Proceedings of The International Conference on Emerging Trends in Artificial Intelligence and Smart Systems, THEETAS 2022, 16-17 April 2022, Jabalpur, India

Research Article

Power Flow Optimization Using MFO to Reduce Energy Losses

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  • @INPROCEEDINGS{10.4108/eai.16-4-2022.2318069,
        author={Pradeep Singh  Chouhan and Akash  Soni and Khushboo  Sharma and Arindam  Singha},
        title={Power Flow Optimization Using MFO to Reduce Energy Losses},
        proceedings={Proceedings of The International Conference on Emerging Trends in Artificial Intelligence and Smart Systems, THEETAS 2022, 16-17 April 2022, Jabalpur, India},
        publisher={EAI},
        proceedings_a={THEETAS},
        year={2022},
        month={6},
        keywords={renewable energy electrical load power flow optimization mfo energy losses},
        doi={10.4108/eai.16-4-2022.2318069}
    }
    
  • Pradeep Singh Chouhan
    Akash Soni
    Khushboo Sharma
    Arindam Singha
    Year: 2022
    Power Flow Optimization Using MFO to Reduce Energy Losses
    THEETAS
    EAI
    DOI: 10.4108/eai.16-4-2022.2318069
Pradeep Singh Chouhan1,*, Akash Soni1, Khushboo Sharma1, Arindam Singha1
  • 1: Rajasthan Institute of Engineering and Technology, Jaipur
*Contact email: Pradeepsingh2201@gmail.com

Abstract

Loads in energy systems that reduce the complexity in energy systems that cause the electric voltage to produce RES to fluctuate a lot. In order to efficiently address the OPF issues, this article presents a moth flame optimization (MFO) method. The theory of maximizing moth flame (MFO) is influenced by the motion of the moon in the path of the moon. With the modification of the direction of moths in different spirals across the blaze, MFO is mostly focused on the MFO principle. Many well optimization methods equate the simulator accuracy of the suggested method with those received. The proposed model provides the capacity and reliability of the MFO approach to address OPF issues. The findings indicate that contrasted to other methods, the MFO method is successful of identifying correct and good OPF approaches.