Comparison of ANN and P&O MPPT methods for PV applications under changing solar irradiation
This paper presents an artificial neural network (ANN) maximum power point tracking (MPPT) method which is fast and precise in finding and tracking the maximum power point (MPP) in photovoltaic (PV) applications, under rapidly changing of solar irradiation, and is stable under slowly changing of...
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| 主要な著者: | , , |
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| フォーマット: | Conference or Workshop Item |
| 出版事項: |
IEEE
2013
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| オンライン･アクセス: | http://psasir.upm.edu.my/id/eprint/41481/ |
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| 要約: | This paper presents an artificial neural network
(ANN) maximum power point tracking (MPPT) method which is
fast and precise in finding and tracking the maximum power
point (MPP) in photovoltaic (PV) applications, under rapidly
changing of solar irradiation, and is stable under slowly changing of solar irradiation. ANN and P&O MPPT algorithms, and other components of the MPPT control system which are PV module and DC-DC boost converter, are simulated in MATLABSimulink, and their performances under rapidly and slowly changing of solar irradiation are compared as well. Simulation results show that ANN method has very fast and more precise response under fast changes of solar irradiation. In addition, this method performs with less power oscillation under constant or slow changes of solar irradiation. |
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