基于改进遗传算法的电力系统无功优化

遗传算法是一种模拟生物进化过程的优化算法,可用于求解包含离散化变量的复杂优化问题,本文将遗传算法应用于电力系统无功优化,并对常规遗传算法的编码方式、遗传算子以及终止判据等方面进行了改进,使用该文提出的算法对IEEE 6、IEEE 30节点系统进行了无功优化计算,结果表明该改进遗传算法应用于无功优化是合理可行的。     

改进遗传算法在配电网无功优化中的应用

遗传算法是近年来被广泛应用于无功优化的一种新型的优化算法。结合配电网的特征，对遗传算法的选择操作、交叉和变异算子、终止判据等核心操作进行改进，提出一种适合于配电网无功优化的改进遗传算法。计算实例表明，其优化效果优于传统遗传算法。     

基于贝叶斯优化算法的新安江模型参数优化及应用

鉴于参数优化是保证水文模型预报效果的重要途径,以息县流域为例,采用贝叶斯优化算法对新安江模型参数进行优化,并与遗传算法进行比较。结果表明,在息县流域日径流预报问题上,贝叶斯优化算法相比遗传算法效率提升显著,精度略优于遗传算法,非常适用于解决新安江模型参数优化问题。     

部分解约束改进遗传算法在火电厂机组负荷优化分配中的应用

针对实数遗传算法应用于火电厂机组负荷优化分配问题中存在因早熟收敛的难题,首次提出部分解约束结合惩罚函数的改进实数遗传算法,在约束条件的处理、变异策略、初始化等多方面针对该问题的特点对实数遗传算法提出了新的改进思路,解决了遗传算法应用于多峰值优化问题中早熟而收敛于局部极值点的难题,对5台机组的火电厂机组负荷优化分配的仿真表明优化成功率能达到100%。     

基于人工鱼群遗传算法的梯级水库优化调度研究

针对人工鱼群算法的不足,提出了人工鱼群遗传算法(AFSA-GA),采用人工鱼群算法模拟梯级水库优化调度,再用遗传算法进行局部细化搜索。实例结果表明,人工鱼群遗传算法应用于梯级水库优化调度行之有效。     

遗传算法在电炉模型中的应用

遗传算法是实现目标优化的重要算法工具,用于目标函数最小或最大化的优化计算,将遗传算法应用到电炉模型中,实现电炉原料优化配置和电炉生产成本最小化,是非常有意义的方法。阐述了如何使用遗传算法对电炉模型中的成本进行优化,使电炉中各物料合理配置,实现电炉炼钢生产的低成本(或者趋于最小化)。     

遗传算法在电力系统无功优化中的应用综述

遗传算法是近10年来发展的基于自然选择规律的一种优化方法,算法能成功的解决无功变量中的离散问题,避免常规数学优化方法的局部最优现象。本文阐述了简单遗传算法以及遗传算法与其它算法相结合的算法在电力系统无功优化中的应用和今后的发展方向。     

遗传算法优化BP神经网络在大坝扬压力预测中的应用

针对BP神经网络的局部极小和收敛慢等问题,提出了利用遗传算法的选择、交叉和变异操作优化BP神经网络的权值和阈值,将优化后的BP神经网络用于预测大坝扬压力。通过实例应用,将遗传算法优化的BP神经网络与逐步回归、BP神经网络预测相对比,结果表明遗传算法优化的BP神经网络收敛快且预测结果精度高。     

基于遗传算法的直喷式柴油机燃烧系统参数优化分析

应用CFD软件Fire对直喷式柴油机缸内工作过程进行了多维仿真研究,在前期正交设计优化的基础上,进一步选取涡流比、喷油压力、喷油提前角和油束夹角4个参数,以动力性为优化目标,应用遗传算法进行了全局优化。研究结果表明:正交设计优化和遗传算法优化均能大幅度改善原型机的燃烧过程和性能,其指示功率分别比原型机提高了10.3%和11.1%;与正交设计优化方法相比,遗传算法优化结果燃烧持续期更短,燃烧效率更高。     

混沌实码遗传算法在水质模型参数优选中的应用

为提高遗传算法在水质模型参数优选中的计算精度和全局优化能力,利用混沌映射的遍历性和实编码遗传算法的全局优化性,通过在实码遗传算法初始群体中引进50%的混沌变量和50%的均匀随机变量,采用混沌变异,用进化得到的优秀个体群来逐步缩小搜索空间,提出了求解水质模型参数优选的一种新方法———混沌实码遗传算法(CRGA)。应用该方法对1~10个变量的非线性、多峰值测试函数进行了仿真。在收敛速度和全局优化方面,CRGA好于现有的标准二进制编码遗传算法和改进的遗传算法。将CRGA用于求解有关河流横向扩散系数的实际水质模型的参数优选问题,与实编码遗传算法、标准二进制编码遗传算法、传统直线图解法相比,CRGA精度高、收敛速度快。     

基于模拟退火遗传算法的水电站优化调度研究

采用模拟退火遗传算法(SAGA)研究了水电站优化调度问题,与GA和经典的DP算法相比,结果表明该算法具有较强的局部搜索能力和较好的收敛能力,能以较快的速度找到全局最优解,是一种有效的搜索方法,可用于水电站优化调度中。     

Using whole annealing genetic algorithms for the turbine cascade inverse design problem

INTRODUCTIONInthelastfewyearstherehajsbeenagrowinginterestinthenumericalsolutionofconstrainedoptimizationproblemsofturbinegovernedbytheEulerorNavier-Stokesequations.Developmelltofturbinecascadeswithoptimumaerodynamicefficiencyhaslongbeenadesignchalle...     

Aerodynamic Optimum Design of Transonic Turbine Cascades Using Genetic Algorithms

INTRoDUCTIoNInmodernturbomachinery,withtheincreasingstageload,thetransoniccascadesbecomemorewidelyused,sothedesignofhighperformancetransoniccas-cadesisacriticaJpracticalproblem.Performanceop-timizationofaerodynamicshapeplaysanimportantroleinthedesigntasksofttirbomachinery,andisanactiveresearchfield.Awidevarietyoftechniqueshavebeendeve1opedtofacethisprobleml1].FuIthermethodsbasedonartificialintelligence-expertsystemtechniqueshavealsobeenintroducedl2].Butthediffusionofauto-matedshapedesigni…     

A review of electric production simulation and capacity expansion planning programs

This paper surveys currently available computer programs for electric production simulation and capacity planning optimization. Algorithms for electric production simulation and capacity planning optimization are reviewed. Representative programs are then outlined.     

Genetic algorithms development for multiobjective design optimization of compressor cascade

INTRODUCTIONTurbomaChinery,especiallycompressorcascade,optimumdesignpresentsagrandchallengetonumericaloptimization.Thegoalofcompressorcascadedesignistoproducethehighestpressurerisewiththelowesttotalpressurelossattheconstantflowcondition.Parameterofpr...     

Design and control strategies of PV-Diesel systems using genetic algorithms

Hybrid photovoltaic systems (PV-hybrid) use photovoltaic energy combined with other sources of energy, like wind or Diesel. If these hybrid systems are optimally designed, they can be more cost effective and reliable than PV-only systems. However, the design of hybrid systems is complex because of the uncertain renewable energy supplies, load demands and the non-linear characteristics of some components, so the design problem cannot be solved easily by classical optimisation methods. When these methods are not capable of solving the problem satisfactorily, the use of heuristic techniques, such as the Genetic Algorithms, can give better results.The authors have developed the HOGA program (Hybrid Optimisation by Genetic Algorithms), a program that uses a Genetic Algorithm (GA) to design a PV-Diesel system (sizing and operation control of a PV-Diesel system). The program has been developed in C++.In this paper a PV-Diesel system optimised by HOGA is compared with a stand-alone PV-only system that has been dimensioned using a classical design method based on the available energy under worst-case conditions. In both cases the demand and the solar irradiation are the same. The computational results show the economical advantages of the PV-hybrid system. HOGA is also compared with a commercial program for optimisation of hybrid systems.Furthermore, we show a number of results and conclusions about hybrid systems optimised by HOGA.     

基于遗传算法的风力机塔架最优化设计方法

探讨了遗传算法在风力机塔架结构优化设计中的应用.通过对经典遗传算法进行改进,建立了存在约束条件的遗传算法优化设计模型;考虑结构受力特点,给出了塔架体系在屈曲性能约束条件下的最优化设计方法.另外,针对遗传算法容易收敛到局部最优点的缺陷,改变各变量相应初始界限值,进行两次遗传算法操作运算,使工作效率提高,且结果更为精确.最后,对某风力机塔架进行在简化荷载作用下的最优化设计,得到了较满意的结果.     

Molecular dynamics modeling of thermal conductivity enhancement in metal nanoparticle suspensions

A theoretical approach based on molecular dynamics modeling, for the estimation of the enhancement of the thermal conductivity of liquids by the introduction of suspended metallic nanoparticles is proposed. Algorithms are developed for simulating the nanofluid abiding the procedural steps of the molecular dynamics method. The method is presented as a solution to the generic problem of thermal conductivity enhancement of liquids in the presence of nanoparticles, and illustrated using a specific simulation procedure with properties representing water and platinum nanoparticles. The thermal conductivity enhancement estimated using the simulations are compared with existing experimental results and those predicted by conventional effective medium theories. Parametric studies are conducted to obtain the variation of thermal conductivity enhancement with the temperature and the volume fraction of the nanoparticles in the suspension.     

Science of daylighting in buildings

The materials characterisation requirements for accurate yet practical simulation of daylighting in buildings is examined for a range of emerging and existing technologies which seek to raise the contribution of daylighting to overall lighting requirements. Algorithms based on underlying physical models can be established from analysis of data on bi-directional transmission and reflection. Demonstrations of effects in simulation relevant to glare, view and illuminance are given.     

Optimization of heat conduction using combinatorial optimization algorithms

The Volume-to-Point (VP) problem is a base problem of heat conduction optimization. The nonlinear two-dimensional optimization problem of VP is discretized and transformed to a combinatorial optimization problem, which can be solved by some modern optimization algorithms. Algorithms for VP problem using simulated annealing and genetic algorithm are developed. Results for different cases are obtained using these algorithms. Analyses of the results and algorithms are also presented, that shows these algorithms are better than bionic optimization algorithm and constructal theory for VP problem, and can be generalized to complex conditions.