基于改进遗传算法的电力系统无功优化研究

发布时间：2018-03-03 22:19

本文选题：无功优化　切入点：遗传算法　出处：《兰州交通大学》2014年硕士论文　论文类型：学位论文

【摘要】：随着我国经济的迅速增长和工业的发展，国民经济各部门对电能质量的要求也越来越严格。在电力系统中，无功功率起着特殊的作用。一方面，无功功率为电能的交换、输送和转换创造必要的条件；另一方面，如果无功电源和负荷分布不合理，将会影响电力系统的经济性和稳定性，降低电能质量。因而研究电力系统无功优化，对减少电力网络因为无功的不合理分配而产生的额外有功消耗和提升电压运行水平具有显著的现实意义。电力系统的无功优化本质是一个最优化问题，它的变量种类多，目标往往不止一个，数学模型复杂，处理规模大，对算法的实时性要求也高。尤其近年来电力系统规模越来越庞大，技术也越来越复杂。由于传统算法的内在局限性，已经不能很好适应现代大规模的电力系统。近年来，人工智能算法开始应用于电力系统无功优化领域，其中遗传算法与其他智能算法比较起来应用更为广泛。论文在现有研究基础上对遗传算法进行改进，以期进一步提高其求解的速度与精度。论文首先介绍电力系统无功优化的背景及意义，对研究内容和特点进行分析，提出电力系统无功优化模型，该模型综合考虑了网损最小和维持电力系统的稳定性。其次，提出ICGA(Improved Catastrophic Genetic Algorithm,改进灾变遗传算法)应用于电力系统无功优化，该算法在常规遗传算法的基础上，引入灾变算子，，并对产生灾变的范围进行动态控制，解决常规遗传算法的易陷入局部最优问题，同时提升收敛速度。此外，为进一步提高算法的收敛性能，论文还设计动态的交叉概率和变异概率。最后，采用测试函数验证论文提出的算法的效果。论文将ICGA与潮流计算结合运用到电力系统无功优化中，并通过IEEE(Institude ofElectrical and Electronics Engineers,电气和电子工程师协会)推荐的两个标准节点进行仿真以验证本算法的效果。结果表明，ICGA在保持群体多样性和提高搜索效率等方面都具有良好的性能，对电力系统无功优化能产生较理想的效果。
[Abstract]:With the rapid growth of economy and the development of industry in our country, the requirements of power quality are becoming more and more strict in all sectors of the national economy. Reactive power plays a special role in the power system. On the one hand, reactive power is the exchange of electric energy. On the other hand, if the distribution of reactive power and load is not reasonable, it will affect the economy and stability of power system and reduce the power quality. It is of great practical significance to reduce the extra active power consumption caused by the unreasonable distribution of reactive power and to raise the level of voltage operation in power network. The essence of reactive power optimization in power system is an optimization problem. It has many kinds of variables, more than one target, complex mathematical model and large scale of processing. Especially in recent years, the scale of power system is getting larger and larger, the technology is more and more complex. Because of the inherent limitation of traditional algorithm, it can not adapt to modern large-scale power system. Artificial intelligence algorithm has been applied to reactive power optimization of power system, and genetic algorithm is more widely used than other intelligent algorithms. In order to further improve the speed and accuracy of its solution. This paper first introduces the background and significance of reactive power optimization in power system, analyzes the contents and characteristics of the research, and puts forward a reactive power optimization model of power system, which considers the minimum network loss and maintains the stability of power system. ICGA(Improved Catastrophic Genetic algorithm (improved catastrophe genetic algorithm) is applied to reactive power optimization of power system. Based on the conventional genetic algorithm, the catastrophe operator is introduced and the range of catastrophe is dynamically controlled. In order to improve the convergence performance of the conventional genetic algorithm, the dynamic crossover probability and mutation probability are designed. Test function is used to verify the effect of the proposed algorithm. In this paper, ICGA and power flow calculation are applied to reactive power optimization of power system. Two standard nodes recommended by IEEE(Institude ofElectrical and Electronics Engineers are simulated to verify the effectiveness of this algorithm. The results show that ICGA has good performance in maintaining population diversity and improving search efficiency. The optimal reactive power optimization of power system can produce ideal effect.
【学位授予单位】：兰州交通大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TM714.3

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