A Novel Global MPP Tracking of Photovoltaic System based on Whale Optimization Algorithm

*Cherukuri Santhan Kumar  -  JNT University Kakinada, India
Rayapudi Srinivasa Rao  -  JNT University Kakinada, India
Published: 4 Nov 2016.
Open Access Copyright (c) 2016 International Journal of Renewable Energy Development


Citation Format:
Abstract

To harvest maximum amount of solar energy and to attain higher efficiency, photovoltaic generation (PVG) systems are to be operated at their maximum power  point (MPP) under both variable climatic and partial shaded condition (PSC). From literature most of conventional MPP tracking (MPPT) methods are able to guarantee MPP successfully under uniform shading condition but fails to get global MPP as they may trap at local MPP under PSC, which adversely deteriorates the efficiency of Photovoltaic Generation (PVG) system. In this paper a novel MPPT based on Whale Optimization Algorithm (WOA) is proposed to analyze analytic modeling of PV system considering both series and shunt resistances for MPP tracking under PSC. The proposed algorithm is tested on 6S, 3S2P and 2S3P Photovoltaic array configurations for different shading patterns and results are presented. To compare the performance, GWO and PSO MPPT algorithms are also simulated and results are also presented.  From the results it is noticed that proposed MPPT method is superior to other MPPT methods with reference to accuracy and tracking speed.

Article History: Received July 23rd 2016; Received in revised form September 15th 2016; Accepted October 1st 2016; Available online

How to Cite This Article: Kumar, C.H.S and Rao, R.S. (2016) A Novel Global MPP Tracking of Photovoltaic System based on Whale Optimization Algorithm. Int. Journal of Renewable Energy Development, 5(3), 225-232.

http://dx.doi.org/10.14710/ijred.5.3.225-232

Keywords: Maximum power point tracking; Partial shaded condition; Photovoltaic generation system; Single diode model; Whale optimization algorithm

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Section: Original Research Article
Language: EN
In Vol 5, No 3 (2016): October 2016
Statistics: 2937 3141
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