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

doi:10.11985/2017.06.008

Abstract

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.

Key words: MPPT, constant voltage control method, BACA, FNN, weight optimization

CLC Number:

TM615

Yang Donghai,Liu Yang,Wang Yi,Xie Weihua. Research on MPPT Control Algorithm of Photovoltaic System by Binary Ant Colony Algorithm and Fuzzy Neural Network[J]. Journal of Electrical Engineering, 2017, 12(6): 41-46.

Figures/Tables 10

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