【摘要】 最大功率跟踪(MPPT)技术是提高温差发电器(TEG)发电效率的必要手段。然而，传统的多峰值MPPT方法往往只关注其静态搜索能力和光照突变情况下的追踪过程。不同于光伏电池，TEG两端温差无法突变而是呈现出缓慢变化趋势。因此，提出一种基于动态粒子群算法(DPSO)的MPPT算法，用于动态温差环境下温差发电的最大功率点跟踪。DPSO通过多阈值检测和群体定向淘汰，避免算法频繁重启，减小了能量损失，提升了多峰值MPPT算法的动态性能。最后，与扰动观察法、改进粒子群算法进行对比仿真，结果表明所提出的算法在各种环境下可以更加准确并快速地实现MPPT。更多还原

【Abstract】 Maximum power tracking(MPPT) technology is necessary to improve the efficiency of temperature difference generators(TEGs) for power generation. Traditional multi-peak MPPT methods focus solely on their static search capability and tracking process under sudden temperature changes. Unlike photovoltaic modules,the temperature difference between the two ends of thermoelectric materials cannot be abruptly changed but shows a slow change trend. Therefore, an MPPT algorithm based on dynamic particle swarm optimization(DPSO) was proposed for thermoelectric generation maximum power point tracking under dynamic temperature difference environment. Through multi-threshold detection and group-oriented elimination, the frequent restart of the algorithm of DPSO was avoided, energy loss was reduced, and the dynamic performance of the multi-peak MPPT algorithm was improved. Finally, the simulation was compared to the perturbation observation method and the improved particle swarm algorithm, with the results demonstrating that the proposed algorithm could achieve MPPT more precisely and quickly in a variety of environments.更多还原