Intelligent maintenance frameworks of large-scale grid using genetic algorithm and K-Mediods clustering methods

• Published in: World wide web (Bussum) Vol. 23; no. 2; pp. 1177 - 1195
• Main Authors: Wang, Weifeng, Lou, Bing, Li, Xiong, Lou, Xizhong, Jin, Ning, Yan, Ke
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.03.2020; Springer Nature B.V
• Subjects: Clustering; Computer Science; Conditioning; Database Management; Electronic devices; Genetic algorithms; Information Systems Applications (incl.Internet); Maintenance; Measuring instruments; Operating Systems; Schedules; Smart grid; Special Issue on Smart Computing and Cyber Technology for Cyberization; Smart electric power grid; Genetic algorithm; K-mediods clustering; Maintenance planning
• ISSN: 1386-145X; 1573-1413
• DOI: 10.1007/s11280-019-00705-w

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.