Name
Title | ||
|---|---|---|
Intelligent maintenance frameworks of large-scale grid using genetic algorithm and K-Mediods clustering methods |
Abstract | ||
|---|---|---|
Large-scale power grids, especially smart grid systems, consist of a huge amount of complex computerized electronic devices, such as smart meters. A smart maintenance system is desired to schedule and send maintenance worker to locations where any computerized devices become faulty. A grid management system (GMS) is purposely designed in the way that the following three conditions are generally fulfilled: 1) all workers are working on full capacity everyday; 2) the highest severity level faulty devices are fixed the quickest; and 3) the overall traveling distance/time is minimized. In this study, two intelligent grid maintenance framework are proposed considering the above three conditioned based on two state-of-arts algorithms, namely, genetic algorithm and K-mediods clustering method, respectively. Five real-world datasets collected from five different local cities/counties in eastern China are adopted and applied to verify the effectiveness of the two proposed intelligent grid maintenance frameworks. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1007/s11280-019-00705-w | World Wide Web |
Keywords | Field | DocType |
Smart electric power grid, Maintenance planning, Genetic algorithm, K-mediods clustering | Data mining,Maintenance system,Smart grid,Computer science,Grid management,Maintenance planning,Electronics,Cluster analysis,Grid,Genetic algorithm,Distributed computing | Journal |
Volume | Issue | ISSN |
23 | 2 | 1386-145X |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Weifeng Wang | 1 | 0 | 0.34 |
| Bing Lou | 2 | 0 | 0.34 |
| Xiong Li | 3 | 0 | 0.34 |
| Xizhong Lou | 4 | 27 | 6.37 |
| Ning Jin | 5 | 0 | 0.34 |
| Yan Ke | 6 | 78 | 14.14 |