.png){kind=icon}
返回
Opposition-Based Tunicate Swarm Algorithm for Parameter Optimization of Solar Cells
.png){kind=icon}
OA
AI
DOI：10.1109/ACCESS.2021.3110849.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Parameter estimation of photovoltaic modules is an essential step to observe, analyze, and optimize the performance of solar power systems. An efficient optimization approach is needed to obtain the finest value of unknown parameters. Herewith, this article proposes a novel opposition-based tunicate swarm algorithm for parameter estimation. The proposed algorithm is developed based on the exploration and exploitation components of the tunicate swarm algorithm. The opposition-based learning mechanism is employed to improve the diversification of the search space to provide a precise solution. The parameters of three types of photovoltaic modules (two polycrystalline and one monocrystalline) are estimated using the proposed algorithm. The estimated parameters show good agreement with the measured data for three modules at different irradiance levels. Performance of the developed opposition-based tunicate swarm algorithm is compared with other predefined algorithms in terms of robustness, statistical, and convergence analysis. The root mean square error values are minimum (6.83 x 10(-4), 2.06 x 10(-4), and 4.48 x 10(-6)) compared to the tunicate swarm algorithm and other predefined algorithms. Proposed algorithm decreases the function cost by 30.11%, 97.65%, and 99.80% for the SS2018 module, SolarexMSX-60 module, and Leibold solar module, respectively, as compared to the basic tunicate swarm algorithm. The statistical results and convergence speed depicts the outstanding performance of the anticipated approach. Furthermore, the Friedman ranking tests confirm the competence and reliability of the developed approach.
Keyword:
Machine learning
parameter extraction
photovoltaic cells
metaheuristics
tunicate swarm algorithm
opposition-based learning
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
机构
引用论文
Tree Growth Based Optimization Algorithm for Parameter Extraction of Different Models of Photovoltaic Cells and Modules基于树生长的光伏电池和组件不同模型参数提取优化算法.png)
IEEE ACCESS
OAAI
IEEE ACCESSIF3.6
OAAI分享收藏A new high performance method for determining the parameters of PV cells and modules based on guaranteed convergence particle swarm optimization基于保收敛粒子群算法的高性能光伏电池和组件参数确定方法APPLIED ENERGY
APPLIED ENERGYIF11
PREAI
分享收藏Parameter estimation of photovoltaic cells using an improved chaotic whale optimization algorithm基于改进混沌鲸鱼优化算法的光伏电池参数估计APPLIED ENERGY
APPLIED ENERGYIF11
PREAI
分享收藏Identification of PV solar cells and modules parameters using the genetic algorithms: Application to maximum power extraction使用遗传算法识别光伏太阳能电池和组件参数: 在最大功率提取中的应用SOLAR ENERGY
SOLAR ENERGYIF6.6
PREAI
分享收藏分享收藏
查看更多内容