REAL-TIME THERMAL MODEL FOR PHOTOVOLTAIC MODULE BASED ON INERTIAL ELEMENT

Wang Hangyu; Xu Zipeng; Tian Yibo; Zhang Heng; Gao Dan; Chen Haiping

doi:10.19912/j.0254-0096.tynxb.2023-0788

PDF(4143 KB)

Acta Energiae Solaris Sinica ›› 2024, Vol. 45 ›› Issue (7) : 445-452. DOI: 10.19912/j.0254-0096.tynxb.2023-0788

REAL-TIME THERMAL MODEL FOR PHOTOVOLTAIC MODULE BASED ON INERTIAL ELEMENT

Wang Hangyu, Xu Zipeng, Tian Yibo, Zhang Heng, Gao Dan, Chen Haiping

Author information +

History +

Abstract

A real-time computing approach for photovoltaic module temperature via first-order inertial elementsis proposed. First, the heat transmission characteristics of photovoltaic modules are comprehensively analyzed, leading to the derivation of a simplified computation model for photovoltaic module temperature based on first-order inertial elements, utilizing solutions from one-dimensional unsteady heat conduction analysis. Next, employing a genetic algorithm and the Quasi-Newton method in serial optimization, the model parameters are expeditiously identified using a data-centric methodology. Ultimately, adopting the proposed model, a temperature prediction model founded on BP and LSTM and orthodox empirical formulas, the component temperatures of a photovoltaic station are studied and predicted. Comparative results demonstrate that this approach yields excellent predictive precision, boasting an root mean square error less than 2 ℃, and necessitates a more modest computational scale for model deployment, with an operational pace surpassing neural networks by tenfold, thereby facilitating practical control system application. When juxtaposed with neural network methodologies, it is superiorly interpretable and serves as an efficacious method for real-time assessment of photovoltaic module temperature.

Key words

photovoltaic modules / temperature / prediction / real-time thermal model / first-order inertial element

Cite this article

EndNote

Ris (Procite)

Bibtex

Download Citations

Wang Hangyu, Xu Zipeng, Tian Yibo, Zhang Heng, Gao Dan, Chen Haiping. REAL-TIME THERMAL MODEL FOR PHOTOVOLTAIC MODULE BASED ON INERTIAL ELEMENT[J]. Acta Energiae Solaris Sinica. 2024, 45(7): 445-452 https://doi.org/10.19912/j.0254-0096.tynxb.2023-0788

References

[1] 吕鑫, 刘天予, 董馨阳, 等. 2019年光伏及风电产业前景预测与展望[J]. 北京理工大学学报(社会科学版), 2019, 21(2): 25-29.
LYU X, LIU T Y, DONG X Y, et al.Outlook and prospect of the photovoltaic and wind power industry in 2019[J]. Journal of Beijing Institute of Technology (social sciences edition), 2019, 21(2): 25-29.
[2] KIM G G, CHOI J H, PARK S Y, et al.Prediction model for PV performance with correlation analysis of environmental variables[J]. IEEE journal of photovoltaics, 2019, 9(3): 832-841.
[3] 袁建华, 谢斌斌, 何宝林, 等. 基于DTW-VMD-PSO-BP的光伏发电功率短期预测方法[J]. 太阳能学报, 2022, 43(8): 58-66.
YUAN J H, XIE B B, HE B L, et al.Short term forecasting method of photovoltaic output based on DTW-VMD-PSO-BP[J]. Acta energiae solaris sinica, 2022, 43(8): 58-66.
[4] WU Y K, HUANG C L, PHAN Q T, et al.Completed review of various solar power forecasting techniques considering different viewpoints[J]. Energies, 2022, 15(9): 3320.
[5] 马铭遥, 王海松, 马文婷, 等. 基于S-V特性分析的晶硅光伏组件阴影遮挡故障诊断[J]. 太阳能学报, 2022, 43(9): 64-72.
MA M Y, WANG H S, MA W T, et al.Partial shadow fault diagnosis of crystalline silicon photovoltaic module based on S-V characteristic analysis[J]. Acta energiae solaris sinica, 2022, 43(9): 64-72.
[6] SKOPLAKI E, PALYVOS J A.On the temperature dependence of photovoltaic module electrical performance: a review of efficiency/power correlations[J]. Solar energy, 2009, 83(5): 614-624.
[7] 刘吉臻, 王玮, 胡阳, 等. 新能源电力系统控制与优化[J]. 控制理论与应用, 2016, 33(12): 1555-1561.
LIU J Z, WANG W, HU Y, et al.Control and optimization of alternate electrical power system with renewable energy sources[J]. Control theory & applications, 2016, 33(12): 1555-1561.
[8] ALONSO GARCÍA M C, BALENZATEGUI J L. Estimation of photovoltaic module yearly temperature and performance based on Nominal Operation Cell Temperature calculations[J]. Renewable energy, 2004, 29(12): 1997-2010.
[9] 殷豪, 陈云龙, 孟安波, 等. 基于二次自适应支持向量机的光伏输出功率预测[J]. 太阳能学报, 2019, 40(7): 1866-1873.
YIN H, CHEN Y L, MENG A B, et al.Forecasting photovoltaic power based on quadric self-adaptive SVM model[J]. Acta energiae solaris sinica, 2019, 40(7): 1866-1873.
[10] 陈克松. 基于深度学习的光伏组件故障诊断方法[D]. 兰州: 兰州理工大学, 2022.
CHEN K S.Research on fault diagnosis method of photovoltaic modules based on deep learning[D]. Lanzhou: Lanzhou University of Technology, 2022.
[11] 徐瑞东, 戴瀹, 孙晓燕. 基于BP神经网络的光伏阵列温度预测[J]. 工矿自动化, 2012, 38(7): 59-63.
XU R D, DAI Y, SUN X Y.Temperature prediction of photovoltaic array based on BP neural network[J]. Industry and mine automation, 2012, 38(7): 59-63.
[12] 蒋俊梅. 基于深度学习的光伏板温度和光伏发电功率预测[D]. 银川: 宁夏大学, 2020.
JIANG J M.Prediction of photovoltaic panel temperature and photovoltaic power generation based on deep learning[D]. Yinchuan: Ningxia University, 2020.
[13] GILPIN L H, BAU D, YUAN B Z, et al.Explaining explanations: an overview of interpretability of machine learning[C]//2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA). Turin, Italy, 2018: 80-89.
[14] LU Z H, YAO Q.Energy analysis of silicon solar cell modules based on an optical model for arbitrary layers[J]. Solar energy, 2007, 81(5): 636-647.
[15] 郑秀, 赵乙凡, 陈可, 等. 基于能量平衡法的光伏组件瞬态温度场预测[J]. 中国电机工程学报, 2022, 42(13): 4907-4915.
ZHENG X, ZHAO Y F, CHEN K, et al.Transient temperature field prediction of photovoltaic module based on energy balance method[J]. Proceedings of the CSEE, 2022, 42(13): 4907-4915.
[16] 杨宇, 祁昊, 邓志成, 等. 基于惯性环节的汽轮机转子温度计算方法[J]. 动力工程学报, 2013, 33(8): 586-590.
YANG Y, QI H, DENG Z C, et al.Calculation of turbine rotor temperature based on inertial element[J]. Journal of Chinese Society of Power Engineering, 2013, 33(8): 586-590.
[17] AKDEMIR H, BOCU M, NAKIR I.The experimental assessment of different PV cell temperature models under the actual climatic conditions for Cd-Te PV modules[C]//2021 IEEE PES Innovative Smart Grid Technologies-Asia (ISGT Asia). Brisbane, Australia, 2021: 1-5.