The Research of PV MPPT based on RBF-BP Neural Network Optimized by GA
- 10.2991/lemcs-15.2015.274How to use a DOI?
- Photovoltaic (PV) battery; Maximum-power-point-tracking (MPPT); genetic algorithm (GA); Radial Basis Function and Back Propagation (RBF-BP); MATLAB simulation
In order to track nominal output power of photovoltaic (PV) battery effectively and consider the feature of non-linear output, researchers present a Radial Basis Function and Back Propagation (RBF-BP) combination neural network based on genetic algorithm (GA) optimization to use in PV maximum-power-point-tracking (MPPT).First, combination of double hidden layer RBF- BP neural network is presented by researching the output feature of PV battery. In order to predict the maximum-power-point of PV battery more accurately, GA is used to optimize combination neural network. Illumination and temperature which is the main factors influencing the output of PV battery are treated as input to construct the prediction model, and simulate the model through MATLAB. Simulation shows that the system has advantages which increase the accuracy and efficiency of tracking the output maximum-power-point-tracking of PV battery effectively of high tracking accuracy, high speed rate and little iteration.
- © 2015, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Jian Liu AU - Xiaolin Xu AU - Tong Li AU - Meiyan Cao PY - 2015/07 DA - 2015/07 TI - The Research of PV MPPT based on RBF-BP Neural Network Optimized by GA BT - Proceedings of the International Conference on Logistics, Engineering, Management and Computer Science PB - Atlantis Press SP - 1376 EP - 1380 SN - 1951-6851 UR - https://doi.org/10.2991/lemcs-15.2015.274 DO - 10.2991/lemcs-15.2015.274 ID - Liu2015/07 ER -