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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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creator | Khaldi, Naoufel Mahmoudi, Hassan Zazi, Malika Barradi, Youssef |
description | 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. |
doi_str_mv | 10.1109/IRSEC.2014.7059894 |
format | conference_proceeding |
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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. 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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.</abstract><pub>IEEE</pub><doi>10.1109/IRSEC.2014.7059894</doi><tpages>6</tpages></addata></record> |
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source | IEEE Electronic Library (IEL) Conference Proceedings |
subjects | Artificiel neural networks Backpropagation Erbium incremental conductance Load modeling MATLAB MPPT Newton Raphson Perturb and observe Photovoltaic systems Robustness Stability analysis |
title | The MPPT control of PV system by using neural networks based on Newton Raphson method |
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