• 工程技术 •

### 基于二进制蚁群模糊神经网络的光伏系统MPPT控制算法研究

1. 1. 河南龙源许继科技发展股份有限公司 郑州 450011
2. 许继集团有限公司 许昌 461000
• 收稿日期:2016-08-23 出版日期:2017-06-25 发布日期:2019-12-20
• 作者简介:杨东海 男 1973年生,工程师,从事智能电网领域方面的工作。|刘 洋 男 1986年生,博士,研究方向为交直流混合微电网保护与控制。

### Research on MPPT Control Algorithm of Photovoltaic System by Binary Ant Colony Algorithm and Fuzzy Neural Network

Yang Donghai1,Liu Yang2,Wang Yi2,Xie Weihua2

1. 1. Henan Longyuan XJ Technology Development Limited by Share Ltd.Zhengzhou 450011 China
2. XJ Group Corporation Xuchang 461000 China
• Received:2016-08-23 Online:2017-06-25 Published:2019-12-20

Abstract:

Maximum power point tracking (MPPT) control which can make photovoltaic module output power furthest has been a research hotspot for enhancing the output power of the photovoltaic system. In the paper, a MPPT control algorithm of photovoltaic system by binary ant colony algorithm (BACA) and fuzzy neural network (FNN) is proposed. FNN is used for substituting BP neural network to forecast the maximum power point, which solved the big error problem of the constant voltage control method; BACA is used to optimize the weights of the FNN, which overcame the shortcomings of slow scouting speed and local minimum. The voltages gotten at the maximum power points are input to the constant voltage control algorithm, then the maximum power points could be tracked by the constant voltage control method. The simulation environments at different light intensity and different ambient temperature are constructed in the simulation model, the results showed that the MPPT control algorithmproposed in this paper had a high accuracy and good adaptability.