Research on optimizing the networking mode of low-voltage distribution network in Internet of Things system based on improved ACO Article Swipe
Long Xu
,
Xiao Liu
,
Jie Han
,
Qiang Li
,
Y. Celia Huang
,
H S Li
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-6629433/v1
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-6629433/v1
Optimizing the stability and transmission efficiency of low-voltage distribution networks remains a critical challenge in smart grid systems. This study proposes an AI-enhanced optimization framework that integrates an improved Ant Colony Optimization (ACO) algorithm with intelligent Internet of Things (IoT) sensing technologies. The ACO algorithm, inspired by swarm intelligence principles, dynamically adjusts network paths to minimize power loss, while IoT devices provide real-time data on grid load and voltage distribution, enabling adaptive decision-making. Experimental results demonstrate that the AI-driven model reduces power loss by 61.8835 kW (30.53%) and 91.9952 kW (45.39%) in pre- and post-reconstruction scenarios, respectively. Additionally, the minimum node voltage improves from 0.9133 p.u. to 0.9291 p.u. (pre-reconstruction) and 0.9453 p.u. (post-reconstruction), with system-wide voltage stability enhancements. Theoretical and simulation analyses confirm that the synergy of AI-based optimization and IoT intelligence significantly improves transmission efficiency and operational robustness in low-voltage grids.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.21203/rs.3.rs-6629433/v1
- https://www.researchsquare.com/article/rs-6629433/latest.pdf
- OA Status
- gold
- References
- 27
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4411098540
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4411098540Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.21203/rs.3.rs-6629433/v1Digital Object Identifier
- Title
-
Research on optimizing the networking mode of low-voltage distribution network in Internet of Things system based on improved ACOWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-06-06Full publication date if available
- Authors
-
Long Xu, Xiao Liu, Jie Han, Qiang Li, Y. Celia Huang, H S LiList of authors in order
- Landing page
-
https://doi.org/10.21203/rs.3.rs-6629433/v1Publisher landing page
- PDF URL
-
https://www.researchsquare.com/article/rs-6629433/latest.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.researchsquare.com/article/rs-6629433/latest.pdfDirect OA link when available
- Concepts
-
Mode (computer interface), Internet of Things, The Internet, Computer science, Computer network, World Wide Web, Human–computer interactionTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- References (count)
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27Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.Optimization | 32 |
| abstract_inverted_index.distribution | 9 |
| abstract_inverted_index.intelligence | 49, 133 |
| abstract_inverted_index.optimization | 24, 130 |
| abstract_inverted_index.transmission | 5, 136 |
| abstract_inverted_index.Additionally, | 98 |
| abstract_inverted_index.distribution, | 70 |
| abstract_inverted_index.enhancements. | 119 |
| abstract_inverted_index.respectively. | 97 |
| abstract_inverted_index.significantly | 134 |
| abstract_inverted_index.technologies. | 42 |
| abstract_inverted_index.decision-making. | 73 |
| abstract_inverted_index.post-reconstruction | 95 |
| abstract_inverted_index.(pre-reconstruction) | 110 |
| abstract_inverted_index.(post-reconstruction), | 114 |
| abstract_inverted_index.<title>Abstract</title> | 0 |
| cited_by_percentile_year | |
| countries_distinct_count | 2 |
| institutions_distinct_count | 6 |
| citation_normalized_percentile.value | 0.21762912 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |