Structural learning approach to replacing unreliable units in a power system

Shamshul Bahar Yaakob, Tsuguhiro Takahashi, Tatsuki Okamoto, Toshikatsu Tanaka, Tran Duc Minh, Junzo Watada, Zhang Xiaojun

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    7 Citations (Scopus)

    Abstract

    Power supply failure will cause major social loss. Therefore, power supply systems have been required to be highly reliable. This research deals with a significant and effective method to decide the investment in a power system damage of the power supply on the society. In CMD2007 Korea, we proposed mean-variance approach to replacing unreliable units in a power system. This paper extends the method so as to solve a problem efficiently. In this research, we will propose the structural learning of a mutual connective neural network. The proposed method enables us to solve the problem defined in terms of mixed integer quadratic programming. In this research, an analysis is performed by using the concepts of the reliability and risks of units evaluated using a variance-covariance matrix and also the effect and expenses of replacement are measured. Mean-variance analysis is formulated as a mathematical programming with two objectives to minimize the risk and maximize the expected return. Finally, we employ a Boltzmann machine to solve the meanvariance analysis efficiently. The result of our method is exemplified using a power network system in Tokyo Metropolitan. By using this method, a more effective selection of results is obtained. In other words, the decision makers can select the expected investment rate and risk of each ward depending on the given total budget. For this reason, the effectiveness of the decision making process can be enhanced.

    Original languageEnglish
    Title of host publicationProceedings of 2008 International Conference on Condition Monitoring and Diagnosis, CMD 2008
    Pages570-575
    Number of pages6
    DOIs
    Publication statusPublished - 2007
    Event2008 International Conference on Condition Monitoring and Diagnosis, CMD 2008 - Beijing
    Duration: 2008 Apr 212008 Apr 24

    Other

    Other2008 International Conference on Condition Monitoring and Diagnosis, CMD 2008
    CityBeijing
    Period08/4/2108/4/24

    Keywords

    • Boltzmann machine
    • Mean-variance analysis
    • Power system maintenance
    • Unit replacing

    ASJC Scopus subject areas

    • Industrial and Manufacturing Engineering
    • Safety, Risk, Reliability and Quality

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  • Cite this

    Yaakob, S. B., Takahashi, T., Okamoto, T., Tanaka, T., Minh, T. D., Watada, J., & Xiaojun, Z. (2007). Structural learning approach to replacing unreliable units in a power system. In Proceedings of 2008 International Conference on Condition Monitoring and Diagnosis, CMD 2008 (pp. 570-575). [4580352] https://doi.org/10.1109/CMD.2008.4580352