Proposal Title

Solar Module(s) Based Design Modeling

Primary Faculty Mentor’s Name

Dr. Adel El Shahat

Proposal Track

Student

Session Format

Poster

Abstract

Solar Module(s) Based Design Modeling

Name:

Supervisor: Dr. Adel El Shahat

Department of Electrical Engineering

Allen E. Paulson College of Engineering and Information Technology,

Georgia Southern University

As petroleum reserves around the world dwindle, the price of petroleum products, most importantly crude oil used for energy, rises. Due to this, renewable energy sources (solar, wind, tidal, geothermal etc.) are attracting more attention as alternative energy sources. Solar energy has emerged as the current front runner of the alternative energy race, and has become very important in the power systems we use today. Solar energy is gathered primarily by solar panels and solar cells, which use the photoelectric effect to emit electrons from semi-conducting materials, and sometimes even superconducting metals, within the panels and cells when light is shone upon them. Solar panels and cells are often referred to as photovoltaic cells and systems. Photovoltaic (PV) systems have become a part of our modern power grids. Their implementation and importance in and to our power grids increases every year, to the point that they have nearly become integral to our power grids. For engineers, it is very important to design and simulate the systems that serve the solar plants and power grids so that they can be tested, monitored, maintained, and improved. The PV modules and systems are modeled by the use of the manufacturers' data listed in the lookup table module. The lookup table module is found in and performed by the MATLAB/Simulink toolbox functions and programs. Moreover, the Artificial Neural Network (ANN) numerical technique is used to simulate and evaluate the designed modules. Using both the lookup table modules and the ANN numerical technique allows the engineer and/or designer to select a specific PV module according to that module’s power load. This allows for and promotes the design of PV systems and fields for many different power applications, whether they are large or small scale. This same method can be used to help investors determine the most cost effective PV modules and implementations. The data collected from lookup table modules and the ANN numerical technique has been found to closely match available commercial data for the PV modules and systems, which proves that the lookup table modules and ANN numerical technique are an effective way to model solar power systems.

Keywords

Photovoltaic (PV); MATLAB/Simulink; Artificial Neural Networks (ANN)

Location

Concourse/Atrium

Presentation Year

2014

Start Date

11-15-2014 9:40 AM

End Date

11-15-2014 10:55 AM

Publication Type and Release Option

Presentation (Open Access)

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Nov 15th, 9:40 AM Nov 15th, 10:55 AM

Solar Module(s) Based Design Modeling

Concourse/Atrium

Solar Module(s) Based Design Modeling

Name:

Supervisor: Dr. Adel El Shahat

Department of Electrical Engineering

Allen E. Paulson College of Engineering and Information Technology,

Georgia Southern University

As petroleum reserves around the world dwindle, the price of petroleum products, most importantly crude oil used for energy, rises. Due to this, renewable energy sources (solar, wind, tidal, geothermal etc.) are attracting more attention as alternative energy sources. Solar energy has emerged as the current front runner of the alternative energy race, and has become very important in the power systems we use today. Solar energy is gathered primarily by solar panels and solar cells, which use the photoelectric effect to emit electrons from semi-conducting materials, and sometimes even superconducting metals, within the panels and cells when light is shone upon them. Solar panels and cells are often referred to as photovoltaic cells and systems. Photovoltaic (PV) systems have become a part of our modern power grids. Their implementation and importance in and to our power grids increases every year, to the point that they have nearly become integral to our power grids. For engineers, it is very important to design and simulate the systems that serve the solar plants and power grids so that they can be tested, monitored, maintained, and improved. The PV modules and systems are modeled by the use of the manufacturers' data listed in the lookup table module. The lookup table module is found in and performed by the MATLAB/Simulink toolbox functions and programs. Moreover, the Artificial Neural Network (ANN) numerical technique is used to simulate and evaluate the designed modules. Using both the lookup table modules and the ANN numerical technique allows the engineer and/or designer to select a specific PV module according to that module’s power load. This allows for and promotes the design of PV systems and fields for many different power applications, whether they are large or small scale. This same method can be used to help investors determine the most cost effective PV modules and implementations. The data collected from lookup table modules and the ANN numerical technique has been found to closely match available commercial data for the PV modules and systems, which proves that the lookup table modules and ANN numerical technique are an effective way to model solar power systems.