Design and Evaluation of Fuzzy Adaptive Particle Swarm Optimization Based Maximum Power Point Tracking on Photovoltaic System Under Partial Shading Conditions
Design and Evaluation of Fuzzy Adaptive Particle Swarm Optimization Based Maximum Power Point Tracking on Photovoltaic System Under Partial Shading Conditions
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DOI：
10.3389/fenrg.2021.712175
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发表时间：
2021-07-21
影响因子：
3.4
通讯作者：
Abdul, Nauman Moiz Mohammed
中科院分区：
文献类型：
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作者：
Guo, Liping;Abdul, Nauman Moiz Mohammed
模糊逻辑和粒子群优化（PSO）等人工智能方法已应用于太阳能电池板最大功率点跟踪（MPPT）。在部分遮阳条件下，太阳能电池板的P-V曲线呈现出多个峰值，当太阳能电池板的所有组件都没有接受相同的太阳辐射时。虽然传统的PSO已被证明在均匀日照下表现良好，但它往往无法找到PSC下的全球最大功率点。针对MPPT，提出了模糊自适应粒子群控制器。然而，为了调整每个粒子的粒子群参数，控制器的计算量变得非常大。本文从设计和性能两个方面对基于模糊自适应粒子群算法和基于传统粒子群算法的MPPT进行了比较和评价。设计了一种简单的模糊自适应粒子群控制器，使MPPT在PSC和均匀照射下达到全局最优点。该控制器结合了粒子群控制和模糊控制的优点。模糊控制器通过动态调整粒子群参数来提高算法的收敛速度和全局搜索能力。由于粒子群参数的调整被设计为对所有粒子通用，因此降低了计算复杂度。设计了模糊控制器的规则库，以获得快速的瞬态响应和稳定的稳态响应。利用升压变换器的仿真结果验证了基于模糊自适应pso的MPPT设计。将结果与在PSC下使用传统PSO控制器的结果进行了比较。仿真结果表明，基于模糊自适应粒子群算法的MPPT改进了全局搜索过程，提高了收敛速度。结果表明，基于模糊自适应粒子群的MPPT在PSC下的沉降时间比传统粒子群平均快14%，在均匀辐照下的沉降时间比传统粒子群平均快30%。模糊自适应粒子群控制器与传统粒子群控制器具有相似的输出功率跟踪精度。
Artificial intelligence methods such as fuzzy logic and particle swarm optimization (PSO) have been applied to maximum power point tracking (MPPT) for solar panels. The P-V curve of a solar panel exhibits multiple peaks under partial shading condition (PSC) when all modules of a solar panel do not receive the same solar irradiation. Although conventional PSO has been shown to perform well under uniform insolation, it is often unable to find the global maximum power point under PSC. Fuzzy adaptive PSO controllers have been proposed for MPPT. However, the controller became computation-intensive in order to adjust the PSO parameters for each particle. In this paper, fuzzy adaptive PSO-based and conventional PSO-based MPPT are compared and evaluated in the aspect of design and performance. A simple fuzzy adaptive PSO controller for MPPT was designed to reach the global optimal point under PSC and uniform irradiation. The controller combines the advantages of both PSO and fuzzy control. The fuzzy controller dynamically adjusts the PSO parameter to improve the convergence speed and global search capability. Since tuning of the PSO parameter is designed to be common for all particles, it reduced the computation complexity. The fuzzy controller's rule base is designed to obtain a fast transient response and stable steady state response. Design of the fuzzy adaptive PSO-based MPPT is verified with simulation results using a boost converter. The results are evaluated in comparison to the results using a conventional PSO controller under PSC. Simulation shows the fuzzy adaptive PSO-based MPPT is able to improve the global search process and increase the convergency speed. The comparison indicates the settling time using the fuzzy adaptive PSO-based MPPT is 14% faster under PSC on average and 30% faster under uniform irradiation than the settling time using the conventional PSO. Both the fuzzy adaptive and conventional PSO controllers have similar output power tracking accuracy.