The MPPT control of PV system by using neural networks based on Newton Raphson method

The maximum power point tracking (MPPT) system controls the voltage and the current output of the photovoltaic (PV) system to deliver maximum power to the load. Present work deals a comparative analysis of perturb and observe (PO), incremental conductance (IC) and neural network based MPPT technique...

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Bibliographic Details
Main Authors: Khaldi, Naoufel, Mahmoudi, Hassan, Zazi, Malika, Barradi, Youssef
Format: Conference Proceeding
Language:English
Subjects:
Artificiel neural networks
Backpropagation
Erbium
incremental conductance
Load modeling
MATLAB
MPPT
Newton Raphson
Perturb and observe
Photovoltaic systems
Robustness
Stability analysis
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Summary:The maximum power point tracking (MPPT) system controls the voltage and the current output of the photovoltaic (PV) system to deliver maximum power to the load. Present work deals a comparative analysis of perturb and observe (PO), incremental conductance (IC) and neural network based MPPT techniques. Parameters values were extracted using Newton Raphson method from characteristics of Shell SP75 module. The simulations have been carried out on MATLAB/SIMULINK platform for solar photovoltaic system connected to boost dc-dc converter. For three MPPT algorithms, Performance assessment covers overshoot, time response, oscillation and stability as described further in this paper. These results show that the objective is achieved and the MPPT controller based on Back Propagation (BP) neural networks play an effective role to improve the efficiency and reduce the oscillations of PV power system comparing with others control strategies.
ISSN:2380-7385
DOI:10.1109/IRSEC.2014.7059894