2010 DNV Energy Training Brochure_tcm142-392218

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2010年ARM嵌入式实训题目

5
基于嵌入式ARM的FAT32文件系统的访问
1、从FAT32格式的文件系统存储介质中读取简单文件。
2、读取并简单显示FAT32文件系统的目录
A
12864液晶屏
6
基于嵌入式ARM的贪吃蛇游戏的设计
1Байду номын сангаас游戏分三个级别，体现在蛇的前进速度
2、显示用户得分、游戏级别
3、显示胜利和失败画面
A
12864液晶屏
7
B
12864液晶屏
25
基于嵌入式ARM的FSK调制
1、两路FSK信号
2、FSK载波频率可调
3、手动输入数据，调制输出
B
26
基于嵌入式ARM的简易数控电源的设计
1、可调输出电压1.25V~4.5V
2、步进≤0.3V
3、显示输出电压值
B
27
基于嵌入式ARM的高级密码锁设计
1、密码位数可调
2、密码错误次数超出自动锁死，超级密码功能
2、失败与胜利画面
3、思考时间倒计时
A
12864液晶屏
13
基于嵌入式ARM五子棋游戏
1、记录并显示游戏局数、胜局数
2、失败与胜利画面
3、思考时间倒计时
A
12864液晶屏
14
基于嵌入式ARM拼图游戏
1、记录并显示移动步数，
2、胜利画面
3、游戏时间
A
12864液晶屏
15
基于嵌入式ARM N皇后求解与方案显示
A
20
基于嵌入式点阵图型液晶显示模块的可移植驱动程序设计
1、编写128x64液晶点阵屏的常用驱动程序
2、要求驱动程序功能包括：绘点、绘直线、圆形、多边形

2010年全国职业院校技能大赛高职组“现代物流_储配方案的设计与执行”项目文件

2010年全国职业院校技能大赛高职组“现代物流—储配方案的设计与执行”项目竞赛规程（2010年全国职业院校技能大赛高职组赛事筹备组，2010年4月）一、竞赛项目名称现代物流——储配方案的设计与执行二、竞赛目的适应国家物流业调整与振兴对高素质技能型物流人才的需求，以物流业的核心环节——储配作业为背景安排竞赛，引导相应高职专业明确物流人才的培养定位与规格，引导物流管理专业的教育教学改革；展示参赛选手在组织管理、专业团队协作、现场问题的分析与处理、工作效率、质量与成本控制、安全及文明生产等方面的职业素养；吸引企业参与，促进校企深度融合，提高高职教育的社会认可度。

三、竞赛方式和内容（一）竞赛方式1.比赛以团队方式进行，每支参赛队由3名选手组成，须为同校在籍学生, 其中主管1名（对方案的设计、修订、客户优先等级、外包与否等负主要责任）,性别和年级不限，可配1名指导教师。

2.赛事持续进行3天。

赛程由制定储配方案赛段和实施储配方案赛段二部分组成，安排在不同的时间、不同的竞赛区域进行。

首先进行制定储配方案赛段竞赛，然后进行实施储配方案赛段的竞赛。

（1）制定储配方案赛段：竞赛用时为4个小时。

（2）实施储配方案赛段：竞赛用时为2个小时（实际操作为100分钟）。

3.比赛期间，允许参赛队员在规定时间内按照规则，接受指导教师指导。

参赛选手可自主选择是否接受指导（外包），接受指导的时间计入竞赛外包工时成本。

赛场开放，允许观众按照规定，在不影响选手比赛的前提下现场参观和体验。

4.赛后点评赛项比赛全部结束后，由专家对赛项相关产业的发展进行介绍并对赛项的技术要点、选手表现、比赛过程等进行点评。

（二）竞赛内容1.制定储配方案选手分工并做好工作准备；根据所获取的企业储存、配货、场地、货物、货架、托盘、叉车、月台、客户、工时资料、各种租赁、货位占用费、外包咨询服务费、安全要求等相关信息，进行分析处理；进行货位优化及制定货物入库方案；进行订单处理及生成拣选单；撰写外包委托书；编制可实施的储配作业计划；预测出实施方案可能出现的问题和应对方案。

CMG培训2010-8-12

CMG操作步骤—2.定义网格和油藏
打开构造顶部文件
CMG操作步骤—2.定义网格和油藏
打开方式
单位
CMG操作步骤—2.定义网格和油藏
CMG操作步骤—2.定义网格和油藏
构造顶部文件
CMG操作步骤—2.定义网格和油藏
CMG操作步骤—2.定义网格和油藏
CMG操作步骤—2.定义网格和油藏
CMG操作步骤—2.定义网格和油藏
FLUID AND COMPONENT DEFINITIONS：流体高压物性定义，PVT数据表、油气水密度、油水压缩系数等。
IMEX模块数据体
ROCK-FLUID PROPERTIES：岩石-流体特性，定义相渗曲线，毛管压力。
INITIAL CONDITIONS：初始条件，这部分包括：初始压力（或者参考压力及参考深度），初始的饱和度场（或者油水界面及油气界面） IMEX 数据体
CMG操作步骤—6.数值方法控制
CMG操作步骤—6.数值方法控制
CMG操作步骤—6.数值方法控制
CMG操作步骤—7.井定义和生产动态数据
CMG操作步骤—7.井定义和生产动态数据
CMG操作步骤—7.井定义和生产动态数据
CMG操作步骤—7.井定义和生产动态数据
CMG操作步骤—7.井定义和生产动态数据
热采和化学驱并行软件 STARS II
实现真正的全油藏数模
利用CMG并行模拟器可以将油藏描述的结果直接输入到数模文件中 ,同时数模的文件又可以输回到地质建模软件中。
CMG领先技术—流线模拟
CMG领先技术—立体可视化技术
CMG优势

领导模拟复杂物理过程的新潮流率先在PC机上实现可视化拥有领先的注气驱、火烧、冷采等先进开采过程的数值模拟技术在世界范围内，与多加企业、公司、研究院联合开展研发工作壳牌，BP， TotalFinaElf，日本石油工程公司，巴西石油， IMP/Pemex

2010年新员工培训资料一

1.●什么是“变电站智能环境控制系统”变电站智能环境控制系统是一种基于计算机自动控制技术和空气动力学、热力学理论的智能化应用系统。

其功能主要是通过温湿度、空气品质等环境数据采集与融合、经处理后按设定或自动生成的控制策略驱动风机和气流控制部件实现气流的导向与流量流速控制，辅以风口选择与布置、风机优化与降噪、空气净化、异常判别与告警等专项技术与手段，实现变电站设备按装空间的局部小气候的改造，控制和优化变电站设备的运行环境。

使各项环境参数最优化从而实现与设备运行特性的最佳匹配。

该系统主要用于一体化解决变电站电气设备的诸多运行环境问题。

这些问题包括：大功率设备运行所需的通风散热、保护电气绝缘所必要的防水防潮、防止电气故障所要求的防尘防污、确保工作人员与设备安全所必要的防火排毒等。

其效能可大大改善变电站设备的运行条件和运行环境，降低能耗，提高设备运行和供电的可靠性。

该系统最突出的应用特点是高效与节能。

系统的应用实测表明，与传统通风降温等环境控制措施相比耗电量可下降85%以上。

其一体化的综合效能更是传统措施所无法比拟的。

目前该项技术及其产品主要应用于电力系统所属的变电站、发电厂，特别是设备紧凑型布置的户内变电站或地下变电站应用效果最为显著。

其数据融合与处理具有“物联网”技术基本特征，与未来智能化变电站整合后，有望成为智能电网技术的重要组成部分。

其衍生产品在石油、化工、冶金、铁道等广阔领域蕴含着巨大的应用潜力和发展前景。

2.●产品的研发背景在我国辽阔广袤的大地上，覆盖着一张由阡陌纵横、四通八达输电线路和无数个变电站编织而成的强大电网，源源不断地为飞速发展的我国国民经济和社会生活输送着无穷的动力和不竭的能量。

作为电网重要节点和承担电压变换、电能分配功能的设施，变电站的安全对于电网的安全稳定举足轻重，其设备的健康、可靠与社会经济和人民生活休戚相关。

维护、保障变电站乃至电网的安全和可持续发展，不仅需要电力工作者们呕心沥血、殚精竭虑，也要耗费大量的资源和能量。

10 英文调节能量存储,以适应可变能源资源的高渗透

Abstract —The variability and non-dispatchable nature of wind and solar energy production presents substantial challenges for maintaining system balance. Depending on the economical considerations, energy storage can be a viable solution to balance energyproduction against its consumption. This paper proposesto use discrete Fourier transform (DFT) to decompose the required balancing power into different time-varying periodic components, i.e., intra-week, intra-day, intra-hour, and real-time. Each component can be used to quantify the maximum energy storage requirement for different types of energy storage. This maximumrequirement is the physical limit that could betheoretically accommodated by a power system. The actual energy storage capacity can be further quantified within this limit by the cost-benefit analysis (future work). The proposed approach has been successfully used in a study conducted for the 2030 Western Electricity Coordinating Council (WECC) system model.Some results of this study are provided in this paper.Index Terms —Imbalance power, energy storage, integration of variable resources, discrete Fourier transform, WECC System.I. I NTRODUCTIONigh penetrations of variable energy resourcescreate significant uncertainty in required powergeneration, needed to balance the energy productionagainst the consumption [1-2]. New technologies, suchas new wind and solar forecasting tools, demand-sidecontrol, fast start-up units, and many others have beenproposed to address this balancing issue [1]. Amongthose options, energy storage can be a viable solutionbecause of its fast response and control flexibility [3-4].A. Energy Storage as an Ancillary Service Resource Today, many electricity storage technologies, including pumped hydro, various batteries,Yuri V. Makarov, Michael C.W. Kintner-Meyer, Pengwei Du, and Chunlian Jin are with the Energy Science and Technology Division, Pacific Northwest National Laboratory, P.O. Box 999, MSIN: K5-20, Richland, WA - 99352, USA (e-mail: yuri.makarov@, michael.kintner-meyer@, pengwei.du@,chunlian.jin@). Howard F. Illian is with Energy Mark, Inc. 334 Satinwood Ct,. N.Buffalo Grove, Illinois, 60089 (email:howard.illian@).compressed air, flywheels, capacitors, and others are proposed or already used to control the grid [3-6]. Energy storage (ES) systems can be used to follow the net load changes, stabilize voltage and frequency, manage peak loads, improve power quality, and ultimately support renewable integration. A summary of performance requirements needed for a variety of energy storage applications can be found in [6]. Wind and solar power variations are hard to predict and cause multiple impacts including the impact on system reliability. To maintain balance betweengeneration and load, costly flexible generation resources that have sufficient start up time, ramping speed, and capacity may be employed. Alternatively, energy storage for periods from days to less than 1 hour can help to smooth out unpredicted power fluctuations. For the intra-hour variations, energy storage can provide essential ancillary services such as fast regulation and load following. This would have great advantages because fast regulation may be twice as effective as gas turbines and 20 times more effective than steam turbines [7]. Therefore, the short-term ES represents a new perspective class of ancillary service resource.The 2007 FERC 1Order No. 890 allows so-called “non-generation” resources like energy storage toparticipate in regulation markets on a non-discriminatory basis. Since then, new market ruleshave been developed by some Independent SystemOperators (ISOs). For example, the New York ISOalready started to support the integration of limitedenergy storage resources (LESR) [8].The balancing ancillary services represent anattractive business opportunity for ES. Numerousresearch and demonstration projects in this area have been planned or currently under development. Forexample, Beacon Power Corporation is constructing the grid-scale 20 MW flywheel plant in Stephentown, New York, in an attempt to provide approximately 10% of New York's overall frequency regulation needs. AES has tested an Altairnano lithium-titanatebattery (2MW/500kWh) in a pilot program with California ISO. Furthermore, the Department of Energy’s American Recovery and Reinvestment Act1FERC stands for Federal Energy Regulatory CommissionSizing Energy Storage to Accommodate High Penetration of Variable Energy ResourcesYuri V. Makarov, Michael C.W. Kintner-Meyer, Pengwei Du, Chunlian Jin, and Howard F. IllianH(ARRA) stimulus funding is sponsoring 37 projects with a combined value of 637 million dollars, which combine smart grid and energy storage functionality [9]. This will greatly accelerate the entrance of ES into the power grid, in particular, the module, distributed ES (e.g. community energy storage, plug-in hybrid electric vehicles). If modeled and controlled properly, these aggregated small-size ESs can provide the ancillary services cost-effectively. In view of these, it can be envisioned that ES will become more integral to the grid operation, and play a key role in providing ancillary service to enable a high penetration of wind power and other renewable resources [9].B. Sizing of Energy StorageAmong other characteristics, an energy storage can be characterized by its energy capacity (MWh), power capacity (MW), round-trip efficiency, and ramping capability. The capital cost of energy storage consists of an energy component ($/MWh) and a power component ($/MW). The former represents the cost of the storage medium, and the latter represents the cost associated with the power electronics. The current cost for energy storage is still relatively high. However, as mentioned above, several companies are exploring the competitiveness of their novel storage technologies in very specific high-value markets. These markets usually require short duration energy storage, which power output can be sustained at the rated power capacity level from 15 to 20 minutes. Longer duration energy storage (for over several hours or for a day) are generally pumped hydro or compressed air energy storage technologies, which generally are less flexible in their placement compared to battery or flywheel energy storage. Both from a transmission planning and technology development points of view it is of interest to estimate the total market size for different energy storage systems.In this context, the optimal operation and sizing of ES is a subject of intensive research work. Stochastic optimization has been proposed to find the optimal sizing of energy storage so as to maximize the expected operation profit (or minimize the cost) while taking into account transmission constraints [10-15]. In [16], battery energy storage (BES) is used in conjunction with a wind farm. The capacity of BES is determined to ensure constant dispatched power to the grid while the voltage level across the dc-link of the buffer is kept within preset limits. Some authors used probabilistic methods to model the operation of energy storage [6]. They evaluated two potential control strategies, i.e., the energy is released as soon as the local network can absorb it, or the energy is stored and is sold when the price of electricity is higher. The value of storage in relation to power rating and energy capacity was investigated so as to facilitate appropriate sizing. The BES storage device can be used to reinforce the dc bus during transients, thereby enhancing its low-voltage ride through capability. When properly sized, it can effectively damp short-term power oscillations, and provide superior transient performance over a number of seconds [17]. Using a BES unit to provide frequency regulation was discussed in [11].State-of-the-art ES models that would be appropriate for transmission and distribution uses were reviewed in [9]. They can be used for optimizing storage size for ancillary services.C. Need for Sizing Tools for Power Systems Planners This paper presents a novel perspective on the sizing issue of grid-scale ES for utilities which are concerned with the system flexibility characteristics needed to mitigate the volatility of wind and solar power. Essentially, the maximum size of ES can be decided upon the cycling components of the required balancing power. Previous research work conducted at the Pacific Northwest National Laboratory (PNNL) studied the capacity requirement of energy storage in WECC for year 20302[18]. The follow-up work reported in this paper aims at determining the maximum feasible size of energy storage by identifying different cycling components of the balancing power. This proposed approach does not use either production cost models or comprehensive storage models. It is based on the fact that an energy storage cycles energy within certain frequency range. For example, a flywheel can cycle energy 4 cycles per hour or even faster if the full energy capacity is used. To find the maximum cycling requirements at different frequencies, a frequency decomposition of the balancing power signal is used in the paper. The components of this decomposition are periodic signals with zero total energy, representing the cycling job for the energy storage. These periodic components also indicate the duration requirements for storage technologies. Ultimately an optimal allocation of storage technologies can be determined based on this cycling analysis.This paper is organized as follows. Section II discusses the basic methodology to decompose the 2 Internal PNNL study that estimated the technical potential of the energy storage for meeting new balancing requirements in the WECC for a 88 GW wind power scenario.balancing power using discrete Fourier transform (DFT). Section III presents the simulation results for the 2030 WECC system model. Section IV provides the final discussion and conclusions.II. D ECOMPOSITION OF B ALANCING P OWER U SINGDFTThe balancing process consists of several components, including scheduling, load following, andregulation. While the scheduling component usuallyreflect hourly dispatches of generation units providing most of the energy to the load, the load following and regulation components help to achieve intra-hour balance by covering the gap between the hourly schedules and minute-by-minute system load.A. Balancing Power The power system control objective is to minimize area control error (ACE) to the extent sufficient to comply with the North American Electric Reliability Corporation (NERC) Control Performance Standards (CPS). Therefore, regulation and load following signals are signals that oppose deviations of ACE from zero: ()10()a s a s ACE I I B F F -=--+- (1) where subscript a denotes actual, s denotes schedule, Istands for interchange between control areas, F standsfor system frequency, and B is the system frequency bias (MW/0.1 Hz, a negative value). The generation output consists of two components:a s dev G G G =+(2)where subscript s refers to hour-ahead schedule 3, and dev refers to the deviation from the schedule.Similarly, the load can be separated into twocomponents as follows:_a f ha dev L L L =+ (3)where L f_ha is hour-ahead load forecast.Based on the assumption that_s f haG L =(4)the difference between the actual load, L a , and theforecasted load, L f_ha , represents the load deviation that is compensated by generators (or energy storage) procured for load following and regulation processes. _dev a f ha a s L L L L G =-=- (5) Wind and solar generation can be treated as negative load._w w wa f ha dev G G G =+ (6) 3 Please note that the hour-ahead schedule can be implemented differently in the different markets.where w a G is the actual wind power, _w f ha G is hour-ahead wind power forecast, and wdevG is the deviation from the forecast.Therefore, similarly to the situation without wind, the balancing power can be expressed as follows: __w s f ha f ha G L G =- (7)__()()w s w w dev a a a f ha a f ha L L G G L L G G =--=--- (8)Fig. 1 shows the imbalance power in the WECC model for August 2030. The balancing power needed in the system is opposite to the imbalance. It is assumed that the peak load in 2030 will have grown to 205 GW, and the installed wind capacity will be 88GW (up from about 7 GW in 2008 [18]). The highly fluctuating imbalance signal is attributable to the highvariability of wind power. It also represents the gap between the scheduled generation and actual load. By utilizing energy storage, the imbalance can be reduced by charging the energy storage whenever there is over-generation (imbalance signal is above zero) and discharging the storage during periods of under-generation (imbalance signal is negative). Periodic zero total energy components of the imbalance signal in Fig. 1 correspond to the maximumcharging/discharging job that can be allocated to theenergy storage.time in hoursI m b a l a n c e p o w e r (G W )Fig. 1. Imbalance power imposed by load and wind variability for assumed 88 GW of installed wind capacity (WECC model for Aug.2030).B. DFT Analysis Different energy storage technologies are best suited for operation over different time periods. Theimbalance power, shown in Fig. 1, can be broken down into the components spanning differentfrequency ranges. This decomposition can be achieved by using DFT. Each component of the periodic signal, except for the zero frequency component, representscycling energy that averages to zero over each cycle.Generally, in a discrete form, the DFT analysis and synthesis equations are written as follows [19]:Analysis equation (fast Fourier transform)1[][]N tfNt X f x t W -==∑0,,1f N =- (9) Synthesis equation (inverse Fourier transform) []101[]N tf N f x t X f W --==∑0,,1t N =- (10)where N is the number of the data points in the sequence (x [0], x [1], , x [N -1]), and()2j tf tfN W e π-=.The basic approach to decompose the imbalance signal using DFT consists of five steps, as shown in Table 1.Four different frequency ranges are selected, and thesignal is decomposed into four categories: slowcycling, intra-day, intra-hour and real-timecomponents. The band-pass filter applied to thespectrum is a rectangular window with unit magnitude within the band and zero magnitude outside of the band, as illustrated in Fig. 2 and Table 2. It is symmetric around one half of the sampling frequency. Table 1: Procedures of applying DFT for cycling analysis Steps Description1 Assume that the data sampling x (t ) issampled each minute (or 0.0167 Hz). Thedata window selected for DFT analysis is 2days (2880 samples), which starts at 0:00 and ends at 48:00.2 The data points are increased to 5760 samplewith zero padding.3 The spectrum, X (f ), is obtained by DFT. Aband-pass filter (see Fig. 2) is applied to the spectrum, X (f ).4 The filtered spectrum is converted back tothe time-domain signal, x´(t ), by using inverse DFT.5 The time-domain signal x´(t ) is characterizedby the magnitude and periodicity.frequencies are f l and f u, and the filter is symmetrical about the half ofthe sampling frequency, f s /2)Table 2: Specifications of frequency bands of the balancing signalcomponentsComponent f l (Hz) f u (Hz) Slow cycling 0 2.315e-5Intra-day 2.315e-5 9.259e-5 Intra-hour 9.259e-5 0.00333Real-time 0.00333 0.00833The frequency ranges given in Table 2 have no astrict definition and they are loosely connected to the dispatch intervals. The reason is that a dispatchinterval can contain half cycle, the entire cycle, twocycles, and so on depending on researchers’ judgment. Currently they are set for periods of 3-12 hours (intra-day), 5 minutes –3 hours (intra-hour), and 2–5 minutes (real time).C. Simulation Results The DFT method described in Section II was applied to a simulated WECC system imbalance powermodel reflecting a future high wind penetrationscenario for 2030. Several simplifying assumptionswere made to determine the balancing requirementscurve. The balancing requirement was derived from the uncertainty in the load and wind forecasting. The scenario assumed 88 GW of wind capacity in the WECC system. Furthermore, it was assumed aconsolidation of all WECC balancing areas into onesingle balancing area. This model was derived in a previous PNNL project analyzing the energy storagepotential applications in the WECC system [18]. D. Decomposition of Balancing Power for a Particular DayFig. 3 shows the one-day imbalance power signal (top) and the corresponding spectrum (bottom). Most of the energy is concentrated in the low and middle frequency bands.G WBy applying the filters shown in Fig. 2 and Table 2, in Fig. 4 the imbalance power, x (t ), is decomposed into four components, namely, into slow cycling, intra-day, intra-hour, and real time components, x 1(t ), x 2(t ), x 3(t ) and x 4(t ). By summation of these components, we can reconstruct the original time-domain signal. Thereconstructed imbalance power matches well with the original signal, as shown in Fig. 5.The frequency and magnitude of the decomposed signal play an important role in determining the required energy storage characteristics as well as technologies appropriate for each application.HoursM W(a) Slow cycling component x 1(t )HoursM W(b) Intra-day component x 2(t)HoursM W(c) Intra-hour component x 3(t)HoursM W(d) Real-time component x 4(t )Fig. 4. Decomposition of imbalance signal for a day in August 2030HoursG W-12HoursM WFig. 5. Comparison between original signal and reconstructed signal.The frequency of cycling increases for intra-hour and real time components. This means that the energy capacity requirements are decreasing, while the cycling requirements are increasing. The cycling requirement has implication for the life time of the energy storage.The energy storage power capacity requirement is associated with the magnitude of the cycles. On this particular day, the imbalance power swings between 10.7 GW and -4.1 GW, while intra-hour component swings between 6.1 GW and -4.8 GW, and real-time component swings between 154 MW and -153 MW 4. Therefore, the intra-day balancing process requires more ES power capacity than the intra-hour process by 43. The same fact has also been observed for other days as described below.E. Sizing of Energy StorageTo determine the size of energy storage for slow-cycling, intra-day, and intra-hour balancing processes, the method described in Section II was applied. We assumed a depth of discharge for the ES of 80%. Table 3 shows both the power and energy capacities for the energy storage.In the full balance scenario (second column), the energy storage compensates for all the imbalance power. In the partial balance scenario (third column), the energy storage compensates for only intra-hour and real-time components. In the fourth column, the reduction in ES requirements between the full balance and partial balance is shown.4Despite the asymmetric power capacity requirement, the energy requirement remains symmetric (the positive and negative energy are equal), which is important for the energy storage applications.Table 3: Comparison of the full balance and partial balancescenariosEnergy storage size Full balance Partial balance Reduction inES requirements Power 13.4 GW 7.7 GW 42.6% Energy68.1 GWh4.3 GWh 93.6%A very significant ES energy capacity (68.1 GWh) would be required in the full balance scenario. The state of charge of ES in this scenario is shown in Fig. 6. The size of the energy storage can be reduced to 3.8 GWh for the intra-hour component and to 568 MWhfor the real-time component as shown in Fig. 7.daysG W hFig. 6. State of charge profile for energy storage in Aug 2030(storage size=68.05 GWh)daysG W h(a) Intra-hour component (storage size=3.8 GWh)daysM W h(b) Real-time component (storage size=568 MWh)Fig. 7. State of charge profile for intra-hour and real-timecomponents in August 2030III. C ONCLUSIONSThis paper presents a novel methodology of characterizing maximum energy storage requirements for a balancing area or their interconnection. The approach is particularly useful for the system planning community as well as for the energy storage providers.The introduction of a cycling taxonomy (slow-cycle, intra-day, intra-hour, intra-minute and real-time) offers a new way to characterize the key features of energy[1] J. C. Smith, M. R. Milligan, E. A. DeMeo, B. Parsons, "Utilitywind integration and operating impact state of the art," IEEE Transactions on Power Systems ,vol.22, no.3, pp. 900 - 908, August 2007.[2] Y.V. Makarov, C Loutan, Jian Ma, P de Mello, "Operationalimpacts of wind generation on California power systems," IEEE Transactions on Power Systems , vol. 24, no. 2, pp.1039 – 1050, May 2009.[3] A. Ter-Gazarian, “Energy storage for power systems,” ISBN-10: 0863412645, The Institution of Engineering and Technology, September 1994.[4] Eyer J. and G Corey, “Energy storage for the electricity grid:benefits and market potential assessment guide,” Sandia report SAND 2010–0815, Sandia, New Mexico, 2010.[5] J. N. Baker and A. Collinson, “Electrical energy storage at theturn of themillennium,” Inst. Elect. Eng. Power Eng. J., vol. 13, no. 3, pp. 107–112, June 1999.[6] J.P. Barton and D.G. Infield, "Energy storage and its use withintermittent renewable energy," IEEE Transactions on Energy Conversion , vol. 19, no. 2, pp. 441- 448, June 2004.[7] Y.V. Makarov, "Relative regulation capacity value of theflywheel energy storage resource," November 26, 2005. [8] Ancillary Services Manual, NYISO, September 2010.[9] M.G. Hoffmann, A. Sadovsky, M. C. Kintner-Meyer, J.G.DeSteese, "Analysis tools for sizing and placement of energy storage in grid applications: a literature review,” Pacific Northwest National Laboratory, July 2010.[10] C. Abbey, G. Joos, "A stochastic optimization approach torating of energy storage systems in wind-diesel isolated grids," IEEE Transactions on Power Systems , vol. 24, no. 1, pp. 418-426, 2009.[11] A. Oudalov, D. Chartouni, C. Ohler, "Optimizing a batteryenergy storage system for primary frequency control," IEEE Transactions on Power Systems , vol. 22, no. 3, pp. 1259-1266, 2007.[12] C.H. Lo, M.D. Anderson, "Economic dispatch and optimalsizing of battery energy storage systems in utility load-leveling operations," IEEE Transactions on Energy Conversion , vol. 14, no. 3, pp. 824 - 829, 1999.[13] S. Chakraborty, T. Senjyu, H. Toyama, A.Y. Saber, T.Funabashi, "Determination methodology for optimising the energy storage size for power system," IET Generation, Transmission & Distribution , vol. 3, no. 11, pp. 987-999, 2009.[14] P. Pinson, G. Papaefthymiou, B.Klockl, J.Verboomen,"Dynamic sizing of energy storage for hedging wind power forecast uncertainty," IEEE Power & Energy Society General Meeting 2009, pp. 1-8.[15] Y. M. Atwa, E. F. El-Saadany, “Optimal allocation of ESS indistribution systems with a high penetration of wind energy,” IEEE Transactions on Power Systems , vol. 1, no. 99, pp. 1-8, 2010.[16] X.Y. Wang, D. Mahinda Vilathgamuwa, S.S. Choi,"Determination of battery storage capacity in energy buffer for wind farm," IEEE Transactions on Energy Conversion , vol. 23, no. 3, pp. 868-878, 2008.[17] C. Abbey and G. Joos, "Supercapacitor energy storage forwind energy applications," IEEE Transactions on Industry Applications, vol. 43, no. 3, pp. 769-776, May-June 2007. [18]M. C. W. Kintner-Meyer, P. J. Balducci, C. Jin, TB. Nguyen,MA. Elizondo, VV. Viswanathan, X. Guo, and FK. Tuffner,“Energy storage for power systems applications: a regional assessment for the northwest power pool (NWPP),” Pacific Northwest National Laboratory, Richland, WA, 2010.[19]Alan V. Oppenheim, Ronald W. SchaferJohn, R. Buck,Discrete-Time Signal Processing, Prentice Hall, 1999 (p543) Yuri V. Makarov (SM’99)received the M.Sc. degree in computers and the Ph.D. degree in electrical engineering from the Leningrad Polytechnic Institute (now St. Petersburg State Technical University), Leningrad, Russia. From 1990 to 1997, he was an Associate Professor in the Department of Electrical Power Systems and Networks at St. Petersburg State Technical University. From 1993 to 1998, he conducted research at the University of Newcastle, University of Sydney, Australia, and Howard University, Washington, DC. From 1998 to 2000, he worked at the Transmission Planning Department, Southern Company Services, Inc., Birmingham, AL, as a Senior Engineer. From 2001 to 2005, he occupied a senior engineering position at the California Independent System Operator, Folsom, CA. Now he works for the Pacific Northwest National Laboratory (PNNL), Richland, WA. His activities are around various theoretical and applied aspects of power system analysis, planning, and control. He participated in many projects concerning power system transmission planning (power flow, stability, reliability, optimization, etc.) and operations (control performance criteria, quality, regulation, impacts of intermittent resources, etc.). Dr. Makarov was a member of the California Energy Commission Methods Group developing the Renewable Portfolio Standard for California; a member of the Advisory Committee for the EPRI/CEC project developing short-term and long-term wind generation forecasting algorithms; and a voting member of the NERC Resources Subcommittees and NERC Wind Generation Task Force. For his role in the NERC August 14th Blackout Investigation Team, he received a Certificate of Recognition signed by the U.S. Secretary of Energy and the Minister of Natural Resources, Canada.Michael Kintner-Meyer is a Staff Scientist with the Pacific Northwest National Laboratory (PNNL) in Richland. He has a Master Degree in Mechanical Engineering from the Technical University of Aachen, Germany and a Ph.D. in Mechanical Engineering from the University of Washington. He is leading the energy storage analysis efforts at PNNL.Pengwei Du received the B.Sc. and M.Sc. degrees in electrical engineering from Southeast University, Nanjing, China, in 1997 and 2000, respectively, and his Ph.D. degree in electrical engineering from Rensselaer Polytechnic Institute, Troy, NY in 2006. He is now a research engineer at the Pacific Northwest National Laboratory, Richland, WA. His research interests include Distributed Generation, power system modeling and analysis, and digital signal processing.Chunlian Jin (M’06) received her B.S.E.E. from Northwestern Polytechnic University, Xi’an, China, in 2000, and her M.S.E.E. from Tsinghua University, Beijing, China, in 2003. Her research interests include energy storage analysis, modeling and assessment of power system operations and control performance, and integration of renewable resources. Currently, she is a research engineer with the Energy and Environment Directorate, Pacific Northwest National Laboratory, Richland, WA. She finished PhD courses in University of South Carolina. Howard F. Illian graduated from Carnegie Institute of Technology (Carnegie-Mellon University) in 1970 with a B.S. in Electrical Engineering. From 1970 until 1982 he worked for ComEd in the field of Operations Research, and was Supervisor, Economic Research and Load Forecasting from 1976 until he was reassigned to Bulk Power Operations in 1982 where he was Technical Services Director when he retired in 1998. He is now President of Energy Mark, Inc., a consulting firm specializing in the commercial relationships required by restructuring. He has authored numerous papers in the field of Engineering Economics, and has testified as an expert witness in this field before the Illinois EPA, the Federal EPA, the Illinois Commerce Commission and the Public Utility Commission of Texas. He has developed and applied several new mathematical techniques for use in simulation and decision making. He has served on the NERC Performance Subcommittee, the Interconnected Operations Services Implementation Task Force, the Joint Inadvertent Interchange Task Force, and the NAESB Inadvertent Interchange Payback Task Force. Recent work includes significant contributions to the development of new NERC Control Performance Standards including the Balancing Authority Ace Limit and a suggested mathematical foundation for control based on classical statistics. He first applied discrete Fourier transforms to load analysis in 1991. His current research concentrates on the development of technical definitions for Ancillary or Reliability Services including frequency response and their market implementation.。

技能认证热注高级考试(习题卷6)

技能认证热注高级考试(习题卷6)说明：答案和解析在试卷最后第1部分：单项选择题，共48题，每题只有一个正确答案,多选或少选均不得分。

1.[单选题]以下不属于理想气体的是（）。

A)饱和水蒸汽B)氧气C)氢气D)二氧化碳2.[单选题]安全的本质是在生产或者经营的过程中，避免各种事故的发生，确保（）的安全。

A)人B)设备C)群众D)人和财产3.[单选题]C200H型PLC在MONITOR模式下正常运行时（）亮。

A)RUNB)ERRC)INHD)COMM4.[单选题]ppm是用溶质质量占溶液质量的（）来表示的浓度。

A)万分之一B)十万分之一C)百万分之一D)十亿分之一5.[单选题]只能进行钻进工作的电钻是（）。

A)手电钻B)冲击钻C)锤钻D)电镐6.[单选题]在紊流流动时，流体靠近壁面的薄层中是（）流动。

A)紊流B)层流C)对流D)混流7.[单选题]1kg某工质在2000K的高温热源与300K的低温热源间进行热力循环，此时，根据卡诺定理最高热效率等于（C)0.65D)0.858.[单选题]ppb是用溶质质量占溶液质量的（）来表示的浓度。

A)万分之一B)十万分之一C)百万分之一D)十亿分之一9.[单选题]硬度化验中，缓冲液的pH为（）。

A)9B)10C)11D)1210.[单选题]氧分压与该地区的海拔高度有关，高原地区和平原地区的差可达（）。

A)5%B)10%C)15%D)20%11.[单选题]DX系列无纸记录仪数据保存结束后，按（）键选择外存取出。

A)FUNCB)MENUC)DISP/ENTERESCD)START12.[单选题]卡钳广泛应用于（）的零件尺寸的测量和检验。

A)要求不高B)要求较高C)要求精密D)其它答案均正确13.[单选题]可燃物质与氧气或氧化剂产生的伴有发热.发光的剧烈氧化反应称为（）。

A)爆炸B)闪燃C)自燃D)燃烧14.[单选题]95H压力调节阀输出压力在（）。

Excel2010专业级(Specialist)培训(共95张PPT)

精品资料
任务(rèn wu)5-3
在“测验成绩统计”工作表中，在单元格F3插入(chā rù)函数，计算报名考试的人数；然后再单元格F4建立函数，计算参加测验获得成绩的人数。
金芥子教育
精品资料
任务(rèn wu)5-4
在“出缺勤计算”工作表中，在单元格F3:G8建立函数，可以(kěyǐ)判断员工是否迟到和早退，若迟到，则显示“迟到”，若未迟到，则显示 “***”，若早退，则显示“早退”，若未早退，则显示“***”。
金芥子教育
精品资料
任务(rèn wu)5-7
在“销售资料”工作表中，编辑(biānjí)单元格H4里的公式，以便可以正确地将其复制到单元格H63。请将公式复制到单元格H63。
金芥子教育
精品资料
名称(míngchēng)的建立与管理
定义名称名称管理器的操作以选取区域(qūyù)建立名称公式中运用名称
打印(dǎ yìn)的基本操作页眉和页脚的设定
金芥子教育
精品资料
任务(rèn wu)1-2
在“全球业绩”工作(gōngzuò)表中，将页眉设置为“制作人，日期，页码” 格式，并且在页脚的左侧添加“文件路径”。
金芥子教育
精品资料
任务(rèn wu)1-3
在“全球业绩”工作表中，设置页眉与页脚：
◦ 页眉左侧输入“财务部制作”字样，页眉右侧插入“文件名”； ◦ 在页脚中央插入“页码of总页数”格式(gé shi)，页脚右侧插入“数据表名称”。
金芥子教育
精品资料
页面设置
设置工作表背景(bèijǐng)图案页面设置对话框
◦ 页面选项卡 ◦ 页边距选项卡 ◦ 页眉/页脚选项卡 ◦ 工作表选项卡
金芥子精(j品iè资z料ǐ)教育

2010年下半年非学历证书考试开考课程

附件1．
2010年下半年非学历证书考试开考课程
考试名称
及代码
考次
日期
时间
课程代码
课程名称
备注
中国餐饮业职业经理人资格证书考试（01）
下半年
11月20日
星期六
上午09:00－11:30
09542
餐饮食品安全
中级★
09005
食品卫生与安全
中级
下午14:00－16:30
09541
现代厨政管理
中级★
09003
10500
市场调研与销售预测
销售经理★
下午14:00－16:30
10501
销售渠道管理
销售经理★
11月21日
星期日
上午9:00—11:30
10516
销售客户管理
销售经理★
考试名称
考次
日期
时间
课程代码
课程名称
备注
中英合作商务管理与金融管理证书管理段课程考试（10）
下半年
11月20日
周六
上午9:00—11:45
现代厨房管理
中级
11月21日
星期日
上午09:00－11:30
09543
餐饮连锁经营与管理
高级【计算器】★
09007
餐饮企业财务管理
高级【计算器】
下午14:00－16:30
09545
餐饮企业品牌经营
高级★
09009
餐饮企业战略管理
高级
2010年下半年非学历证书考试开考课程（续表一）
考试名称
及代码
考次
日期
库存管理（一）
初级【计算器】
05373
物流企业管理

Office2010办公软件高级应用实例7 员工档案制作

新建Excel工作簿方法如下：
执行“开始”→“程序”→“Microsoft Office” →”Microsoft Excel 2010”命令，即可启动 Excel程序。一个新建的空白工作簿，默认的名称是Book1，每个工作簿默认包含3个工作表，分别是Sheet1、Sheet2、Sheet3。
Office2010办公软件高级应用
Office2010办公软件高级应用
a=3,b=2
制作员工档案
实例实现
5、单元格合并居中
单元格合并居中方法如下：
1）选择单元格区域A1：H1。
2）选择“开始”选项卡，在“对齐方式”功能组中单击“合并后居中”按钮。
机械工业出版社
CHINA MACHINE PRESS
Office2010办公软件高级应用
机械工业出版社
CHINA MACHINE PRESS
Office2010办公软件高级应用实例教程
实例7：员工档案制作
Office办公软件课程组制作
Office2010办公软件高级应用
学习提纲
1.实例需求 2.实例实现 3.实例小结
机械工业出版社
CHINA MACHINE PRESS
制作员工档案
制作员工档案
实例实现
机械工业出版社
CHINA MACHINE PRESS
3、数据录入
数据录入方法如下：
1）单击A1单元格，输入“企业员工档案”。
2）按<Enter>键，光标移动到A2单元格。在单元格A2中输入列标题“工号”，然后按<Tab> 键或方向键将光标移动到B2，在其中输入“姓名”。
3）用相同的方法，为其他单元格录入数据（注意不同数据的录入），具体操作见视频素材。

静变电源培训Maintena课件

module and the 3 remaining one are placed on a cooling element below the DC/AC module. The combination of the 12-
pulse rectifier, the 12-pulse transformer and the input filter choke means that there is hardly no harmonic feed back into the mains (i.e. no mains pollution/distortion). (12脉冲变压器将3相输入电源变为6相。6相通过12-脉二极管全桥整流。整流器由6 个半导体闸流管/二极管模块组成，其中3个安装在DC/AC模型上，3个安装在DC/AC模型下的散热器上。12-脉冲整流器、12脉冲变压器及输入滤波电感的混合意味几乎没有谐波返回到输入回路.（也就是没有输入电路/畸变）。 )
2. Key and LED:
NORMAL DISP. (按键和指示灯：正常显示)
3. Key and LED:
ALARM DISP. (按键和指示灯：警告显示)
4. Key and LED:
SET-UP GPU. (按键和指示灯：设置机组)
5. Key:
ARROW UP.
(键：向上调整)
6. Key:
静变电源培训Maintena
3
… the safe choice ...
400Hz静变电源原理
静变电源培训Maintena
4
… the safe choice ...
电缆收放装置
介于停靠的飞机和地面电源之间的供电设备，用于存放400Hz输出电缆

纸厂复卷机自动化培训教材(中)

• Safety beam can be acknowledged at panel CP12.
Safety circuit (beam) OK if green light
5
SAFETY DEVICES =04 – Safety beam 2/2
Safety beam safety relay in PC10
7
INDUCTIVE SENSORS
- the most common sensor type used in the winders
TURCK - Bi15-CK40-AP6X2-H1141
Brand of sensors:
Turck
Bi15-CK40-AP6X2-H1141 (15mm, NO) Bi8-M18-VP4X-H1141 (8mm NO/NC) Bi15-CP40-VP4X2 (15mm, NO/NC)
• Status light is red (at the top of the sender part) when safety circuit is open and relay not reseted and green when circuit is ok and relay reseted.
• Safety beam system consists of the following parts: • sender/receiver part • mirror element • safety relay (in PC10) and circuit + reset button
• 卷取部分 safety beam prevents 踢辊器, rider roll and service hatch movements if not reset.

太阳能泵站培训文件共46页

(用于双水箱系统或者两套集热器) • 新型的设计 • 外形更加小巧
Training documentation New solar pump stations
BBT Thermotechnik GmbH. All rights reserved. TT/STR
For internal use only!!
1.4. 产品定位 1.4.1. 品牌的核心
BBT Thermotechnik GmbH. All rights reserved. TT/STR
For internal use only!!
Slide Nr. 5 Date: 2007/02
1. 销售论据
1.2. 方案目标
• 2 大产品系列 (单管泵站, 双管泵站) • 泵站可应用于1到50组集热器 • 结合太阳能控制器 Logamatic SC20, SC40 和 SM 10 • 排气组件集成 (屋面排气可以被取消) • 冲压站专用的连接 • 单管泵站和双管泵站可以组合
太阳能泵站培训文件
太阳能泵站培训文件
太阳能泵站 KS系列…
country-specific please adapt
Training documentation New solar pump stations
BBT Thermotechnik GmbH. All rights reserved. TT/STR
2.1.1. Variants country-specific
13
2.1.2. Package contents
16
2.1.3. Differences previous / new model country-specific
18
2.1.4. Fitting dimensions