Integrating MPPT and Artificial Neural Networks for Efficient DC-DC and DC-AC Conversion in Photovoltaic Applications

International Journal of Electrical and Electronics Engineering

© 2023 by SSRG - IJEEE Journal

Volume 10 Issue 8

Year of Publication : 2023

Authors : S. Muthukaruppasamy, E. Parimalasundar, V. Rajagopal, P. Duraipandy

10.14445/23488379/IJEEE-V10I8P105

How to Cite?

S. Muthukaruppasamy, E. Parimalasundar, V. Rajagopal, P. Duraipandy, "Integrating MPPT and Artificial Neural Networks for Efficient DC-DC and DC-AC Conversion in Photovoltaic Applications," SSRG International Journal of Electrical and Electronics Engineering, vol. 10,  no. 8, pp. 51-61, 2023. Crossref, https://doi.org/10.14445/23488379/IJEEE-V10I8P105

Abstract:

This paper describes integrating Maximum Power Point Tracking (MPPT) algorithms and Artificial Neural Networks (ANNs) to optimize DC-DC and DC-AC transformation in photovoltaic applications. By continuously adjusting the operating point to the maximum power point of the PV module, MPPT techniques are frequently used to maximise the power output from PV systems. However, environmental changes and system configurations impact the extent to which conventional MPPT methods perform. An ANN-based MPPT approach is proposed to address this problem, and it uses ANNs' capacity for learning to track the maximum power point under various circumstances adaptively. The simulation results confirm that the integrated system outperforms traditional MPPT techniques regarding power extraction effectiveness. The proposed approach improves the stability and reliability of monitoring the maximum power point, enabling optimal energy collection from PV systems. The efficacy of the integrated system has been exhibited through extensive modelling, highlighting its potential for practical implementation in real-world PV applications.

Keywords:

Artificial Neural Networks, Boost converter, MPPT, Multilevel inverter, Photovoltaic system, Power efficiency.

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