改进自适应禁忌退火粒子群算法的电力系统环境经济调度  

Improved Adaptive Tabu Annealing Particle Swarm Optimization Algorithm for Environmental Economic Dispatch of Power System

作　　者：史磊 熊国江 潘政 刘宇 何其多 付子怡 SHI Lei;XIONG Guojiang;PAN Zheng;LIU Yu;HE Qiduo;FU Ziyi(College of Electrical Engineering,Guizhou University,Guiyang 550025,China)

机构地区：[1]贵州大学电气工程学院,贵阳550025

出　　处：《实验室研究与探索》2022年第10期116-120,127,共6页Research and Exploration In Laboratory

基　　金：国家自然科学基金项目(51907035);贵州省教育厅青年科技人才成长项目(黔教合KY字[2018]108)。

摘　　要：为提高粒子群算法的全局搜索能力,避免陷入局部极值,提出一种改进自适应禁忌退火粒子群算法(IATAPSO),用于求解电力系统环境经济调度。采用修改平均价格罚因子将环境经济调度转化为单目标优化问题。在IATAPSO中,惯性系数采用反正切函数控制策略,学习因子按照余弦函数策略进行变换,实现全局搜索能力与局部搜索能力之间的协调与配合;为避免出现前期大量粒子聚集的“早熟”现象,引入禁忌激励的退火选择机制,来选择全局最优位置的替代解,增大粒子跳出局部极值的概率。通过15机系统仿真验证了IATAPSO算法的可行性和有效性。In order to improve the global search ability of particle swarm algorithm,an improved adaptive tabu annealing particle swarm optimization(IATAPSO)algorithm is proposed to solve the environmental economic dispatch problem of power system.The modified average price penalty factor is used to convert the environmental economic dispatch problem into a single objective optimization problem.In IATAPSO,the inertia coefficient is controlled by arctangent function,and the learning factor is transformed by cosine function to achieve the coordination and cooperation between global and local search capabilities.Concurrently,in order to avoid the premature phenomenon of a large number of particles clustering in the early stage,a tabu-excitation annealing selection mechanism is introduced to select an alternative solution for the global optimal location,thereby increasing the probability of particles jumping out of the local extreme value.Finally,the feasibility and validity of IATAPSO algorithm are verified by 15-machine system simulation.

关 键 词：电力系统 环境经济调度 价格罚模型 退火粒子群 自适应参数 禁忌激励 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程] TM732[自动化与计算机技术—控制科学与工程]