基于Bezier曲线的薄膜太阳电池参数逆最小二乘辨识

朱显辉; 高彬; 师楠; 朱晓强; 钟敬文

doi:10.19912/j.0254-0096.tynxb.2022-0142

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太阳能学报 ›› 2023, Vol. 44 ›› Issue (6) : 204-212. DOI: 10.19912/j.0254-0096.tynxb.2022-0142

基于Bezier曲线的薄膜太阳电池参数逆最小二乘辨识

朱显辉¹, 高彬¹, 师楠^2,3, 朱晓强¹, 钟敬文¹

作者信息 +

INVERSE LEST SQUARES IDENTIFICATION OF THIN FILM SOLAR CELL PARAMETERS BASED ON BEZIER CURVES

Zhu Xianhui¹, Gao Bin¹, Shi Nan^2,3, Zhu Xiaoqiang¹, Zhong Jingwen¹

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摘要

针对光伏参数求解繁琐、准确度不高、需要实验测试的弊端,提出一种利用两条二阶Bezier曲线和逆最小二乘法相结合的薄膜太阳电池参数辨识方法。首先,构造过最大功率点且平行于开路电压点和短路电流点连线的平行线,并在该平行线上寻找两条二阶Bezier函数的最佳控制点,进而给出控制位置与薄膜太阳电池填充因子的线性规律,实现对I-V曲线简单、准确地刻画;然后,利用等效变换简化薄膜太阳电池输出特性超越方程,引入5个新的变量将超越方程改造为代数方程,随机选取Bezier曲线上的5个点,通过代数计算和欧几里得范数的（伪）解给出5个变量的值,并基于所得的变量结果,利用逆最小二乘法反向求解出超越方程的5个参数。最后,以5种不同型号薄膜太阳电池为例,对不同条件下的参数和求解时间进行对比和分析,验证该方法具备快速性、准确性和适用性。

Abstract

Aiming at the drawbacks of tedious solving of photovoltaic parameters, low accuracy and need for experimental testing, a method of identifying parameters of thin film cells using a combination of two second-order Bezier curves and the inverse least squares method is proposed. Firstly, a parallel line is constructed through the maximum power point and parallel to the line connecting the open-circuit voltage point and the short-circuit current point, and the best control point of the two second-order Bezier functions is found on the parallel line. And then the linear law of the control position and the filling factor of the thin-film cell is obtained to achieve a simple and accurate portrayal of the I-V curve. Thirdly, the output characteristic transcendence equation of the thin-film cell is simplified by using the equivalent transformation, and the Five new variables are introduced to transform the transcendental equation into an algebraic equation, five points on the Bezier curve are randomly selected, and the values of the five variables are given by algebraic calculation and （pseudo-）solution of Euclidean parametrization, and the five parameters of the transcendental equation are solved backwards by inverse least squares method using the obtained variable results. Finally, the parameters and solution times under different operating conditions are compared and analyzed for five different types of thin-film cells to verify the rapidity, accuracy and applicability of the method.

导出引用

朱显辉, 高彬, 师楠, 朱晓强, 钟敬文. 基于Bezier曲线的薄膜太阳电池参数逆最小二乘辨识[J]. 太阳能学报. 2023, 44(6): 204-212 https://doi.org/10.19912/j.0254-0096.tynxb.2022-0142

Zhu Xianhui, Gao Bin, Shi Nan, Zhu Xiaoqiang, Zhong Jingwen. INVERSE LEST SQUARES IDENTIFICATION OF THIN FILM SOLAR CELL PARAMETERS BASED ON BEZIER CURVES[J]. Acta Energiae Solaris Sinica. 2023, 44(6): 204-212 https://doi.org/10.19912/j.0254-0096.tynxb.2022-0142

中图分类号： TM615

参考文献

[1] ZIDANCE T E K, ADZMAN M R B, TAJUDDIN M F N, et al. Optimal configuration of photovoltaic power plant using grey wolf optimizer: a comparative analysis considering CdTe and c-Si PV modules[J]. Solar energy, 2019, 188: 247-257.
[2] 庄一展, 刘飞, 黄艳辉, 等. 模块化串联光伏直流变换器的环形功率均衡拓扑及效率优化策略[J]. 中国电机工程学报, 2022, 42(5): 1657-1669.
ZHUANG Y Z, LIU F, HUANG Y H, et al.Research development of biomass conversion technology[J]. Proceedings of the CSEE, 2022, 42(5): 1657-1669.
[3] AMAR S, BAHICH M, BENTAHAR Y, et al.A study of the temperature influence on different parameters of mono-crystalline silicon photovoltaic module[J]. Journal of power and energy engineering, 2021, 9(6): 29-42.
[4] 刘恒, 马铭遥, 张志祥, 等. 热斑晶硅光伏组件I-V曲线分析及特征模拟研究[J]. 太阳能学报, 2021, 42(4): 239-246.
LIU H, MA M Y, ZHANG Z X, et al.Analysis and characterization of I-V curve of hot spot crystalline silicon photovoltaic modules[J]. Acta energiae solaris sinica, 2021, 42(4): 239-246.
[5] 朱瑞, 贾科, 施志明, 等. 基于主动注入的光伏直流并网系统早期故障定位方法[J]. 电网技术, 2021, 45(1): 67-75.
ZHU R, JIA K, SHI Z M, et al.Early fault location method of PV DC grid-connected system based on active injection[J]. Power grid technology, 2021, 45(1): 67-75.
[6] 马铭遥, 张志祥, 刘恒, 等. 基于I-V特性分析的晶硅光伏组件故障诊断[J]. 太阳能学报, 2021, 42(6): 130-137.
MA M Y, ZHANG Z X, LIU H, et al.Fault diagnosis of crystalline silicon photovoltaic modules based on I-V characteristic analysis[J]. Acta energiae solar sinica, 2021, 42(6): 130-137.
[7] MICELI R, ORIOLI A, GANGI A D, et al.A procedure to calculate the I-V characteristics of thin-film photovoltaic modules using an explicit rational form[J]. Applied energy, 2015, 155: 613-628.
[8] BAI J B, LIU S, HAO Y Z, et al.Development of a new compound method to extract the five parameters of PV modules[J]. Energy conversion and management, 2014, 79: 294-303.
[9] WEI D, WEI M Y, CAI H, et al.Parameters extraction method of PV model based on key points of I-V curve[J]. Energy conversion and management, 2020, 209: 112656.
[10] 师楠, 朱显辉, 李一丹, 等. 不同迭代算法求解光伏模块参数的收敛速度[J]. 电力科学与技术学报, 2020, 35(3): 55-60.
SHI N, ZHU X H, LI Y D, et al.Convergence speed of different iterative algorithms for solving PV module parameters[J]. Journal of power science and technology, 2020, 35(3): 55-60.
[11] YANG B, WANG J B, ZHANG X S, et al.Comprehensive overview of meta-heuristic algorithm applications on PV cell parameter identification[J]. Energy conversion and management, 2020, 208: 112595.
[12] GUDE S, JANA K C.Parameter extraction of photovoltaic cell using an improved cuckoo search optimization[J]. Solar energy, 2020, 204: 280-293.
[13] IBRAHIM I A, HOSSAIN M J, DUCK B C, et al.An adaptive wind-driven optimization algorithm for extracting the parameters of a single-diode PV cell model[J]. IEEE transactions on sustainable energy, 2019, 11(2): 1054-1066.
[14] YAN Z Y, LI C Q, SONG Z S, et al.An improved brain storming optimization algorithm for estimating parameters of photovoltaic models[J]. IEEE access, 2019, 7: 77629-77641.
[15] 吴忠强, 于丹琦, 康晓华. 基于改进蚁狮优化算法的太阳电池模型参数辨识[J]. 太阳能学报, 2019, 40(12): 3435-3443.
WU Z Q, YU D Q, KANG X H.Parameter identification of solar cell model based on improved ant-lion optimization algorithm[J]. Acta energiae solaris sinica, 2019, 40(12): 3435-3443.
[16] 吴忠强, 刘重阳, 赵德隆, 等. 基于IEHO算法的太阳电池模型参数辨识[J]. 太阳能学报, 2021, 42(9): 97-103.
WU Z Q, LIU C Y, ZHAO D L, et al.Identification of solar cell model parameters based on IEHO algorithm[J]. Acta energiae solaris sinica, 2021, 42(9): 97-103.
[17] DROUICHE I, HARROUNI S, ARAB A H.A new approach for modelling the aging PV module upon experimental I-V curves by combining translation method and five-parameters model[J]. Electric power systems research, 2018, 163: 231-241.
[18] WU L J, CHEN Z C, LONG C, et al.Parameter extraction of photovoltaic models from measured I-V characteristics curves using a hybrid trust-region reflective algorithm[J]. Applied energy, 2018, 232: 36-53.
[19] PANCHAL A K.A per unit single diode model parameter extraction algorithm: a high-quality solution without reduced dimensions search[J]. Solar energy, 2020, 207: 1070-1077.
[20] LIM L H I, YE Z, YE J, et al. A linear identification of diode models from single I-V characteristics of PV panels[J]. IEEE transactions on industrial electronics, 2015, 62(7): 4181-4193.
[21] 韩平平, 范桂军, 孙维真, 等. 基于数据测试和粒子群优化算法的光伏逆变器LVRT特性辨识[J]. 电力自动化设备, 2020, 40(2): 49-54.
HAN P P, FAN G J, SUN W Z, et al.Identification of LVRT characteristics of PV inverters based on data testing and particle swarm optimization algorithm[J]. Power automation equipment, 2020, 40(2): 49-54.
[22] TOLEDO F J, BLANES J M.Analytical and quasi-explicit four arbitrary point method for extraction of solar cell single-diode model parameters[J]. Renewable energy, 2016, 92: 346-356.
[23] LAUDANI A, FULGINEI F R, SALVINI A.High performing extraction procedure for the one-diode model of a photovoltaic panel from experimental I-V curves by using reduced forms[J]. Solar energy, 2014, 103: 316-326.
[24] 师楠, 朱显辉. CdTe组件输出特性显式模型拟合规律[J]. 仪器仪表学报, 2021, 42(7): 208-215.
SHI N, ZHU X H.Fitting law of CdTe module output characteristic curve based on explicit model[J]. Chinese journal of scientific instrument, 2021, 42(7): 208-215.
[25] 师楠, 朱显辉, 苏勋文. 基于Bezier曲线的CIGS薄膜光伏电池I-V曲线简单拟合方法[J]. 电力自动化设备, 2021, 41(11): 199-204.
SHI N, ZHU X H, SU X W.Simple fitting method for I-V curve of CIGS thin-film PV cell based on Bezier curve[J]. Electric power automation equipment, 2021, 41(11): 199-204.
[26] 徐岩, 靳伟佳, 朱晓荣. 基于遗传粒子群算法的光伏并网逆变器参数辨识[J]. 太阳能学报, 2021, 42(7): 103-109.
XU Y, JIN W J, ZHU X R.Parameter identification of grid-connected photovoltaic inverters based on genetic particle swarm algorithm[J]. Acta energiae solaris sinica, 2021, 42(7): 103-109.
[27] 叶进, 卢泉, 王钰淞, 等. 基于级联随机森林的光伏故障诊断模型研究[J]. 太阳能学报, 2021, 42(3): 358-362.
YE J, LU Q, WANG Y S, et al.Research on PV fault diagnosis model based on cascaded random forest[J]. Acta energiae solaris sinica, 2021, 42(3): 358-362.
[28] 高伟, 黄俊铭. 基于SSELM的光伏组件故障智能诊断方法[J]. 太阳能学报, 2021, 42(12): 465-470.
GAO W, HUANG J M.An intelligent diagnosis method for PV module faults based on SSELM[J]. Acta energiae solaris sinica, 2021, 42(12): 465-470.
[29] POLO J, MARTÍN C N, ALONSO G M C, et al. Modeling I-V curves of photovoltaic modules at indoor and outdoor conditions by using the Lambert function[J]. Energy conversion and management, 2019, 195: 1004-1011.
[30] 丁坤, 陈富东, 翁帅, 等. 基于I-V特性灰色关联分析的光伏阵列健康状态评估[J]. 电网技术, 2021, 45(8): 3087-3095.
DING K, CHEN F D, WENG S, et al.Health state assessment of photovoltaic arrays based on gray correlation analysis of I-V characteristics[J]. Power grid technology, 2021, 45(8): 3087-3095.
[31] JENA D, RAMANA V V.Simple and accurate method of modeling photovoltaic module: a different approach[C]//ICGCE 2013, International Conference on Green Computing, Communication and Conservation of Energy, Chennal, India, 2013.