NSGA2遗传算法改进研究及其在微电网配置中的应用

发布时间：2019-06-02 12:09

【摘要】：进化算法是一种极具适用性的多目标优化方法,对全局寻优极具优势,算法思想是自然界生物进化原则和优胜劣汰法则。实际工程应用领域的优化问题通常以多场景、多时段、多影响因素等为特征,并且附带各种性质的约束限制条件,这为问题的解决加大了难度。优化问题的约束处理有多种方法可以实现,其中罚函数法受到很多学者的广泛关注和研究,但是该方法存在固有缺陷即罚因子的设置问题。快速非支配排序遗传算法(Non-dominated Sorting Genetic Algorithm 2,NSGA2)是一种典型多目标遗传算法。本文以经典NSGA2算法为基础加以改进,提出改进型INSGA2算法(Improved Non-dominated Sorting Genetic Algorithm 2),用以解决含约束条件的多目标优化问题。改进型INSGA2算法处理约束多目标优化问题时,将约束条件转化为待优化目标之一,又因NSGA2算法在解决三目标以上优化问题时,算法性能明显下降,故本文只研究带约束两目标优化问题。在INSGA2算法中,对不可行域内性能较好的个体加以利用,将可行解和不可行解执行遗传操作以促使搜索加快向可行域方向靠拢,并自适应调整执行遗传操作的进化代数以减少进化后期低效冗余的遗传操作;并为算法在可行域内的搜索设置存活条件,即允许保留的个体必须满足一定限制条件,此操作设计可以强化进化进程中的选择压力,防止进化出现停滞甚至退化现象,使进化趋优趋势更为明显;在种群进化后期,性状相似的个体过度拥挤过度重叠可能引发搜索的局部收敛,针对此问题,提出在种群进化后期执行边际变异操作。在算例验证分析中,选取约束优化基准测试函数和多模态优化基准测试函数进行两种算法的对比实验验证,实验结果表明改进型算法是具有一定优势的。传统供配电网络远距离大范围互联互通,发配电集中操作与控制,此种运作模式的缺陷日益显现。逐渐受到重视的分布式发电(Distributed Generating Power,DG)和微电网(Microgrid,以下简称微网)应用很大程度上弥补了大规模集中式供电的不足,提高了供电可靠性,加快电网智能化进程。然而DG的不当并网会给基于线路损耗、电能质量、经济因素、环境因素等考量的前期规划产生干扰和冲击,因此需对DG的定址定容进行规划优化。为使系统更加安全可靠和高效运行,本文从供电质量、经济成本、环境效益等角度出发,研究分析DG并入微网的配置问题,以线路损耗、电压偏移、初期经济成本、寿命周期碳排量为目标,并将不同目标进行两两组合,考虑系统正常运行的各项约束限制条件,以IEEE33节点配电网系统为实验对象,进行基于NSGA2算法与INSGA2算法的微网规划实例仿真验证,实验结果表明算法和模型是合理有效的。
[Abstract]:Evolutionary algorithm is a very applicable multi-objective optimization method, which has great advantages in global optimization. The idea of the algorithm is the principle of biological evolution in nature and the rule of survival of the fittest. The optimization problems in the field of practical engineering application are usually characterized by multi-scene, multi-time period, multi-influencing factors and so on, and attach various constraints and constraints, which makes it more difficult to solve the problem. There are many methods to deal with the constraints of optimization problems, among which the penalty function method has been widely concerned and studied by many scholars, but this method has inherent defects, that is, the setting of penalty factors. Fast undominated sorting genetic algorithm (Non-dominated Sorting Genetic Algorithm 2, NSGA 2) is a typical multi-objective genetic algorithm. In this paper, based on the classical NSGA2 algorithm, an improved INSGA2 algorithm (Improved Non-dominated Sorting Genetic Algorithm 2) is proposed to solve the multi-objective optimization problem with constraints. When the improved INSGA2 algorithm deals with constrained multi-objective optimization problem, the constraint condition is transformed into one of the objectives to be optimized, and the performance of NSGA2 algorithm is obviously degraded when solving the optimization problem of more than three objectives. Therefore, this paper only studies the two-objective optimization problem with constraints. In INSGA2 algorithm, individuals with good performance in infeasible domain are used to perform genetic operation of feasible solution and infeasible solution to promote the search to move closer to feasible domain. The evolutionary algebra that performs genetic operation is adaptively adjusted to reduce the inefficient redundant genetic operation in the later stage of evolution. The survival conditions are set for the search in the feasible domain, that is, the reserved individuals must meet certain constraints. This operation design can strengthen the selection pressure in the process and prevent the stagnation or even degradation of evolution. It makes the trend of evolution more obvious. In the late stage of population evolution, overcrowding and overoverlap of individuals with similar traits may lead to local convergence of search. In order to solve this problem, it is proposed to perform marginal variation operation at the later stage of population evolution. In the verification analysis of an example, the constrained optimization benchmark function and the multimodal optimization benchmark function are selected to verify the comparison between the two algorithms. The experimental results show that the improved algorithm has certain advantages. The defects of the traditional power supply and distribution network are becoming more and more obvious because of the long distance and large range interconnection and centralized operation and control of the power supply and distribution network. The application of distributed generation (Distributed Generating Power,DG and microgrid (Microgrid,), which has been paid more and more attention, makes up for the deficiency of large-scale centralized power supply to a great extent, improves the reliability of power supply and speeds up the process of intelligence of power grid. However, the improper grid connection of DG will interfere and impact the preliminary planning based on line loss, power quality, economic factors, environmental factors and so on, so it is necessary to optimize the location and capacity of DG. In order to make the system more safe, reliable and efficient, this paper studies and analyzes the configuration of DG integrated into microgrid from the aspects of power supply quality, economic cost and environmental benefit, with line loss, voltage offset and initial economic cost. The life cycle carbon emission is taken as the goal, and the different targets are combined in pairs. considering the constraints and limitations of the normal operation of the system, the IEEE33 node distribution network system is taken as the experimental object. The simulation results of microgrid planning based on NSGA2 algorithm and INSGA2 algorithm show that the algorithm and model are reasonable and effective.
【学位授予单位】：兰州理工大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TP18;TM727

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