基于改进蜂群算法的波浪发电最大功率跟踪  

The Maximum Power Point Tracking of Wave Energy Converter Based on Modified Artificial Bee Colony Algorithm

作　　者：熊锋俊 杨俊华[1] 谢东燊 吴丹琦 XIONG Feng-jun;YANG Jun-hua;XIE Dong-shen;WU Dan-qi(Automation School,Guangdong University of Technology,Guangdong Guangzhou 510006,China)

机构地区：[1]广东工业大学自动化学院,广东广州510006

出　　处：《计算机仿真》2020年第1期87-93,233,共8页Computer Simulation

基　　金：国家自然科学基金资助项目(51370265);广东省科技计划项目(2016B090912006);广东省自然科学基金项目(2015A030313487);广东省教育部产学研合作专项资金(2013B090500089)。

摘　　要：在波浪发电装置最大功率点跟踪中,浮子受到水动力的非线性导致传统群智能算法收敛速度不佳,易陷入局部最优。为此提出纵横交叉优化的人工蜂群算法(CABC)控制方案。引入纵横交叉算法(CSO)横向交叉算子的个体间变量全交叉思想,优化引导蜂、采蜜蜂搜索方式,增强CABC算法局部搜索能力。引入CSO纵向交叉算子优化侦查蜂,使侦查蜂能利用已知蜜源信息探索未知可行解域,提升CABC算法全局搜索能力。优化蜜源选择概率和人工蜂群结构,进一步改善CABC算法性能。仿真表明,CABC算法全局寻优能力强,收敛速度快,适用于波浪发电装置最大功率点跟踪。In the maximum power point tracing of wave energy converter,the nonlinearity of buoy hydrodynamic leads to poor convergence speed and strong tendency of trapped in local optimum of traditional swarm intelligence opti-mization.Therefore,a control scheme based on crisscross optimized artificial bee colony(CABC)algorithm is pro-posed in the paper.The horizontal crossover operator of CSO,which implements arithmetic crossover between all vari-ables of two individuals,was introduced to train the searching skill of director bees and forager bees,and the local searching capability of CABC algorithm was improved.Vertical crossover operator of CSO was introduced to enhance the searching ability of detecting bees,so that the known information of nectar sources can be used by detecting bees to explore unknown feasible solution region.Both nectar source selection criterion and artificial bee colony configura-tion were modified,and performance of CABC algorithm was further improved.The simulation shows that the CABC algorithm has excellent global optimization capacity and rapid convergence rate,which is suitable for maximum power point tracking of wave energy converter.

关 键 词：波浪发电装置 最大功率点跟踪 人工蜂群 纵横交叉优化 群智能算法 

分 类 号：TP391.9[自动化与计算机技术—计算机应用技术]