Open AccessMaximum Power Point Tracking Algorithms for Photovoltaic System
Author(s) -
M. Gobikha,
S. Akila,
M. Madhavi Latha,
R. Dhanlakshmi,
A. Bhavani Sankar
Publication year - 2021
Publication title -
international journal of advanced research in science, communication and technology
Language(s) - English
Resource type - Journals
ISSN - 2581-9429
DOI - 10.48175/ijarsct-916
Subject(s) - maximum power point tracking , photovoltaic system , maximum power principle , artificial neural network , matlab , power (physics) , computer science , control theory (sociology) , algorithm , point (geometry) , tracking (education) , engineering , mathematics , artificial intelligence , electrical engineering , control (management) , physics , psychology , pedagogy , geometry , quantum mechanics , inverter , operating system
This paper helps us analyse three different MPPT techniques like Perturb and Observe, Incremental Conductance and Artificial Neural Network. As the output characteristic depends on temperature and irradiance, therefore the maximum power point tracking (MPPT) is not always constant. Hence it is necessary to ensure that the PV panel is operating at its maximum power point. There are many different MPPT techniques but, the confusion lies in selecting which MPPT technique is best as every algorithm has its own merit and demerit. In order to extract maximum power from PV arrangement, Artificial Neural Network algorithm is proposed. Algorithms are implemented using the Boost converter. Results of simulations are presented in order to demonstrate the effectiveness of Artificial Neural Network algorithm, when compared to Perturb and Observe (P&O) and Incremental Conductance (INC). To simulate the proposed system MATLAB/ SIMULINK power system tool box is used.