Qalaai Zanist Scientific Journal
گۆڤارى قەڵاى زانست

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Qalaai Zanist Scientific Journal  
Volume 2, Issue 2, April 2017

Copyright Statement: This is an open access publication licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

 
Paper Title: A Hybrid Neur-Fuzzy Optimization Method for PV Steady State Improvement in Maximum Power Point Tracking Controller
Author (s):

Ashty M. Aaref 
Ali M. Humada

https://doi.org/10.25212/lfu.qzj.2.2.04
   
Index Terms:

Maximum Power Point Tracking (MPPT), PV system, Oscillation of MPPT, Neural-Fuzzy control method. P&O MPPT method

Abstract:

This paper presents ahybrid Neural-Fuzzy optimization method for the purpose of assuringthe steady state case of the Maximum Power Point (MPP) for a Photovoatic (PV) system. Also, it works to decrease the oscillation occurring around MPP. The proposed method implemented via using an improved intelligent control method, neural fuzzy networks (Nueural-Fuzzy). The results showed that the PV system with Maximum Power Point Tracking (MPPT) always tracks the peak powerpoint of the PV module under various operating conditions. Afterwards, these results are validated by the comparison with the conventional and most used methods (P&O) and showed that the proposed MPPT method has better steady state and therefore less oscillation around the MPP than the P&O. It is also shown that, the increase in the output power due to usingthe MPPT is about 48.2% for a clear weather conditions and 34.8% for a shadow conditions.

   
Cite This Paper (APA):

M. Aaref, A., & M. Humada, A. (2017). A Hybrid Neur-Fuzzy Optimization Method for PV Steady State Improvement in Maximum Power Point Tracking Controller. Qalaai Zanist Scientific Journal, 2(2). doi:10.25212/lfu.qzj.2.2.04

Text Language: English
Pp.: 28 - 35
Full Text:
 
 


INVITATION

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