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

Honglu Zhu,Cao Yu,Yang Hu,Lingxing Lu,Weiwei Lian,Peter Kamunyu,Songyuan Dai

doi:10.1155/2017/6759295

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.

Highlights

Since PV power plants mainly adopt silicon-based PV modules throughout the world, the operating temperature is an important parameter which can greatly affect the siliconbased PV modules’ electrical performance and efficiency [1]
The arrangement of the paper is as follows: Section 2 analyses the effect of temperature on the properties of silicon-based PV modules; Section 3 describes the calculation method of PV power generation based on a physics-based model and analyses the error and error sources; in Section 4, the calculation models for operating temperature of silicon-based PV module based on back propagation artificial neural network (BP-ANN) are proposed; in Section 5, the comparative analysis for the performance of the algorithm is analysed; and Section 6 gives the corresponding conclusions
The error of physics-based model is big for the calculation of operating temperature of silicon-based PV modules in engineering application

Summary

Introduction

Since PV power plants mainly adopt silicon-based PV modules throughout the world, the operating temperature is an important parameter which can greatly affect the siliconbased PV modules’ electrical performance and efficiency [1]. The calculation models for operating temperature based on the BP-ANN method using historical data could avoid the complex analysis process of a physics-based model and could be applied to the related engineering calculation effectively. The arrangement of the paper is as follows: Section 2 analyses the effect of temperature on the properties of silicon-based PV modules; Section 3 describes the calculation method of PV power generation based on a physics-based model and analyses the error and error sources; in Section 4, the calculation models for operating temperature of silicon-based PV module based on BP-ANN are proposed; in Section 5, the comparative analysis for the performance of the algorithm is analysed; and Section 6 gives the corresponding conclusions

The Operation Temperature of Silicon-Based PV Modules

Findings

Result and Discussion

Conclusion