Skip to main content
SpringerLink
Log in

Reliability enhancement of power systems through a mean–variance approach

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Recently, power-supply failures have caused major social losses. Therefore, power-supply systems need to be highly reliable. The objective of this study is to present a significant and effective method of determining a productive investment to protect a power-supply system from damage. In this study, the reliability and risks of each of the units are evaluated with a variance–covariance matrix, and the effects and expenses of replacement are analyzed. The mean–variance analysis is formulated as a mathematical program with the following two objectives: (1) to minimize the risk and (2) to maximize the expected return. Finally, a structural learning model of a mutual connection neural network is proposed to solve problems defined by mixed-integer quadratic programming and is employed in the mean–variance analysis. Our method is applied to a power system network in the Tokyo Metropolitan area. This method enables us to select results more effectively and enhance decision making. In other words, decision-makers can select the investment rate and risk of each ward within a given total budget.

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

Access this article

Log in via an institution

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

Similar content being viewed by others

References

  1. Nash HO, Wells MF (1984) Power system disturbances and considerations for power conditioning. In: Conference of recommended industrial and commercial power system technical conferences, pp 1–10

  2. National Fire Protection Association (2006) NFPA 70B: recommended practice for electrical equipment maintenance. In: 2006 Edition

  3. IEEE-SA Standards Board (1997) IEEE recommended practice for the design of reliable industrial and commercial power systems. In: Revision of IEEE Std, pp 493–1997

  4. Handa K, Matsumoto S, Nakamoto M, Uchihira N (2005) An optimization method for investment and maintenance planning of power plants under uncertain environments. IEICE Trans Fundam E88-A(6):1481–1486

    Article  Google Scholar 

  5. Ang BW, Huang JP, Poh KL (1999) Break-even price of distributed generation under uncertainty. Ene J 24(7):579–589

    Article  Google Scholar 

  6. Hoff TE (1997) Using distributed resources to manage risk caused by demand uncertainty. Ene J 21(Special Issue Distributed Resources):63–83

    Google Scholar 

  7. Markowitz H (1987) Mean–variance analysis in portfolio choice and capital markets. In: Blackell

  8. Wei J, Takahashi T, Watada J (2006) Mean–variance approach to replacing unreliable units in a power system. In: International conference on condition monitoring and diagnosis

  9. Toni J, Ahti S (2009) Multicriteria partner selection in virtual organizations with transportation costs and other network interdependencies. IEEE Trans Syst Man Cybern C Appl Rev l 39(1):124–129

    Google Scholar 

  10. Lin C-J, Chen C-H, Lin C-T (2009) A hybrid of cooperative particle swarm optimization and cultural algorithm for neural fuzzy networks and its prediction applications. IEEE Trans Syst Man Cybern C Appl Rev 39(1):55–68

    Article  Google Scholar 

  11. Chen J, Lin S (2004) A neural network approach-decision neural network (DNN) for preference assessment. IEEE Trans Syst Man Cybern C Appl Rev 34(2):219–225

    Article  Google Scholar 

  12. Yu L, Zhang Y-Q (2005) Evolutionary fuzzy neural networks for hybrid financial predictions. IEEE Trans Syst Man Cybern C Appl Rev 35(2):244–249

    Article  Google Scholar 

  13. Figueiredo M, Ballini R, Soares S, Andrade M, Gomide F (2004) Learning algorithm for a class of neurofuzzy network and application. IEEE Trans Syst Man Cybern C Appl Rev 34(3):293–301

    Article  Google Scholar 

  14. Tan KK, Huang SN, Lim SY, Leow YP, Liaw HC (2006) Geometrical error modeling and compensation using neural networks. IEEE Trans Syst Man Cybern C Appl Rev 36(6):797–809

    Article  Google Scholar 

  15. Shouhong W, Hai W (2008) Password authentication using Hopfield neural network. IEEE Trans Syst Man Cybern C Appl Rev 38(2):265–268

    Article  Google Scholar 

  16. Pei-Chang C, Chin-Fuan F, Chen-Hao L (2009) Integrating a piecewise linear representation method and a neural network model for stock trading points prediction. IEEE Trans Syst Man Cybern C Appl Rev 39(1):80–92

    Article  Google Scholar 

  17. Dongsong Z, Lina Z (2004) Discovering golden nuggets: data mining in financial application. IEEE Trans Syst Man Cybern C Appl Rev 34(4):513–522

    Article  Google Scholar 

  18. Ackley DH, Hinton GE, Sejnowski TJ (1985) A learning algorithm for Boltzmann machines. Cogn Sci 9:147–169

    Article  Google Scholar 

  19. Hopfield JJ (1982) Neural networks and physical systems with emergent collective computational abilities. In: Proceedings of national science, pp 2554–2558

  20. Watada J (1997) Fuzzy portfolio selection and its applications to decision making. Tatra Moutains Math Publ 13:219–248

    MathSciNet  MATH  Google Scholar 

  21. Watanabe T, Watada J, Oda K (1999) Hierarchical decision making in strategic investment by a Boltzmann machine. Int J Uncertain Fuzziness Knowl Syst 7(4):429–437

    Article  MATH  Google Scholar 

  22. Watada J, Oda K (2000) Formulation of a two-layered Boltzmann machine for portfolio selection. Int J Fuzzy Syst 2(1):39–44

    Google Scholar 

  23. Watanabe T, Watada J (2004) A meta-controlled Boltzmann machine for rebalancing portfolio selection. Cent Euro J Oper Res 12(1):93–103

    MathSciNet  MATH  Google Scholar 

  24. Hobbs BF (1995) Optimization methods for electric utility resource planning. Euro J Oper Res 83(1):1–20

    Article  MATH  Google Scholar 

  25. Nishikawa Y, Tezuka T, Kita H, Nakano S (1992) An optimal electric power-plant planning under uncertainty of demand by means of the scenario aggregation algorithm. The Institute of Electrical Engineers of Japan Trans. Electron Inform Syst 112-C(6):356–363

    Google Scholar 

  26. Yasuda K, Tsunematsu M, Watanabe T (2001) A study on the robust generation mix under uncertain environments. The Institute of Electrical Engineers of Japan Trans. Power Ene 121-B(2):164–171

    Google Scholar 

  27. Muraoka Y, Oyama T (2003) Generation planning including distributed generator under uncertainty of demand growth. The Institute of Electrical Engineers of Japan Trans. Power Ene 123-B(2):162–168

    Google Scholar 

Download references

Acknowledgments

The first author would like to thank Universiti Malaysia Perlis and the Ministry of Higher Education Malaysia for a study leave in Waseda University under the SLAI-KPT scholarship.

Author information

Authors and Affiliations

  1. Graduate School of IPS, Waseda University, 2-7 Hibikino, Kitakyushu, 808-0135, Japan

    Shamshul Bahar Yaakob & Junzo Watada

  2. Electric Power Engineering Research Laboratory, Central Research Institute of Electric Power Industry, Yokotsuka, Kanagawa, 240-0196, Japan

    Tsuguhiro Takahashi & Tatsuki Okamoto

Authors
  1. Shamshul Bahar Yaakob
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Junzo Watada
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Tsuguhiro Takahashi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Tatsuki Okamoto
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shamshul Bahar Yaakob.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Yaakob, S.B., Watada, J., Takahashi, T. et al. Reliability enhancement of power systems through a mean–variance approach. Neural Comput & Applic 21, 1363–1373 (2012). https://doi.org/10.1007/s00521-011-0580-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-011-0580-z

Keywords

Search

Navigation