| 引用本文: | 葛双冶,杨凌帆,刘倩,周杭霞.基于改进CPSO的动态阴影环境下光伏MPPT仿真研究[J].电力系统保护与控制,2019,47(6):151-157.[点击复制] |
| GE Shuangye,YANG Lingfan,LIU Qian,ZHOU Hangxia.Research on photovoltaic MPPT simulation under dynamic shadow environment based on modified CPSO[J].Power System Protection and Control,2019,47(6):151-157[点击复制] |
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| 摘要: |
| 动态阴影下，传统最大功率点追踪(MPPT)算法易陷入局部极值，而常规粒子群(PSO)算法实现的MPPT控制易给系统带来较大的振荡。针对上述问题，提出一种自适应精英策略改进混沌粒子群(AEM-CPSO)算法的MPPT控制策略。该算法对粒子前三次迭代进行混沌搜索，使粒子在初始状态具有全局遍历性。自适应精英策略运用于粒子搜索后期，用于缓解算法后期振荡的问题。仿真结果表明，AEM-CPSO算法在全局搜索性，追踪速度以及暂态稳定性都优于传统方法。 |
| 关键词: 动态阴影 最大功率点追踪 粒子群算法 自适应精英策略 混沌搜索 |
| DOI：10.7667/PSPC180415 |
| 投稿时间：2018-04-13修订日期：2018-11-02 |
| 基金项目:浙江省基础公益研究计划项目资助(LGF18F0 20017) |
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| Research on photovoltaic MPPT simulation under dynamic shadow environment based on modified CPSO |
| GE Shuangye,YANG Lingfan,LIU Qian,ZHOU Hangxia |
| (China Jiliang University, Hangzhou 310016, China) |
| Abstract: |
| Under the dynamic shadow, the traditional MPPT algorithm is easy to fall into the local extremum, while the MPPT control implemented by the conventional Particle Swarm Optimization (PSO) algorithm is easy to introduce a large shock to the system. To solve the problems above, an adaptive MPPT control strategy based on the improved Chaotic Particle Swarm Optimization (AEM-CPSO) algorithm is proposed. The algorithm performs chaotic search on the first three iterations of the particle, which makes the particle have global ergodicity in the initial state. The adaptive elite strategy is used in the late phase of particle search to mitigate the problem of late oscillation of the algorithm. Simulation results show that AEM-CPSO algorithm is superior to traditional methods in global search, tracking speed and transient stability. This work is supported by Basic Public Benefit Research Program of Zhejiang Province (No. LGF18F020017). |
| Key words: dynamic shadow maximum power point tracking particle swarm optimization adaptive elite mutation chaotic search |
