Title: Maximum power point tracking for grid tied solar fed DTC controlled IM drive using artificial neural network with energy management

Authors: S. Senthamizh Selvan

Addresses: Electrical and Electronics Engineering Department, National Institute of Technology Puducherry, Karaikal, India

Abstract: In the mechanised world, carbon-less emission of energy production is vitalised. Despite varied renewable energy sources available, solar PV seems to be an optimum choice due to its ease of installation and maintenance. Though conventional algorithm exists for extracting maximum power, non-conventional algorithm by soft computing is foreseen for high stability during a sudden change in irradiation and load transients. In this article, artificial neural network-based maximum power point tracking is focused. A comparative analysis is carried out between single layer neural network and multi-layer neural network for varied parameters. The multi-layer neural network is found to be advantageous in the case of neuron's requirement, implementation complexity and testing MSE. Hence, the trained neural model is implemented in PV-grid fed DTC-IM drive system with various operating conditions. Simulation results are found to satisfactory. Added energy management condition is also validated for various irradiations.

Keywords: artificial neural network; ANN; single layer feed forward; multi-layer feed forward neural network; maximum power point tracking.

DOI: 10.1504/IJETM.2024.135556

International Journal of Environmental Technology and Management, 2024 Vol.27 No.1/2, pp.151 - 172

Received: 04 Jan 2021
Accepted: 26 Mar 2021

Published online: 18 Dec 2023 *

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