Power loss reduction using MOALO technique: enhancing power system

Osman, Muhamad Danial, Robson, S. ORCID: https://orcid.org/0000-0003-3156-1487, Omar, Saodah, Widjonark, Widjonarko, Ariprihart, Aripriharta and Asri Samat, Ahmad 2025. Power loss reduction using MOALO technique: enhancing power system. Presented at: UPEC 2024, Cardiff, Wales, 02-06 September 2024. Proceedings 59th International Universities Power Engineering Conference (UPEC). IEEE, 10.1109/upec61344.2024.10892430

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Abstract

This research focuses on minimizing power losses in distribution networks by integrating renewable energy sources through AI techniques. Distribution networks often experience significant losses due to factors such as line resistance, unbalanced loads, and inefficient power distribution. The study aims to develop an AI-based approach, specifically using the MOALO, to identify high-loss areas within the distribution network and optimize the bus system by injecting the renewable energy sources. The proposed methodology involves utilizing solar power to reduce the total power loss and MOALO will optimize the bus network. By employing MOALO, the research considers multiple objectives, such as power losses and voltage stability, to enhance renewable energy integration. Through extensive data collection and simulation, the study evaluates the effectiveness of the AI technique in reducing power losses. Key metrics include power loss reduction, voltage stability improvement, and renewable energy utilization efficiency. The findings of this research contribute to the advancement of sustainable energy systems by providing strategies for optimizing renewable energy integration and mitigating power losses in distribution networks. Overall, this study provides valuable insights into the potential of AI techniques to enhance the efficiency and sustain ability of electrical distribution networks.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Published Online
Status:	Published
Schools:	Schools > Engineering
Publisher:	IEEE
ISBN:	979-8-3503-7973-0
Last Modified:	10 Mar 2025 14:30
URI:	https://orca.cardiff.ac.uk/id/eprint/176771

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