Skip to main content

Advertisement

Log in

Optimization of maintenance personnel dispatching strategy in smart grid

  • Published:
World Wide Web Aims and scope Submit manuscript

Abstract

Efficient and timely dispatch of maintenance personnel for fault detection and failure recovery play a key role towards safe operation of power grid and has become a challenging issue. To address this challenge, this paper proposes a new optimal strategy, namely adaptive NSGAII (NSGAII/A), for dispatching maintenance personnel in the event of security risk that can improve manpower efficiency and maintain the security of power grid at a minimum cost. To solve this optimization problem, we firstly model the optimization objectives and constraints in the process of maintenance personnel scheduling. Secondly, we improve the legacy nondominated sorting genetic algorithm II (NSGAII) to combat its shortcomings in the calculation of congestion and the strategy of individual selection. On one hand, NSGAII/A takes the absolute value of the average congestion degree minus the standard deviation as the individual congestion degree, so as to reduce the impact of the congestion degree of individual optimization objectives. Furthermore, it can prevent the algorithm from converging too fast and causing the problem of local optimization. On the other hand, we adopt the elitist control strategy to replace the elitist retention strategy of NSGAII. The extensive experimental results demonstrate that NSGAII/A has advantages in terms of the average value of optimization objectives, the maintenance completion degree, and the distribution of non-dominated solution set in the process of population optimization.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. Henry, D., Ramirez - Marquez, J.E.: , On the impacts of power outages during hurricane sandy—a resilience - based analysis. Systems Engineering 9(1), 59–75 (2016)

    Article  Google Scholar 

  2. Wanik, D., Anagnostou, E., Astitha, M., Hartman, B., Lackmann, G., Yang, J., Cerrai, D., He, J., Frediani, M.: A case study on power outage impacts from future hurricane sandy scenarios. Journal of Applied Meteorology and Climatology 57(1), 51–79 (2018)

    Article  Google Scholar 

  3. Baojie, L., Jinbo, L., Hongjie, L.: Analysis of turkey blackout on march 31 2015 and lessons on china power grid. Proc. CSEE 36, 5788–5795 (2016)

    Google Scholar 

  4. Lin, W., Guo, Q., Yi, J.: Analysis on blackout in argentine power grid on june 16, 2019 and its enlightenment to power grid in china. In: Proc. CSEE (2020)

  5. Du, G., Liang, X., Sun, C.: Scheduling optimization of home health care service considering patients’ priorities and time windows. Sustainability 9(2), 253 (2017)

    Article  Google Scholar 

  6. Pakpoom, P., Charnsethikul, P.: A column generation approach for personnel scheduling with discrete uncertain requirements. In: 2018 2nd International conference on informatics and computational sciences (ICICoS). pp. 1–6 (2018)

  7. Du, G., Zheng, L., Ouyang, X.: Real-time scheduling optimization considering the unexpected events in home health care. Journal of Combinatorial Optimization 37(1), 196–220 (2019)

    Article  MATH  Google Scholar 

  8. Porto, A.F., Henao, C.A., López-Ospina, H., González, E.R.: Hybrid flexibility strategy on personnel scheduling: Retail case study. Computers & Industrial Engineering 133, 220–230 (2019)

    Article  Google Scholar 

  9. Li, H., Mi, S., Li, Q., Wen, X., Qiao, D., Luo, G.: A scheduling optimization method for maintenance, repair and operations service resources of complex products. Journal of Intelligent Manufacturing 31(7), 1673–1691 (2020)

    Article  Google Scholar 

  10. Cai, T., Li, J., Mian, A.S., Sellis, T., Yu, J.X., et al.: Target-aware holistic influence maximization in spatial social networks. IEEE Transactions on Knowledge and Data Engineering (2020)

  11. Jiongzhe, H., Bin, L., Weida, W., Xinze, S., Longshui, Y.: Optimization of scheduling rules for aircraft cabin cleaners. Mechanical & Electrical Engineering Technology 49(02), 25–29 (2020)

    Google Scholar 

  12. Yannibelli, V., Amandi, A.: A knowledge-based evolutionary assistant to software development project scheduling. Expert Systems with Applications 38(7), 8403–8413 (2011)

    Article  Google Scholar 

  13. Xindong, W., Fei, Y.: Task model and optimization of metro dispatcher for fully automatic operation. China Safety Science Journal 29(S1), 94 (2019)

    Google Scholar 

  14. Özder, E.H., Özcan, E., Eren, T.: Sustainable personnel scheduling problem optimization in a natural gas combined-cycle power plant. Processes 7(10), 702 (2019)

    Article  Google Scholar 

  15. Zhou, Y., Pahwa, A., Yang, S.-S.: Modeling weather-related failures of overhead distribution lines. IEEE Transactions on Power Systems 21(4), 1683–1690 (2006)

    Article  Google Scholar 

  16. Lin, X., Ke, S., Li, Z., Weng, H., Han, X.: A fault diagnosis method of power systems based on improved objective function and genetic algorithm-tabu search. IEEE Transactions on Power Delivery 25(3), 1268–1274 (2010)

    Article  Google Scholar 

  17. Teive, R., Coelho, J., Camargo, C.D.B., Charles, P., Lange, T., Cimino, L.: A bayesian network approach to fault diagnosis and prognosis in power transmission systems. In: 2011 16th International conference on intelligent system applications to power systems. pp. 1–6 (2011)

  18. Sun, Q., Wang, C., Wang, Z., Liu, X.: A fault diagnosis method of smart grid based on rough sets combined with genetic algorithm and tabu search. Neural Computing and Applications 23(7), 2023–2029 (2013)

    Article  Google Scholar 

  19. Bošnjak, I., Madarász, R.: Compatibility of fuzzy power relations. Fuzzy Sets and Systems 298, 44–55 (2016)

    Article  MATH  Google Scholar 

  20. Li, Z., Wang, X., Li, J., Zhang, Q.: Deep attributed network representation learning of complex coupling and interaction. Knowledge-Based Systems 212, 106618 (2021)

    Article  Google Scholar 

  21. Kleilat, I., Al-Sheikh, H., Moubayed, N., Hoblos, G.: Robust fault diagnosis of sensor faults in power converter used in hybrid electric vehicle. IFAC-PapersOnLine 51(24), 326–331 (2018)

    Article  Google Scholar 

  22. Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: Nsga-ii. IEEE Transactions on Evolutionary Computation 6(2), 182–197 (2002)

    Article  Google Scholar 

  23. Srinivas, N., Deb, K.: Muiltiobjective optimization using nondominated sorting in genetic algorithms. Evolutionary Computation 2(3), 221–248 (1994)

    Article  Google Scholar 

  24. Esfe, M.H., Hajmohammad, H., Moradi, R., Arani, A.A.A.: Multi-objective optimization of cost and thermal performance of double walled carbon nanotubes/water nanofluids by nsga-ii using response surface method. Applied Thermal Engineering 112, 1648–1657 (2017)

    Article  Google Scholar 

  25. Rabbani, M., Farrokhi-Asl, H., Asgarian, B.: Solving a bi-objective location routing problem by a nsga-ii combined with clustering approach: application in waste collection problem. Journal of Industrial Engineering International 13(1), 13–27 (2017)

    Article  Google Scholar 

  26. Rabbani, M., Heidari, R., Yazdanparast, R.: A stochastic multi-period industrial hazardous waste location-routing problem: Integrating nsga-ii and monte carlo simulation. European Journal of Operational Research 272(3), 945–961 (2019)

    Article  MATH  Google Scholar 

  27. Wang, P., Xue, F., Li, H., Cui, Z., Xie, L., Chen, J.: A multi-objective dv-hop localization algorithm based on nsga-ii in internet of things. Mathematics 7(2), 184 (2019)

    Article  Google Scholar 

  28. Li, Y., Wang, S., Duan, X., Liu, S., Liu, J., Hu, S.: Multi-objective energy management for atkinson cycle engine and series hybrid electric vehicle based on evolutionary nsga-ii algorithm using digital twins. Energy Conversion and Management 230, 113788 (2021)

    Article  Google Scholar 

  29. Zitzler, E., Laumanns, M., Thiele, L.: Spea2: Improving the strength pareto evolutionary algorithm, TIK-report 103 (2001)

  30. Corne, D.W., Jerram, N.R., Knowles, J.D., Oates, M.J.: Pesa-ii: Region-based selection in evolutionary multiobjective optimization. In: Proceedings of the 3rd annual conference on genetic and evolutionary computation, pp. 283–290 (2001)

  31. Kennedy, J., Eberhart, R.: Particle swarm optimization, In: Proceedings of ICNN’95-international conference on neural networks, vol. 4, pp. 1942–1948 (1995)

  32. Chen, J., Zhong, M., Li, J., Wang, D., Qian, T., Tu, H.: Effective deep attributed network representation learning with topology adapted smoothing. IEEE Transactions on Cybernetics (2021)

  33. Dorigo, M., Maniezzo, V., Colorni, A.: Ant system: optimization by a colony of cooperating agents, IEEE Transactions on Systems, Man, and Cybernetics. Part B (Cybernetics) 26(1), 29–41 (1996)

    Article  Google Scholar 

  34. Kirkpatrick, S., Gelatt, C.D., Vecchi, M.P.: Optimization by simulated annealing. Science 220(4598), 671–680 (1983)

    Article  MATH  Google Scholar 

  35. Tian, Y., Cheng, R., Zhang, X., Jin, Y.: Platemo: A matlab platform for evolutionary multi-objective optimization [educational forum]. IEEE Computational Intelligence Magazine 12(4), 73–87 (2017)

    Article  Google Scholar 

  36. Yang, Y., Guan, Z., Li, J., Zhao, W., Cui, J., Wang, Q.: Interpretable and efficient heterogeneous graph convolutional network. IEEE Transactions on Knowledge and Data Engineering (2021)

Download references

Acknowledgements

This paper is partially supported by National Natural Science Foundation of China (No.62076224, No.U1711266 and No.41925007)

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jining Yan.

Ethics declarations

Conflicts of interest

The authors have no conficts of interest to declare that are relevant to the content of this article.

Additional information

Yunliang Chen, Nian Zhang, Guishui Zhu and Geyong Min contributed equally to this work.

This article belongs to the Topical Collection: Special Issue on Decision Making in Heterogeneous Network Data Scenarios and Applications

Guest Editors: Jianxin Li, Chengfei Liu, Ziyu Guan, and Yinghui Wu

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chen, Y., Zhang, N., Yan, J. et al. Optimization of maintenance personnel dispatching strategy in smart grid. World Wide Web 26, 139–162 (2023). https://doi.org/10.1007/s11280-022-01019-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11280-022-01019-0

Keywords

Navigation