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A reliable approach for modeling the photovoltaic system under partial shading conditions using three diode model and hybrid marine predators-slime mould algorithm

机译:一种可靠的方法,用于使用三二极管模型和混合船舶捕食者 - 粘液模具算法在局部阴影条件下建模光伏系统

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In this article, the triple diode model (TDM) is studied for modeling the Canadian-Solar-CS6P-240P poly-crystalline PV module, Kyocera Solar KC200GT multi-crystalline PV module, Sharp NU-(Q250W2) mono-crystalline PV module, and Pythagoras Solar Large PVGU Window mono-crystalline PV module. A novel hybrid algorithm of the marine predator's algorithm (MPA) and slime mould algorithm (SMA) (HMPA) is proposed to enhance the MPA exploitation phase while identifying the TDM parameters. The HMPA results are compared to several recent algorithms that are equilibrium optimizer (EO), manta ray foraging optimization (MRFO), transient search optimization (TSO), jellyfish optimizer (JS), and forensic-based optimizer (FBI), besides the basic versions of MPA and SMA. For unbiased comparison, several statistical analyses and non-parametric tests are applied. The convergence curves are used to evaluate the convergence property of the proposed algorithm compared to their counterparts. The HMPA confirms its efficiency in handling the complex multi-modal and multi-dimensional optimization process of identifying the TDM parameters. HMPA provides the least root mean square error (RMSE) between the measured and estimated datasets with the least standard deviation (STD). For Canadian Solar (CS6P-240P) module, the proposed HMPA achieves the minimum RMSE of 0.00037313 with STD of 0.0030488; for Kyocera Solar (KC200GT) module, HMPA attains RMSE +/- STD of 0.0033042 +/- 0.0061813. For SharpNU-(Q250W2) PV module and Pythagoras Solar Large PVGU Window, HMPA outperforms the other counterparts with RMSEs +/- STDs of 0.00027661 +/- 0.0053002 and 0.00285 +/- 0.0020075, respectively. Accordingly, the HMPA provides the slightest deviation between the estimated datasets and the experimental ones with high consistency over several independent runs. The convergence curves of the proposed HMPA affirm its fast response while handling the optimization problem of TDM. The reliability of the identified parameters is tested to emulate the PV modules' characteristics at different irradiation levels. Furthermore, the robustness of the identified parameters is examined for integrated systems of series string and series-parallel arrays under partial shading conditions. The PV solar modules/strings/arrays characteristics confirm the accuracy of the identified parameters as the attained main points on the characteristics are defined with high quality.
机译:在本文中,研究了三级二极管模型(TDM),用于建模加拿大太阳能-CS6P-240P多晶硅电池模块,Kyocera Solar KC200GT多晶硅电镀PV模块,Sharp Nu-(Q250W2)单晶PV模块,毕达哥拉斯太阳能大型PVGU窗口单晶光伏模块。提出了一种新颖的海洋捕食者算法(MPA)和粘液模具算法(SMA)(HMPA)的混合算法,以增强MPA开发阶段,同时识别TDM参数。将HMPA结果与均衡优化器(EO),Manta射线觅食优化(MRFO),瞬态搜索优化(TSO),水母优化器(JS)和基于法医的优化器(FBI)进行比较的算法结果进行了比较。 MPA和SMA的版本。对于无偏的比较,应用了几种统计分析和非参数测试。收敛曲线用于评估所提出的算法的收敛性,与其对应物相比。 HMPA确认其效率处理识别TDM参数的复杂多模态和多维优化过程。 HMPA在具有最小标准偏差(STD)之间的测量和估计数据集之间提供最小的均方根误差(RMSE)。对于加拿大太阳能(CS6P-240P)模块,所提出的HMPA实现了0.00037313的最小RMSE,STD为0.0030488;对于Kyocera Solar(KC200GT)模块,HMPA达到RMSE +/- STD为0.0033042 +/- 0.0061813。对于Sharpnu-(Q250W2)PV模块和Pythagoras Solar大PVGu窗口,HMPA分别优于RMSE +/- STD的其他对应物,分别为0.00027661 +/- 0.0053002和0.00285 +/- 0.0020075。因此,HMPA提供估计的数据集和实验组之间的最轻微的偏差,并且在几个独立的运行中具有高一致性的实验性。提议的HMPA的收敛曲线确认了处理TDM优化问题的同时确认其快速响应。测试了所识别的参数的可靠性以使PV模块的特性模拟不同的辐射水平。此外,在部分着色条件下检查串联串和串联阵列的集成系统的稳健性。光伏太阳能模块/弦/阵列特性确认所识别的参数的准确性,因为达到了特性的达到的主要点,以高质量定义。

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