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%。     

基于改进狼群算法的水电站厂内优化运行研究

针对水电站厂内运行中机组负荷分配问题的高维度、多约束的特点,提出了一种基于差分进化算法的自适应狼群算法,结合水电站厂内优化运行的优化目标和约束条件,以龙潭水电站日常运行数据为基础,采用该算法进行求解,并与遗传算法和原狼群算法计算结果进行比较。结果表明,该算法在计算耗时和收敛精度上均较遗传算法和原狼群算法有一定的改善,能满足电站实时性和精度要求,具有良好的全局搜索能力,在计算高维多机组负荷分配问题时能快速有效摆脱局部最优情况趋向全局最优解。     

改进遗传算法在梯级电站日优化运行中的应用

针对基本遗传算法存在求解精度与收敛速度间的矛盾，研究了一种新的改进自适应遗传算法（IAGA），并用于求解梯级电站日优化调度问题，基于提出的评价种群“早熟”程度新指标，自适应调整遗传算法法的交叉概率和变异概率，同时依据当前最优个体解码所对应调度方案对约束条件的违反量，动态调整相应的惩罚系数，使调度方案获得期望的约束程度，仿真结果验证了算法的有效性和可靠性。     

基于改进遗传算法的某型涡轮对转改造一维优化设计

为提高某型燃气涡轮性能,利用改进遗传算法对该型涡轮进行了对转改造一维设计。设计过程中将涡轮效率作为优化目标,出口气动参数、叶片强度和效率等作为约束条件,将一维设计构建为有约束的非线性优化问题。在简单遗传算法基础上增加自适应机制、改进的交叉方式和惩罚函数处理约束条件等改进方式,选取涡轮的设计变量作为决策变量进行优化计算。优化后的结果显示涡轮的效率较原型涡轮提高了0.6%。     

太阳能电动汽车储能系统的优化配置

在太阳能电动汽车(SEEV)系统中,储能系统的优化配置是一个重要且具挑战性任务.太阳能电动汽车储能系统的优化配置可以看成一个具有约束的优化问题:以储能系统的成本最小为优化目标,以表达系统可靠性指标的负载失电率为约束.决策变量不仅包含传统方法中的蓄电池充电电流而且还包含储能飞轮的质量.优化算法是采用基于遗传算法和神经网络的组合优化方法,即把机会约束遗传算法中比较耗时的个体检验部分交给神经网络处理.研究结果表明,基于遗传算法和神经网络的组合优化算法在被应用于太阳能电动汽车储能系统的优化配置时,算法收敛良好,计算时间少且可行.     

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.     

风光互补独立供电系统的优化设计

在设计风光互补独立供电系统时,系统中需要优化的不仅有光伏电池和蓄电池的容量,还应该有风力发电机种类和容量以及光伏电池的倾角。优化目标为系统安装成本,约束条件为供电可靠性,其指标负载缺电率LP- SP需经仿真运行得到。本问题属于非线性整数规划,也是一个NP-hard问题。用包含精英策略的遗传算法优化,以自适应罚函数法处理约束。计算和验证表明本文采用的算法收敛,能同时优化风力发电机类型和容量、光伏电池的容量和倾角以及蓄电池的容量,并且计算效率高。     

含液流电池的微网环境经济调度模型及应用

鉴于储能技术的应用可提高微网运行的经济性与可靠性,将含液流电池的微网作为研究对象,建立了基于机会约束规划的含液流电池的微网环境经济调度模型,该模型以微网运行成本与污染物治理费用最小为优化目标,以旋转备用容量在某一置信水平下满足系统可靠性要求作为概率约束条件,采用基于自适应变异的改进遗传算法对模型进行求解,并采用蒙特卡洛模拟法处理模型中的概率约束条件。实例应用结果表明,模型具有合理性与有效性。     

基于改进粒子群算法的机组负荷优化分配

针对标准粒子群算法存在容易早熟收敛的问题,在分析机组负荷优化问题的基础上,提出了一种基于解约束机制、边界反弹规则、高斯分布序列和混沌序列的改进粒子群算法。算法采用解约束机制和边界反弹规则处理优化问题的约束条件,同时在粒子移动过程中引入了高斯分布序列和混沌序列,从而克服了算法过早收敛的缺陷,提高了算法的全局优化能力。实例计算结果表明,该算法具有稳定的全局优化能力,为机组负荷优化分配问题的求解提供了新的方法。     

考虑光滑性约束的水库发电调度图优化方法研究

提出一种改进的基于实数编码的加速遗传算法(RAGA),并用于优化水库发电调度图,用廊道约束并结合平滑函数约束限制调度线的波动,分析比较了不同的编码方案和光滑性约束条件下的计算结果.结果表明,仅对上、下基本调度线进行优化,调度线以每个时段一个实数进行编码,以上、下基本调度线计算加大、降低出力线,利用改进的RAGA结合廊道约束可计算光滑、总发电量较大且能充分反映来水过程的调度图,为水库发电调度图求解提供了新途径.     

基于改进遗传算法的水库群防洪优化调度

为解决标准遗传算法(SGA)在处理高维复杂问题中寻优能力不足、计算时间过长、易早熟的问题,首先改进映射规则,仅选择部分决策变量进行编码,采取新的映射规则将其解码映射至解空间,实现高维问题的降维;其次使用针对不同约束条件的自适应罚函数对算法收敛性进行改进;最后以北方某流域并联水库防洪调度为例,建立防洪优化调度模型,并运用改进遗传算法进行求解。结果表明,该方法在解决高维复杂水库群防洪优化调度问题时具有求解速度快、寻优结果好的优点,有一定的实用性。     

Enhancement of combined heat and power economic dispatch using self adaptive real-coded genetic algorithm

In this paper, a self adaptive real-coded genetic algorithm (SARGA) is implemented to solve the combined heat and power economic dispatch (CHPED) problem. The self adaptation is achieved by means of tournament selection along with simulated binary crossover (SBX). The selection process has a powerful exploration capability by creating tournaments between two solutions. The better solution is chosen and placed in the mating pool leading to better convergence and reduced computational burden. The SARGA integrates penalty parameterless constraint handling strategy and simultaneously handles equality and inequality constraints. The population diversity is introduced by making use of distribution index in SBX operator to create a better offspring. This leads to a high diversity in population which can increase the probability towards the global optimum and prevent premature convergence. The SARGA is applied to solve CHPED problem with bounded feasible operating region which has large number of local minima. The numerical results demonstrate that the proposed method can find a solution towards the global optimum and compares favourably with other recent methods in terms of solution quality, handling constraints and computation time.     

基于粒子群算法的水电站中长期优化调度研究

以洪家渡水电站为例,探讨了粒子群算法在水电站中长期优化调度的应用方法及效果。实例计算结果表明,该算法可以求解复杂约束条件的非线性水库优化调度,精度高、收敛速度快,为解决水电站中长期优化调度问题提供了一种有效的方法。     

Investigation of wind farm location planning by considering budget constraints

The growing awareness about the adverse effects of fossil fuels and the ensuing pollution has encouraged and promoted the use of renewable energies throughout the world. This in turn has encouraged governments and investors to show more interest in generation of renewable energy through construction of facilities such as wind farms. The novelty of this research work is the budget constraints consideration for the first time in wind energy projects. This work is done for the Khorasan provinces of Iran. This study is based on regional and municipal data pertaining to the year 2016, and uses the fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method to deal with the uncertainty of the data. The fuzzy TOPSIS method is used to evaluate and rank the candidate cities, and the results are then used to develop a budget-constrained model by fuzzy linear programming with the Maxmin objective function. The resulting mathematical model is optimised with GAMS software. The modelling results show that in the presence of budget constraints, of 10 Khorasan cities considered as candidate locations, Afriz, Rudab, and Fadashk exhibit the best conditions for the construction of wind farms.     

智能算法求解机组组合的可行解检验方法

针对智能算法求解电力系统机组组合时存在的经济调度无解问题,提出了一种简便、高效率的检验方法.提出的算法可用于检验智能算法中较难处理的爬坡约束和直流潮流安全约束,有效地避免对不可行机组状态组合的经济调度.算例表明,该方法检验效率高,可有效节省计算时间,具有较强的实用意义.     

A GENETIC ALGORITHM FOR THE SHAPE OPTIMIZATION OF PARTS SUBJECTED TO THERMAL LOADING

In many technical situations, the optimization of the mechanical behavior of structures proceeds from the search for the ideal shape satisfying thermal, mechanical, technological, and geometrical constraints. In this article, the shape optimization of mono- and two-dimensional structures is handled by means of a new genetic algorithm (GA). The method is in general well suited to the resolution of nonconstrained optimization problems: The algorithm presented here has been modified by taking into account the imposed design constraints in the selection of the "individuals" belonging to a given population. The crossover operation between individuals and the mutation process in their original forms are applied to derive the optimal shape of parts subjected to thermal loadings. The algorithm exhibits a good convergence toward the optimal solution and the numerical results of its application show a good numerical accuracy.     

Stress constrained thermo-elastic topology optimization based on stabilizing control schemes

Abstract

This article proposes two effective stabilizing control schemes for addressing the stress constrained thermo-elastic topology optimization in a non-uniform temperature field. Based on the density interpolation scheme, two linear elastic equations for coupling a thermo-elastic problem are considered. For comparison, different topology problem formulations for minimizing compliance or volume subject to stress constraints are solved. By virtue of a stabilization transform method, two stabilizing control schemes combined with the grouped aggregation method are developed to handle the challenging difficulties stemming from the local nature of highly nonlinear stress constraints. Moreover, the adjoint method is adopted to perform the sensitivity analysis. The design variables are updated by utilizing the method of moving asymptotes. The results of several typical numerical examples verify the validity of the proposed methodology, including the present stabilizing control schemes which can be employed to obtain clear topological design and fast convergence rate for thermo-elastic coupling problems. Meanwhile, compliance minimization design with stress constraints is appropriate to achieve balance between stress level and stiffness.     

Meshless local Petrov Galerkin method for 2D/3D nonlinear convection–diffusion equations based on LS-RBF-PUM

In this article, we propose a meshless local Petrov Galerkin (MLPG) method based on least square radial basis function partition of unity method (LS-RBF-PUM), which is applied to the nonlinear convection–diffusion equations. The proposed method is not sensitive to the node layout, and has good stability and flexibility to complex domain. In order to treat nonlinear term, Picard iterative scheme is employed to confirm the convergence of iterative process. Error estimates are derived by the radial basis function interpolation method and convergence rate is proven to be second order. Numerical examples are performed for the nonlinear convection–diffusion equations in two and three space dimensions (2D/3D), which not only supports the theoretical results but also finds out superconvergence of third order.     

水电系统及水库库群优化调度的非线性网络法

利用水电系统各机组出力的非线性目标函数，结合各水库流量和库存量具有网络流特点的约束，采用非线性网络规划对各水电站出水或水库水量进行优化调度，本计算方法可充分 各种因素，具有速度快、收敛精度高，占有内存少的特点。