Citation

Jenitha, R. and Rajesh, K. and Alaguselvi, R. and You, Ah Heng and Wong, Wai Kit and Ng, Poh Kiat (2026) Implementation of deep learning-based controller for microgrid system for irrigation purpose. Results in Engineering, 32. p. 113275. ISSN 2590-1230

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Abstract

The hybrid wind and solar renewable involvement to meet the energy needs practically faces a lot of challenges. The main target to achieve is the power point tracking for both of the energy systems. In order to address the human-less irrigation process, this paper advances the deep learning-centered single-controller approach. The system is designed for the stand-alone energy system. So, the controller is responsible for achieving the peak power point for hybrid energy systems, battery, and load management. To carry out the said tasks, a deep learning-based MPPT controller is implemented and analyzed with the hardware setup. The federated learning algorithm is utilized as the controller, which by itself learns and teaches the controller to train at every moment for its effective learning and implementation process. The results show that the system performs well, with the tracking time for the MPPT achievement is 0.024 s with the designed algorithm by carrying out multiple tasks at a time.

Item Type:	Article
Uncontrolled Keywords:	Maximum power point tracking, Deep learning, Hybrid energy sources
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines
Divisions:	Faculty of Engineering and Technology (FET)
Depositing User:	Ms Suzilawati Abu Samah
Date Deposited:	29 Oct 2026 04:44
Last Modified:	29 Oct 2026 04:44
URII:	http://shdl.mmu.edu.my/id/eprint/16990

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