A maximum power point tracker for photovoltaic energy systems based on fuzzy neural networks

2009 ◽  
Vol 10 (2) ◽  
pp. 263-270 ◽  
Author(s):  
Chun-hua Li ◽  
Xin-jian Zhu ◽  
Guang-yi Cao ◽  
Wan-qi Hu ◽  
Sheng Sui ◽  
...  
2011 ◽  
Vol 291-294 ◽  
pp. 2771-2774
Author(s):  
Hai Bin Su ◽  
Zhi Chong Cheng

This paper proposes a algorithm of maximum power point tracking using fuzzy Neural Networks for grid-connected photovoltaic systems. The system is composed of a VSI converter, the maximum power point tracking algorithm based on fuzzy Neural Networks outputs a reference voltage as voltage loop import variable. The voltage controller outputs a reference current to control inverter output current in side grid. The fuzzy Neural Networks provide attractive features such as fast response, good performance. Therefore, the system is able to deliver energy to grid. This proposed algorithm is simulated and implemented to evaluate performance. From the simulation and experimental results, the fuzzy Neural Networks can deliver more power than the other algorithm.


2018 ◽  
Vol 47 (8) ◽  
pp. 4519-4532 ◽  
Author(s):  
Jose Manuel Lopez-Guede ◽  
Josean Ramos-Hernanz ◽  
Necmi Altın ◽  
Saban Ozdemir ◽  
Erol Kurt ◽  
...  

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