Enhanced Electric Eel Foraging Algorithm for Integrated Optimization of Distributed Generation and Distribution Static Compensators with Network Reconfiguration
COMPUTERS & ELECTRICAL ENGINEERING（2025）
Madan Mohan Malaviya Univ Technol
Abstract
This research introduces the Enhanced Electric Eel Foraging Optimization (EEEFO) algorithm, a hybrid optimization approach for simultaneously sizing and placing distributed generation (DG) units at optimal power factor, optimizing distribution static compensators (DSC), and performing optimal network reconfiguration (ONR) on large-scale electrical distribution networks. The EEEFO algorithm leverages the electric eel foraging optimizer's skills with genetic operators to address the challenging issues of optimum design of large electrical distribution networks. The primary objective of this study is to minimize power loss and enhance voltage stability of distribution networks. The effectiveness of the EEEFO algorithm is demonstrated through its application to several large distribution networks, including 70-bus, 85-bus, 118-bus, 136-bus, 141-bus, and 415-bus systems. Additionally, the EEEFO algorithm's efficiency is evaluated against other algorithms and previous research in the area. Simulation findings demonstrate the usefulness of optimizing DG at the optimum power factor while also optimizing DSC with ONR to improve power system performance. Specifically, the active power loss is reduced by 80.46 %, 97.00 %, 87.76 %, 88.71 %, 92.14 %, and 82.18 %, while reactive power loss is reduced by 73.62 %, 97.73 %, 87.41 %, 91.92 %, 92.58 %, and 81.22 % for the 70-bus, 85-bus, 118-bus, 136-bus, 141-bus, and 417-bus systems, respectively. Furthermore, this strategy considerably improves the system's voltage stability, demonstrating its usefulness and scalability across a wide range of network topologies.
MoreTranslated text
Key words
Electric eel foraging optimization,Genetic operators,Large distribution systems,Distributed generation,Distribution static compensator
求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined