Please use this identifier to cite or link to this item:
http://hdl.handle.net/2080/4056
Title: | Study on Artificial Neural Network based MPPT Algorithm in PV Application |
Authors: | Shyam, Swaroop Ghosh, Arnab |
Keywords: | Maximum power point tracking (MPPT) artificial neural network (ANN) perturbation and observation (P&O) photovoltaic (PV). |
Issue Date: | Aug-2023 |
Citation: | IEEE 3rd International Conference on Sustainable Energy and Future Electric Transportation (SeFeT), ITER, Siksha 'O' Anusandhan, 09-12 August 2023 |
Abstract: | This paper presents a comparative study between the Artificial Neural Network (ANN) and Perturb and Observe (P&O) Maximum Power Point Tracking (MPPT) method for photovoltaic (PV) applications. The study includes a detailed analysis of the fundamental principles and operational aspects of ANN and P&O MPPT methods. The MATLAB Simulink is used to simulate the PV module, DC-DC boost converter, and the ANN and P&O MPPT algorithms of the MPPT control system. The simulation also compares the system's performance under varying solar irradiation rates, both fast and slow. The simulation results demonstrate that ANN-based MPPT outperforms the P&O method in terms of efficiency and accuracy, particularly under dynamic weather and shading conditions. The proposed study provides a comprehensive understanding of the benefits and limitations of ANN and P&O MPPT methods and highlights the potential for future research. |
Description: | Copyright belongs to proceeding publisher |
URI: | http://hdl.handle.net/2080/4056 |
Appears in Collections: | Conference Papers |
Files in This Item:
File | Description | Size | Format | |
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2023_IEEE-SEFET_SShyam_Study.pdf | 733.82 kB | Adobe PDF | View/Open |
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