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

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