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Fault Detection and Localization for Overhead 11-kV Distribution Lines With Magnetic Measurements.

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Title: Fault Detection and Localization for Overhead 11-kV Distribution Lines With Magnetic Measurements.
Authors: Kazim, Muhammad1 (AUTHOR) arsalan@uspcase.nust.edu.pk, Khawaja, Arsalan Habib1 (AUTHOR), Zabit, Usman2 (AUTHOR), Huang, Qi3 (AUTHOR)
Source: IEEE Transactions on Instrumentation & Measurement. May2020, Vol. 69 Issue 5, p2028-2038. 11p.
Subjects: Power distribution networks, Magnetic measurements, Magnetic sensors, Magnetic fields
Abstract: Electric power distribution network holds a critical role in uninterruptable power supply for modern societies. The distribution network comprising of complex of primary and secondary distribution lines is vulnerable to various short-circuit (SC) faults caused by lightning strikes, storms, growing vegetation, animals, insulation breakdown, and other environmental situations. Most SC faults result in breakdowns that need to be repaired in order to restore electric supply to consumers. Rapid and accurate fault localization method helps in an accelerated system restoration process and thus reduces power outage time. In this paper, a new approach related to the noncontact magnetic field (MF)-based measurement system to localize SC faults on 11-kV overhead distribution lines is presented. The method uses highly sensitive and energy-efficient magnetic sensors to detect variations in MF levels measured along the distribution lines. The fault localization algorithm is developed which identifies SC fault on main feeders and its subbranches and then localize the fault by analyzing MF over window size of 20 cycles. The developed algorithm is implemented on a complex distribution system and tested for different cases. Laboratory experiments were conducted to detect and localize SC faults in a multibranch configuration to validate the proposed method, where measurement uncertainty of less than 5% is observed for the worst case scenario. [ABSTRACT FROM AUTHOR]
Copyright of IEEE Transactions on Instrumentation & Measurement is the property of IEEE and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Fault Detection and Localization for Overhead 11-kV Distribution Lines With Magnetic Measurements.
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  Data: <searchLink fieldCode="AR" term="%22Kazim%2C+Muhammad%22">Kazim, Muhammad</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> arsalan@uspcase.nust.edu.pk</i><br /><searchLink fieldCode="AR" term="%22Khawaja%2C+Arsalan+Habib%22">Khawaja, Arsalan Habib</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zabit%2C+Usman%22">Zabit, Usman</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Huang%2C+Qi%22">Huang, Qi</searchLink><relatesTo>3</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22IEEE+Transactions+on+Instrumentation+%26+Measurement%22">IEEE Transactions on Instrumentation & Measurement</searchLink>. May2020, Vol. 69 Issue 5, p2028-2038. 11p.
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– Name: Abstract
  Label: Abstract
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  Data: Electric power distribution network holds a critical role in uninterruptable power supply for modern societies. The distribution network comprising of complex of primary and secondary distribution lines is vulnerable to various short-circuit (SC) faults caused by lightning strikes, storms, growing vegetation, animals, insulation breakdown, and other environmental situations. Most SC faults result in breakdowns that need to be repaired in order to restore electric supply to consumers. Rapid and accurate fault localization method helps in an accelerated system restoration process and thus reduces power outage time. In this paper, a new approach related to the noncontact magnetic field (MF)-based measurement system to localize SC faults on 11-kV overhead distribution lines is presented. The method uses highly sensitive and energy-efficient magnetic sensors to detect variations in MF levels measured along the distribution lines. The fault localization algorithm is developed which identifies SC fault on main feeders and its subbranches and then localize the fault by analyzing MF over window size of 20 cycles. The developed algorithm is implemented on a complex distribution system and tested for different cases. Laboratory experiments were conducted to detect and localize SC faults in a multibranch configuration to validate the proposed method, where measurement uncertainty of less than 5% is observed for the worst case scenario. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
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  Data: <i>Copyright of IEEE Transactions on Instrumentation & Measurement is the property of IEEE and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1109/TIM.2019.2920184
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      – Code: eng
        Text: English
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        PageCount: 11
        StartPage: 2028
    Subjects:
      – SubjectFull: Power distribution networks
        Type: general
      – SubjectFull: Magnetic measurements
        Type: general
      – SubjectFull: Magnetic sensors
        Type: general
      – SubjectFull: Magnetic fields
        Type: general
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      – TitleFull: Fault Detection and Localization for Overhead 11-kV Distribution Lines With Magnetic Measurements.
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            NameFull: Zabit, Usman
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            NameFull: Huang, Qi
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              M: 05
              Text: May2020
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              Y: 2020
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