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202306012.pdf
文章编号： 1672-6987（2023）06-0095-07； DOI： 10.16351/j.1672-6987.2023.06.012
牟星辰, 孟祥忠*(青岛科技大学 自动化与电子工程学院， 山东 青岛 266061)
摘要： 以某地区的配电网为研究对象，对该区域配电网的供电方式及负荷性质进行了分析，针对引起该区域电压不合格及无功不足问题的原因，建立了考虑成本效益回报和线损率的多目标无功优化模型。将模拟退火算法(SA)中的Boltzmann策略，加入到传统遗传算法(GA)中，构成改进遗传算法(SA-GA)，以提升遗传算法的全局搜索能力。通过实际配电网仿真算例，验证了本模型及算法对解决该区域无功及电压问题的有效性。
关键词： 无功补偿； 改进遗传算法； 低压配电网； 无功优化
中图分类号： TQ 207+.2文献标志码： A
引用格式： 牟星辰, 孟祥忠. 基于改进遗传算法的配电网多目标无功优化设计［J］. 青岛科技大学学报(自然科学版), 2023, 44(6): 95-101.
MU Xingchen， MENG Xiangzhong. Multi-objective reactive power optimization design of distribution network based on improved genetic algorithm［J］. Journal of Qingdao University of Science and Technology（Natural Science Edition), 2023， 44（6）： 95-101.
Multi-objective Reactive Power Optimization Design of Distribution
Network Based on Improved Genetic Algorithm
MU Xingchen，MENG Xiangzhong
(College of Automation and Electronic Engineering, Qingdao University of Science and Technology, Qingdao 266061, China)
Abstract: Taking the distribution network in a certain area as the research object, the power supply mode and load nature of the distribution network in this area are analyzed. According to the causes of the unqualified voltage and insufficient reactive power in this area, A multi-objective reactive power optimization model with more cost-benefit return and less line-loss rate is established. The Boltzmann strategy in the simulated annealing algorithm (SA) is added to the traditional genetic algorithm (GA) to form an improved genetic algorithm (SA-GA) to improve the global search ability of the genetic algorithm. Through the simulation example of the actual distribution network, the effectiveness of the model and algorithm in this paper to solve the reactive power and voltage problems in this area is verified.
Key words: reactive power compensation; improved genetic algorithm; low voltage distribution network; reactive power optimizationed.
收稿日期： 2022-11-18
基金项目： 山东省重大科技创新工程项目（2021SFGC0601）.
作者简介： 牟星辰(1996-),男,硕士研究生.*通信联系人.
