改进模糊控制的光伏发电系统最大功率点跟踪

MPPT for photovoltaic power generating system based on fuzzy neural networks

研究了光伏发电系统最大功率点跟踪的问题,由于其存在着随机性,且往往不够充分与准确,容易导致系统稳态剧烈震荡或无法准确跟踪。鉴于传统人工总结模糊控制规则难度高,提出了模糊神经网络控制算法,将T-S模糊推理方法与神经网络理论相结合,选择混合法作为训练方法,网格法作为生成算法,由实测数据自动生成模糊控制规则,将其嵌入到模糊控制器当中,从而实现了MPPT控制功能。仿真结果表明,采用该方法生成的模糊规则实用准确,系统稳态性能与动态性能均十分优越。实验证明人工神经网络法与模糊控制技术相结合,实现光伏发电MPPT高效准确。

The issues of maximum power point tracking（MPPT） for photovoltaic power generating system were discussed in this paper. Currently, the fuzzy control rules were generally summed up by the artificial. However, this conclusion is difficult, random, inadequate and incomplete, which leads to steady-state turbulent or inaccurate tracing,so fuzzy neural network algorithms were proposed. The fuzzy control rules were adopted using combination of T-S fuzzy reasoning method and neural network theory with measured data, and hybrid was selected as training method,and grid partition as generation algorithm. These rules were embedded in fuzzy controller, in order to achieve the MPPT control. The simulation results show fuzzy rules generated by this method are practical and accurate, and steady and dynamic-state performance are very advantageous. The experiments show the performance of MPPT algorithm becomes much active and precise with the help of artificial neural network and fuzzy control.