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

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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.

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Acknowledgements

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.

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Authors and Affiliations

  1. State Grid Zhejiang Electric Power Co., Ltd, Hangzhou, 310007, China

    Weifeng Wang & Xiong Li

  2. Zhejiang Huayun Information Technology Co., Ltd, Hangzhou, 310008, China

    Bing Lou

  3. College of Information Engineering, China Jiliang University, 258 Xueyuan Street, Hangzhou, 310018, China

    Xizhong Lou, Ning Jin & Ke Yan

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  1. Weifeng Wang
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Correspondence to Ke Yan.

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This article belongs to the Topical Collection: Special Issue on Smart Computing and Cyber Technology for Cyberization

Guest Editors: Xiaokang Zhou, Flavia C. Delicato, Kevin Wang, and Runhe Huang

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Wang, W., Lou, B., Li, X. et al. Intelligent maintenance frameworks of large-scale grid using genetic algorithm and K-Mediods clustering methods. World Wide Web 23, 1177–1195 (2020). https://doi.org/10.1007/s11280-019-00705-w

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