Analysis of mathematical modeling of PV module with MPPT algorithm

Babu, B. Chitti; Cermak, Tomas; Gurjar, Suresh; Leonowicz, Zbigniew; Piegari, Luigi

doi:10.1109/eeeic.2015.7165415

Cited by 12 publications

(1 citation statement)

References 7 publications

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“…The perturbation cycle is repeated until the maximum power is reached at the dP/ dV=0. [19][20][21][22] The perturbation size is kept very small intentionally. It helps to keep the power variation small.…”

Section: Classical Pando Algorithmmentioning

confidence: 99%

Modeling of a photovoltaic array in MATLAB simulink and maximum power point tracking using neural network

Khanam¹,

Foo²

2018

EETOAJ

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In this paper, we present our work on Maximum Power Point Tracking (MPPT) using neural network. The MATLAB/Simulink is used to establish a model of photovoltaic array. The Simulink model is tested with different temperature and irradiation and resultant I-V and P-V characteristics proved the validation of Simulink model of PV array. We collected a set of data from the Simulink model of PV array after simulated under a range of irradiation and temperature. The data collected from the system is used to train the neural network. When we tested the neural network with different irradiance and temperature, we see that the neural network can accurately predict the maximum power point of a photovoltaic array. In this paper, the backpropagation training algorithm is used to train the neural network. Comparisons of MPPT with P & O algorithm and without MPPT tracker are also shown in this paper. It is demonstrated that the neural network based MPPT tracking require less time and provide more accurate results than the P&O algorithm based MPPT.

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Section: Classical Pando Algorithmmentioning

confidence: 99%