power generation area.This paper briefly introduces the structure and working principle of brushless doubly fed machine.By analyzing the structure and working principle of BDFM,mathematical model based on the rotor speed d-q coordinate has been ing the mathematical model,MATLAB/Simulink has been used to conduct the computer simulation research for the motor running status.The simulation waveforms under various operating mode have been obtained.The simulation results have confirmed the mathematical model's accuracy and some beneficial conclusions have been obtained.Key words:brushless doubly fed machine （BDFM ）,rotor speed,mathematical model,simulatio无刷双馈电机（BDFM ）作为一种新型电机,它与一般电机相比,在运行时要求容量较小的变频器,降低了系统成本,它既可运行于亚同步速也可以运行在超同步速,同时电机本身没有滑环和电刷,既降低了电机的成本,又提高了系统运行的可靠性,比较适合于变速恒频恒压发电领域,特别适用于风力发电、水力发电等可再能源的开发、利用[1-2].1无刷双馈电机的结构及原理1.1无刷双馈电机的基本结构无刷双馈电机的定子上装有两套不同极数的三相对称绕组,一套接至工频电源称为功率绕组（主绕组）；一套接至变频电源称为控制绕组（副绕组）[3].无刷双馈电机结构原理图如图1所示.doi:10.3969/j.issn.1008-7516.2011.04.020第39卷第4期394Vol.No.河南科技学院学报Journal of Henan Institute of Science and Technology 2011年8月2011Aug.收稿日期：2011-05-23作者简介：靳雷（1974-）,男,河南扶沟人,硕士,讲师.主要从事自动控制技术教学与应用研究.P p+P c P c 图1无刷双馈电机结构原理1.2“极调制”原理对无刷双馈电机来说,当功率绕组接入工频（频率为）电源、控制绕组接入变频（频率为）电源后,由于两套定子绕组同时有电流流过,因此在气隙中产生两个不同极对数的旋转磁场,这两个磁场通过转子的调制发生交叉耦合,在转子中产生相同极对数和转速的旋转磁场,从而使两个原本不会发生直接磁耦合的定子磁场通过转子的中介发生了磁耦合,使能量在两不同极对数、不同旋转速度的定子磁场以及转子磁场之间发生传递转换.转子的这种“中介”作用被称为“极调制”机理[4].根据“极调制”原理可知,电机稳定运行时,定子功率绕组和控制绕组在转子绕组中感应的电流频率应相等,因此,转子运行频率为：（1）所以,转子机械转速n r 为：（2）式（2）中的“±”号取决于定子两套绕组的相对相序.当功率绕组电源和控制绕组电源相序相反时取“+”号,反之取“-”.当f c 时的转速称为自然同步速.f c 前取负号的速度,称为亚同步速,反之称为超同步速.由式（2）可以看出,无刷双馈电机作电动机运行时,可通过调节控制绕组的供电频率f c 来调节转子转速,作发电机运行时,在不同机械转速下调节控制绕组的供电频率,可保证定子功率绕组输出恒定频率的交流电能,即实现了变速恒频发电[5].2无刷双馈电机的转子速d-q 模型对无刷双馈电机来说,两个子系统通过转子绕组发生耦合,在转子绕组上建立一个合适的坐标系统将给无刷双馈电机的数学模型的建立和分析带来方便,这样转子速d-q 坐标轴将是最好的选择.假定转子以逆时针方向旋转,由于无刷双馈电机两个子系统中旋转磁场的转向一般不同,为了得到一个统一的转子速d-q 坐标系,在磁场逆时针方向旋转的子系统中,选q 轴与转子第一相绕组的轴线重合,d 轴在旋转方向上落后90°；在磁场顺时针方向旋转的子系统中,q 轴仍与转子第一相绕组的轴线重合,d 轴在旋转方向上超前90°.由于这两个坐标系以同一个转子速度旋转,这两个d-q 轴坐标系可合并为同一个转子速d-q 轴坐标系[6].利用坐标变换理论,并考虑到BDFM 转子采用鼠笼式结构,这样,就得到无刷双馈电机在转子速d-q 坐标系下,以定转子绕组的电流作为状态变量的电压矩阵方程为：ÁÂÃÁÂf f f p p ÁÂÃÁÂ60()f f n p p −2011年河南科技学院学报（自然科学版）式（3）中,r p 、L sp 、M pr 和r c 、L sc 、M cr 分别为功率绕组和控制绕组的电阻、自感和绕组与转子之间的互感；r r 、L r 、分别为转子的电阻、自感和机械角速度；u qp 、u dp 、u qc 、u dc 、i qp 、i dp 、i qc 、i dc 、i qr 、i dr 为电压和电流瞬时值,下标“p ”表示功率绕组,“c ”表示控制绕组,“r ”表示转子,“q ”表示q 轴分量,“d ”表示d 轴分量.电磁转矩方程式如下：（4）机械运动方程如下：（5）式（4）、式（5）中T e 、T ep 、T ec 分别为电磁总转矩、功率绕组产生的转矩和控制绕组产生的电磁转矩,J 、K d 分别为转子机械惯量、转动阻尼系数,T L 为负载转矩.式（3）、式（4）和式（5）就构成了无刷双馈电机在转子速d-q 轴坐标系上的数学模型.3无刷双馈电机的运行仿真采用MATLAB/Simulink 对系统进行仿真研究,仿真所用到的无刷双馈电机模型电机参数为：p p =3,L sp =71.38mH,M p =69.31mH,r p =0.435Ω,p c =1,L sc =65.33mH,M c =60.21mH,r c =0.435Ω,L r =142.8mH,r r =1.63Ω,J =0.03kg·m 2,K d =0.利用无刷双馈电机在转子速d-q 轴坐标系上的数学模型,建立了如图2所示的动态仿真系统模型,它是由多个封装模块（子系统）构成[7].图2BDFM 仿真系统结构以BDFM 封装模块为例,包括6个电压方程和1个转矩方程的封装模块,如图3所示.其中,以Uqp 的封装模块为例,它的构成如图4所示.（3）Á?e ep ec p pr qp dr dp qr c cr qc dr dc qr ()()T T T p M i i i i p M i i i i ??????ÁÂÃÄÁd 1()d T T K t J?−??靳雷等：无刷双馈电机的建模与仿真第4期图3BDFM 封装模型图4BDFM 封装模型（局部）3.1单馈异步运行仿真无刷双馈电机运行在异步模式时,功率绕组星形连接,接380V 、50Hz 工频电源,控制绕组出线端abc 直接短路,即u qc =u dc =0,波形图如图5所示（其中图a 为转速波形,图b 为电磁转矩波形）.开始时,电机空载启动,经过一定时间的震荡后,电机转速稳定在自然同步速750r/min,在1s 时电机突加10Nm 的负载,则电机转速略有下降,稳定后转速大约为710r/min,这体现了无刷双馈电机作为异步电机的特性,与理论值相符.（a ）转速波形（b ）电磁转矩波形图5单馈异步运行动态特性3.2同步运行特性仿真2s 时控制控制绕组突加两并一串（U a =U b =10V,U c =-5V ）的直流励磁电源,则无刷双馈电机牵入同步运行,稳定后电机转速达到自然同步转速750r/min,与式（2）相符.若改变控制绕组直流电压的大小,过渡过程改变,但稳定转速不变.波形图如图6所示（其中图a 为转速波形,图b 为电磁转矩波形）.3s 时负载转矩由10Nm 突增到20Nm,稳定后,无刷双馈电机仍然可以维持同步速运行,也就是说,负载转矩在稳定允许的范围内改变时,对转速没有影响,此时无刷双馈电机显示出同步电机的特性.波形图如图7所示（其中图a 为转速波形,图b 为电磁转矩波形）.（a ）转速波形（b ）电磁转矩波形图6单馈运行状态过渡到同步运行状态的动态特性2011年河南科技学院学报（自然科学版）（a ）转速波形（b ）电磁转矩波形图7同步运行状态负载突变的动态特性3.3双馈运行特性仿真4s 时控制绕组突加同相序三相电压（100V,10Hz ）时,无刷双馈电机由同步运行状态过渡到“超同步”双馈运行状态,稳态转速从750r/min 变为900r/min,无刷双馈电机由空载同步运行状态过渡到“超同步”双馈运行状态,波形图如图8所示（其中图a 为转速波形,图b 为电磁转矩波形）.5s 控制绕组频率突然变为反相序三相电压（100V,10Hz ）时,稳态转速从900r/min 变为600r/min,无刷双馈电机由超同步双馈运行状态过渡到“亚同步”双馈运行状态,波形图如图9所示（其中图a 为转速波形,图b 为电磁转矩波形）.在理论上均与式（2）相符.（a ）转速波形（b ）电磁转矩波形图8同步运行状态过渡到超同步双馈运行状态时的动态特性（a ）转速波形（b ）电磁转矩波形图9超同步双馈运行过渡到亚同步双馈运行的动态特性4结语本文借助电机的坐标变换理论,推导出无刷双馈电机的转子速d-q 数学模型,对无刷双馈电机几种运行方式进行了M ATLAB 仿真研究,仿真结果表明了该模型的正确性,同时也说明无刷双馈电机可实现电机的软起动、异步、同步和双馈等多种运行方式,另外,仿真模型的构建为以后对无刷双馈电机更深入的研究奠定了基础.（下转93页）靳雷等：无刷双馈电机的建模与仿真第4期武艳等：发电机参数聚合及其动态仿真第4期5结论将连续域的变量区域进行网格划分,即可将离散优化问题的蚁群算法拓展应用到连续域寻优中,通过全局搜索和局部搜索两步获得最优解,具备全局寻优能力.同调发电机聚合参数的好坏对等值后系统的动态特性有很大的影响,对复杂大系统而言更为突出,因此对等值机参数的寻优应尽可能与同调机群聚合函数逼近.同调发电机参数的聚合可以表示为连续域的优化问题,因此可将蚁群算法应用于其中,通过算例分析以及与梯度法的效果对比,验证了该方法在同调发电机参数聚合中的良好效果.参考文献：[1]倪以信,陈寿孙,张宝霖.动态电力系统的理论和分析[M].北京：清华大学出版社.2002：240-242.[2]许剑冰,薛禹胜,张启平,等.电力系统同调动态等值的述评[J].电力系统自动化.2005,29（14）：91-95.[3]胡杰,余贻鑫.电力系统动态等值参数聚合的实用方法[J].电网技术.2006,30（24）：26-30.[4]李士勇.蚁群算法及其应用[M].哈尔滨：哈尔滨工业大学出版社,2004：1-59.[5]段海滨.蚁群算法原理及其应用[M].北京：科学出版社,2005：24-38.[6]Dorigo M,M aniezzo V,Colorni A.Ant system：optimization by a colony of cooperating agents[J].IEEE Transaction on System,M an,and Cybernetics-Part B,1996,26（1）：29-41.[7]Bilchev G A,Parmee I C.The ant colony metaphor for searching continuous spaces[J].Lecture Notes in Computer Science.1995,993：25-39.[8]Wang L,Wu Q D.Ant system algorithm for optimization in continuous space[J].Proceedings of the2001IEEE InternationalConference on Control Application,2001：385-400.[9]段海滨,马冠军,王道波,等.一种求解连续空间优化问题的改进蚁群算法[J].系统仿真学报,2007,19（5）：974-977.[10]陈礼义,孙丹峰.电力系统动态等值中发电机详细模型的参数集合[J].中国电机工程学报,1989,9（5）：30-39.[11]Benchluch S M,Chow J H.A trajectory sensitivity method for the identification of nonlinear excitation system models[J].IEEEtrans on Energy Conversion,1993,8（2）：159-164.[12]Carvalho V F,EI-kady M A,Fouad A.A direct analysis of transient stability for large power systems[R].California：EPRI,1986.（责任编辑：卢奇）（上接87页）参考文献：[1]卞松江.变速恒频发电关键技术研究[D].杭州：浙江大学,2003.[2]张志刚,王毅,黄守道,等.无刷双馈电机在变速恒频风力发电系统中的应用[J].电气传动,2005,35（4）：61-64.[3]邓先明,姜建国.无刷双馈电机的工作原理及电磁设计[J].中国电机工程学报,2003,23（11）：126-132.[4]章玮.无刷双馈电机系统及其控制研究[D].杭州：浙江大学,2001.[5]伍小杰,柴建云,王祥珩.变速恒频双馈风力发电系统交流励磁综述[J].电力系统自动化,2004（10）：92-96.[6]Li R,Wallace A,Spee R.Two-Axis M odel Development of Cage-Rotor Doubly-Fed M achines[J].IEEE Transactions on EnergyConversion,1991,6（3）：453-560.[7]薛定宇,陈阳泉.基于M atlab/Simulink的系统仿真技术与应用[M].北京：清华大学出版社,2002.（责任编辑：卢奇）。
圆柱形永磁体磁场建模及仿真研究
物质是由原子组成的， 每个原子又由原子核和电子 组成。电子绕原子核转动形成电流， 这些环流定向排列
基金项目： 江苏省自然科学基金资助项目 （BK20151182） 。 作者简介： 周恩权 （1984-） ， 男， 硕士， 工程师， 研究方向： 机械设计理论及高可靠性磁力系统。
·140·
圆பைடு நூலகம்形永磁体磁场建模及仿真研究
总 623 期第十一期 2017 年 11 月
河南科技 Henan Science and Technology
能源与化学
圆柱形永磁体磁场建模及仿真研究
周恩权 1 郑仲桥 2 张燕红 2 王奇瑞 3
常州 213032； 镇江 212013） （1. 海安交睿机器人科技有限公司， 江苏 南通 226000； 2. 常州工学院， 江苏 3. 江苏大学现代农业装备与技术教育部重点实验室， 江苏
Modeling and Simulation for Cylinder Permanent Magnetic Field
Zhou Enquan1 Zheng Zhongqiao2 Zhang Yanhong2 Wang Qirui3
面向计算机科学的数理逻辑系统建模与推理英文原版第二版教学设计
Teaching Design of Modeling and Reasoning in Mathematical Logic for Computer Science IntroductionMathematical Logic is a fundamental branch of mathematics thatstudies reasoning and inference. It provides a systematic approach to reasoning and problem-solving that can be applied to various fields, including computer science. In this teaching design, we will be usingthe second edition of the English original text entitled Modeling and Reasoning with Mathematical Logic: An Introduction for Computer Scientists.The m of this teaching design is to introduce students to the basics of mathematical logic and its applications to computer science. We will cover topics such as propositional logic, predicate logic, set theory, and proofs. In addition, we will use examples and exercises that are relevant to computer science, such as programming language semantics, databases, and artificial intelligence.Course ObjectivesUpon completion of this course, students should be able to:1.the principles of mathematical logic and its applications in computer science.2.and reason about various problems usingpropositional and predicate logic. 3.the basics of set theory and itsuse in modeling problems. 4.simple theorems using mathematical reasoning.5.the principles of mathematical logic in programming language semantics, database design, and artificial intelligence.Course OutlineChapter 1: Introduction to Mathematical LogicIn this chapter, we will provide a brief introduction tomathematical logic and its history. We will also introduce the syntaxand semantics of propositional logic, including truth tables and logical equivalence.Chapter 2: Reasoning with Propositional LogicIn chapter 2, we will cover the basics of reasoning withpropositional logic, including deductions, proofs, and the resolution method. We will also use examples and exercises that are relevant to computer science, such as circuit design and programming language semantics.Chapter 3: Predicate LogicChapter 3 introduces predicate logic, which extends propositional logic by adding quantifiers and predicates. We will cover the syntax and semantics of predicate logic, as well as the first-order logic. We will also use examples and exercises that are relevant to computer science, such as databases and artificial intelligence.Chapter 4: Set TheoryChapter 4 introduces the basics of set theory, including set operations, relations, and functions. We will cover the axiomatic foundations of set theory, as well as the ZFC axioms. We will also useexamples and exercises that are relevant to computer science, such as programming language semantics and databases.Chapter 5: Reasoning with Sets and RelationsIn chapter 5, we will apply our knowledge of set theory andpredicates to reason about sets and relations. We will cover basic set operations, equivalence relations, and partial orders. We will also use examples and exercises that are relevant to computer science, such as database normalization and graph algorithms.Chapter 6: Proofs and TheoremsChapter 6 introduces the basics of mathematical proofs and theorem proving. We will cover various proof techniques, including direct proofs, proofs by contradiction, and mathematical induction. We will also use examples and exercises that are relevant to computer science, such as program verification and testing.Teaching MethodologyThe teaching methodology will include lectures, in-class problem-solving, and assignments. In the lectures, we will cover the theory and principles of mathematical logic and their applications in computer science. In the problem-solving sessions, we will work through examples and exercises to reinforce the concepts covered in the lectures. Finally, the assignments will be designed to test students’ understanding of the course material.AssessmentThe assessment will be based on assignments (40%), mid-term examination (30%), and final examination (30%).ConclusionIn conclusion, this teaching design provides a systematic approachto teaching mathematical logic and its applications in computer science. By the end of the course, students will have a solid foundation in mathematical logic that they can apply to various areas in computer science.。
基于Saber的地铁牵引异步电动机建模与仿真
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(1. School of Electrical Engineering, Southeast University, Nanjing 210096, China; 2. College of Power Engineering, Nanjing University of Science & Technology, Nanjing 210094, China)
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基于 Saber的地铁牵引异步电动机建模与仿真 王 伟 , 等
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纯电动汽车动力性匹配设计与模型仿真
Modeling and Simulation 建模与仿真, 2020, 9(3), 357-366Published Online August 2020 in Hans. /journal/moshttps:///10.12677/mos.2020.93036Dynamic Matching Design and ModelSimulation of Pure Electric VehicleWentao Zhang, Li Ye, Zhijun Zhang, Huan Ye, Mengya ZhangSchool of Power Engineering, University of Shanghai for Science and Technology, ShanghaiReceived: Aug. 6th, 2020; accepted: Aug. 20th, 2020; published: Aug. 27th, 2020AbstractBased on the selection of basic vehicle parameters and the determination of performance indica-tors, this paper carries out the design matching of dynamic performance parameters of pure elec-tric vehicles. Then, a pure electric vehicle dynamic simulation model is established by vehicle si-mulation software, and the vehicle dynamic performance index is simulated and analyzed by in-putting relevant parameters. Finally, the rationality of simulation model and parameter matching is verified by real car test. This study can provide theoretical basis for the matching design of var-ious systems in the initial stage of pure electric vehicles, carry out range and performance test evaluation of vehicle performance, and provide reference for the analysis of dynamic performance and economic index of pure electric vehicles.KeywordsPure Electric Vehicle, Parameter Design Matching, Vehicle Power Model, Simulation Analysis纯电动汽车动力性匹配设计与模型仿真张文韬，叶立，张志军，叶欢，张梦伢上海理工大学动力工程学院，上海收稿日期：2020年8月6日；录用日期：2020年8月20日；发布日期：2020年8月27日摘要本文基于对整车基本参数的选取与性能指标的确定，进行了纯电动汽车动力性能参数的设计匹配。
Theory of modeling and simulation
THEORY OF MODELING AND SIMULATIONby Bernard P. Zeigler, Herbert Praehofer, Tag Gon Kim2nd Edition, Academic Press, 2000, ISBN: 0127784551Given the many advances in modeling and simulation in the last decades, the need for a widely accepted framework and theoretical foundation is becoming increasingly necessary. Methods of modeling and simulation are fragmented across disciplines making it difficult to re-use ideas from other disciplines and work collaboratively in multidisciplinary teams. Model building and simulation is becoming easier and faster through implementation of advances in software and hardware. However, difficult and fundamental issues such as model credibility and interoperation have received less attention. These issues are now addressed under the impetus of the High Level Architecture (HLA) standard mandated by the U.S. DoD for all contractors and agencies.This book concentrates on integrating the continuous and discrete paradigms for modeling and simulation. A second major theme is that of distributed simulation and its potential to support the co-existence of multiple formalisms in multiple model components. Prominent throughout are the fundamental concepts of modular and hierarchical model composition. These key ideas underlie a sound methodology for construction of complex system models.The book presents a rigorous mathematical foundation for modeling and simulation. It provides a comprehensive framework for integrating various simulation approaches employed in practice, including such popular modeling methods as cellular automata, chaotic systems, hierarchical block diagrams, and Petri Nets. A unifying concept, called the DEVS Bus, enables models to be transparently mapped into the Discrete Event System Specification (DEVS). The book shows how to construct computationally efficient, object-oriented simulations of DEVS models on parallel and distributed environments. In designing integrative simulations, whether or not they are HLA compliant, this book provides the foundation to understand, simplify and successfully accomplish the task.MODELING HUMAN AND ORGANIZATIONAL BEHAVIOR: APPLICATION TO MILITARY SIMULATIONSEditors: Anne S. Mavor, Richard W. PewNational Academy Press, 1999, ISBN: 0309060966. Hardcover - 432 pages.This book presents a comprehensive treatment of the role of the human and the organization in military simulations. The issue of representing human behavior is treated from the perspective of the psychological and organizational sciences. After a thorough examination of the current military models, simulations and requirements, the book focuses on integrative architectures for modeling theindividual combatant, followed by separate chapters on attention and multitasking, memory and learning, human decision making in the framework of utility theory, models of situation awareness and enabling technologies for their implementation, the role of planning in tactical decision making, and the issue of modeling internal and external moderators of human behavior.The focus of the tenth chapter is on modeling of behavior at the unit level, examining prior work, organizational unit-level modeling, languages and frameworks. It is followed by a chapter on information warfare, discussing models of information diffusion, models of belief formation and the role of communications technology. The final chapters consider the need for situation-specific modeling, prescribe a methodology and a framework for developing human behavior representations, and provide recommendations for infrastructure and information exchange.The book is a valuable reference for simulation designers and system engineers.HANDBOOK OF SIMULATOR-BASED TRAININGby Eric Farmer (Ed.), Johan Reimersma, Jan Moraal, Peter JornaAshgate Publishing Company, 1999, ISBN: 0754611876.The rapidly expanding area of military modeling and simulation supports decision making and planning, design of systems, weapons and infrastructure. This particular book treats the third most important area of modeling and simulation – training. It starts with thorough analysis of training needs, covering mission analysis, task analysis, trainee and training analysis. The second section of the book treats the issue of training program design, examining current practices, principles of training and instruction, sequencing of training objectives, specification of training activities and scenarios, methodology of design and optimization of training programs. In the third section the authors introduce the problem of training media specification and treat technical issues such as databases and models, human-simulator interfaces, visual cueing and image systems, haptic, kinaesthetic and vestibular cueing, and finally, the methodology for training media specification. The final section of the book is devoted to training evaluation, covering the topics of performance measurement, workload measurement, and team performance. In the concluding part the authors outline the trends in using simulators for training.The primary audience for this book is the community of managers and experts involved in training operators. It can also serve as useful reference for designers of training simulators.CREATING COMPUTER SIMULATION SYSTEMS:An Introduction to the High Level Architectureby Frederick Kuhl, Richard Weatherly, Judith DahmannPrentice Hall, 1999, ISBN: 0130225118. - 212 pages.Given the increasing importance of simulations in nearly all aspects of life, the authors find that combining existing systems is much more efficient than building newer, more complex replacements. Whether the interest is in business, the military, or entertainment or is even more general, the book shows how to use the new standard for building and integrating modular simulation components and systems. The HLA, adopted by the U.S. Department of Defense, has been years in the making and recently came ahead of its competitors to grab the attention of engineers and designers worldwide. The book and the accompanying CD-ROM set contain an overview of the rationale and development of the HLA; a Windows-compatible implementation of the HLA Runtime Infrastructure (including test software). It allows the reader to understand in-depth the reasons for the definition of the HLA and its development, how it came to be, how the HLA has been promoted as an architecture, and why it has succeeded. Of course, it provides an overview of the HLA examining it as a software architecture, its large pieces, and chief functions; an extended, integrated tutorial that demonstrates its power and applicability to real-world problems; advanced topics and exercises; and well-thought-out programming examples in text and on disk.The book is well-indexed and may serve as a guide for managers, technicians, programmers, and anyone else working on building simulations.HANDBOOK OF SIMULATION:Principles, Methodology, Advances, Applications, and Practiceedited by Jerry BanksJohn Wiley & Sons, 1998, ISBN: 0471134031. Hardcover - 864 pages.Simulation modeling is one of the most powerful techniques available for studying large and complex systems. This book is the first ever to bring together the top 30 international experts on simulation from both industry and academia. All aspects of simulation are covered, as well as the latest simulation techniques. Most importantly, the book walks the reader through the various industries that use simulation and explains what is used, how it is used, and why.This book provides a reference to important topics in simulation of discrete- event systems. Contributors come from academia, industry, and software development. Material is arranged in sections on principles, methodology, recent advances, application areas, and the practice of simulation. Topics include object-oriented simulation, software for simulation, simulation modeling,and experimental design. For readers with good background in calculus based statistics, this is a good reference book.Applications explored are in fields such as transportation, healthcare, and the military. Includes guidelines for project management, as well as a list of software vendors. The book is co-published by Engineering and Management Press.ADVANCES IN MISSILE GUIDANCE THEORYby Joseph Z. Ben-Asher, Isaac YaeshAIAA, 1998, ISBN 1-56347-275-9.This book about terminal guidance of intercepting missiles is oriented toward practicing engineers and engineering students. It contains a variety of newly developed guidance methods based on linear quadratic optimization problems. This application-oriented book applies widely used and thoroughly developed theories such LQ and H-infinity to missile guidance. The main theme is to systematically analyze guidance problems with increasing complexity. Numerous examples help the reader to gain greater understanding of the relative merits and shortcomings of the various methods. Both the analytical derivations and the numerical computations of the examples are carried out with MATLAB Companion Software: The authors have developed a set of MATLAB M-files that are available on a diskette bound into the book.CONTROL OF SPACECRAFT AND AIRCRAFTby Arthur E. Bryson, Jr.Princeton University Press, 1994, ISBN 0-691-08782-2.This text provides an overview and summary of flight control, focusing on the best possible control of spacecraft and aircraft, i.e., the limits of control. The minimum output error responses of controlled vehicles to specified initial conditions, output commands, and disturbances are determined with specified limits on control authority. These are determined using the linear-quadratic regulator (LQR) method of feedback control synthesis with full-state feedback. An emphasis on modeling is also included for the design of control systems. The book includes a set of MATLAB M-files in companion softwareMATHWORKSInitial information MATLAB is given in this volume to allow to present next the Simulink package and the Flight Dynamics Toolbox, providing for rapid simulation-based design. MATLAB is the foundation for all the MathWorks products. Here we would like to discus products of MathWorks related to the simulation, especially Code Generation tools and Dynamic System Simulation.Code Generation and Rapid PrototypingThe MathWorks code generation tools make it easy to explore real-world system behavior from the prototyping stage to implementation. Real-Time Workshop and Stateflow Coder generate highly efficient code directly from Simulink models and Stateflow diagrams. The generated code can be used to test and validate designs in a real-time environment, and make the necessary design changes before committing designs to production. Using simple point-and-click interactions, the user can generate code that can be implemented quickly without lengthy hand-coding and debugging. Real-Time Workshop and Stateflow Coder automate compiling, linking, and downloading executables onto the target processor providing fast and easy access to real-time targets. By automating the process of creating real-time executables, these tools give an efficient and reliable way to test, evaluate, and iterate your designs in a real-time environment.Real-Time Workshop, the code generator for Simulink, generates efficient, optimized C and Ada code directly from Simulink models. Supporting discrete-time, multirate, and hybrid systems, Real-Time Workshop makes it easy to evaluate system models on a wide range of computer platforms and real-time environments.Stateflow Coder, the standalone code generator for Stateflow, automatically generates C code from Stateflow diagrams. Code generated by Stateflow Coder can be used independently or combined with code from Real-Time Workshop.Real-Time Windows Target, allows to use a PC as a standalone, self-hosted target for running Simulink models interactively in real time. Real-Time Windows Target supports direct I/O, providing real-time interaction with your model, making it an easy-to-use, low-cost target environment for rapid prototyping and hardware-in-the-loop simulation.xPC Target allows to add I/O blocks to Simulink block diagrams, generate code with Real-Time Workshop, and download the code to a second PC that runs the xPC target real-time kernel. xPC Target is ideal for rapid prototyping and hardware-in-the-loop testing of control and DSP systems. It enables the user to execute models in real time on standard PC hardware.By combining the MathWorks code generation tools with hardware and software from leading real-time systems vendors, the user can quickly and easily perform rapid prototyping, hardware-in-the-loop (HIL) simulation, and real-time simulation and analysis of your designs. Real-Time Workshop code can be configured for a variety of real-time operating systems, off-the-shelf boards, and proprietary hardware.The MathWorks products for control design enable the user to make changes to a block diagram, generate code, and evaluate results on target hardware within minutes. For turnkey rapid prototyping solutions you can take advantage of solutions available from partnerships between The MathWorks and leading control design tools:q dSPACE Control Development System: A total development environment forrapid control prototyping and hardware-in-the-loop simulation;q WinCon: Allows you to run Real-Time Workshop code independently on a PC;q World Up: Creating and controlling 3-D interactive worlds for real-timevisualization;q ADI Real-Time Station: Complete system solution for hardware-in-the loopsimulation and prototyping.q Pi AutoSim: Real-time simulator for testing automotive electronic control units(ECUs).q Opal-RT: a rapid prototyping solution that supports real-time parallel/distributedexecution of code generated by Real-Time Workshop running under the QNXoperating system on Intel based target hardware.Dynamic System SimulationSimulink is a powerful graphical simulation tool for modeling nonlinear dynamic systems and developing control strategies. With support for linear, nonlinear, continuous-time, discrete-time, multirate, conditionally executed, and hybrid systems, Simulink lets you model and simulate virtually any type of real-world dynamic system. Using the powerful simulation capabilities in Simulink, the user can create models, evaluate designs, and correct design flaws before building prototypes.Simulink provides a graphical simulation environment for modeling dynamic systems. It allows to build quickly block diagram models of dynamic systems. The Simulink block library contains over 100 blocks that allow to graphically represent a wide variety of system dynamics. The block library includes input signals, dynamic elements, algebraic and nonlinear functions, data display blocks, and more. Simulink blocks can be triggered, enabled, or disabled, allowing to include conditionally executed subsystems within your models.FLIGHT DYNAMICS TOOLBOX – FDC 1.2report by Marc RauwFDC is an abbreviation of Flight Dynamics and Control. The FDC toolbox for Matlab and Simulink makes it possible to analyze aircraft dynamics and flight control systems within one softwareenvironment on one PC or workstation. The toolbox has been set up around a general non-linear aircraft model which has been constructed in a modular way in order to provide maximal flexibility to the user. The model can be accessed by means of the graphical user-interface of Simulink. Other elements from the toolbox are analytical Matlab routines for extracting steady-state flight-conditions and determining linearized models around user-specified operating points, Simulink models of external atmospheric disturbances that affect the motions of the aircraft, radio-navigation models, models of the autopilot, and several help-utilities which simplify the handling of the systems. The package can be applied to a broad range of stability and control related problems by applying Matlab tools from other toolboxes to the systems from FDC 1.2. The FDC toolbox is particularly useful for the design and analysis of Automatic Flight Control Systems (AFCS). By giving the designer access to all models and tools required for AFCS design and analysis within one graphical Computer Assisted Control System Design (CACSD) environment the AFCS development cycle can be reduced considerably. The current version 1.2 of the FDC toolbox is an advanced proof of concept package which effectively demonstrates the general ideas behind the application of CACSD tools with a graphical user- interface to the AFCS design process.MODELING AND SIMULATION TERMINOLOGYMILITARY SIMULATIONTECHNIQUES & TECHNOLOGYIntroduction to SimulationDefinitions. Defines simulation, its applications, and the benefits derived from using the technology. Compares simulation to related activities in analysis and gaming.DOD Overview. Explains the simulation perspective and categorization of the US Department of Defense.Training, Gaming, and Analysis. Provides a general delineation between these three categories of simulation.System ArchitecturesComponents. Describes the fundamental components that are found in most military simulations.Designs. Describes the basic differences between functional and object oriented designs for a simulation system.Infrastructures. Emphasizes the importance of providing an infrastructure to support all simulation models, tools, and functionality.Frameworks. Describes the newest implementation of an infrastructure in the forma of an object oriented framework from which simulation capability is inherited.InteroperabilityDedicated. Interoperability initially meant constructing a dedicated method for joining two simulations for a specific purpose.DIS. The virtual simulation community developed this method to allow vehicle simulators to interact in a small, consistent battlefield.ALSP. The constructive, staff training community developed this method to allow specific simulation systems to interact with each other in a single joint training exercise. HLA. This program was developed to replace and, to a degree, unify the virtual and constructive efforts at interoperability.JSIMS. Though not labeled as an interoperability effort, this program is pressing for a higher degree of interoperability than have been achieved through any of the previous programs.Event ManagementQueuing. The primary method for executing simulations has been various forms of queues for ordering and releasing combat events.Trees. Basic queues are being supplanted by techniques such as Red-Black and Splay trees which allow the simulation store, process, and review events more efficiently than their predecessors.Event Ownership. Events can be owned and processed in different ways. Today's preference for object oriented representations leads to vehicle and unit ownership of events, rather than the previous techniques of managing them from a central executive.Time ManagementUniversal. Single processor simulations made use of a single clocking mechanism to control all events in a simulation. This was extended to the idea of a "master clock" during initial distributed simulations, but is being replaced with more advanced techniques in current distributed simulation.Synchronization. The "master clock" too often lead to poor performance and required a great deal of cross-simulation data exchange. Researchers in the Parallel Distributed Simulation community provided several techniques that are being used in today's training environment.Conservative & Optimistic. The most notable time management techniques are conservative synchronization developed by Chandy, Misra, and Bryant, and optimistic synchronization (or Time Warp) developed by David Jefferson.Real-time. In addition to being synchronized across a distributed computing environment, many of today's simulators must also perform as real-time systems. These operate under the additional duress of staying synchronized with the human or system clock perception of time.Principles of ModelingScience & Art. Simulation is currently a combination of scientific method and artistic expression. Learning to do this activity requires both formal education and watching experienced practitioners approach a problem.Process. When a team of people undertake the development of a new simulation system they must follow a defined process. This is often re-invented for each project, but can better be derived from experience of others on previous projects.Fundamentals. Some basic principles have been learned and relearned by members of the simulation community. These have universal application within the field and allow new developers to benefit from the mistakes and experiences of their predecessors.Formalism. There has been some concentrated effort to define a formalism for simulation such that models and systems are provably correct. These also allow mathematical exploration of new ideas in simulation.Physical ModelingObject Interaction. Military object modeling is be divided into two pieces, the physical and the behavioral. Object interactions, which are often viewed as 'physics based', characterize the physical models.Movement. Military objects are often very mobile and a great deal of effort can be given to the correct movement of ground, air, sea, and space vehicles across different forms of terrain or through various forms of ether.Sensor Detection. Military object are also very eager to interact with each other in both peaceful and violent ways. But, before they can do this they must be able to perceive each other through the use of human and mechanical sensors.Engagement. Encounters with objects of a different affiliation often require the application of combat engagement algorithms. There are a rich set of these available to the modeler, and new ones are continually being created.Attrition. Object and unit attrition may be synonymous with engagement in the real world, but when implemented in a computer environment they must be separated to allow fair combat exchanges. Distributed simulation systems are more closely replicating real world activities than did their older functional/sequential ancestors, but the distinction between engagement and attrition are still important. Communication. The modern battlefield is characterized as much by communication and information exchange as it is by movement and engagement. This dimension of the battlefield has been largely ignored in previous simulations, but is being addressed in the new systems under development today.More. Activities on the battlefield are extremely rich and varied. The models described in this section represent some of the most fundamental and important, but they are only a small fraction of the detail that can be included in a model.Behavioral ModelingPerception. Military simulations have historically included very crude representations of human and group decision making. One of the first real needs for representing the human in the model was to create a unique perception of the battlefield for each group, unit, or individual.Reaction. Battlefield objects or units need to be able to react realistically to various combat environments. These allow the simulation to handle many situations without the explicit intervention of a human operator.Planning. Today we look for intelligent behavior from simulated objects. Once form of intelligence is found in allowing models to plan the details of a general operational combat order, or to formulate a method for extracting itself for a difficult situation.Learning. Early reactive and planning models did not include the capability to learn from experience. Algorithms can be built which allow units to become more effective as they become more experienced. They also learn the best methods for operating on a specific battlefield or under specific conditions.Artificial Intelligence. Behavioral modeling can benefit from the research and experience of the AI community. Techniques of value include: Intelligent Agents, Finite State Machines, Petri Nets, Expert and Knowledge-based Systems, Case Based Reasoning, Genetic Algorithms, Neural Networks, Constraint Satisfaction, Fuzzy Logic, and Adaptive Behavior. An introduction is given to each of these along with potential applications in the military environment.Environmental ModelingTerrain. Military objects are heavily dependent upon the environment in which they operate. The representation of terrain has been of primary concern because of its importance and the difficulty of managing the amount of data required. Triangulated Irregular Networks (TINs) are one of the newer techniques for managing this problem. Atmosphere. The atmosphere plays an important role in modeling air, space, and electronic warfare. The effects of cloud cover, precipitation, daylight, ambient noise, electronic jamming, temperature, and wind can all have significant effects on battlefield activities.Sea. The surface of the ocean is nearly as important to naval operations as is terrain to army operations. Sub-surface and ocean floor representations are also essential for submarine warfare and the employment of SONAR for vehicle detection and engagement.Standards. Many representations of all of these environments have been developed.Unfortunately, not all of these have been compatible and significant effort is being given to a common standard for supporting all simulations. Synthetic Environment Data Representation and Interchange Specification (SEDRIS) is the most prominent of these standardization efforts.Multi-Resolution ModelingAggregation. Military commanders have always dealt with the battlefield in an aggregate form. This has carried forward into simulations which operate at this same level, omitting many of the details of specific battlefield objects and events.Disaggregation. Recent efforts to join constructive and virtual simulations have required the implementation of techniques for cross the boundary between these two levels of representation. Disaggregation attempts to generate an entity level representation from the aggregate level by adding information. Conversely, aggregation attempts to create the constructive from the virtual by removing information.Interoperability. It is commonly accepted that interoperability in these situations is best achieved though disaggregation to the lowest level of representation of the models involved. In any form the patchwork battlefield seldom supports the same level of interoperability across model levels as is found within models at the same level of resolution.Inevitability. Models are abstractions of the real world generated to address a specific problem. Since all problems are not defined at the same level of physical representation, the models built to address them will be at different levels. The modeling an simulation problem domain is too rich to ever expect all models to operate at the same level. Multi-Resolution Modeling and techniques to provide interoperability among them are inevitable.Verification, Validation, and AccreditationVerification. Simulation systems and the models within them are conceptual representations of the real world. By their very nature these models are partially accurate and partially inaccurate. Therefore, it is essential that we be able to verify that the model constructed accurately represents the important parts of the real world we are try to study or emulate.Validation. The conceptual model of the real world is converted into a software program. This conversion has the potential to introduce errors or inaccurately represent the conceptual model. Validation ensures that the software program accurately reflects the conceptual model.Accreditation. Since all models only partially represent the real world, they all have limited application for training and analysis. Accreditation defines the domains and。
Geometric Modeling
Geometric ModelingGeometric modeling is a crucial aspect of computer-aided design and manufacturing, playing a fundamental role in various industries such as engineering, architecture, and animation. It involves the creation of digital representations of objects and environments using mathematical and computational techniques. This process enables designers and engineers to visualize, simulate, and analyze complex structures and shapes, leading to the development ofinnovative products and solutions. In this discussion, we will explore the significance of geometric modeling from different perspectives, considering its applications, challenges, and future advancements. From an engineering standpoint, geometric modeling serves as the cornerstone of product design and development. By representing physical components and systems through digital models, engineers can assess the performance, functionality, and manufacturability of their designs.This enables them to identify potential flaws or inefficiencies early in thedesign process, leading to cost savings and improved product quality. Geometric modeling also facilitates the creation of prototypes and simulations, allowing engineers to test and validate their ideas before moving into the production phase. As such, it significantly accelerates the innovation cycle and enhances theoverall efficiency of the product development process. In the field ofarchitecture and construction, geometric modeling plays a pivotal role in the conceptualization and visualization of building designs. Architects leverage advanced modeling software to create detailed 3D representations of structures, enabling clients and stakeholders to gain a realistic understanding of the proposed designs. This not only enhances communication and collaboration but also enables architects to explore different design options and assess their spatialand aesthetic qualities. Furthermore, geometric modeling supports the analysis of structural integrity and building performance, contributing to the creation of sustainable and resilient built environments. In the realm of animation andvisual effects, geometric modeling is indispensable for the creation of virtual characters, environments, and special effects. Artists and animators utilize sophisticated modeling tools to sculpt and manipulate digital surfaces, defining the shape, texture, and appearance of virtual objects. This process involves theuse of polygons, curves, and mathematical equations to create lifelike and dynamic visual elements that form the basis of compelling animations and cinematic experiences. Geometric modeling not only fuels the entertainment industry but also finds applications in scientific visualization, medical imaging, and virtual reality, enriching our understanding and experiences in diverse domains. Despite its numerous benefits, geometric modeling presents several challenges,particularly in dealing with complex geometries, large datasets, and computational efficiency. Modeling intricate organic shapes, intricate details, and irregular surfaces often requires advanced techniques and computational resources, posing a barrier for designers and engineers. Moreover, ensuring the accuracy and precision of geometric models remains a critical concern, especially in applications where small errors can lead to significant repercussions. Addressing these challenges demands continuous research and development in geometric modeling algorithms, data processing methods, and visualization technologies. Looking ahead, the future of geometric modeling holds tremendous promise, driven by advancements in artificial intelligence, machine learning, and computational capabilities. The integration of AI algorithms into geometric modeling tools can revolutionize the way designers and engineers interact with digital models, enabling intelligent automation, predictive analysis, and generative design. This paves the way for the creation of highly personalized and optimized designs, tailored to specific requirements and constraints. Furthermore, the convergence of geometric modeling with virtual and augmented reality technologies opens up new possibilities for immersive design experiences, interactive simulations, and digital twinning applications. In conclusion, geometric modeling stands as a vital enabler of innovation and creativity across various disciplines, empowering professionals to visualize, analyze, and realize their ideas in the digital realm. Its impact spans from product design and manufacturing to architecture, entertainment, and beyond, shaping the way we perceive and interact with the physical and virtual worlds. As we continue to push the boundaries of technology and imagination, geometric modeling will undoubtedly remain at the forefront of transformative advancements, driving progress and unlocking new frontiers of possibility.。
simulation modelling practice and theory sci
simulation modelling practice and theory sci全文共四篇示例，供读者参考第一篇示例：仿真建模实践与理论科学是一门旨在研究仿真技术在不同领域中的应用和发展的学科。
它涵盖了模型建立、仿真实验、数据分析等方面的内容，是一门跨学科的综合性学科。
仿真建模实践与理论科学的发展源远流长，它的发展史可以追溯到数学、物理学等领域的建模实践。
在当今信息化、数字化的时代，仿真建模已经成为了许多领域的重要工具，为我们认识和解决现实世界中的问题提供了新的途径。
在仿真建模实践与理论科学领域中，科学家们通过数学和计算机技术建立模型，通过对模型的仿真实验来观察和分析系统行为，并从中获取有关系统的信息。
这些信息可以帮助我们更好地理解系统的运行机理，指导我们做出相应的决策，提高系统的效率和性能。
在不同领域中，仿真建模都发挥着重要的作用，比如在工程领域中，仿真建模可以帮助工程师们设计和优化产品，提高产品的质量和性能；在医学领域中，仿真建模可以帮助医生们理解疾病的发生和发展机理，指导他们制定治疗方案等。
除了在实践中发挥着重要作用外，仿真建模实践与理论科学也在理论上不断地得到拓展和深化。
科学家们运用数学模型和计算机技术，探索系统的动力学行为、性质、规律等方面的规律，推动了系统科学和计算科学的发展。
仿真建模的理论也逐渐由简单的数学模型扩展到了包括多尺度、多模态、多组分等多种因素的复杂系统建模，使仿真建模更加贴近实际问题，更具有针对性和预见性。
在仿真建模实践与理论科学的研究中，还存在着一些困难和挑战。
复杂系统的建模和仿真需要大量的计算资源和数据支持，这对仿真建模的算法和技术提出了更高的要求；仿真建模需要在实际系统的基础上建立模型，并进行验证和验证，这对科学家们的理论功底和经验积累提出了更高的要求；不同领域之间的交叉和融合也需要科学家们具备跨学科的知识和思维能力，这为仿真建模的发展带来了更多的机遇和挑战。
电动汽车用异步电机参数辨识及优化
电动汽车用异步电机参数辨识及优化李强【摘要】The vector control system for induction motor used in electric vehicle was analyzed. A parameter identification and optimization algorithm for induction motor was proposed. Two different kinds of frequency sine signals generated by sinusoidal pulse width modulation were injected into the motor respectively, to identify the rotor resistance, mutual inductance and leakage inductance. On the d-axis, a sinusoidal current with DC bias was applied while the q-axis current was controlled to be zero, to optimize the rotor resistance. A step signal was injected into the induction motor to optimize the rotor time constant . The experiments of parameter identification and optimization using the algorithm were carried out on the 7. 5 kW induction motor. All conclusions indicate that the identification and optimization results are correct.%对电动汽车用异步电机矢量控制系统进行了分析，提出一种电动汽车用异步电机参数辨识及优化算法。
Introduction_to_Modeling_and_Simulation[1]
![Introduction_to_Modeling_and_Simulation[1]](https://img.taocdn.com/s3/m/25c398d8b14e852458fb571b.png)
INTRODUCTION TO MODELING AND SIMULATIONAnu MariaState University of New York at Binghamton Department of Systems Science and Industrial Engineering Binghamton, NY 13902-6000, U.S.A.ABSTRACTThis introductory tutorial is an overview of simulation modeling and analysis. Many critical questions are answered in the paper. What is modeling? What is simulation? What is simulation modeling and analysis? What types of problems are suitable for simulation? How to select simulation software? What are the benefits and pitfalls in modeling and simulation? The intended audience is those unfamiliar with the area of discrete event simulation as well as beginners looking for an overview of the area. This includes anyone who is involved in system design and modification - system analysts, management personnel, engineers, military planners, economists, banking analysts, and computer scientists. Familiarity with probability and statistics is assumed.1WHAT IS MODELING?Modeling is the process of producing a model; a model is a representation of the construction and working of some system of interest. A model is similar to but simpler than the system it represents. One purpose of a model is to enable the analyst to predict the effect of changes to the system. On the one hand, a model should be a close approximation to the real system and incorporate most of its salient features. On the other hand, it should not be so complex that it is impossible to understand and experiment with it. A good model is a judicious tradeoff between realism and simplicity. Simulation practitioners recommend increasing the complexity of a model iteratively. An important issue in modeling is model validity. Model validation techniques include simulating the model under known input conditions and comparing model output with system output.Generally, a model intended for a simulation study is a mathematical model developed with the help of simulation software. Mathematical model classifications include deterministic (input and output variables are fixed values) or stochastic (at least one of the input or output variables is probabilistic); static (time is not taken into account) or dynamic (time-varying interactions among variables are taken into account). Typically, simulation models are stochastic and dynamic.2WHAT IS SIMULATION?A simulation of a system is the operation of a model of the system. The model can be reconfigured and experimented with; usually, this is impossible, too expensive or impractical to do in the system it represents. The operation of the model can be studied, and hence, properties concerning the behavior of the actual system or its subsystem can be inferred. In its broadest sense, simulation is a tool to evaluate the performance of a system, existing or proposed, under different configurations of interest and over long periods of real time.Simulation is used before an existing system is altered or a new system built, to reduce the chances of failure to meet specifications, to eliminate unforeseen bottlenecks, to prevent under or over-utilization of resources, and to optimize system performance. For instance, simulation can be used to answer questions like: What is the best design for a new telecommunications network? What are the associated resource requirements? How will a telecommunication network perform when the traffic load increases by 50%? How will a new routing algorithm affect its performance? Which network protocol optimizes network performance? What will be the impact of a link failure?The subject of this tutorial is discrete event simulation in which the central assumption is that the system changes instantaneously in response to certain discrete events. For instance, in an M/M/1 queue - a single server queuing process in which time between arrivals and service time are exponential - an arrival causes the system to change instantaneously. On the other hand, continuous simulators, like flight simulators and weather simulators, attempt to quantify the changes in a system continuously over time in response toProceedings of the 1997 Winter Simulation Conferenceed. S. Andradóttir, K. J. Healy, D. H. Withers, and B. L. Nelson7controls. Discrete event simulation is less detailed (coarser in its smallest time unit) than continuous simulation but it is much simpler to implement, and hence, is used in a wide variety of situations.Figure 1 is a schematic of a simulation study. The iterative nature of the process is indicated by the system under study becoming the altered system which then becomes the system under study and the cycle repeats. In a simulation study, human decision making is required at all stages, namely, model development, experiment design, output analysis, conclusion formulation, and making decisions to alter the system under study. The only stage where human intervention is not required is the running of the simulations, which most simulation software packages perform efficiently. The important point is that powerful simulation software is merely a hygiene factor - its absence can hurt a simulation study but its presence will not ensure success. Experienced problem formulators and simulation modelers and analysts are indispensable for a successful simulation study.Figure 1: Simulation Study Schematic The steps involved in developing a simulation model, designing a simulation experiment, and performing simulation analysis are:Step 1.Identify the problem.Step 2.Formulate the problem.Step 3.Collect and process real system data.Step 4.Formulate and develop a model.Step 5.Validate the model.Step 6.Document model for future use.Step 7.Select appropriate experimental design.Step 8.Establish experimental conditions for runs.Step 9.Perform simulation runs.Step 10.Interpret and present results.Step 11.Recommend further course of action. Although this is a logical ordering of steps in a simulation study, many iterations at various sub-stages may be required before the objectives of a simulation study are achieved. Not all the steps may be possible and/or required. On the other hand, additional steps may have to be performed. The next three sections describe these steps in detail.3HOW TO DEVELOP A SIMULATION MODEL?Simulation models consist of the following components: system entities, input variables, performance measures, and functional relationships. For instance in a simulation model of an M/M/1 queue, the server and the queue are system entities, arrival rate and service rate are input variables, mean wait time and maximum queue length are performance measures, and 'time in system = wait time + service time' is an example of a functional relationship. Almost all simulation software packages provide constructs to model each of the above components. Modeling is arguably the most important part of a simulation study. Indeed, a simulation study is as good as the simulation model. Simulation modeling comprises the following steps:Step 1.Identify the problem. Enumerate problems with an existing system. Produce requirements for a proposed system.Step 2.Formulate the problem. Select the bounds of the system, the problem or a part thereof, to be studied. Define overall objective of the study and a few specific issues to be addressed. Define performance measures - quantitative criteria on the basis of which different system configurations will be compared and ranked. Identify, briefly at this stage, the configurations of interest and formulate hypotheses about system performance. Decide the time frame of the study, i.e., will the model be used for a one-time decision (e.g., capital expenditure) or over a period of time on a regular basis (e.g., air traffic scheduling). Identify the end user of the simulation model, e.g., corporate management versus a production supervisor. Problems must be formulated as precisely as possible.Step 3.Collect and process real system data. Collect data on system specifications (e.g., bandwidth for a communication network), input variables, as well as8Mariaperformance of the existing system. Identify sources of randomness in the system, i.e., the stochastic input variables. Select an appropriate input probability distribution for each stochastic input variable and estimate corresponding parameter(s).Software packages for distribution fitting and selection include ExpertFit, BestFit, and add-ons in some standard statistical packages. These aids combine goodness-of-fit tests, e.g., χ2 test, Kolmogorov-Smirnov test, and Anderson-Darling test, and parameter estimation in a user friendly format.Standard distributions, e.g., exponential, Poisson, normal, hyperexponential, etc., are easy to model and simulate. Although most simulation software packages include many distributions as a standard feature, issues relating to random number generators and generating random variates from various distributions are pertinent and should be looked into. Empirical distributions are used when standard distributions are not appropriate or do not fit the available system data. Triangular, uniform or normal distribution is used as a first guess when no data are available. For a detailed treatment of probability distributions see Maria and Zhang (1997).Step 4.Formulate and develop a model. Develop schematics and network diagrams of the system (How do entities flow through the system?). Translate these conceptual models to simulation software acceptable form. Verify that the simulation model executes as intended. Verification techniques include traces, varying input parameters over their acceptable range and checking the output, substituting constants for random variables and manually checking results, and animation.Step 5.Validate the model. Compare the model's performance under known conditions with the performance of the real system. Perform statistical inference tests and get the model examined by system experts. Assess the confidence that the end user places on the model and address problems if any. For major simulation studies, experienced consultants advocate a structured presentation of the model by the simulation analyst(s) before an audience of management and system experts. This not only ensures that the model assumptions are correct, complete and consistent, but also enhances confidence in the model.Step 6.Document model for future use. Document objectives, assumptions and input variables in detail.4 HOW TO DESIGN A SIMULATION EXPERIMENT?A simulation experiment is a test or a series of tests in which meaningful changes are made to the input variables of a simulation model so that we may observe and identify the reasons for changes in the performance measures. The number of experiments in a simulation study is greater than or equal to the number of questions being asked about the model (e.g., Is there a significant difference between the mean delay in communication networks A and B?, Which network has the least delay: A, B, or C? How will a new routing algorithm affect the performance of network B?). Design of a simulation experiment involves answering the question: what data need to be obtained, in what form, and how much? The following steps illustrate the process of designing a simulation experiment.Step 7.Select appropriate experimental design. Select a performance measure, a few input variables that are likely to influence it, and the levels of each input variable. When the number of possible configurations (product of the number of input variables and the levels of each input variable) is large and the simulation model is complex, common second-order design classes including central composite, Box-Behnken, and full-factorial should be considered. Document the experimental design.Step 8.Establish experimental conditions for runs. Address the question of obtaining accurate information and the most information from each run. Determine if the system is stationary (performance measure does not change over time) or non-stationary (performance measure changes over time). Generally, in stationary systems, steady-state behavior of the response variable is of interest. Ascertain whether a terminating or a non-terminating simulation run is appropriate. Select the run length. Select appropriate starting conditions (e.g., empty and idle, five customers in queue at time 0). Select the length of the warm-up period, if required. Decide the number of independent runs - each run uses a different random number stream and the same starting conditions -by considering output data sample size. Sample size must be large enough (at least 3-5 runs for each configuration) to provide the required confidence in the performance measure estimates. Alternately, use common random numbers to compare alternative configurations by using a separate random number stream for each sampling process in a configuration. Identify output data most likely to be correlated.Step 9.Perform simulation runs. Perform runs according to steps 7-8 above.5 HOW TO PERFORM SIMULATION ANALYSIS?Introduction to Modeling and Simulation 9Most simulation packages provide run statistics (mean,standard deviation, minimum value, maximum value) on the performance measures, e.g., wait time (non-time persistent statistic), inventory on hand (time persistent statistic). Let the mean wait time in an M/M/1 queue observed from n runs be n 21W ...,,W ,W . It is important to understand that the mean wait time W is a random variable and the objective of output analysis is to estimate the true mean of W and to quantify its variability.Notwithstanding the facts that there are no data collection errors in simulation, the underlying model is fully known, and replications and configurations are user controlled, simulation results are difficult to interpret. An observation may be due to system characteristics or just a random occurrence. Normally, statistical inference can assess the significance of an observed phenomenon, but most statistical inference techniques assume independent, identically distributed (iid) data. Most types of simulation data are autocorrelated, and hence, do not satisfy this assumption. Analysis of simulation output data consists of the following steps.Step 10.Interpret and present results. Compute numerical estimates (e.g., mean, confidence intervals) of the desired performance measure for each configuration of interest. To obtain confidence intervals for the mean of autocorrelated data, the technique of batch means can be used. In batch means, original contiguous data set from a run is replaced with a smaller data set containing the means of contiguous batches of original observations.The assumption that batch means are independent may not always be true; increasing total sample size and increasing the batch length may help.Test hypotheses about system performance.Construct graphical displays (e.g., pie charts, histograms)of the output data. Document results and conclusions.Step 11.Recommend further course of action. This may include further experiments to increase the precision and reduce the bias of estimators, to perform sensitivity analyses, etc.6AN EXAMPLEA machine shop contains two drills, one straightener, and one finishing operator. Figure 2 shows a schematic of the machine shop. Two types of parts enter the machine shop.in sequence. Type 2 parts require only drilling and finishing. The frequency of arrival and the time to be routed to the drilling area are deterministic for both types of parts.Step 1.Identify the problem. The utilization of drills, straightener, and finishing operator needs to be assessed. In addition, the following modification to the original system is of interest: the frequency of arrival of both parts is exponential with the same respective means as in the original system.Step 2.Formulate the problem. The objective is to obtain the utilization of drills, straightener, and finishing operator for the original system and the modification . The assumptions include:♦The two drills are identical♦There is no material handling time between the threeoperations.♦Machine availability implies operator availability.♦Parts are processed on a FIFO basis.♦All times are in minutes.Step 3.Collect and process real system data. At the job shop, a Type 1 part arrives every 30 minutes, and a Type 2 part arrives every 20 minutes. It takes 2 minutes to route a Type 1 part and 10 minutes to route a Type 2 part to the drilling area. Parts wait in a queue till one of the two drilling machines becomes available. After drilling, Type 1parts are routed to the straightener and Type 2 parts are10Mariarouted to the finishing operator. After straightening, Type 1 parts are routed to the finishing operator.The operation times for either part were determined to be as follows. Drilling time is normally distributed with mean 10.0 and standard deviation 1.0. Straightening time is exponentially distributed with a mean of 15.0. Finishing requires 5 minutes per part.Step 4.Formulate and develop a model. A model of the system and the modification was developed using a simulation package. A trace verified that the parts flowed through the job shop as expected.Step 5.Validate the model. The utilization for a sufficiently long run of the original system was judged to be reasonable by the machine shop operators.Step 6.Document model for future use. The models of the original system and the modification were documented as thoroughly as possible.Step 7.Select appropriate experimental design. The original system and the modification described above were studied.Step 8.Establish experimental conditions for runs. Each model was run three times for 4000 minutes and statistical registers were cleared at time 1000, so the statistics below were collected on the time interval [1000, 4000]. At the beginning of a simulation run, there were no parts in the machine shop.Step 9.Perform simulation runs. Runs were performed as specified in Step 8 above.Step 10.Interpret and present results. Table 1 contains the utilization statistics of the three operations for the original system and the modification (in parentheses).Table 1: Utilization StatisticsDrilling Straightening Finishing Mean Run #1 0.83 (0.78) 0.51 (0.58) 0.42 (0.39) Mean Run #2 0.82 (0.90) 0.52 (0.49) 0.41 (0.45) Mean Run #3 0.84 (0.81) 0.42 (0.56) 0.42 (0.40) Std. Dev. Run #1 0.69 (0.75) 0.50 (0.49) 0.49 (0.49) Std. Dev. Run #2 0.68 (0.78) 0.50 (0.50) 0.49 (0.50) Std. Dev. Run #3 0.69 (0.76) 0.49 (0.50) 0.49 (0.49) Mean utilization represents the fraction of time a server is busy, i.e., busy time/total time. Furthermore, the average utilization output for drilling must be divided by the number of drills in order to get the utilization per drill. Each drill is busy about 40% of the time and straightening and finishing operations are busy about half the time. This implies that for the given work load, the system is underutilized. Consequently, the average utilization did not change substantially between the original system and the modification; the standard deviation of the drilling operation seems to have increased because of the increased randomness in the modification. The statistical significance of these observations can be determined by computing confidence intervals on the mean utilization of the original and modified systems.Step 11.Recommend further course of action. Other performance measures of interest may be: throughput of parts for the system, mean time in system for both types of parts, average and maximum queue lengths for each operation. Other modifications of interest may be: the flow of parts to the machine shop doubles, the finishing operation will be repeated for 10% of the products on a probabilistic basis.7 WHAT MAKES A PROBLEM SUITABLE FOR SIMULATION MODELING AND ANALYSIS?In general, whenever there is a need to model and analyze randomness in a system, simulation is the tool of choice. More specifically, situations in which simulation modeling and analysis is used include the following:♦ It is impossible or extremely expensive to observe certain processes in the real world, e.g., next year's cancer statistics, performance of the next space shuttle, and the effect of Internet advertising on a company's sales.♦ Problems in which mathematical model can be formulated but analytic solutions are either impossible (e.g., job shop scheduling problem, high-order difference equations) or too complicated (e.g., complex systems like the stock market, and large scale queuing models).♦ It is impossible or extremely expensive to validate the mathematical model describing the system, e.g., due to insufficient data.Applications of simulation abound in the areas of government, defense, computer and communication systems, manufacturing, transportation (air traffic control), health care, ecology and environment, sociological and behavioral studies, biosciences, epidemiology, services (bank teller scheduling), economics and business analysis.8 HOW TO SELECT SIMULATION SOFTWARE?Although a simulation model can be built using general purpose programming languages which are familiar to the analyst, available over a wide variety of platforms, and less expensive, most simulation studies today are implemented using a simulation package. TheIntroduction to Modeling and Simulation 11advantages are reduced programming requirements; natural framework for simulation modeling; conceptual guidance; automated gathering of statistics; graphic symbolism for communication; animation; and increasingly, flexibility to change the model. There are hundreds of simulation products on the market, many with price tags of $15,000 or more. Naturally, the question of how to select the best simulation software for an application arises. Metrics for evaluation include modeling flexibility, ease of use, modeling structure (hierarchical v/s flat; object-oriented v/s nested), code reusability, graphic user interface, animation, dynamic business graphics, hardware and software requirements, statistical capabilities, output reports and graphical plots, customer support, and documentation.The two types of simulation packages are simulation languages and application-oriented simulators (Table 2). Simulation languages offer more flexibility than the application-oriented simulators. On the other hand, languages require varying amounts of programming expertise. Application-oriented simulators are easier to learn and have modeling constructs closely related to the application. Most simulation packages incorporate animation which is excellent for communication and can be used to debug the simulation program; a "correct looking" animation, however, is not a guarantee of a valid model. More importantly, animation is not a substitute for output analysis.Table 2: Simulation PackagesType OfSimulationPackageExamplesSimulation languages Arena (previously SIMAN), AweSim! (previously SLAM II), Extend, GPSS, Micro Saint,SIMSCRIPT, SLXObject-oriented software: MODSIM III, SIMPLE++ Animation software: Proof AnimationApplication -Oriented Simulators Manufacturing: AutoMod, Extend+MFG,FACTOR/AIM, ManSim/X, MP$IM,ProModel, QUEST, Taylor II, WITNESS Communications/computer: COMNET III,NETWORK II.5, OPNET Modeler, OPNETPlanner, SES/Strategizer, SES/workbench Business: BP$IM, Extend+BPR, ProcessModel, ServiceModel, SIMPROCESS, Time machine Health Care: MedModel9BENEFITS OF SIMULATION MODELING AND ANALYSISAccording to practitioners, simulation modeling and analysis is one of the most frequently used operations research techniques. When used judiciously, simulation modeling and analysis makes it possible to:♦Obtain a better understanding of the system by developing a mathematical model of a system ofinterest, and observing the system's operation in detail over long periods of time.♦Test hypotheses about the system for feasibility.♦Compress time to observe certain phenomena over long periods or expand time to observe a complex phenomenon in detail.♦Study the effects of certain informational, organizational, environmental and policy changes on the operation of a system by altering the system's model; this can be done without disrupting the real system and significantly reduces the risk of experimenting with the real system.♦Experiment with new or unknown situations about which only weak information is available.♦Identify the "driving" variables - ones that performance measures are most sensitive to - and the inter-relationships among them.♦Identify bottlenecks in the flow of entities (material, people, etc.) or information.♦Use multiple performance metrics for analyzing system configurations.♦Employ a systems approach to problem solving.♦Develop well designed and robust systems and reduce system development time.10WHAT ARE SOME PITFALLS TO GUARD AGAINST IN SIMULATION?Simulation can be a time consuming and complex exercise, from modeling through output analysis, that necessitates the involvement of resident experts and decision makers in the entire process. Following is a checklist of pitfalls to guard against.♦Unclear objective.♦Using simulation when an analytic solution is appropriate.♦Invalid model.♦Simulation model too complex or too simple.♦Erroneous assumptions.♦Undocumented assumptions. This is extremely important and it is strongly suggested that assumptions made at each stage of the simulation modeling and analysis exercise be documented thoroughly.♦Using the wrong input probability distribution.♦Replacing a distribution (stochastic) by its mean (deterministic).♦Using the wrong performance measure.♦Bugs in the simulation program.♦Using standard statistical formulas that assume independence in simulation output analysis.♦Initial bias in output data.♦Making one simulation run for a configuration.12MariaIntroduction to Modeling and Simulation 13♦ Poor schedule and budget planning.♦ Poor communication among the personnel involvedin the simulation study.REFERENCESBanks, J., J. S. Carson, II, and B. L. Nelson. 1996.Discrete-Event System Simulation, Second Edition,Prentice Hall.Bratley, P., B. L. Fox, and L. E. Schrage. 1987. A Guideto Simulation, Second Edition, Springer-Verlag.Fishwick, P. A. 1995. Simulation Model Design andExecution: Building Digital Worlds, Prentice-Hall.Freund, J. E. 1992. Mathematical Statistics, Fifth Edition,Prentice-Hall.Hogg, R. V., and A. T. Craig. 1995. Introduction toMathematical Statistics, Fifth Edition, Prentice-Hall.Kleijnen, J. P. C. 1987. Statistical Tools for SimulationPractitioners, Marcel Dekker, New York.Law, A. M., and W. D. Kelton. 1991. SimulationModeling and Analysis, Second Edition,McGraw-Hill.Law, A. M., and M. G. McComas. 1991. Secrets ofSuccessful Simulation Studies, Proceedings of the1991 Winter Simulation Conference, ed. J. M.Charnes, D. M. Morrice, D. T. Brunner, and J. J.Swain, 21-27. Institute of Electrical and ElectronicsEngineers, Piscataway, New Jersey.Maria, A., and L. Zhang. 1997. Probability Distributions,Version 1.0, July 1997, Monograph, Department ofSystems Science and Industrial Engineering, SUNYat Binghamton, Binghamton, NY 13902.Montgomery, D. C. 1997. Design and Analysis ofExperiments, Third Edition, John Wiley.Naylor, T. H., J. L. Balintfy, D. S. Burdick, and K. Chu.1966. Computer Simulation Techniques, John Wiley.Nelson, B. L. 1995. Stochastic Modeling: Analysis andSimulation, McGraw-Hill.AUTHOR BIOGRAPHYANU MARIA is an assistant professor in the departmentof Systems Science & Industrial Engineering at the StateUniversity of New York at Binghamton. She receivedher PhD in Industrial Engineering from the University ofOklahoma. Her research interests include optimizing theperformance of materials used in electronic packaging(including solder paste, conductive adhesives, andunderfills), simulation optimization techniques, geneticsbased algorithms for optimization of problems with alarge number of continuous variables, multi criteriaoptimization, simulation, and interior-point methods.。
双馈感应风力发电机低电压穿越控制策略
双馈感应风力发电机低电压穿越控制策略甄佳宁;陈益广;王颖【摘要】针对双感应馈风力发电机低电压穿越问题,提出了一种新型的转子侧变流器控制策略,该控制策略采用基于定子磁链定向的矢量控制方法,对转子稳态励磁电流添加补偿量,得到新的转子电流励磁给定值,抵消了定子磁链直流分量和负序分量对转子电流的部分影响,并能够有效地抑制双馈感应发电机转子过电流,增强了双馈感应风力发电机的低电压穿越能力.Matlab仿真结果验证了该控制策略的有效性和可行性.【期刊名称】《电力系统及其自动化学报》【年(卷),期】2013(025)005【总页数】4页(P88-91)【关键词】风力发电;双馈感应风力发电机;低电压穿越;电压骤降;转子变换器【作者】甄佳宁;陈益广;王颖【作者单位】天津大学智能电网教育部重点实验室,天津300072;天津大学智能电网教育部重点实验室,天津300072;天津经济技术开发区汉沽现代产业区总公司,天津300480【正文语种】中文【中图分类】TM614随着风力发电技术的不断发展，风力发电装机容量在不断扩大，并网风电机组对电网的影响越来越显著，诸多的风电并网问题突显出来。
电网电压跌落是电网故障中最为常见的问题之一，后果通常也最为严重。
当电网电压发生大幅度跌落故障后，如果风电机组不具备低电压穿越能力，将导致风电机组大规模解列，失去对电网电压的支撑，对电网稳定运行和电能质量产生严重影响。
为此世界上许多电网运营商均对风电机组低电压穿越能力提出了要求：当电网电压发生跌落后，风电机组必须在一定时间内与电网连接而不解列，甚至在过渡过程中为电网提供一定无功支撑以帮助电网电压快速恢复。
为了使风力发电能够实现大规模并网，风力发电机必须具备一定的低电压穿越能力[1]。
双馈感应风力发电机DFIG（doubly fed induction generator）具有变流器容量较小、有功和无功可独立解耦控制等特点，目前在兆瓦级风电机组中得到了广泛研究和应用[2～7]。
System Modeling and Simulation
System Modeling and Simulation System modeling and simulation are two key concepts in the field of engineering and technology. They are used to design, analyze, and optimize complex systems in various industries such as aerospace, automotive, and manufacturing. In this essay, we will discuss the importance of system modeling and simulation, the benefits they offer, and the challenges associated with their implementation.System modeling is the process of creating a mathematical representation of a system. It involves identifying the inputs, outputs, and components of the system and defining their relationships. Simulation, on the other hand, is the process of using the model to predict how the system will behave under different conditions. By combining these two processes, engineers can create virtual prototypes of complex systems and test them before they are built.One of the main benefits of system modeling and simulation is that they allow engineers to identify and correct design flaws early in the development process. By simulating the behavior of a system, engineers can test various scenarios and evaluate the performance of the system under different conditions. This helps to reduce the risk of costly errors and delays during the manufacturing and testing phases of the project.Another benefit of system modeling and simulation is that they enable engineers to optimize the performance of a system. By analyzing the simulation results, engineers can identify areas where the system can be improved and make the necessary adjustments. This can lead to significant cost savings and improved efficiency in the final product.System modeling and simulation also offer benefits in terms of safety. By simulating the behavior of a system, engineers can identify potential safety hazards and take steps to mitigate them. This is particularly important in industries such as aerospace and automotive, where safety is a critical concern.Despite the many benefits of system modeling and simulation, there are also challenges associated with their implementation. One of the main challenges is the complexity of the systems being modeled. As systems become more complex, the models required to simulate their behavior become more complex as well. This canmake it difficult to create accurate models and can increase the time and resources required to complete the simulation.Another challenge is the availability of data. In order to create an accurate model, engineers need access to data on the behavior of the system under different conditions. This data can be difficult to obtain, particularly in industries where the systems being modeled are new or proprietary.In conclusion, system modeling and simulation are critical tools for engineers and designers in a variety of industries. They offer numerous benefits, including the ability to identify and correct design flaws early in the development process, optimize system performance, and improve safety. However, there are also challenges associated with their implementation, including the complexity of the systems being modeled and the availability of data. Despite these challenges, the benefits of system modeling and simulation make them an essential part of modern engineering and design.。
计算长电缆电机端电压的递推算法
计算长电缆电机端电压的递推算法俞光【摘要】针对变流器与电机间由长距离电缆连接时机端过电压问题,提出根据单程传输时间前的变流器电压和往返传输时间前的电机端电压而递推出当前电机端电压的方法.利用该方法,首先推导出在已知变流器输出梯形波的情况下,电机端电压的表达式.然后,严格论证了在上升时间之后的一个单程传输时间点电机端电压取得最大值,并给出电机端电压与上升时间的关系曲线.说明了电机端电压的最大值并不是变流器输出电压上升时间的单调递减函数,而是周期性地增减.相比于行波反射理论,所提出的方法更便于定量分析.最后,通过仿真和在双馈风力发电机变流器系统中实际观测的电压波形对比,说明所提结论的正确性.【期刊名称】《电气传动》【年(卷),期】2015(045)011【总页数】5页(P76-80)【关键词】变流器;电机端电压;过电压;递推算法;上升时间【作者】俞光【作者单位】南京工程学院工业中心,江苏南京211167【正文语种】中文【中图分类】TM46在风力发电等大功率场合，变流器与电机间通常由上百m的长线电缆连接，而变流器输出电压为上升时间仅几μs 的调制波，经过长线电缆传输后，在电机端发生反射现象［1-4］，导致机端过电压问题［5-7］，最严重的情况下机端电压会达到变流器输出电压的2倍，威胁绕组绝缘，以及带来严重的电磁干扰［8-9］。
在电机端进行阻抗匹配能有效抑制过电压，但是安装不方便。
目前最常见的处理方法是在变流器中安装RLC滤波器［10-17］，以滤除调制波的陡峭上升沿，使得变流器的输出电压具有一定的上升时间。
理论分析和实践已验证该方法的确可以起到抑制电机端过电压的效果，但抑制效果与滤波器输出电压的上升时间具有密切联系。
因此研究电机端电压的大小与上升时间的关系具有重要意义。
文献［9-12］根据均匀传输线和反射理论进行定性分析，给出了在变流器输出电压为梯形波情形下电机端电压的反射过程，但在上升时间是多次传输时间时，反射理论分析显得非常困难，而且很难得到电机端电压在整个时域上的表达式。