Dynamic leader based collective intelligence for maximum power point tracking of PV systems affected by partial shading condition
Dynamic leader based collective intelligence for maximum power point tracking of PV systems affected by partial shading condition
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基于动态领导者的集体智能，用于跟踪受部分阴影条件影响的光伏系统的最大功率点
DOI：
10.1016/j.enconman.2018.10.074
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发表时间：
2019
影响因子：
10.4
通讯作者：
Jiang Lin
中科院分区：
文献类型：
--
作者：
Yang Bo;Yu Tao;Zhang Xiaoshun;Li Haofei;Shu Hongchun;Sang Yiyan;Jiang Lin
提出了一种基于动态领导者集体智能的光伏系统最大功率点跟踪算法。与传统的元启发式算法不同，DLCI算法由多个子优化器组成，可以充分协调各种搜索机制的优化能力，而不是单一的搜索机制，从而实现更广泛的探索。为了实现更深层次的开发，选择具有当前最佳解的子优化器作为动态领导者，以有效地搜索指导其他子优化器。虽然DLCI的多个子优化器会导致更高的计算复杂度，它可以提供一个增强的搜索能力和更稳定的收敛相比，传统的元启发式算法。由于DLCI不对系统模型进行响应，因此它可以很容易地应用于其他优化任务。四个案例研究，包括启动测试，太阳辐射与恒温的阶跃变化，太阳辐射和温度的渐变，和每天的太阳辐射和温度在香港的现场数据，进行。他们试图评估DLCI的有效性和优势相比，传统的增量电导（INC）和八个典型的元启发式算法，例如，遗传算法（GA）、粒子群算法（PSO）、人工蜂群算法（ABC）、布谷鸟搜索算法（CSA）、灰狼优化算法（GWO）、蛾焰优化算法（MFO）、鲸鱼优化算法（WOA）和基于教学的优化算法（TLBO）。最后，基于dSpace的硬件在环（HIL）测试验证了基于DLCI的MPPT技术实现的可行性。算例分析和HIL测试表明，通过多个子优化器的有效协调，DLCI的搜索能力得到了显著提高，与采用单一搜索机制的其他方法相比，DLCI可以使光伏系统产生更多的能量（最高可达36.64%）和更小的功率波动（最高可达21.17%）。
This study presents a novel maximum power point tracking (MPPT) algorithm via dynamic leader based collective intelligence (DLCI) of PV systems affected by partial shading condition (PSC). Different from the conventional meta-heuristic algorithms, DLCI is consisted of multiple sub-optimizers, which can achieve a much wider exploration by fully collaborating the optimization ability of various searching mechanisms instead of a single searching mechanism. In order to achieve a deeper exploitation, the sub-optimizer with the current best solution is chosen as the dynamic leader for an efficient searching guidance to other sub-optimizers. Although the multiple sub-optimizers of DLCI will result in a higher computational complexity, it can offer an enhanced searching ability and a more stable convergence compared to that of conventional meta-heuristic algorithms. Since it does not reply on the system model, DLCI can be easily applied to other optimization tasks. Four case studies, including start-up test, step change in solar irradiation with constant temperature, gradual change in both solar irradiation and temperature, and daily field data of solar irradiation and temperature in Hong Kong, are undertaken. They attempt to evaluate the effectiveness and advantages of DLCI in comparison to that of conventional incremental conductance (INC) and eight typical meta-heuristic algorithms, e.g., genetic algorithm (GA), particle swarm optimization (PSO), artificial bees colony (ABC), Cuckoo search algorithm (CSA), grey wolf optimizer (GWO), moth-flame optimization (MFO), whale optimization algorithm (WOA), and teaching–learning-based optimization (TLBO), respectively. Lastly, a dSpace based hardware-in-the-loop (HIL) test is carried out to validate the implementation feasibility of DLCI based MPPT technique. Both the case studies and HIL test demonstrate that the searching ability of DLCI can be significantly improved via an effective coordination between multiple sub-optimizers, which can make the PV system generate more energy (up to 36.64%) and smaller power fluctuation (up to 21.17%) than other methods with a single searching mechanism.
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DOI：
--
发表时间：
1997-08
期刊：
--
影响因子：
--
作者：
Pierre Lévy;Robert Bononno
通讯作者：
Pierre Lévy;Robert Bononno
影响因子：
2.1
作者：
Devesh K. Jha;Pritthi Chattopadhyay;S. Sarkar;A. Ray
通讯作者：
Devesh K. Jha;Pritthi Chattopadhyay;S. Sarkar;A. Ray
DOI：
10.1016/j.eswa.2016.03.016
发表时间：
2016-09
期刊：
Expert Syst. Appl.
影响因子：
--
作者：
A. Asgari;Kaveh Hassani;Won-sook Lee
通讯作者：
A. Asgari;Kaveh Hassani;Won-sook Lee
影响因子：
9
作者：
Lei Guo;Z. Meng;Yize Sun;Libiao Wang
通讯作者：
Lei Guo;Z. Meng;Yize Sun;Libiao Wang
影响因子：
4.9
作者：
Mohanty, Satyajit;Subudhi, Bidyadhar;Ray, Pravat Kumar
通讯作者：
Ray, Pravat Kumar