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  4. Maximum power point tracking using hybrid fuzzy based p&o and back propagation (BP) neural network for photovoltaic system
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Maximum power point tracking using hybrid fuzzy based p&o and back propagation (BP) neural network for photovoltaic system

Author(s)
Konghuayrob, Poom
Kaitwanidvilai, Somyot
Date Issued
October 1, 2014
Type
Article
Abstract
Photovoltaic system is one of the most popular renewable energy sources to solve the problem of energy crisis. The important problem of solar PV systems is their low efficiency and nonlinear output characteristics in the changing weather that causes the difficulty in tracking of maximum power. To overcome this problem, this paper proposes “Hybrid Fuzzy based P&O and Back Propagation (BP) neural network” for improving the efficiency and reducing the power oscillation of PV system. The proposed system composes of two parts which are fuzzy based P&O and Neural Network. The fuzzy based P&O is used to find the maximum power point (MPP) while the neural network is used to find the appropriate modulation index (ma). The proposed algorithm is adopted in the AC module flyback inverter in which modulation index (Δma) is used as the control variable to track the MPP of the PV array. Simulation results verify that the proposed technique can effectively track the power from PV system, and is better than the conventional P&O techniques
Citation
International Journal of Innovative Computing Information and Control, 10(5), 1949-1960, 2014
Subjects

BP neural network

Fuzzy logic control

Maximum power point t...

Perturbation and obse...

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