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An Improved Maximum Power Point Tracking Controller for PV Systems Using Artificial Neural Network
| Content Provider | Semantic Scholar |
|---|---|
| Author | Younis, M. A. A. Khatib, Tamer Najeeb, Muhammad Ariffin, Azrul Mohd |
| Copyright Year | 2012 |
| Abstract | This paper presents an improved maximum power point tracking (MPPT) controller for PV systems. An Artificial Neural Network and the classical PO the response of the proposed MPPT controller is faster than the classical P&O algorithm. Moreover, the average tracking efficiency of the developed algorithm was 95.51% as compared to 85.99% of the classical P&O algorithm. Such developed controller increases the conversion efficiency of a PV system. Streszczenie. W artykule zaprezentowano ulepszony uklad śledzenia maksymalnej mocy w systemie fotowoltaicznym. Zastosowano siec neuronową i klasyczny algorytm P&O. Siec neuronowa w sprzezeniu zwrotnym ma cztery wejścia: promieniowanie sloneczne, temperatura otoczenia i wspolczynniki temperaturowe ISC i Voc. Wyjściem jest optymalne napiecie systemu. (Ulepszona metoda śledzenia maksymalnej mocy systemu fotowoltaicznego z wykorzystaniem sieci neuronowej) |
| Starting Page | 116 |
| Ending Page | 121 |
| Page Count | 6 |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://pe.org.pl/articles/2012/3b/28.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |