为进一步提高光伏发电系统的输出效率和动态调节性能,设计基于神经网络的改进扰动观察法最大功率点跟踪（MPPT）控制。该方法在扰动观察法基础上引入神经网络算法,使得在环境剧烈变化时,系统动态调节速度加快。通过系统仿真和硬件实测,得出该改进扰动观察法相比于传统扰动观察法和神经网络法,在一定程度上能加快动态调节速度和减小稳态误差的结论。
Abstract
In order to further improve the output efficiency and dynamic control performance of the photovoltaic power generation system, an improved perturbation observation method MPPT control is designed in this paper based on neural network. A neural network algorithm is introduced based on the perturbation and observation method, so the dynamic adjustment speed is accelerated when the environment changed drastically. Through system simulation and hardware measurement, it’s concluded that, compared with the traditional perturbation observation and neural network method, the dynamic adjustment speed can be accelerated and steady error can be reduced to some extent with the improved perturbation observation method.
关键词
神经网络 /
光伏组件 /
MPPT /
扰动观察法
Key words
neural network /
photovoltaic module /
MPPT /
perturbation observation method
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基金
教育部 2017 年首批新工科研究与实践项目（教高厅函（2018）17号）
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