Skip to main content

Advertisement

Springer Nature Link
Log in

An approach for Condition Based Maintenance strategy optimization oriented to multi-source data

  • Published:
Cluster Computing Aims and scope Submit manuscript

Abstract

Condition Based Maintenance (CBM) scheduling for power equipment is based on some types of data in power system, such as the health condition of components, the load level of the substation and Life Cycle Cost (LCC), etc. Due to the lack of necessary data, traditional CBM strategy optimization models are usually established without enough data, and the optimization results are usually difficult to obtain the enough accuracy. With the development of Chinese power industry, the data volume in power system is increasing dramatically in recent years, and the power industry is undoubtedly entering the era of Big Data. To handle and store these increasing data, more and more data management systems have been developed for different application. For example, Energy Management System, Production Management System and Management Information System have been widely applied. The multi-source data in these systems gives an opportunity to improve the accuracy and rationality for CBM optimization results. Thus, it is of great significance to make full use of these data in maintenance strategy optimization process. This paper presents a new approach to optimize the CBM strategy for components in the substation. After analyzing the interconnection relations of different types of components, the substation is divided into different maintenance units, the components in the same unit can be maintained together. To quantitatively evaluate the reliability of the components before and after repaired, two failure rate calculation models based on Health Index (HI) and age reduction factor are established respectively, and all the alternative maintenance strategies for the abnormal components are proposed based on the location and severity of the faults or defects of the components. According to the theory of LCC, the CBM optimization model is established when determines the minimum total cost as the optimization goal during the maintenance period. The total cost consists of the reparation cost, the interruption cost and the maintenance cost. Finally, an application example in a 220 kV substation is proposed, and these multi-source data are fully applied in the optimization process. The calculation results indicate that these types of data have benefit to improve the accuracy and rationality of the optimization results, and the strategy after optimized can obviously improve both the power supply reliability and the economy of the substation.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. Du, X., Liu, W.: Evaluation of power system reliability based on the maintenance state. In: 2011 4th International Conference on Electric Utility Deregulation and Restructuring and Power Technologies (DRPT), F 6–9 July 2011, pp. 1016–1020 (2011)

  2. Huynh, K.T., Castro, I.T., Barros, A., et al.: On the use of mean residual life as a condition index for condition-based maintenance decision-making. IEEE Trans. Syst. Man Cybern. 44(7), 877–893 (2014)

    Article  Google Scholar 

  3. Li, Y., Han, X., Xu, B. et al.: A condition-based maintenance approach to an optimal maintenance strategy considering equipment imperfect maintenance model. In: 2015 5th International Conference on Electric Utility Deregulation and Restructuring and Power Technologies (DRPT), F 26–29 Nov 2015, pp. 1466–1471 (2015)

  4. Wang, Z., Xu, Y., Liu, S.: Condition Monitoring and Fault Diagnosis for Power Equipment. Shanghai Jiaotong University Press, Shang Hai (2012)

    Google Scholar 

  5. IEEE Recommended Practice for the Maintenance of Industrial and Commercial Power Systems. IEEE Std 30072-2010, pp. 1–56 (2010)

  6. Lu, K., Zhang, Y., Suo, M.: Study on the maintenance strategy of power equipment based on optimal LCC. In: 2011 Asia-Pacific Power and Energy Engineering Conference (APPEEC), F 25–28 Mar 2011, pp. 1–5 (2011)

  7. Herald, T.E., Ramirez-Marquez, J.E.: System element obsolescence replacement optimization via life cycle cost forecasting. IEEE Trans. Compon. Packag. Manuf. Technol. 2(8), 1394–1401 (2012)

    Article  Google Scholar 

  8. Li, Y., Han, X., Xu, B. et al.: A condition-based maintenance approach to an optimal maintenance strategy considering equipment imperfect maintenance model. In: 2015 5th International Conference on Electric Utility Deregulation and Restructuring and Power Technologies (DRPT), F 26–29 Nov 2015, pp. 1466–1471 (2015)

  9. Hongdong, F., Changhua, H., Maoyin, C., et al.: Cooperative predictive maintenance of repairable systems with dependent failure modes and resource constraint. IEEE Trans. Reliab. 60(1), 144–157 (2011)

    Article  Google Scholar 

  10. IEEE Guide for Maintenance Operation, and safety of industrial and commercial power systems (Yellow Book). IEEE Std. 902, 21–28 (1998)

  11. Aldhubaib, H.A., Salama, M.M.A.: A novel approach to investigate the effect of maintenance on the replacement time for transformers. IEEE Trans. Power Deliv. 29(4), 1603–1612 (2014)

    Article  Google Scholar 

  12. Abbasghorbani, M., Mashhadi, H.R., Damchi, Y.: Reliability-centred maintenance for circuit breakers in transmission networks. IET Gener. Transm. Distrib. 8(9), 1583–1590 (2014)

    Article  Google Scholar 

  13. Corporation, The State Grid: Guide for Condition Based Maintenance Strategy of SF6 High-Voltage Circuit Breaker Q/GDW 172–2008. The State Grid Corporation, Beijing (2008)

    Google Scholar 

  14. Corporation, The State Grid: Guide for Condition Based Maintenance Strategy of Oil-immersed Power Transformers (Reactors). The State Grid Corporation, Beijing (2008)

    Google Scholar 

  15. Camci, F.: System maintenance scheduling with prognostics information using genetic algorithm. IEEE Trans. Reliab. 58(3), 539–552 (2009)

    Article  Google Scholar 

  16. Vu, H.C., Do, P., Barros, A.: A stationary grouping maintenance strategy using mean residual life and the Birnbaum importance measure for complex structures. IEEE Trans. Reliab. 65(1), 217–234 (2016)

    Article  Google Scholar 

  17. Bo, X., Xueshan, H., Changgen, L., et al.: Condition-based maintenance decision-making model for power system based on association sets decomposition. Autom. Electr. Power Syst. 39, 46–52 (2015)

    Google Scholar 

  18. Huang, Y., Zhou, X.: Knowledge model for electric power big data based on ontology and semantic web. CSEE J. Power Energy Syst. 1(1), 19–27 (2015)

    Article  Google Scholar 

  19. Xiaosheng, Peng, Diyuan, Deng, Shijie, Chen, et al.: Key technologies of electric power big data and its application prospects in smart grid. Proc. CSEE 35, 503–511 (2016)

    Google Scholar 

  20. Zhang, P., Yang, H., Xu, Y.: Power big data and its application scenarios in power grid. Proc. CSEE 34, 85–92 (2014)

    MathSciNet  Google Scholar 

  21. Chinese Society for Electrical Engineering Information Committee. Chinese Electric Power Big Data Development White Paper. Chinese Society for Electrical Engineering, Beijing, China (2013)

  22. Lin, L., Liu, X., Han, Y.: Optimization model and algorithm for maintenance scheduling of power supply equipments. Electr. Power Autom. Equip. 32(8), 91–94 (2012)

    Google Scholar 

  23. Peng, X., Deng, Y., et al.: Key technologies of electric power big data and its application prospects in smart grid. Proc. CSEE 35(3), 503–511 (2015)

    MathSciNet  Google Scholar 

  24. Fei, S.W., Sun, Y.: Forecasting dissolved gases content in power transformer oil based on support vector machine with genetic algorithm. Electr. Power Syst. Res. 78(3), 507–514 (2008)

    Article  Google Scholar 

  25. Ghunem, R.A., Assaleh, K., El-Hag, A.H.: Artificial neural networks with stepwise regression for predicting transformer oil furan content. IEEE Trans. Dielectr. Electr. Insulation 19(2), 414–420 (2012)

    Article  Google Scholar 

  26. Changchang, W.A.N.G., Qifu, L.I., Shengyou, G.A.O.: On-Line Monitoring and Fault Diagnosis for Power Equipment. Tsinghua University Press, Beijing (2006)

    Google Scholar 

  27. Lezhen, P., Zhang, Y., Yu, G., et al.: Prediction of electrical equipment failure rate for condition-based maintenance decision-making. Electr. Power Autom. Equip. 30(2), 91–94 (2010)

    Google Scholar 

  28. Corporation, The State Grid: Guide for Condition Evaluation of SF6 High-Voltage Circuit Breaker Q/GDW 172–2008. The State Grid Corporation, Beijing (2008)

    Google Scholar 

  29. Corporation, The State Grid: Guide for Evaluation of Oil-immersed Power Transformers(Reactors). The State Grid Corporation, Beijing (2008)

    Google Scholar 

  30. Li, W.: Risk Assessment of Power Systems: Models, Methods, and Applications. Wiley, New York (2005)

    Google Scholar 

  31. Corporation, The State Grid: Guide for Risk Assessment of Transmission and Transformation Components Q/GDW 172–2008. The State Grid Corporation, Beijing (2008)

    Google Scholar 

  32. Rui, Y., Fu, G.: The Modern Reliability Fesign. National Defence Industry Press, Beijing (2007)

    Google Scholar 

  33. Sun, X., Lu, C.: Reliability Engineering. National Defence Industry Press, Beijing (2005)

    Google Scholar 

  34. Han, B., Fan, X., Ma, D.: Optimal policy research of preventive maintenance in finite time horizon. J. Shanghai Jiaotong Univ. 37(5), 679–682 (2003)

    Google Scholar 

  35. Li, W., Korczynski, J.: A reliability based approach to transmission maintenance planning and its application in BC hydro system. IEEE Trans. Power Deliv. 19, 303–308 (2004)

    Article  Google Scholar 

  36. Billinton, R., Li, W.: Reliability Assessment of Electric Power Systems Using Monte Carlo Approaches. Plenum Press, New York (1994)

    Book  MATH  Google Scholar 

  37. Mingxin, Z., Sige, L., Hai, C. et al.: Risk assessment based Life-Cycle-Cost model for distribution network. In: 2012 IEEE Innovative Smart Grid Technologies—Asia (ISGT Asia), F 21–24 May 2012, pp. 1–5 (2012)

  38. Junqin, S.: Life Cycle Management for Electric Enterprise Assets: Theory, Approaches and Applications. China Electric Power Press, Beijing (2010)

    Google Scholar 

Download references

Acknowledgments

The work is in part supported by the National High-tech R&D Program of China (863 Program, 2015AA050204) and Science and Technology Program of State Grid Corporation of China (No.GY71-15-045).

Author information

Authors and Affiliations

  1. State Key Laboratory of Power Transmission Equipment & System Security and New Technology, School of Electrical Engineering, Chongqing University, Chongqing, 400044, China

    Youyuan Wang & Hang Liu

  2. China Electric Power Research Institute, Beijing, 100192, China

    Jiangang Bi, Feng Wang & Chunyu Yan

  3. Anhui Electric Power Research Institute, Hefei, 200030, China

    Taiyun Zhu

Authors
  1. Youyuan Wang
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Hang Liu
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Jiangang Bi
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Feng Wang
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Chunyu Yan
    View author publications

    You can also search for this author inPubMed Google Scholar

  6. Taiyun Zhu
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Hang Liu.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, Y., Liu, H., Bi, J. et al. An approach for Condition Based Maintenance strategy optimization oriented to multi-source data. Cluster Comput 19, 1951–1962 (2016). https://doi.org/10.1007/s10586-016-0626-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10586-016-0626-1

Keywords