Improved Multi-swarm PSO Based Maintenance Schedule of Power Communication Network

  • Minchao ZhangEmail author
  • Xingyu Chen
  • Yue Hou
  • Guiping Zhou
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 905)


Maintenance schedule is an important and complex task in power communication system. This paper builds a maintenance schedule model that considers decreasing average waiting time of maintenance as well as some constraints. This paper uses Hadoop and MapReduce to handle huge amount of information in power communication network. An improved multi-swarm PSO (Particle Swarm Optimization) algorithm is proposed to schedule maintenance. This algorithm combines the MPSO algorithm with the bacterial chemotaxis. Experiment demonstrates accuracy and efficiency of the improved MPSO algorithm.


Power communication network Particle swarm optimization Maintenance schedule Dynamic learning factor Population diversity 



This work is supported by National Key R&D Program of China (2016YFB0901200).


  1. 1.
    Jiang, D., Tian, D., Liu, X., Shen, Z.: Security analysis of topology structure of electric power communication network. In: 2016 First IEEE International Conference on Computer Communication and the Internet, Wuhan, China, pp. 76–79. IEEE (2016)Google Scholar
  2. 2.
    Zhu, Q., Peng, H., Timmermans, B., van Houtum, G.J.: A condition-based maintenance model for a single component in a system with scheduled and unscheduled downs. Int. J. Prod. Econ. 193, 365–380 (2017)CrossRefGoogle Scholar
  3. 3.
    Xie, C., Liu, W., Wen, J., Wang, J.: An auto-generated method of day-ahead forecast powerflow for security correction of power grid maintenance scheduling. In: 2012 Asia-Pacific Power and Energy Engineering Conference, Shanghai, China, pp. 1–4. IEEE (2012)Google Scholar
  4. 4.
    Samuel, G.G., Rajan, C.C.A.: Hybrid particle swarm optimization – genetic algorithm and particle swarm optimization – evolutionary programming for long-term generation maintenance scheduling. In: 2013 International Conference on Renewable Energy and Sustainable Energy, Coimbatore, India, pp. 237–232. IEEE (2014)Google Scholar
  5. 5.
    Zeng, M., Huang, L., Qiu, L., Tian, K., The risk-based optimal maintenance scheduling for transmission system in smart grid. In: 2010 International Conference on Electrical and Control Engineering, Wuhan, China, pp. 4446–4449. IEEE (2010)Google Scholar
  6. 6.
    Suresh, K., Kumarappan, N.: Coordination mechanism of maintenance scheduling using modified PSO in a restructured power market. In: 2013 IEEE Symposium on Computational Intelligence in Scheduling, Singapore, Singapore, pp. 36–43. IEEE (2013)Google Scholar
  7. 7.
    Wilhelm, P.A.: Pheromone Particle Swarm Optimization of Stochastic Systems. Iowa State University, Ames (2008)CrossRefGoogle Scholar
  8. 8.
    Thuraisingham, B., Khan, L.R., Husain, M.F.: Data intensive query processing for semantic web data using Hadoop and MapReduce. The University of Texas at Dallas, Richardson (2011)Google Scholar
  9. 9.
    Gaonkar, V., Nanannavar, R.B., Manjunatha: Power system congestion management using sensitivity analysis and particle swarm optimization. In: 2017 International Conference on Energy, Communication, Data Analytics and Soft Computing, Chennai, India, pp. 1268–1271 (2017)Google Scholar
  10. 10.
    Yang, L.H., Wang, Y.J., Zhu, C.M.: Study on fuzzy energy management strategy of parallel hybrid vehicle based on quantum PSO algorithm. Int. J. Multimed. Ubiquit. Eng. 11(05), 147–158 (2016)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Minchao Zhang
    • 1
    Email author
  • Xingyu Chen
    • 1
  • Yue Hou
    • 2
  • Guiping Zhou
    • 3
  1. 1.Institute of Network TechnologyBeijing University of Posts and TelecommunicationsBeijingChina
  2. 2.Beijing Guodiantong Network Technical Co. Ltd.BeijingChina
  3. 3.State Grid Liaoning Electric Power Co., Ltd.ShenyangChina

Personalised recommendations