多态蚁群–细菌觅食算法实现部分遮蔽下光伏系统最大功率跟踪

针对传统最大功率跟踪技术容易陷入局部最大功率点的问题，提出多态蚁群-细菌觅食算法（polymorphic ant colony - bacterial foraging algorithm, PACO-BFOA）来实现部分遮蔽条件下光伏系统的最大功率输出。该算法在传统蚁群算法的基础上引入信息素扩散机制、多态蚁群的概念和细菌的趋化行为，使算法的全局开发和局部探索能力得到了增强。并在太阳辐照恒定、突变和缓慢变化3种环境下进行算法仿真对比验证，结果证明所提出的算法在部分遮蔽及变化光照下均能快速、稳定地在线寻得全局最大功率点。

Maximum Power Tracking of Photovoltaic System Under Partial Shading Based on PACO-BFOA

In allusion to the fact that traditional maximum power point tracking algorithms are easy to fall into local maximum power point, for this reason, a polymorphic ant colony-bacterial foraging optimization algorithm (PACO-BFOA) was proposed to realize the maximum power output of the photovoltaic system under partial shading condition (abbr. PSC). On the basis of traditional ant colony algorithm the proposed algorithm led in the pheromone diffusion mechanism, the concept of polymorphic ant colony and the chemotactic behavior of bacteria to make both global development and local exploration ability of this algorithm enhanced. Under three different environments, i.e., constant irradiance, abruptly varying irradiance, and slowly varying irradiance, the simulation verification on the effectiveness of the PACO-BFOA was carried out. Verification results show that by use of the proposed algorithm the global maximum power point can be online fast and steady searched out under the conditions of partial shading and varying solar irradiation.