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

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 fram...

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Main Authors: Huizhong Song, Ming Dong, Rongjie Han, Fushuan Wen, Md. Abdus Salam, Xiaogang Chen, Hua Fan, Jian Ye
Format: Article
Language:English
Published: MDPI AG 2018-09-01
Series:Energies
Subjects:
Online Access:http://www.mdpi.com/1996-1073/11/10/2565
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author Huizhong Song
Ming Dong
Rongjie Han
Fushuan Wen
Md. Abdus Salam
Xiaogang Chen
Hua Fan
Jian Ye
author_facet Huizhong Song
Ming Dong
Rongjie Han
Fushuan Wen
Md. Abdus Salam
Xiaogang Chen
Hua Fan
Jian Ye
author_sort Huizhong Song
collection DOAJ
description 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.
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spelling doaj.art-d8ae585102224ba7b1b308859a8230972022-12-22T04:10:35ZengMDPI AGEnergies1996-10732018-09-011110256510.3390/en11102565en11102565Stochastic Programming-Based Fault Diagnosis in Power Systems Under Imperfect and Incomplete InformationHuizhong Song0Ming Dong1Rongjie Han2Fushuan Wen3Md. Abdus Salam4Xiaogang Chen5Hua Fan6Jian Ye7State Grid Hangzhou Xiaoshan Power Supply Company, Beiganshan Road 12, Hangzhou 311201, ChinaSchool of Electrical Engineering, Zhejiang University, No. 38 Zheda Rd., Hangzhou 310027, ChinaState Grid Hangzhou Xiaoshan Power Supply Company, Beiganshan Road 12, Hangzhou 311201, ChinaDepartment for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh City, VietnamDepartment of Electrical and Electronic Engineering, Universiti Teknologi Brunei, Bandar Seri Begawan BE1410, BruneiState Grid Hangzhou Xiaoshan Power Supply Company, Beiganshan Road 12, Hangzhou 311201, ChinaState Grid Hangzhou Xiaoshan Power Supply Company, Beiganshan Road 12, Hangzhou 311201, ChinaState Grid Hangzhou Xiaoshan Power Supply Company, Beiganshan Road 12, Hangzhou 311201, ChinaWhen 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.http://www.mdpi.com/1996-1073/11/10/2565power systemfault diagnosisanalytic modelchance-constrained programming
spellingShingle Huizhong Song
Ming Dong
Rongjie Han
Fushuan Wen
Md. Abdus Salam
Xiaogang Chen
Hua Fan
Jian Ye
Stochastic Programming-Based Fault Diagnosis in Power Systems Under Imperfect and Incomplete Information
Energies
power system
fault diagnosis
analytic model
chance-constrained programming
title Stochastic Programming-Based Fault Diagnosis in Power Systems Under Imperfect and Incomplete Information
title_full Stochastic Programming-Based Fault Diagnosis in Power Systems Under Imperfect and Incomplete Information
title_fullStr Stochastic Programming-Based Fault Diagnosis in Power Systems Under Imperfect and Incomplete Information
title_full_unstemmed Stochastic Programming-Based Fault Diagnosis in Power Systems Under Imperfect and Incomplete Information
title_short Stochastic Programming-Based Fault Diagnosis in Power Systems Under Imperfect and Incomplete Information
title_sort stochastic programming based fault diagnosis in power systems under imperfect and incomplete information
topic power system
fault diagnosis
analytic model
chance-constrained programming
url http://www.mdpi.com/1996-1073/11/10/2565
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AT fushuanwen stochasticprogrammingbasedfaultdiagnosisinpowersystemsunderimperfectandincompleteinformation
AT mdabdussalam stochasticprogrammingbasedfaultdiagnosisinpowersystemsunderimperfectandincompleteinformation
AT xiaogangchen stochasticprogrammingbasedfaultdiagnosisinpowersystemsunderimperfectandincompleteinformation
AT huafan stochasticprogrammingbasedfaultdiagnosisinpowersystemsunderimperfectandincompleteinformation
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