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

Hang YU; Yang LIU; Weiwei LIAN; Honglu ZHU

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

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

New and Renewable Energy | 更新时间：2025-07-04

- An Online Soft Sensing Approach of Operating Temperature for Silicon-Based Photovoltaic Module Based on Neural Network
- Power Generation Technology Vol. 39, Issue 6, Pages: 566-573(2018)
- 作者机构：
  
  1.龙源(北京)太阳能技术有限公司, 北京市西城区 100034
  2.华北电力大学可再生能源学院, 北京市昌平区 102206
- 作者简介：
- 基金信息：
  
  the National Key R & D Plan(2017YFB0902100)
- DOI：10.12096/j.2096-4528.pgt.18050
  CLC：
- Received：10 September 2018，
  
  Published：31 December 2018
- 稿件说明：
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Hang YU, Yang LIU, Weiwei LIAN, et al. 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.
DOI：

Hang YU, Yang LIU, Weiwei LIAN, et al. 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. DOI： 10.12096/j.2096-4528.pgt.18050.

摘要

硅基光伏组件的运行温度对组件电气性能和发电效率具有显著影响，是光伏系统建模和性能评估的重要参数，它的精确计算对于光伏系统分析和最大功率跟踪等算法的应用等具有重要意义。通过对组件运行温度经典计算方法进行实例验证，发现该算法在不同季节、天气条件下的精度不一致，使得光伏电站发电量的计算与实际状况存在较大误差。针对这一问题，提出了一种基于多层反向传播（back propagation，BP）神经网络的硅基光伏组件运行温度在线建模方法，它分别以实测太阳辐照度、环境温度和输出功率作为模型输入，以组件运行温度作为模型输出，实现了组件运行温度的在线软测量。通过实际运行数据的对比表明上述方法是有效的，在条件允许时，也应该将风速作为模型输入之一。

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.

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