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

Huizhong Song, Ming Dong, Rongjie Han, Fushuan Wen, Md. Abdus Salam, Xiaogang Chen, Hua Fan and Jian Ye
Additional contact information
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
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
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

Energies, 2018, vol. 11, issue 10, 1-13

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.

Keywords: power system; fault diagnosis; analytic model; chance-constrained programming (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2018
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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