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Stochastic Programming-Based Fault Diagnosis in Power Systems Under Imperfect and Incomplete Information

Author

Listed:
  • Huizhong Song

    (State Grid Hangzhou Xiaoshan Power Supply Company, Beiganshan Road 12, Hangzhou 311201, China)

  • Ming Dong

    (School of Electrical Engineering, Zhejiang University, No. 38 Zheda Rd., Hangzhou 310027, China
    State Grid Dalian Power Supply Company, Zhongshan Road 102, Dalian 116000, China)

  • Rongjie Han

    (State Grid Hangzhou Xiaoshan Power Supply Company, Beiganshan Road 12, Hangzhou 311201, China)

  • Fushuan Wen

    (Department for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh City, Vietnam
    Faculty of Electrical and Electronics Engineering, Ton Duc Thang University, Ho Chi Minh City, Vietnam)

  • Md. Abdus Salam

    (Department of Electrical and Electronic Engineering, Universiti Teknologi Brunei, Bandar Seri Begawan BE1410, Brunei)

  • Xiaogang Chen

    (State Grid Hangzhou Xiaoshan Power Supply Company, Beiganshan Road 12, Hangzhou 311201, China)

  • Hua Fan

    (State Grid Hangzhou Xiaoshan Power Supply Company, Beiganshan Road 12, Hangzhou 311201, China)

  • Jian Ye

    (State Grid Hangzhou Xiaoshan Power Supply Company, Beiganshan Road 12, Hangzhou 311201, China)

Abstract

When a fault occurs in a section or a component of a given power system, the malfunctioning of protective relays (PRs) and circuit breakers (CBs), and the false and missing alarms, may manifestly complicate the fault diagnosis procedure. It is necessary to develop a methodologically appropriate framework for this application. As a branch of stochastic programming, the well-developed chance-constrained programming approach provides an efficient way to solve programming problems fraught with uncertainties. In this work, a novel fault diagnosis analytic model is developed with the ability of accommodating the malfunctioning of PRs and CBs, as well as the false and/or missing alarms. The genetic algorithm combined with Monte Carlo simulations are then employed to solve the optimization model. The feasibility and efficiency of the developed model and method are verified by a real fault scenario in an actual power system. In addition, it is demonstrated by simulation results that the computation speed of the developed method meets the requirements for the on-line fault diagnosis of actual power systems.

Suggested Citation

  • Huizhong Song & Ming Dong & Rongjie Han & Fushuan Wen & Md. Abdus Salam & Xiaogang Chen & Hua Fan & Jian Ye, 2018. "Stochastic Programming-Based Fault Diagnosis in Power Systems Under Imperfect and Incomplete Information," Energies, MDPI, vol. 11(10), pages 1-13, September.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:10:p:2565-:d:172112
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    Cited by:

    1. Ziyu Bai & Guoqiang Sun & Haixiang Zang & Ming Zhang & Peifeng Shen & Yi Liu & Zhinong Wei, 2019. "Identification Technology of Grid Monitoring Alarm Event Based on Natural Language Processing and Deep Learning in China," Energies, MDPI, vol. 12(17), pages 1-19, August.

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