Work

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

Year: 2023

Type: article

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.

Source: International Journal of Electrical and Electronics Engineering

DOI: 10.14445/23488379/ijeee-v10i8p105

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

Institutions: Anna University, Chennai, Mohan Babu University, Dire Dawa University

Language: English

Topic: Photovoltaic System Optimization Techniques

Subfield: Renewable Energy, Sustainability and the Environment

Field: Energy

Domain: Physical Sciences

SDGs: Affordable and clean energy, Industry, innovation and infrastructure

Open Access status: hybrid

OpenAlex ID: W4386926705