Solar PV parameter forecast using generalized neural networks

Solar photovoltaic (PV) is widely used in the world due to the increased demand for renewable energy. The environmental concern and depletion of natural energy sources are also a factor that leads to the shift to renewable solutions. An efficient operation of solar PV depends on the accuracy of the...

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Main Author: Ong, Pei Qi
Other Authors: Gooi Hoay Beng
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2023
Subjects:
Online Access:https://hdl.handle.net/10356/167581
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1675812023-07-07T15:53:35Z Solar PV parameter forecast using generalized neural networks Ong, Pei Qi Gooi Hoay Beng School of Electrical and Electronic Engineering EHBGOOI@ntu.edu.sg Engineering::Electrical and electronic engineering Solar photovoltaic (PV) is widely used in the world due to the increased demand for renewable energy. The environmental concern and depletion of natural energy sources are also a factor that leads to the shift to renewable solutions. An efficient operation of solar PV depends on the accuracy of the modelling and control of the module prior to the installation. This research work proposes a new neural network algorithm, Generalised Linear Hopfield Neural Network (GLHNN) to determine the maximum power point (MPP) of a solar photovoltaic (PV) module accurately under dynamic environmental condition, while comparing the accuracy with the commonly used Newton Raphson (NR) method. A single diode model is used and whose equivalent circuit parameters are derived from the mathematical expressions developed. The five parameters of the model such as the series resistance (R_se), shunt resistance (R_sh), ideality factor of the diode (A), light generated current (I_LG) and diode reverse saturated current (I_sat) are used to analyse the operating performance of PV panel and to estimate the maximum power at MPP. Bachelor of Engineering (Electrical and Electronic Engineering) 2023-05-30T04:01:17Z 2023-05-30T04:01:17Z 2023 Final Year Project (FYP) Ong, P. Q. (2023). Solar PV parameter forecast using generalized neural networks. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/167581 https://hdl.handle.net/10356/167581 en application/pdf Nanyang Technological University
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Electrical and electronic engineering
spellingShingle Engineering::Electrical and electronic engineering
Ong, Pei Qi
Solar PV parameter forecast using generalized neural networks
description Solar photovoltaic (PV) is widely used in the world due to the increased demand for renewable energy. The environmental concern and depletion of natural energy sources are also a factor that leads to the shift to renewable solutions. An efficient operation of solar PV depends on the accuracy of the modelling and control of the module prior to the installation. This research work proposes a new neural network algorithm, Generalised Linear Hopfield Neural Network (GLHNN) to determine the maximum power point (MPP) of a solar photovoltaic (PV) module accurately under dynamic environmental condition, while comparing the accuracy with the commonly used Newton Raphson (NR) method. A single diode model is used and whose equivalent circuit parameters are derived from the mathematical expressions developed. The five parameters of the model such as the series resistance (R_se), shunt resistance (R_sh), ideality factor of the diode (A), light generated current (I_LG) and diode reverse saturated current (I_sat) are used to analyse the operating performance of PV panel and to estimate the maximum power at MPP.
author2 Gooi Hoay Beng
author_facet Gooi Hoay Beng
Ong, Pei Qi
format Final Year Project
author Ong, Pei Qi
author_sort Ong, Pei Qi
title Solar PV parameter forecast using generalized neural networks
title_short Solar PV parameter forecast using generalized neural networks
title_full Solar PV parameter forecast using generalized neural networks
title_fullStr Solar PV parameter forecast using generalized neural networks
title_full_unstemmed Solar PV parameter forecast using generalized neural networks
title_sort solar pv parameter forecast using generalized neural networks
publisher Nanyang Technological University
publishDate 2023
url https://hdl.handle.net/10356/167581
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