Hybrid Moth-Flame Optimization Algorithm and Incremental Conductance for Tracking Maximum Power of Solar PV/Thermoelectric System under Different Conditions

Hegazy Rezk, Ziad Mohammed Ali, Omer Abdalla, Obai Younis, Mohamed Ramadan Gomaa and Mauia Hashim
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Hegazy Rezk: College of Engineering at Wadi Addawaser, Prince Sattam Bin Abdulaziz University, Al Kharj 11991, Saudi Arabia
Ziad Mohammed Ali: College of Engineering at Wadi Addawaser, Prince Sattam Bin Abdulaziz University, Al Kharj 11991, Saudi Arabia
Omer Abdalla: College of Engineering at Wadi Addawaser, Prince Sattam Bin Abdulaziz University, Al Kharj 11991, Saudi Arabia
Obai Younis: College of Engineering at Wadi Addawaser, Prince Sattam Bin Abdulaziz University, Al Kharj 11991, Saudi Arabia
Mohamed Ramadan Gomaa: Mechanical Department, Faculty of Engineering, Mu’tah University, Al-Karak 61710, Jordan
Mauia Hashim: Sudan Academy of Science, Khartoum 11111, Sudan

Mathematics, 2019, vol. 7, issue 10, 1-21

Abstract: For an efficient energy harvesting by the PV/thermoelectric system, the maximum power point tracking (MPPT) principle is targeted, aiming to operate the system close to peak power point. Under a uniform distribution of the solar irradiance, there is only one maximum power point (MPP), which easily can be efficiently determined by any traditional MPPT method, such as the incremental conductance (INC). A different situation will occur for the non-uniform distribution of solar irradiance, where more than one MPP will exist on the power versus voltage plot of the PV/thermoelectric system. The determination of the global MPP cannot be achieved by conventional methods. To deal with this issue the application of soft computing techniques based on optimization algorithms is used. However, MPPT based on optimization algorithms is very tedious and time consuming, especially under normal conditions. To solve this dilemma, this research examines a hybrid MPPT method, consisting of an incremental conductance (INC) approach and a moth-flame optimizer (MFO), referred to as (INC-MFO) procedure, to reach high adaptability at different environmental conditions. In this way, the combination of the two different algorithms facilitates the utilization of the advantages of the two methods, thereby resulting in a faster speed tracking with uniform radiation distribution and a high accuracy in the case of a non-uniform distribution. It is very important to mention that the INC method is used to track the maximum power point under normal conditions, whereas the MFO optimizer is most relevant for the global search under partial shading. The obtained results revealed that the proposed strategy performed best in both of the dynamic and the steady-state conditions at uniform and non-uniform radiation.

Keywords: energy efficiency; photovoltaic module; MPPT; optimization; computational fluid dynamics (CFD) (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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