Affiliations: [a] Department of Electrical and Electronic Engineering, University College of Engineering Nagercoil, (A Constituent College of Anna University Chennai), Tamil Nadu, India
| [b] Department of Mechanical Engineering, Stella Mary’s College of Engineering, Aruthenganvilai, Azhikal Post, Tamil Nadu, India
| [c] Department of CSE, Sathyabama Institute of Science and Technology, Chennai, Tamil Nadu, India
Correspondence:
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Corresponding author. S. Sahaya Elsi, Assistant Professor, Department of Electrical and Electronic Engineering, University College of Engineering Nagercoil, (A Constituent College of Anna University Chennai), Tamil Nadu, India. E-mail: elsismailbox@rediffmail.com.
Abstract: Grey wolf-optimized artificial neural networks used in DC–AC hybrid distribution networks, to regulate the energy consumption, is presented in this study. Energy management system that takes into consideration, the distributed generation, load demand, and battery state of charge are being considered. The artificial neural network have been trained, utilising the profile data, based on the energy storage system’s charging and discharging characteristics, under various distribution network power conditions. Moreover, the error rate was kept, well under 10%. The suggested energy management system, that employs an artificial neural network, has been trained to function in the optimal mode, utilising grey wolf optimization for each grid-connected power converter. Small-scale hybrid DC/AC microgrids have been developed and tested, in order to simulate and verify the proposed energy management system. The grey wolf optimized neural network energy management system has been proven to provide 99.48 % efficiency, which is superior when compared to other methods existing in the literatures.
