Improved artificial neural network method for predicting photovoltaic output performance

• 作者: Chen Zhang Ming Yang Siyi Wang Yunpeng Zhang
• 来源期刊: 全球能源互联网（英文）
• 年，卷（期）: 2020,（6）
• 所属期刊栏目: Clean energy
• 页码范围: 553-561
• 页数: 9页
• 语种: 英文
• DOI: 10.14171/j.2096-5117.gei.2020.06.005

摘要

To ensure the safety and stability of power grids with photovoltaic (PV) generation integration, it is necessary to predict the output performance of PV modules under varying operating conditions. In this paper, an improved artificial neural network (ANN) method is proposed to predict the electrical characteristics of a PV module by combining several neural networks under different environmental conditions. To study the dependence of the output performance on the solar irradiance and temperature, the proposed neural network model is composed of four neural networks, it called multi-neural network (MANN). Each neural network consists of three layers, in which the input is solar radiation, and the module temperature and output are five physical parameters of the single diode model. The experimental data were divided into four groups and used for training the neural networks. The electrical properties of PV modules, including I-V curves, P-V curves, and normalized root mean square error, were obtained and discussed. The effectiveness and accuracy of this method is verified by the experimental data for different types of PV modules. Compared with the traditional single-ANN (SANN) method, the proposed method shows better accuracy under different operating conditions.