智能全站仪精密三角高程测量替代二等水准测量

鉴于用传统几何水准的方法进行垂直位移监测存在效率低、受环境和地形因素影响大等问题，探讨了用智能全站仪进行精密三角高程测量来监测垂直位移。简述了TCA2003全站仪的自动目标识别（ATR）功能和使用方法，研究了ATR精密三角高程测量的误差来源及精度;结合在某大坝的实践，分析了TCA2003全站仪ATR三角高程测量的实测精度及其替代二等水准测量的可行性和可靠性，给出了智能全站仪精密三角高程测量替代二等几何水准测量的条件、减弱误差的方法及提高精度的措施等。     

基于EGM2008重力场模型的电厂区高程传递方法研究

本文提出了一种快速、准确传递距已知水准点较远厂区控制点高程的新方法,即基于国际上最先进的EGM2008重力场模型结合似大地水准面的方法传递高程法。本文首先介绍了EGM2008模型的精度,并详细阐述了似大地水准面法的原理。然后在山东地区比较了不同方法拟合高程异常残差的精度,结果表明：加权反距离法的精度优于其他方法。工程实例反映出该方法满足厂区高程控制联测精度要求。     

GPS测高技术在电力工程中的应用

以电厂厂区和管线工程GPS水准高程为依据,从局部拟合法和大地水准面模型法分析了GPS拟合的精度,总结了GPS在厂区和带状地形图水准高程拟合精度的注意事项及直接应用WGS-84大地高差进行高程控制的方法。     

全站仪中间法进行三角高程测量的分析

介绍了全站仪中间法原理,详细分析了全站仪中间法的误差影响及其注意事项,并与传统三角高程测量和水准测量进行了全面比较.     

长距离跨江高程传递在苏通GIL综合管廊工程中的应用

在苏通GIL综合管廊工程中采用三角高程传递的方法完成了长江两岸长距离跨江的高程传递,其精度满足了国家二等水准测量的技术要求。同时采用改进的三角高程测量方法进行了测量实验验证了该方法在长距离跨江测量中运用的可行性;通过测量成果与GPS拟合高程结果比较,验证了跨江高程传递结果的可靠性。     

丁字尺与角度配合法测量全站仪高度的方法

当受到地形条件限制,以对向观测法或单向观测法进行三角高程测量时,仍需对全站仪高度进行比较精确地测量.为此提出丁字尺与角度配合法.利用丁字尺尺头与尺身相互垂直的特点,结合全站仪对中器激光束(或光学对中器十字丝)和垂直角,在几何上构建一个直角三角形,根据三角函数关系可得仪器高度.经过与二等水准测量对比,该法获得的仪器高度比...     

基于EGM2008重力场模型的电厂区高程传递方法研究

本丈提出了一种快速、准确传递距已知水准点较远厂区控制点高程的新方法,即基于国际上最先进的EGM2008重力场模型结合似大地水准面的方法传递高程法.本文首先介绍了EGM2008模型的精度,并详细阐述了似大地水准面法的原理.然后在山东地区比较了不同方法拟合高程异常残差的精度,结果表明:加权反距离法的精度优于其他方法.工程实...     

火电厂沉降观测中水准尺倾斜的数据处理研究

沉降观测成果是衡量火电厂建设质量的重要指标。本文论述了电厂建(构)筑物沉降观测过程中,由于施工、安装、设备堆放等不确定性因素,观测点水准尺临时无法正常竖直、水准尺倾斜时如何有效减弱其对沉降观测结果的影响。并通过工程实例,验证其有效性和可操作性。     

特大型桥梁施工的三角高程控制测量

以自动型全站仪的自动目标识别技术为硬件基础进行跨河三角高程试验,采用严格同步对向观测消除大气折光影响,设计观测路线构成具有外部检核条件的跨越四边形,根据精度分析提出新的定权方法.试验结果:1080米跨径的跨河三角高程达到±0.7mm的精度.     

桂林电厂以大代小技改工程2×135 MW机组烟囱地基加固处理

从施工流程、施工技术、质量保证措施等方面,详细论述应用水泥灌浆法解决桂林电厂以大代小技改工程180 m高烟囱不均匀沉降的问题.     

基于FPGA的惯性平台嵌入式调平系统设计

嵌入式调平系统是惯性平台“三自(自主功能检测、自主误差标定、自主初始对准)”技术发展首先要解决的问题.在深入研究了某型号惯性平台调平原理的基础上,设计了1套全新的嵌入式调平系统.该系统主要是通过AD650采集失调电压信号,然后送入FPGA进行分析处理,最后通过施矩施矩电路的工作来实现惯性平台的调平.实验表明此系统不仅能...     

惯性平台调平回路的自抗扰控制

针对惯性平台调平回路的设计要求,研究了惯性平台调平回路自抗扰控制的设计,并进行了计算机仿真。结果表明,自抗扰控制方案具有快速,无超调的特点,而且在干扰力矩下,动态误差小。采用自抗扰控制器提高了惯性平台调平时的可靠性和精度,是一种应用在实际平台系统中理想的控制器。     

深厚软基火电厂主要结构桩筏基础设计方法及应用

桩筏基础因其具有承载力高、沉降小以及调节不均匀沉降能力强等优点而被广泛应用于主厂房、锅炉等电厂重要结构物中。首先介绍了考虑土-桩-筏协调变形的桩筏基础设计方法,该方法主要涉及群桩下卧层附加应力的计算、群桩沉降的确定以及桩筏共同作用分析等方面。与其他桩筏基础分析方法相比,该方法计算群桩中的单桩刚度相对简单准确,可以考虑成层地基、不规则及变厚度筏板、存在相邻基础等复杂情况。对某电厂桩筏基础进行了分析计算,并将计算沉降与实测沉降进行了对比。     

Optimization of the electric power leveling system by using superconducting magnetic energy storage with genetic algorithm

A new optimization method of the electric power leveling system using an SMES is proposed. The SMES is parallelly connected with rolling mills in steel works. The leveling control is based on fuzzy reasoning. The SMES capacity and the scaling factors of the fuzzy controller will be optimized so that the power leveling control can be achieved and then the total cost of the added SMES cost and reduced contract electricity rate becomes lower. The optimization is carried out using a genetic algorithm and a cost reduction of 7.76 billion yen can be achieved. Power leveling simulation confirms that the proposed optimization method is very effective for designing the power leveling system. © 2004 Wiley Periodicals, Inc. Electr Eng Jpn, 150(3): 62–69, 2005; Published online in Wiley InterScience ( www.interscience.wiley.com ). DOI 10.1002/eej.20049     

A control strategy for leveling load power fluctuations with a successive learning fuzzy-neural network based on prediction of average load power

The effective usage of power facilities can be realized by leveling the fluctuating active power and compensating the reactive power. A fuzzy control strategy of superconducting magnetic energy storage (SMES) has been proposed for this purpose. The control results depend on the values of the scaling factors in fuzzy reasoning. Therefore, to obtain better control results, the scaling factor should be successively adjusted according to the load power fluctuations. In this paper, a control strategy based on autotuning of scaling factors and a fuzzy singleton reasoning method using backpropagation in a neural network is proposed for leveling load fluctuations. The prediction and revision of the teaching signal in terms of the energy of the SMES is proposed. The learning rate and the revision of the teaching signal are discussed. Better leveling of load power fluctuation is shown to be achievable by using fuzzy logic and neural networks. © 1997 Scripta Technica, Inc. Electr Eng Jpn, 120(2): 72–81, 1997     

碎石垫层在百万千瓦级机组主厂房地基处理中的应用

碎石垫层具有压实性能好、填筑密度大、抗剪强度高、变形小、承载力高等特性。邹县电厂百万千瓦级机组主厂房地基采用碎石垫层换填方案,通过试验确定了合理的施工工艺和设计参数。本文以建筑物沉降观测数据为基础,详细分析了碎石垫层地基建筑物的沉降与变形特征,采用三维数值模拟分析了垫层地基的变形和受力特征。     

GPS和水准测量相结合在电力工程测量中的应用

文章结合通辽电厂水源工程,详细论述了GPS和水准测量相结合的基本原理及工作方法,并得出了有益的结论,对今后类似的工程具有一定的借鉴作用。     

Development of a scheduling algorithm for resource leveling in local time intervals

A scheduling algorithm has been developed for resource leveling in a plant construction schedule. The algorithm facilitates resource profile smoothing as well as resource peak flattening, through a combination of global leveling with a variance-based performance index and local leveling. The local resource leveling, done after the global leveling, further improves minor fluctuations and local peaks in the resource profile that are insensitive to the variance-based performance index. The local resource leveling is based on two steps: definition of a target resource profile for chosen local time intervals; and heuristic optimization which shifts the resource profile towards the target curve. The scheduling algorithm developed has been experimentally applied to a practical problem of simulating a plant construction schedule with about 5000 tasks. It was confirmed from the numerical results that local resource leveling is a promising way to refine the resource profile after global resource leveling. © 1998 Scripta Technica. Electr Eng Jpn, 122(4): 34–44, 1998     

Control strategy of leveling load power fluctuations based on fuzzy neural networks by tuning coefficients of learning rate with genetic algorithm

The effective usage of the power facilities can be realized by leveling the fluctuating active power and compensating the reactive power. The fuzzy and fuzzy neural network control strategy of superconducting magnetic energy storages (SMES) was proposed for this purpose. The control results depend on the values of coefficients of learning rate in fuzzy neural networks. Therefore, it is desirable to obtain better control results by tuning the coefficients of learning rate to their optimum values. In this paper, the control strategy based on an autotuning of scaling factors with neural network and tuning of coefficients of learning rate of neural network with genetic algorithm is proposed for leveling load fluctuations. Encoding and decoding of coefficients of learning rate and selection, crossover, and mutation within genetic operations are shown, and crossover rate and mutation rate are discussed. Through these methods, we can achieve a better leveling of load power fluctuation by using fuzzy neural network with genetic algorithm. © 1998 Scripta Technica, Electr Eng Jpn, 125(1): 65–72, 1998     

促进新能源消纳的电力交易偏差结算补偿机制

随着新能源参与市场交易的比例不断扩大,由于其波动性强、预测准确度低等特性,引起新能源电厂基数电量分配不均、合同电量滚动到年底仍无法结算等问题,使得设计新能源电厂参与电力市场交易偏差结算机制尤为关键。文中立足新能源参与电力交易现状,提出新能源替代利益补偿办法。首先,基于结算日新能源电厂实际发电量减去事前合同电量,按机组装机容量确定基数电量。然后,针对市场中各电厂参与交易的电量和电价,确定新能源电厂结算综合单价,核定超发电厂对少发电厂的经济补偿。最后,通过具体算例得出该方法可以使得市场份额高的电厂获得高利润,同时,按月结算电费保证了交易合同的执行力。