An Online Soft Sensing Approach of Operating Temperature for Silicon-Based Photovoltaic Module Based on Neural Network

doi:10.12096/j.2096-4528.pgt.18050

Abstract

Abstract:

The operating temperature of silicon-based photovoltaic (PV) modules has a significant impact on the electrical performance and power generation efficiency, which is an important parameter of PV system modeling and performance evaluation. Its precise calculation is very important for PV system analysis and maximum power tracking. Based on physics-based method of the operating temperature of silicon-based PV modules, it is found that the accuracy of the algorithm always changes with seasons and weather conditions, which resulted in a big error between the calculated and actual output of the PV plant. Aiming at the above problem, the paper proposes an online modelling method for the operating temperature of silicon-based PV modules, which adopts the multi-layer back propagation artificial neural network (BP-ANN) algorithm and uses the measured solar irradiance, ambient temperature, output power of PV modules as the inputs and the PV module operating temperature as the model output. And the online soft sensor of the operating temperature of the silicon-based PV module is realized. The proposed method is verified based on the actual operating data, whose comparative results show that the above method is effective. If conditions permit, wind speed should also be one of the inputs to the model.

Key words: PV power generation, operating temperature of PV modules, neural network, online modelling

Hang YU,Yang LIU,Weiwei LIAN,Honglu ZHU. An Online Soft Sensing Approach of Operating Temperature for Silicon-Based Photovoltaic Module Based on Neural Network[J]. Power Generation Technology, 2018, 39(6): 566-573.

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