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

  • Weifeng Wang
  • Bing Lou
  • Xiong Li
  • Xizhong Lou
  • Ning Jin
  • Ke YanEmail author
Part of the following topical collections:
  1. Special Issue on Smart Computing and Cyber Technology for Cyberization


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.


Smart electric power grid Maintenance planning Genetic algorithm K-mediods clustering 



This study is supported by National Natural Science Foundation of China (No. 61850410531). It is also partially supported by Zhejiang Provincial Natural Science Foundation of China under Grant No. LY19F020016 and National Natural Science Foundation of China (No. 61602431). A previous version of this paper titled ‘Data-Driven Intelligent Maintenance Planning of Smart Meter Reparations for Large-Scale Smart Electric Power Grid’ was published with The 4th IEEE International Conference on Internet of People (IoP 2018) on October 8-12, 2018, at Guangzhou in China.

Compliance with Ethical Standards

Conflict of interests

All authors declare that there is no conflict of interest regarding the publication of this manuscript.


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© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.State Grid Zhejiang Electric Power Co., LtdHangzhouChina
  2. 2.Zhejiang Huayun Information Technology Co., LtdHangzhouChina
  3. 3.College of Information EngineeringChina Jiliang UniversityHangzhouChina

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