Intelligent maintenance frameworks of large-scale grid using genetic algorithm and K-Mediods clustering methods
| Autor: | Weifeng Wang, Bing Lou, Ning Jin, Xizhong Lou, Xiong Li, Ke Yan |
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| Rok vydání: | 2019 |
| Předmět: |
Schedule
Computer Networks and Communications Computer science Distributed computing 02 engineering and technology Grid Power (physics) Smart grid Hardware and Architecture 020204 information systems Genetic algorithm 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Electronics Scale (map) Cluster analysis Software |
| Zdroj: | World Wide Web. 23:1177-1195 |
| ISSN: | 1573-1413 1386-145X |
| DOI: | 10.1007/s11280-019-00705-w |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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