九动态模型DynamicModeling

机械系统的动态建模与参数辨识机械系统是指由各种机械元件组成的系统，如齿轮传动、弹簧系统等。

为了对机械系统进行分析和控制，我们需要对其进行动态建模和参数辨识。

动态建模是指通过数学模型来描述机械系统的运动规律。

首先，我们需要明确机械系统的输入和输出变量。

输入变量通常是外部施加的力、力矩或位移，而输出变量则是系统的状态或响应。

其次，我们可以根据机械系统的特性和工作原理选择合适的数学模型，如微分方程、差分方程或状态空间模型等。

最后，利用物理原理和运动学关系，我们可以建立起机械系统的动态模型。

在动态建模的过程中，参数辨识起着重要的作用。

参数辨识是指通过实验或数据分析，对机械系统中的参数进行估计和辨识。

由于机械系统中的参数通常很难直接测量或获取，我们需要借助于辨识方法来对这些参数进行估计。

常见的参数辨识方法包括最小二乘法、极大似然法等。

参数辨识的过程可以分为离线辨识和在线辨识。

离线辨识是指在事先收集好的实验数据基础上进行参数辨识，而在线辨识则是指在系统运行过程中不断对参数进行更新和辨识。

无论是离线辨识还是在线辨识，我们都需要选择合适的辨识算法和模型结构。

辨识算法的选择通常需要考虑辨识误差、计算复杂度和辨识时间等因素。

而模型结构的选择则需要结合机械系统的特性和实际需求。

机械系统的动态建模和参数辨识对于机械工程领域具有重要意义。

通过建立准确的数学模型，我们可以深入理解机械系统的工作原理和运动规律，为系统分析和控制提供有力支持。

同时，通过参数辨识，我们可以对机械系统的参数进行精确估计，为系统设计和优化提供依据。

然而，机械系统的动态建模和参数辨识也存在一些挑战和限制。

首先，机械系统的运动规律通常是非线性的，因此需要采用适当的非线性模型和辨识方法。

其次，机械系统中存在着各种不确定因素，如摩擦、载荷变化等，这些因素会对参数辨识的准确性和稳定性造成影响。

此外，由于机械系统的复杂性和多样性，动态建模和参数辨识的过程也需要一定的专业知识和经验。

时磊忖呎DSGE模型在房产税影响分析的应用1•模型综述动态随机一般均衡模型（dynamic stochastic general equilibrium model，即DSGE,是以微观和宏观经济理论为基础，采用动态优化方法考察个行为主体（家庭、厂商等）的决策，即在家庭最大化其一生效用、厂商最大化其利润的假设下得到个行为主体的行为方程。

各行为主体在决策时必须考虑其行为的当期影响，以及未来的后续影响，同时，现实经济中存在诸多的不确定性，因此，DSGE模型在引入各种外生随机冲击的情况下，研究各主体之间的相互作用和相互影响。

（Dynamic stochastic general equilibrium modeling （abbreviated DSGE or sometimes SDGE or DGE） is a branch of applied gen eral equilibrium theory that is in flue ntial in con temporary macroec ono mics. The DSGE methodology attempts to expla in aggregate econo mic phe nomena, such as econo mic growth, bus in ess cycles, and the effects of mon etary and fiscal policy, on the basis of macroec ono mic models derived from microec ono mic prin ciples.）其主要特征有：（1）动态“动态”指经济个体考虑的是跨期最优选择（In ter-temporal Optimal Choice）。

因此，模型得以探讨经济体系中各变量如何随时间变化而变化的动态性质。

（2）随机“随机”则指经济体系受到各种不同的外生随机冲击所影响。

数学建模动态优化模型数学建模是一种通过建立数学模型来解决实际问题的方法。

动态优化模型则是指在一定的时间尺度内，通过调整决策变量，使系统在约束条件下达到最优效果的数学模型。

本文将介绍数学建模中动态优化模型的基本原理、方法和应用。

动态优化模型是一种考虑时间因素的优化模型。

在解决实际问题时，往往需要考虑到系统随时间变化的特性，因此单纯的静态优化模型可能无法满足需求。

动态优化模型对系统的演化过程进行建模，通过引入时间因素，能够更准确地描述系统的行为，并找到最优的策略。

动态优化模型的核心是建立一个数学模型来描述系统的演化过程。

在建模过程中，需要确定决策变量、目标函数、约束条件和系统的动态特性。

决策变量是指在不同时间点上的决策变量值，目标函数是指目标的数量指标，约束条件是系统必须满足的条件，系统的动态特性是指系统状态随时间的变化规律。

动态优化模型的建模方法有很多种，常见的方法包括状态空间建模、差分方程建模和优化控制建模等。

其中，状态空间建模是一种通过描述系统状态和系统状态之间的关系来建立模型的方法；差分方程建模是一种通过描述离散时间点上系统的状态之间的关系来建立模型的方法；优化控制建模则是一种将优化方法和控制方法相结合的建模方法。

动态优化模型在实际问题中有广泛的应用。

例如，在生产调度问题中，我们需要根据不同时间的产销情况来安排生产任务，以使得产能得到充分利用并满足市场需求；在交通控制问题中，我们需要根据交通流量的变化来调整信号灯的配时方案，以最大程度地减少交通拥堵；在能源管理问题中，我们需要根据电网的负荷变化来调整发电机组的出力，以实现能源的有效利用。

在建立动态优化模型时，需要考虑到模型的复杂性和求解的难度。

一方面，动态优化模型往往比静态优化模型复杂，需要考虑到系统的动态特性和约束条件的演化；另一方面，求解动态优化模型需要考虑到系统的运行时间和求解算法的效率。

因此，在建立动态优化模型时，需要合理选择模型和算法，以保证模型的可行性和求解的可行性。

说明动态模型的特征。

动态模型是指描述系统随时间变化的模型。

它能够帮助我们预测未来，并为决策提供更好的依据。

以下是动态模型的几个特征。

1. 可变性
动态模型能够表示和解释系统随时间变化的复杂性和变化性。

由于系
统中很多因素都是不固定的、不断变化的，动态模型能够不断更新和
改进模型以适应这些变化。

2. 系统性
动态模型涉及到整个系统，而不仅仅是其个别组成部分。

这种方法可
以捕捉系统中重要的因素，发现它们之间的关系并预测它们对整个系
统的影响。

3. 多样性
动态模型能够处理多种系统，包括生态系统、经济系统和社会系统等。

它可以适应各种领域的需要，是一种广泛适用的方法。

4. 依赖于时间
由于动态模型是基于时间的，它能够帮助我们了解系统中各个部分之
间的相互作用和关系是如何随着时间变化而改变的。

这种时间性质使
得动态模型可以帮助我们预测未来的发展趋势和结果。

5. 重视过程
动态模型强调过程和变化，能够揭示系统中的动态机制，并为我们提
供预测系统未来行为的依据。

通过建立动态模型，我们可以知道指定
变量所对应的过程，以及这些过程如何受到其他因素的影响。

综上所述，动态模型是一种可以处理复杂系统的广泛适用方法，它不
断更新和改进以适应系统中的变化。

它能够证明系统因素之间的相互
作用和关系，并为我们预测未来提供帮助。

为了建立适当的动态模型，我们必须密切关注过程和时间，并理解系统的复杂性，这样才能使模
型发挥最大的让益。

附录A 英文参考文献Dynamic modeling and direct power control of wind turbine driven DFIG under unbalanced network voltage condition INTRODUCTION,Wind farms based on the doubly-fed inductiongenerators(DFIG)with converters rated at 25%~30%ofthe generator rating for a given rotor speedvariation range of±25%are becoming pared with the wind turbines using fixedspeed induction generators or fully-fed synchronousgenerators with full-size convertersthe DFIG-basedwind turbines offer not only theadvantages of variable speed operation and four-quadrant active andreactive power capabilities,but also lower convertercost and power losses(Pena et al.,1996).However,both transmission and distribution networks couldusually have small steady state and large transientvoltage unbalance.If voltage unbalance is not considered by the DFIG control system,the stator currentcould become highly unbalanced even with a smallunbalanced stator voltage.The unbalanced currentscreate unequal heating on the stator windings,andpulsations in the electromagnetic torque and statoroutput active and reactive powers(Chomatetal.,2002;Jang et al.,2006;Zhou et al.,2007;Pena et al.,2007;Hu et al.,2007;Xu and Wang,2007;Hu andHe,2008).Control and operation of DFIG wind turbinesystems under unbalanced network conditions istraditionally based on either stator-flux-oriented(SFO)(Xuand Wang,2007)or stator-voltage-oriented(SVO)vector control(Jang et al.,2006;Zhou et al.,2007;Hu et al.,2007;Hu and He,2008).The schemein(Jang et al.,2006;Zhou et al.,2007;Xu and Wang,2007;Hu et al.,2007)employs dual-PI(proportionalintegral)current regulators implemented in thepositive and negative synchronously rotating referenceframes,respectively,which has to decompose themeasured rotor current into positive and negative sequence components to control them individually.One main drawback of this approach is that,the timedelays introduced by decomposing the sequentialcomponents of rotor current can affect the overallsystem stability and dynamic response.Thus,acurrent control scheme based on a proportionalresonant(PR)regulator in the stator stationaryreference frame was proposed in(Hu and He,2008),which can directly control the rotor current withoutthe need of sequential decomposition.Whereas,theperformance of the vector control scheme highlydepends on the accurate machine parameters such asstator/rotor inductances and resistances used in thecontrol system.Similar to direct torque control(DTC)ofinduction machines presented a few decades ago,which behaves as an alternative to vector control,direct power control(DPC)of DFIG-based windturbine systems has been proposed recently(Gokhaleet al.,2002;Xu and Cartwright,2006;Zhi and Xu,2007).In(Gokhale et al.,2002),the control schemewas based on the estimated rotor flux.Switchingvectors were selected from the optimal switchingtable using the estimated rotor flux position and theerrors of rotor flux and active power.The rotor fluxreference was calculatedusing the reactive powerreference.Since the rotor supply frequency,equal tothe DFIG slip frequency,might be very low,the rotorflux estimation could be significantly affected by themachine parameter variations.In(Xu and Cartwright,2006),a DPC strategy based on the estimated statorflux was proposed.As the stator voltage is relativelyharmonics-free and fixed in frequency,a DFIGestimated stator flux accuracy can then be guaranteed.Switching vectors were selected from the optimalswitching table using the estimated stator fluxposition and the errors of the active and reactivepowers.Thus,the control system was simple and themachine parameters’impact on the systemperformance was found to be negli able.However,like a conventional DTC,DPC has the problem ofunfixed switching frequency,due to the significantinfluence of the active and reactive power variations,generator speed,and power controllers’hysteresisbandwidth.More recently,a modified DPC strategyhas been proposed in(Zhi and Xu,2007)based onSFO vector control in the synchronous referenceframe for DFIG-based wind power generationsystems with a constant switching frequency.The control method directly calculates the required rotorcontrol voltage within each switching period,basedon the estimated stator flux,the active and reactivepowers and their errors.The control strategy providesimproved transient performance with the assumptionof the stator(supply)voltage being strictly balanced.However,the operation could be deteriorated duringthe supply voltage unbalance and there is no reportyet on DFIGDPC under unbalanced networkvoltage conditions.This paper investigates an improved DPCscheme for a DFIG wind power generation systemunder unbalanced network conditions.In the SVO dqreference frame,a mathematical DPC model of aDFIG system with balanced supply is presented,which is referred to as the conventional model in thispaper.Then during network unbalance,a modifiedDFIG DPC model in the SVO positive dqandnegative dqreference frames is developed.Based onthe developed model,a system control strategy isproposed by eliminating the stator output activepower oscillations under unbalanced network conditions.Finally,simulation results on a 2-MW DFIGwind generation system are presented to demonstratethe correctness and feasibility of the proposed controlstrategy.SIMULATION STUDIES,Simulations of the proposed DPC strategy for aDFIG-based wind power generation system wereconducted using PSCAD/EMTDC.A single-phase load at the primary side of the coupling transformer was used to generate the voltageunbalance.The nominal DC link voltage was set at1 200 V and the switching frequencies for both converters were 2 000 Hz.The main target of the grid sideconverter was assigned to control the DC link voltagewith the similar method used in(Song and Nam,1999;Hu et al.,2007).As shown in Fig.7,a high frequencyAC filter is shunt-connected to the stator side to absorb the switching harmonics generated by the twoconverters.Initial studies with various active and reactivepower steps were carried out to test the dynamic response using the conventional control scheme shownin Fig.4 in the conditions of balanced supply voltage.First,the DFIG was assumed to be in speed control,viz.,the rotor speed was set externally,as thelargeinertia of the wind turbine resulting in a slow changeof the rotor speed.The activeand reactive powers were initially set at 0 MW and0.5 MV·A,respectively,whererefers to absorbing reactive power.Various power steps were applied,viz.,active and reactive power references werechanged from 0 to2 MW at the instant of 1.3 s.CONCLUSION，This paper has proposed an analysis and an improved DPC design for a DFIG-based wind powergeneration system during network voltage unbalance.Simulation results were presented to demonstrate thefeasibility of the proposed control scheme.Conclusions can be drawn as follows:(1)The conventional DPC scheme without net-work unbalance considered can provide pretty gooddynamic system performance when the supplyvoltage is strictly balanced.However,once the net-work is slightly unbalanced,the performance deteriorates with high stator/rotor current unbalances andsignificant oscillations in the stator active/reactivepower and electromagnetic torque.(2)The proposed DPC scheme,which is implemented in the SVO positive dq+ and negative dqreference frames,gets rid of the decompositionprocess of positive and negative sequence rotor currents in the vector control scheme using dual-PI rotorenhanced by the elimination of the stator output activepower oscillations and the reduction of the electro-magnetic torque pulsations during network unbalance.附录B 中文参考文献动态建模与驱动的双馈风力发电机直接供电网络的电压不平衡条件下的控制风力发电场的双馈感应发电机与转换器在25％〜30给定转子速度变化范围± 25％额定发电机（双馈）的基础正在变得越来越受欢迎。

第二十四章动态模型(Dynamic Models)动态电动机起动、暂态稳定性和发电机起动分析中都要用到电动机动态模型。

发电机动态模型和一些相关的控制单元(如励磁器，电力系统稳定器和调速器等)只在暂态稳定分析中用到。

另外，电动机起动分析和暂态稳定分析也要求有负荷转矩特性。

风力涡轮发电机动态模型以及相关的控制需要进行动态仿真研究。

如果需要等效负荷的动力研究，那么等效负荷动态模型也是必须的。

ETAP为各种分析提供了各种感应电机和同步电机模型，风力涡轮发电机，等效负荷动态模型以及全面的励磁器，调速器库和电力系统稳定器以供你选择。

在运用电机模型进行动态电机加速分析中，只有加速的电动机需要动态模型，也就是说，发电机，励磁器和调速器都不需要动态模型。

在暂态稳定分析中，所有的发电机，励磁器和调速器都是动态模型的。

有动态模型的电动机和在分析案例中被设为动态模型的电动机都会被动态模拟。

在发电机起动和依赖于频率的暂态稳定分析中，所有的发电机、励磁器和调速器都必须是依赖于频率的模型。

本章描述了不同类型的电机模型，电机控制单元模型和负荷模型并解释了他们在电动机起动和暂态稳定性分析中的应用。

也介绍了在选择模型和设定模型参数时所用到的工具。

感应电机模型部分介绍了五种不同的感应电机模型和这些模型的依赖于频率形式，分别是电路模型 (Single1、Single2、DBL1、DBL2) 和特性曲线模型。

介绍了五种不同的同步电机模型和这些模型的依赖于频率形式，这些模型在同步电机这章中提供给大家。

这些模型分别是等值电路模型，隐极电机的暂态模型，隐极电机的次暂态模型，凸极电机的暂态模型和凸极电机的次暂态模型。

电动机起动分析和暂态稳定性分析也把等效电网系统模拟成一个等值电机。

在等效电网部分介绍了等效电网系统的模型。

在励磁器和自动电压调节器模型部分定义不同类型的励磁器和自动电压调节器模型，包括标准IEEE模型和商用特有模型。

在调速器-涡轮部分列举了以IEEE标准和商用产品手册为准的调速器-涡轮模型。

系统动力学9种模型引言系统动力学是一种研究动态系统行为的方法论，它通过构建系统模型来分析系统的各种因果关系和变化规律。

在系统动力学中，有9种基本模型被广泛应用于各种领域的问题分析和解决。

本文将对这9种模型进行全面、详细、完整且深入地探讨。

1. 积累模型积累模型是系统动力学中最基本的模型之一，它描述了一个变量或者一组变量的积累过程。

例如，当我们考虑人口增长的问题时，可以使用积累模型来描述人口数量随时间的变化。

积累模型通常使用微分方程表示。

1.1. 特点 - 变量之间存在流入和流出的关系； - 变量之间的积累是连续的； - 流入量和流出量可以是恒定的或者变化的。

1.2. 应用示例积累模型在生态学、经济学、工程管理等领域得到了广泛的应用。

例如，在生态学中，可以使用积累模型来研究物种数量的变化；在经济学中，可以使用积累模型来研究货币的流通和储蓄；在工程管理中，可以使用积累模型来研究项目进展和资源分配。

1.3. 示例方程dP/dt = b*P - d*P其中，P表示人口数量，t表示时间，b表示出生率，d表示死亡率。

2. 流动模型流动模型描述了一个变量或者一组变量之间的流动过程。

它通常用来研究物质、能量、信息等在系统中的传递和传播。

例如，在物流管理中，可以使用流动模型来研究物料的流动和分配。

2.1. 特点 - 变量之间存在流动的关系； - 流动可以是单向的或者双向的； -流动可以是连续的或者离散的。

2.2. 应用示例流动模型在供应链管理、信息传输、能量传递等领域具有广泛的应用。

例如，在供应链管理中，可以使用流动模型来优化物料的流动和库存的控制；在信息传输中，可以使用流动模型来研究信息的传播和处理；在能量传递中，可以使用流动模型来分析能量的转化和利用。

2.3. 示例方程dQ/dt = f - k*Q其中，Q表示物料的数量，t表示时间，f表示流入量，k表示流失率。

3. 动力平衡模型动力平衡模型描述了一个变量或者一组变量在达到平衡状态时的行为。

说明动态模型的特征。

动态模型是指一种描述物体或系统在时间上变化的模型。

它可以用来分析和预测复杂系统的行为，帮助我们理解事物的演变规律和规划未来的发展方向。

动态模型的特征主要包括以下几个方面：1. 时间变化：动态模型关注事物在时间上的演变过程，通过描述系统随时间的变化来分析系统的行为。

它可以反映系统的历史状况，预测未来的发展趋势，并帮助我们做出相应的决策。

2. 多变量关系：动态模型通常涉及多个变量之间的相互作用和影响。

它可以揭示事物之间的复杂关系，帮助我们理解系统的内部结构和外部环境对系统的影响。

通过分析这些关系，我们可以找到系统的关键因素，识别问题所在，并采取相应的措施来改进系统的性能。

3. 非线性特性：动态模型通常具有非线性特性，即系统的行为不仅受到输入的线性组合影响，还受到系统本身的非线性特性和外部环境的非线性影响。

这使得系统的行为变得更加复杂，需要采用更加灵活和综合的方法来进行建模和分析。

4. 反馈机制：动态模型通常包含反馈机制，即系统的输出会影响系统的输入，从而产生反馈效应。

这种反馈机制可以使系统的行为变得更加复杂和多样化，同时也增加了系统的稳定性和鲁棒性。

通过分析反馈机制，我们可以预测系统的稳定状态和稳定性边界，并采取相应的措施来控制系统的行为。

5. 系统动力学：动态模型通常基于系统动力学理论进行构建和分析。

系统动力学是一种综合运筹学、控制论和信息论等多学科知识的方法，可以描述和分析复杂系统的行为和演化规律。

通过应用系统动力学方法，我们可以建立系统的数学模型，研究系统的稳定性和动态特性，并通过模拟和仿真来验证模型的有效性。

6. 预测和优化：动态模型可以用来预测系统的未来行为和优化系统的性能。

通过分析系统的历史数据和当前状态，我们可以建立系统的预测模型，预测系统未来的发展趋势和可能出现的问题。

同时，通过优化模型，我们可以找到系统的最优解，最大化系统的效益，并制定相应的决策和措施来改进系统的性能。

系统动力学的9种模型解析标题：系统动力学的9种模型解析引言：系统动力学是一种研究动态复杂系统行为的数学方法，广泛应用于经济学、生态学、管理学等领域。

本文将深入探讨系统动力学的9种常见模型，并分析其理论基础和应用领域。

通过对这些模型的解析，旨在帮助读者更深入地理解系统动力学及其在实践中的作用。

第一部分：系统动力学概述在介绍具体的模型之前，有必要先了解系统动力学的基本概念和原理。

系统动力学着重于分析系统内部各个组成部分之间的相互关系，通过建立微分方程等数学模型来描述系统的演化过程。

这一方法注重动态演化和非线性特性，在解决复杂问题时具有独特的优势。

第二部分：9种系统动力学模型1. 常微分方程模型：系统动力学的基础，用于描述动态系统的变化过程。

2. 资源流模型：关注系统内资源的流动和变化，适用于生态学、能源管理等领域的研究。

3. 增长模型：研究系统中因子的增长和衰减，可应用于经济学、人口学等领域。

4. 循环模型：探讨系统中的循环过程，如经济周期的波动，可应用于宏观经济研究。

5. 积聚模型：研究系统中积聚和堆积的过程，如资本积累，适用于经济学和企业管理等领域。

6. 信息流模型：研究系统中信息传递和决策的影响，可用于管理学和组织行为学的研究。

7. 优化模型：优化系统中某些指标的值，如最大化效益或最小化成本，适用于运筹学等领域。

8. 非线性模型：考虑系统中的非线性效应，如混沌和复杂性的产生，广泛应用于自然科学和社会科学。

9. 策略模型：研究系统中不同决策对结果的影响，适用于战略管理和政策制定等领域。

第三部分：系统动力学的理论与实践系统动力学的理论基础包括建模、仿真和分析等方法。

通过系统动力学模型，我们可以深入研究系统的行为、寻找潜在问题，并基于模型结果做出合理的决策。

在实践中，系统动力学可应用于企业管理、政策制定、环境保护等领域，为问题解决提供了一种全面和系统的方法。

第四部分：总结与回顾通过对系统动力学的9种模型的解析，我们可以看到系统动力学对于复杂问题的分析和理解具有重要意义。

Dynamic modelling of a PV pumping system with special consideration on waterdemandPietro Elia Campana a ,⇑,Hailong Li a ,Jinyue Yan a ,b ,⇑a School of Sustainable Development of Society and Technology,Mälardalen University,SE-72123Västerås,Sweden bSchool of Chemical Science,Royal Institute of Technology,SE-144Stockholm,Swedenh i g h l i g h t s"Evaluation of water demand and solar energy is essential for PV pumping system."The design for a PV water pumping system has been optimized based on dynamic simulations."It is important to conduct dynamic simulations to check the matching between water demand and water supply."AC pump driven by the fixed PV array is the most cost-effective solution.a r t i c l e i n f o Article history:Received 25September 2012Received in revised form 17December 2012Accepted 22December 2012Available online 29January 2013Keywords:Renewable energy resources Solar energyPhotovoltaic system Pumping system Water demanda b s t r a c tThe exploitation of solar energy in remote areas through photovoltaic (PV)systems is an attractive solu-tion for water pumping for irrigation systems.The design of a photovoltaic water pumping system (PVWPS)strictly depends on the estimation of the crop water requirements and land use since the water demand varies during the watering season and the solar irradiation changes time by time.It is of signif-icance to conduct dynamic simulations in order to achieve the successful and optimal design.The aim of this paper is to develop a dynamic modelling tool for the design of a of photovoltaic water pumping sys-tem by combining the models of the water demand,the solar PV power and the pumping system,which can be used to validate the design procedure in terms of matching between water demand and water supply.Both alternate current (AC)and direct current (DC)pumps and both fixed and two-axis tracking PV array were analyzed.The tool has been applied in a case study.Results show that it has the ability to do rapid design and optimization of PV water pumping system by reducing the power peak and selecting the proper devices from both technical and economic viewpoints.Among the different alternatives con-sidered in this study,the AC fixed system represented the best cost effective solution.Ó2013Elsevier Ltd.All rights reserved.1.IntroductionThe availability of electricity in remote areas is one of the main issues regarding the design and operation of irrigation systems.Nevertheless,it is quite common in the developing countries that the access to the electric grid is unavailable.With the development of photovoltaic (PV)technology that can convert the solar energy to electricity,using PV cells has become a more attractive solution to provide the required power for the water pumping system,especially in the areas that have abundant solar energy resources [1].The high technical reliability of PVWPSs for irrigation purposes,their long term economic viability and recent developments as well as the weaknesses have been shown by several studies and field experiences.The knowledge and the competencies achieved in this field resulted as starting point and recommendations for further and future programmes worldwide [2,3].For example,in 2009the Government of Bangladesh has set as target for 2014to install more than 10,000PVWPS for irrigation with a total installed capacity of 10MW p .Only in 2010India has installed more than 50MW p PV off-grid systems of which pumping system represent a large part [4].Many studies have been carried out in the development of the PVWPS focusing on the system sizing,system modelling,economic performance and environmental feasibility.Models have been presented for the estimation of water demand [5,6],assessment of the solar energy [7,8],PV generator and controller [8,9]and for the motor-pump system [10].Some demonstration projects that link the power output from the photovoltaic generator,pumping system power consumption and instantaneous water flow output have been conducted [11].Based on the available0306-2619/$-see front matter Ó2013Elsevier Ltd.All rights reserved./10.1016/j.apenergy.2012.12.073⇑Corresponding authors.Address:School of Sustainable Development of Societyand Technology,Mälardalen University,SE-72123Västerås,Sweden (J.Yan).E-mail address:pietro.campana@mdh.se (P.E.Campana).models,the approaches regarding the system optimization have been developed[12].In addition,economic and environmental evaluations showed the feasibility of photovoltaic pumping system compared to traditional systems driven by diesel engines[13].The main R&D gaps for the implementation of the PVWPSs exist not only in the technologies of PV and pump.Problems related to the local peculiarity need to be considered[14].The local peculiar-ity includes water resources availability,water demand,different pumping system configurations,acceptance and management of the system.These issues need to be investigated in order to achieve the success of a photovoltaic pumping project.In addition,in the current state of the art the capital cost of a PVWPS is still higher than the traditional system driven by diesel engine,which is con-sidered as the major barrier for the large scale commercialization, although the operation costs are much lower.Therefore,as regards the optimization,efforts are mainly focused on minimizing the cost.Dynamic operation is one of the most important characteristics of the PVWP systems.Due to the dynamic variation of solar irradi-ation and the precipitation,the PV power output and the water de-mand of irrigation vary time by time.Meanwhile,as the solar irradiation varies,the dynamic PV power output would affect the performance of pump,resulting in a dynamic variation of pump efficiency and power consumption.In order to achieve the success-ful and optimal design and minimize the costs,the system dynamic characteristic has to be considered.The impacts of the dynamic variation of solar irradiation on the dynamic variation of water de-mand and pump performance have been investigated thoroughly. The objective of this paper is to develop a dynamic simulation tool and conduct dynamic simulations for a PVWP system,by integrat-ing all of the dynamic variations of water demand,solar irradiation, PV power output and pump performances.Both AC and DC pump and bothfixed and two-axis sun tracking systems were investi-gated from a technical and economic viewpoint.A dynamic water demand model was developed based on the local climatic conditions,soil characteristics and type of crops.With the predicted dynamic water demand the instant performances of PVWP system were studied.Such a dynamic simulation can be used to evaluate the existing design,checking if there is mismatch between the pumped water and the demanded water.The results would also give some guidelines or suggestion concerning system optimization from the perspective of dynamic water demand.2.Description of the systemA PVWPS is basically composed of a PV array,a power control-ling system and a pumping system connected to the distribution system that can be a water tank or directly an irrigation system.A schematic diagram of the photovoltaic water pumping system studied in this work and the related models adopted is presented in Fig.1.The photovoltaic array consists of photovoltaic modules that are connected in series or in parallel depending on the voltage and current output requirements.In this work bothfixed and two-axis sun tracking systems were investigated and compared technically and economically.The power controlling system is an interface between the PV modules and the motor-pump system with the function to improve the coupling performances.The power conditioning system can be a DC/DC converter or a DC/AC inverter depending on the motor-pump technology.Both converter and inverter are usually equipped with a maximum power point tracker(MPPT)device in order to maximize the power extraction from the solar array.In this study,both multistage centrifugal DC and AC pump and the related power controllers were adopted in order to investigate and compare the performances,especially in terms of power consumption,water pumped and costs.3.MethodologyThis study is divided into three parts:the estimation of the water demand for irrigation,the assessment of the exploitableNomenclatureAbbreviationAC alternate currentDC direct currentICC initial investment costMPPT maximum power point trackerPV photovoltaicPVWPS photovoltaic water pumping systemSCS soil conservation serviceSymbolse a actual vapour pressure(kPa)E h hydraulic energy(kW h/day)e s saturation vapour pressure(kPa)ET0reference evapotranspiration(mm/day)ET c evapotranspiration in cultural conditions(mm/day) EX extra-terrestrial radiation(kW h/m2)g gravity acceleration(m/s2)G soil heatflux density(MJ/m2day)GH global horizontal radiation(kW h/m2)H total dynamic head(m)I b beam radiation(W h/m2)I d diffuse radiation(W h/m2)I tot global radiation on the array(W h/m2)I tot,d daily total radiation(kW h/m2/day)K b(h)incidence angle modifierK c cultural coefficientK d incidence modifier for diffuse radiation LI long wave incoming radiation(kW h/m2) LO long wave outgoing radiation(kW h/m2) NOCT nominal operating cell temperature(°C) P precipitation(mm)P(T,h)power output(W)Q waterflow(l/s)RH relative humidity(%)R n net radiation at crop surface(MJ/m2day) T temperature(°C)T a ambient temperature(°C)T c cell temperature(°C)T r reference temperature(°C)u2wind speed at2m height(m/s)W g water gross volume(mm/day])WS wind speed(m/s)W t watered height(mm)a power temperature coefficient(%/°C)c psychrometric constant(kPa/°C)D slope vapour pressure curve(kPa/°C)g0b optical efficiency for beam radiation(%) g s system efficiency(%)g p pump efficiency(%)g PV,T PV module thermal efficiency(%)g w electric wires efficiency(%)H angle of incidence(°)q water density(kg/m3)636P.E.Campana et al./Applied Energy112(2013)635–645solar energy and related power output from the PV array,and siz-ing and dynamic modelling of the system.The assessment of water demand depends on a lot of factors such as the type of crop,type of soil,irrigated area,rainfall regime,average temperatures,wind speed and solar radiation.Here the FAO Penman-Monteith method was used to estimate the water demand for growing Alfalfa (Medi-cago Sativa )in a sandy soil with some assumptions regarding the soil characteristics [5].Based on this model both the assessment of the monthly water demand that is the input data for the design procedure,and the hourly water demand used in the dynamic modelling can be obtained.The assessment of the solar energy available and power output from the solar array was made on the basis of data provided by a global climatic database and pro-cessed by the program WINSUN considering different tilt angles and system configurations [7,8].The design process was carried out through the estimation of the water demand and hydraulic head for growing Alfalfa in order to estimate the power of the pumping system.The PV array power peak was then calculated on the basis of the daily required hydraulic energy,daily collect-able solar energy and system efficiency.The worst conditions in terms of available solar energy and required water demand were chosen for the design procedure.The dynamic modelling of the photovoltaic water pumping system was used to prove and opti-mize the sizing process,underlining the match between water de-mand and water supply.A describing flow chart of the designing process and dynamic simulations carried out in this paper and the related parameters affecting both processes are presented in Fig.2.The dynamic simulations were done based on the hourly data of solar radiation,angle of incidence and temperature in order to esti-mate the hourly power output from the PV array.The PV power output was then used to estimate the hourly water output of the pumping system according to the power input-instantaneous flow characteristic curve of the chosen pumps and power controller effi-ciency.The match between water supply and water demand wasanalyzed using the results achieved by the hourly dynamic model-ling of water pumped and estimated water requirements on monthly basis.The economic analysis carried out in this work was mainly focused on the differences in initial capital costs be-tween system equipped with AC and DC pump,fixed PV array and sun tracking array.The economic investigation was based on the prices referring to the Chinese market and taken from an on-line business-to-business trading platform [15].P.E.Campana et al./Applied Energy 112(2013)635–6456373.1.Climatic dataThe site chosen for this study was in Xining,the capital city of Qinghai Province,China,located on the eastern edge of the Qing-hai-Tibet Plateau(Latitude:36°370N;Longitude:101°460E;Alti-tude:2275m a.s.l.).This location is featured by a continental cold semi-arid climate with high potential in solar energy.The monthly daily average temperatures range fromÀ6.0°C in January up to22.2°C in July.The annual precipitation is269mm and is mainly distributed between May and September.The annual global radiation on horizontal plane is1542kW h/m2with2701sunshine hours.The climatic data referring to Xining were taken from the global database provided by Meteonorm including temperature, relative humidity,precipitation,wind speed,global radiation on a horizontal plane,extra-terrestrial radiation,and incoming and out-going long wave radiation as given in Table1[7].The monthly sta-tistical data were used for the estimation of the monthly average daily water demand and the sizing of the PVWPS.Whereas the hourly data elaborated by the software applying stochastic method were used for the dynamic modelling of the water requirements and the photovoltaic pumping system.3.2.Water demandThe model adopted for the estimation of the water demand was based on assumptions regarding the crop and soil characteristics. In this study Alfalfa was chosen as growing crop whereas the char-acteristics of the ground referred to a sandy soil.Both characteris-tic parameters of the growing crop and soil used in this model and equations for the assessment of both average daily water require-ments were taken from guidelines provided by FAO[5].The refer-ence evapotranspiration was estimated through the method FAO Penman-Monteith that is a procedure based on the climatic data of the site chosen for the irrigation system.The daily trend of the reference evapotranspiration ET0was cal-culated taking into account the monthly average daily climatic data regarding solar radiation,temperature,humidity,vapour pressure and wind speed through the following equation:ET0¼0:408DðR nÀGÞþc900Tþ273u2ðe sÀe aÞc2ð1Þwhere D is the slope of the vapour pressure curve,R n is the daily net radiation at the crop surface,G is the soil heatflux density,c is the psychrometric constant,e s is the saturation vapour pressure,e a is the average daily actual vapour pressure and u2is the average monthly daily wind speed.The net radiation can be estimated as difference between the incoming net shortwave radiation and the net outgoing long wave radiation.Based on the hourly data of the involved parameters,the hourly water demand can be calculated from Eq.(1)adjusted for one hour time step.The evapotranspiration in standard cultural conditions,ET c,was estimated from the reference value on the basis of the growing crop,climatic conditions,and soil characteristic parameters and the vegetative phase.These previous considerations are summed up in the cultural coefficient K c.Then,ET c is given by:ET c¼K c ET0ð2ÞIn the specific case of Alfalfa K c varies from0.4to0.95depend-ing on the growing phase of the crop:K c equal to0.4in develop-ment phase,0.95during the intermediate phase and0.9in the final phase.The development phase runs from the sowing to the effective full ground cover,the intermediate stage from the effec-tive full cover up to the crop ageing and thefinal stage from the ageing up to the harvesting.In order to size the system,in this study K c was assumed equal to0.95.The daily gross water volume needed by the crop W g in mm/day can be estimated taking into ac-count evapotranspiration in the standard cultural conditions,effec-tive rainfall,potential application efficiency(PAE)and leaching requirement(LR).The gross water volume in mm/day is given by the following equation:W g¼ET cÀP eð1ÀLRÞPAEÀÁð3Þwhere P e is the effective rainfall,which was estimated from the monthly precipitation data by applying the Soil Conservation Ser-vice(SCS)method developed by the United States Department of Agriculture[16].In this equation,LR implies the amount of water needed in order to remove residual salts from the root zone whereas PAE refers to the efficiency of the irrigation plant.LR and PAE were set equal to0.18and0.8correspondingly,when assuming to use a micro irrigation system.Another important parameter for PVWPS is the irrigation turn that permits the planning of the irrigation.The irrigation turn was estimated as the ratio between the amount of water released during an irrigation turn,W t,and the daily gross water volume.W t represents the maximum water volume that the crop can absorb without water losses.It depends on the water fraction absorbed by the crop,the wet surface due to the irrigation system,the roots depth and the soil water content[17].The sizing of the system was based on the monthly average dai-ly water demand,whereas the dynamic modelling was based on hourly values.The estimated hourly water demand was then com-pared with the hourly water supplied by the PV pumping system. Comparisons between water demand and water supply were made also considering a time step equal to the irrigation turn and on monthly basis for the whole season.3.3.Photovoltaic arrayThe power output provided by the PV array varies especially due to the different conditions of solar radiation and temperature. Indeed those previous parameters affect the characteristic curve of the PV modules.The dynamic modelling of the PV system consid-ered the estimation of the hourly power output of the solar array P(T,h),depending on the hourly beam radiation I b and diffuse radiation I d,incidence angle h and temperature T.The followingTable1Climatic data for Xining.January February March April May June July August September October November DecemberT(°C)À6.0À2.0 5.011.616.819.822.221.115.59.7 1.9À4.8 RH(%)65.665.060.557.656.560.665.567.466.963.665.370.2 P(mm)0.738.31633.337.35060.236.618.7 4.50 WS(m/s) 2.8 3.0 3.6 3.7 3.7 3.2 3.0 2.9 2.8 2.9 3.1 2.9 GH(kW h/m2)75.696.6144.3168.5182.2187.2184.5157.7127.589.376.153.5 EX(kW h/m2)149.8176.3253.0297.8344.6346.7349.7319.4262.8212.8156.9136.8 LI(kW h/m2)160.9156.4195.4210.8239.0247.2269.0267.1235.9219.5184.0170.6 LO(kW h/m2)209.3204.3254.6272.5303.3306.6326.0319.6285.5269.4232.2212.9638P.E.Campana et al./Applied Energy112(2013)635–645equation was used to evaluate the hourly power output from 1kW p PV array[18]:PðT;hÞ¼½g0bK bðhÞI bþg0b K d I d ½1þðT cÀT rÞa ð4Þwhere g0b is the optical efficiency for the beam radiation,K b(h)is the incidence angle modifier,K d is the incidence modifier for diffuse radiation T c is the cell temperature,T r is the reference temperature (25°C)and a is the power temperature coefficient.Thefirst term of Eq.(4)represents the power output from1kW p PV modules at the reference temperature,whilst the second term accounts for the power losses due to temperature deviation from the reference value.The influence of the temperature on the PV modules perfor-mance was taken into account through the cell temperature T c that is affected by the ambient temperature T a and the global solar radi-ation I tot through the following equation:T c¼T aþðNOCTÀ20Þ800!I totð5Þwhere NOCT is the nominal operating cell temperature.Simulations of the power output from the PV array were conducted with WIN-SUN that is software based on TRNSYS system simulation[9].The dynamic modelling of the solar array power output was estimated taking into account the hourly values of beam radiation,diffuse radiation,incidence angle and ambient temperature.The calcula-tions carried out with WINSUN considered the effects of both opti-cal efficiencies and angle modifiers,whereas the effect of the temperature was estimated separately through a MATLAB script. Bothfixed array and fully tracking array were investigated in this work.The sizing of the PV water pumping system was carried out through a simple approach based on the daily hydraulic energy E h required to lift the water demand,the average daily radiation on the plane of the array I tot,d and the overall system efficiency g s.This approach is summed up in the following equation[19]:P peak¼E hI tot;d g sð6ÞThe daily hydraulic energy was estimated from the daily water demand and hydraulic head with the following formula:E h¼q gHW gð7Þwhere q is the water density and g is the gravity acceleration.In Eq.(7)W g is expressed in m3/ha day(1mm/day corresponds to10m3/ ha day).The efficiency of the system takes into account the effi-ciency of the MPPT system,controller or inverter,electric engine, centrifugal pump and system losses[20,21].3.4.DC/DC converter-motor-pumpThe model used in this work for the converter and inverter was based on the assumption that the output power is equal to the in-put power from the photovoltaic generator less the unavoidable power losses associated.Normally the efficiency varies between the80%up to the95%depending on working conditions,especially temperature,and power available.The power losses were taken into account on the basis of an average efficiency of the power con-trolling system.The motor-pump was sized on the basis of instantaneous water flow,estimated from daily water demand and daily operating hours,and hydraulic discharge.The total dynamic head was calcu-lated taking into account several contributions such as outlet min-imum pressure required by the irrigation system,height of the of the outlet pipe above the ground surface,depth of the static water level,depth of the dynamic water level and friction losses due to the pipeline circuit.In this study a hydraulic discharge measured infield tests was used.The sizing of the centrifugal motor-pump in kW was carried out with the following equation:P¼q gQH1000g pð8Þwhere Q is theflow expressed in m3/s,1000is a conversion factor and g p is the efficiency of the motor pump system.The motor-pump system was modelled on the basis of the governing equations of the electric engines,affinity laws and hydraulic power.The main input data regarding the minimum and maximum head and the correspondingflows and efficiencies, were taken from motor-pump datasheet provided by pumps man-ufacturer companies[22,23].The dynamic modelling of the pump was carried out considering the pump characteristic curve that expresses the instantaneous waterflow in m3/h versus the instan-taneous feeding power to the motor-pump system.A typical expression of the relationship between waterflow Q,hydraulic dis-charge H and power input P in is given by the following third grade polynomial:QðHmP inÞ¼c1ðHÞP3inþc2ðHÞP2inþc3ðHÞP inþC4ðHÞð9Þwhere c1,c2,c3and c4are experimental coefficients.The previ-ous curves,both for the DC and AC pump,were obtained from PVsyst(v5.55)through the specific tool for pumping system and adjusted with the curvefitting function in MATLAB.4.Results and discussionsThis section shows the results regarding the assessment of water demand and solar energy,sizing and modelling of the system,matching between water demand and water supply and economics analysis between DC and AC pump andfixed and fully tracking PV array.In this work an irrigated area of1ha was considered.4.1.Assessment of the water demandThe monthly average water demand for the growing of Alfalfa on a sandy soil and the trend of the monthly average precipitation are presented in Fig.3.It is clear that the trend of the daily water demand for irrigation is affected mainly by the evaporation related to the growing phase and rainfall.The evapotranspiration registered a peak during the sunniest months of the year whereas the precipitation registered the highest values in the period May–September.The irrigation season for the crop chosen isfive months,this study interested then only the months from May to September.In this work it was assumed that in May takes place the development phase,in June and July the intermediate phase and in August and September thefinal phase.The Alfalfa water demand trend shows then a peak during the month of June of47m3/ha and it decreases during the remaining months.The minimum daily water demand estimated for this period corresponded to the water requirements in May which is equal to10.4m3/ha.The irrigation turn estimated by the model was10days.In this work an irrigated area of1ha was considered.The validation of the results obtained from the water demand model was carried out through personal communication withfield expert and with the results obtained infield studies con-ducted in the same region[24].The former proved that the daily maximum water requirement for irrigation is50m3/ha.Whereas the results obtained in previousfield studies showed an irrigation duty of600m3/ha for an irrigation turn of14days corresponding to40m3/ha day.P.E.Campana et al./Applied Energy112(2013)635–645639radiation and its variation with the tilt angleshown in Fig.4.In this study it was as-with an azimuth angle equal to0°thatoriented towards south.The results offor thefixed system the best tilt anglewith a corresponding collectable solaryear.For the simulations carried outseason,from May to September,the bestcollecting854kW h/m2season.The10°used in our study.As regards the fullycollected solar radiation on the planeyear whereas1120kW h/m2during the irrigation season.This corresponds to a collected solar energy30%higher compared to the optimalfixed system.The power output fromfixed and fully tracking PV system with a capacity of1kW p during a sunny day in June is shown in Fig.5. The energy collected by the10°tilted system was7.0kW h/m2 whereas the solar energy collected by the fully tracking array it was equal to10kW h/m2corresponding to40%more energy than thefixed system.The better performances of the sun fully tracking system are mainly due to the system,varying continuously its tilt and azimuth angle in order to follow the sun,optimizes the har-nessing of available solar radiation guaranteeing a wider range of working hours at higher power output compared to thefixed system.It is clear that the solar generator power output depends on the variation of the available solar power and is mainly sensitive to the variation of ambient temperatures.The typical effect of the hourly array during the sunniest and warmest hours of the day due to the difference between cell temperature and reference tempera-ture.The maximum drop of the efficiency and the subsequent drop of power generation were registered at1pm and it was equal to 198W representing a loss of17%.The high value of power waste was due to the theoretical approach used in this study to perform the effect of temperature on the PV modules efficiency.The previous approach tends to overestimate the power losses due to temperature,usually in the range of10%,on behalf of guaranteeing more accurate water supply forecasts.4.3.Pump modellingThe sized PV systems were used in dynamic simulations in order to estimate the hourly power output and hourly water pumped.The dynamic modelling of the photovoltaic pumping sys-tem could further verify if the sized system could fulfil the dynamic water requirements.The water pumped under different PV power output was estimated on the basis of the pump characteristic curve flow rate against power input.Obviously,the instantaneous pumped waterflow is mainly affected by the variation of the power coming from the solar array.Fig.7shows the instantaneous waterflow at different motor power input.The system is controlled by the power conditioning system, DC/AC variable frequency inverter for the AC pump and DC/DC converter for the DC pump.The main function of the power condi-tioning unit is,apart providing the matching between the power output from the solar array and the power-electrical needs of the motor-pump,guaranteeing the safety of the system against mal-functioning.In the case of the AC technology the engine starts to drive the pump when is reached a minimum feeding power of 0.37kW.The instantaneous waterflow increases with the input power until reaching1.5kW.The motor-pump speed is governedFig.3.Monthly daily estimated water demand.Fig.4.Solar energy available depending on the tilt angle and system technology.Fig.5.1kW p power output during a sunny day in June.。

dynamical model 动力学模型英文说法1. 引言1.1 概述在科学研究和工程实践中，动力学模型是描述系统行为和演化的重要工具。

它们被广泛应用于生物学、物理学、社会科学以及许多其他领域。

动力学模型可以帮助我们理解自然现象背后的基本原理，揭示系统内部的相互作用和变化规律。

本文将介绍动力学模型的定义与原理，并探讨其在科学研究中的应用。

文章还将覆盖构建动力学模型的方法和技巧，并对未来动力学模型发展趋势进行展望。

1.2 文章结构本文分为五个主要部分：第一部分是引言，概述了动力学模型的重要性和应用领域，并介绍了文章的组织结构。

第二部分将阐述动力学模型的定义与原理。

我们将讨论动力学模型概念的含义，以及如何通过动力学方程和变量定义来描述系统演化过程。

此外，我们还将探讨动力学模型所依赖的基本假设和限制条件。

第三部分将详细介绍动力学模型在科学研究中的应用。

我们将以生物学、物理学和社会科学领域为例，说明动力学模型如何被用来解释和预测自然界和社会现象的行为。

第四部分将探讨构建动力学模型的方法和技巧。

我们将讨论实验数据收集与分析方法，参数估计与拟合技术，以及模拟和预测验证方法。

这些技术将有助于研究人员从实际观测中提取出系统的关键动力学特征，并验证模型的准确性。

最后一部分是结论和未来展望。

我们将总结主要研究结果，并对动力学模型发展趋势进行展望，探讨可能的研究方向和新兴应用领域。

1.3 目的本文的目的是提供一个全面且清晰的介绍动力学模型及其在科学研究中应用的文章。

通过阐述动力学模型背后的原理和基本假设，读者可以更好地理解系统演化过程中内在机制和相互作用规律。

同时，本文还旨在帮助读者掌握构建动力学模型所需的方法与技巧，并对未来该领域的发展趋势进行展望。

2. 动力学模型的定义与原理2.1 动力学模型的概念动力学模型是科学研究中常用的工具，用于描述和预测系统随时间演化的行为。

它是基于物理、生物或社会系统内部因素之间相互作用关系的数学表达式。

dynamical model 动力学模型英文说法
摘要：
1.动力学模型的概念
2.动力学模型的英文说法
3.动力学模型的应用领域
4.动力学模型的构建方法
5.动力学模型的发展趋势
正文：
动力学模型是一种用来描述和预测系统行为的数学模型，它考虑了系统内部各个部分之间的相互作用和影响。

动力学模型广泛应用于自然科学、社会科学和工程领域，帮助我们理解复杂系统的动态行为。

动力学模型的英文说法是“dynamical model”。

这个词由“dynamic”（动态的）和“model”（模型）组成，突显了动力学模型关注的是系统随时间演变的过程。

动力学模型的应用领域非常广泛，包括但不限于以下几个方面：
- 物理学：用于描述物理系统的运动和力学过程，如牛顿力学、量子力学等；
- 生物学：用于研究生物体内的生化反应、生长和发育过程，如基因表达调控、神经网络等；
- 经济学：用于分析经济系统的供求关系、价格波动等，如凯恩斯经济学、新古典经济学等；
- 工程学：用于设计和优化工程系统，如控制系统、信号处理系统等。

构建动力学模型的方法因具体应用领域而异，但一般包括以下几个步骤：
1.确定研究对象和目标：明确要研究的系统及其特性；
2.建立数学模型：选择适当的数学工具来描述系统的动态行为；
3.参数估计：通过实验或观测数据来估计模型参数；
4.模型验证与优化：检验模型的准确性和稳定性，并根据需要进行调整。

随着科学技术的不断发展，动力学模型在各个领域的应用也在不断拓展和深化。

动态能力模型名词解释嘿，朋友！咱今天来聊聊“动态能力模型”这个听起来有点高深，但其实也没那么难理解的东西。

你想想，生活就像一场不停变化的冒险之旅，有时候道路平坦，有时候崎岖难行。

而我们在这个过程中，需要不断地适应和改变，才能走得顺利。

“动态能力模型”就像是我们在这场冒险中的导航仪。

它可不是那种死板的、一成不变的东西哦。

比如说，你是个运动员，一开始你的优势是速度快，但随着比赛规则改变或者对手变得更强，你就得调整策略，可能要加强耐力训练，或者学习新的技巧。

这就是动态，根据变化来调整自己。

再打个比方，一个企业就像一艘在大海中航行的船。

市场就像大海，时而风平浪静，时而波涛汹涌。

如果这个企业只有一套固定的经营模式，那遇到风浪的时候可能就会翻船。

但要是它能像“动态能力模型”说的那样，敏锐地感知市场的变化，迅速调整产品、服务和管理策略，那它就能在大海中稳稳前行。

动态能力模型强调的是对变化的感知、把握和应对。

就好像你在玩游戏，关卡一直在变，你得随时准备好换上新的装备，学会新的技能，才能顺利通关。

咱身边也有很多这样的例子。

比如说手机行业，以前大家都喜欢按键手机，可突然之间，触摸屏手机成了主流。

那些能迅速跟上潮流，研发和生产触摸屏手机的企业就发展得越来越好，而那些反应慢的就被淘汰了。

这难道不是动态能力模型在起作用吗？又比如说，一个人在职场上，刚开始可能靠着专业知识做得不错。

但随着行业发展，新技术、新理念不断涌现，如果这个人不学习，不改变，还守着原来那点东西，能行吗？肯定不行！他得根据行业的动态，提升自己的综合能力，比如沟通能力、团队协作能力等等。

总之，动态能力模型就是告诉我们，世界在变，我们不能不变。

我们要像灵活的猴子一样，在树枝间跳跃时能迅速抓住新的树枝，适应新的环境。

只有这样，我们才能在这个充满变化和挑战的世界里站稳脚跟，取得成功。

所以说，别害怕变化，拥抱动态能力模型，让自己成为生活和工作中的强者！。

动态模拟的特点描述对象时间参数动模即动态模拟，是通过在RTDS（实时数字仿真装置）或实际等值系统上模拟实际电力系统的各种运行工况及故障状态，对在电力系统中运行的保护和控制装置的功能和性能进行考核，以确保保护和控制装置在现场的可靠运行的试验。

动态模拟dynamic要参数随时间变化时，simulation被模拟的过程对象的主则在数学模型中时间成为一个主要自变量，参数随时间变化的规律常用微分方程组来描述，这类模拟称为动态模拟。

例如研究化工过程在外部干扰作用引起的不稳定过程，开停车过程和一些间歇操作过程、就需要采用动态模拟。

稳态模拟是对稳态系统的模拟。

关注点是模拟时间趋向无穷的情况下系统的稳定性和其他行为特性。

要求模拟时间足够长以获得高质量的估计结果。

为消除模拟初始状态对模拟结果的影响，通常需设置足够长的预热期，在预热期间不收集统计信息。

通常也需要有足够多的重复运行，以获得较好的模拟结果。

终态模拟是对终态系统的模拟。

在给定模拟时段[0,h]内对系统进行模拟，关注点是该时段内系统的行为特性，其中指特定事件E 所发生的时刻。

由于模拟的运行性能依赖于系统的初始状态，一般要求终态模拟的初始状态和终止条件能符合终态系统的真实状况，并对模拟程序独立运行多次，在足够多的样本之上，再对结果做统计分析。

终态模拟不需设置预热期。