Optimize Grouping and Path of Pylon Inspection in Power System Article Swipe
Zhao-Long Hu
,
Jiahui Li
,
An Chen
,
Feiming Xu
,
Riheng Jia
,
Feilong Lin
,
Changbing Tang
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1109/access.2020.3001435
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1109/access.2020.3001435
In order to ensure the normal operation of the power system, it is an essential concern for optimizing inspection path based on limited human and material resources. Despite a wide body of literatures for path planning, however, a framework to optimize grouping and inspection path with minimum number of inspection teams is still lacking. Given the target transmission lines and constrained work hours for each inspector, we study the theoretical solution of the minimum number of inspection teams for task assignment. Furthermore, we develop an improved k-means algorithm, and combine with heuristic intelligent algorithms, such as ant colony algorithm and simulated annealing algorithm, we put forward a universal framework for optimizing grouping and inspection path with minimum number of inspection teams. By applying our framework to both synthetic transmission line and the real transmission lines in Jinhua city, the results verify the theoretical solution of the minimum number of inspection teams. In addition, experimental results demonstrate that our framework can provide quasi-optimal inspection paths and balance work hours for each team. By comparison of the results with different algorithms, we find that the simulated annealing algorithm works the best. Our work paves a new way to solve the vehicle routing problem, travelling salesman problem and some other related problems.
Related Topics
Concepts
Simulated annealing
Computer science
Travelling salesman problem
Mathematical optimization
Path (computing)
Motion planning
Ant colony optimization algorithms
Shortest path problem
Heuristic
Ant colony
Routing (electronic design automation)
Algorithm
Artificial intelligence
Graph
Robot
Theoretical computer science
Mathematics
Programming language
Computer network
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/access.2020.3001435
- https://ieeexplore.ieee.org/ielx7/6287639/8948470/09113311.pdf
- OA Status
- gold
- Cited By
- 4
- References
- 70
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3035214516
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3035214516Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1109/access.2020.3001435Digital Object Identifier
- Title
-
Optimize Grouping and Path of Pylon Inspection in Power SystemWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2020Year of publication
- Publication date
-
2020-01-01Full publication date if available
- Authors
-
Zhao-Long Hu, Jiahui Li, An Chen, Feiming Xu, Riheng Jia, Feilong Lin, Changbing TangList of authors in order
- Landing page
-
https://doi.org/10.1109/access.2020.3001435Publisher landing page
- PDF URL
-
https://ieeexplore.ieee.org/ielx7/6287639/8948470/09113311.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://ieeexplore.ieee.org/ielx7/6287639/8948470/09113311.pdfDirect OA link when available
- Concepts
-
Simulated annealing, Computer science, Travelling salesman problem, Mathematical optimization, Path (computing), Motion planning, Ant colony optimization algorithms, Shortest path problem, Heuristic, Ant colony, Routing (electronic design automation), Algorithm, Artificial intelligence, Graph, Robot, Theoretical computer science, Mathematics, Programming language, Computer networkTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
4Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1, 2023: 1, 2022: 1, 2020: 1Per-year citation counts (last 5 years)
- References (count)
-
70Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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