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International Journal of Photoenergy
Volume 2017, Article ID 6759295, 13 pages
https://doi.org/10.1155/2017/6759295
Research Article

Online Modelling and Calculation for Operating Temperature of Silicon-Based PV Modules Based on BP-ANN

1The State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Changping District, Beijing, China
2The School of Renewable Energy, North China Electric Power University, Changping District, Beijing, China
3China Three Gorges New Energy Co. Ltd., Beijing, China

Correspondence should be addressed to Yang Hu; nc.ude.upecn@gnuoyooh

Received 12 July 2017; Revised 2 October 2017; Accepted 11 October 2017; Published 29 November 2017

Academic Editor: K. R. Justin Thomas

Copyright © 2017 Honglu Zhu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

The operating temperature of silicon-based solar modules has a significant effect on the electrical performance and power generation efficiency of photovoltaic (PV) modules. It is an important parameter for PV system modeling, performance evaluation, and maximum power point tracking. The analysis shows that the results of physics-based methods always change with seasons and weather conditions. It is difficult to measure all the needed variables to build the physics-based model for the calculation of operating temperature. Due to the above problem, the paper proposes an online method to calculate operating temperature, which adopts the back propagation artificial neural network (BP-ANN) algorithm. The comparative analysis is carried out using data from the empirical test platform, and the results show that both the BP-ANN and the support vector machine (SVM) method can reach good accuracy when the dataset length was over six months. The SVM method is not suitable for the temperature modeling because its computing time is too long. To improve the performance, wind speed should be taken as one of the models’ input if possible. The proposed method is effective to calculate the operating temperature of silicon-based solar modules online, which is a low-cost soft-sensing solution.