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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| Format: | Final Year Project (FYP) |
| Language: | English |
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Nanyang Technological University
2023
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| Online Access: | https://hdl.handle.net/10356/167581 |
| _version_ | 1826128530128240640 |
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| author | Ong, Pei Qi |
| author2 | Gooi Hoay Beng |
| author_facet | Gooi Hoay Beng Ong, Pei Qi |
| author_sort | Ong, Pei Qi |
| collection | NTU |
| 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. |
| first_indexed | 2024-10-01T07:26:05Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/167581 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T07:26:05Z |
| publishDate | 2023 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-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 |
| spellingShingle | Engineering::Electrical and electronic engineering Ong, Pei Qi Solar PV parameter forecast using generalized neural networks |
| title | 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_short | Solar PV parameter forecast using generalized neural networks |
| title_sort | solar pv parameter forecast using generalized neural networks |
| topic | Engineering::Electrical and electronic engineering |
| url | https://hdl.handle.net/10356/167581 |
| work_keys_str_mv | AT ongpeiqi solarpvparameterforecastusinggeneralizedneuralnetworks |