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Development of artificial neural network based MPPT for photovoltaic system during shading condition

Mahamad, Abd Kadir and Saon, Sharifah (2014) Development of artificial neural network based MPPT for photovoltaic system during shading condition. In: 2013 International Conference on Renewable Energy and Environmental Technology (REET 2013), 21-22 September 2013, Jilin, China.


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This paper presents Feedforward Neural network (FFNN) and Elman network controllers to control the maximum power point tracking (MPPT) of photovoltaic (PV). MPPT is a method used to extract the maximum available power from photovoltaic module by designs them to operate efficiently. Thus, cell temperatures and solar irradiances are two critical variable factors to determine PV output powers. The performances of the controller is analyzed in four conditions which are i) constant irradiation and temperature, ii) constant irradiation and variable temperature, iii) constant temperature and variable irradiation and iv) variable temperature and irradiation. The proposed systems are simulated by using MATLAB-SIMULINK. Based on the results, FFNN controller has shown the better performance compare to the Elman network controller during partial shading conditions.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: FFNN; Elman network: photovoltaic; MPPT; maximum power
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7800-8360 Electronics
Divisions: Faculty of Electrical and Electronic Engineering > Department of Computer Engineering
Depositing User: Mr. Mohammad Shaifulrip Ithnin
Date Deposited: 13 Aug 2018 03:20
Last Modified: 13 Aug 2018 03:20
URI: http://eprints.uthm.edu.my/id/eprint/9408
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