Integrated Intelligent Technique for Loss Control in Distribution System via Distributed Generation Installation Under Load Variations

As power consumption rises, it is essential to minimize line loss to maintain uninterrupted service to the end customer. Expansion of existing power plants has numerous environmental and economic hurdles, necessitating a careful analysis of local best options. As a result, several Distributed Genera...

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Bibliographic Details
Main Authors: Abdullah A., Musirin I., Othman M.M., Rahim S.R.A., Mansor M.H., Shaaya S.A., Senthil Kumar A.V.
Other Authors: 57197864035
Format: Conference paper
Published: Springer Science and Business Media Deutschland GmbH 2025
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Summary:As power consumption rises, it is essential to minimize line loss to maintain uninterrupted service to the end customer. Expansion of existing power plants has numerous environmental and economic hurdles, necessitating a careful analysis of local best options. As a result, several Distributed Generation (DG) technologies have been integrated into the power system. This paper introduces Integrated Immune Moth Flame Evolutionary Programming (IIMFEP) as a new hybrid method for planning distributed generation in distribution systems. The IIMFEP determines the optimal sizing and placement of DG Type II that supplies reactive power under load variations to mitigate power loss in a system. Utilizing the IEEE 69-Bus Radial Distribution Systems (RDS), a comparison is made between the IIMFEP method and Moth Flame Optimization (MFO), Artificial Immune System (AIS) and Evolutionary Programming (EP). The IIMFEP outperformed the other three techniques, which managed to achieve the lowest total system power loss. As the system?s real and reactive loads increase, so does the system?s overall power loss. Therefore, DGs must inject more reactive power to compensate for the power loss. The research findings�indicate that the proposed IIMFEP reduces system power losses more effectively. The proposed IIMFEP is, therefore, a viable alternative solution addressing optimal DG difficulties in RDS. ? The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.