New ANN method for multi-terminal HVDC protection relaying

Qingqing Yang; Simon Le Blond; Raj Aggarwal; Yawei Wang; Jianwei Li

doi:10.1016/j.epsr.2017.03.024

New ANN method for multi-terminal HVDC protection relaying

Qingqing Yang, Simon Le Blond, Raj Aggarwal, Yawei Wang, Jianwei Li

University of Bath

Research output: Contribution to journal › Article › peer-review

137 Citations (Scopus)

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Abstract

This paper proposes a comprehensive novel multi-terminal HVDC protection scheme based on artificial neural network (ANN) and high frequency components detected from fault current signals only. The method is shown to accurately detect, classify and locate overhead line faults. Unlike existing travelling wave based methods which must capture the initial wavefront and require high sampling rates, the new approach is more robust since it gives accurate fault detection and fault location over a range of windowed post-fault signals. Furthermore, the proposed method is fault resistance independent meaning even a very high fault impedance has no effect on accurate fault location. A three-terminal VSC-HVDC system is modelled in PSCAD/EMTDC, which is used for obtaining the fault current data for transmission line terminals. The method is verified by studying different cases with a range of fault resistances in various fault locations, and in addition, external faults. The results show that the proposed method gives fast (<5 ms) and reliable (100%) fault detection and classification and accurate location (<1.16%) for DC line faults.

Original language	English
Pages (from-to)	192-201
Number of pages	10
Journal	Electric Power Systems Research
Volume	148
Early online date	7 Apr 2017
DOIs	https://doi.org/10.1016/j.epsr.2017.03.024
Publication status	Published - Jul 2017
Externally published	Yes

Bibliographical note

NOTICE: this is the author’s version of a work that was accepted for publication in Electric Power Systems Research. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Electric Power Systems Research, 148 (2017) DOI: 10.1016/j.epsr.2017.03.024

© 2017, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/

Keywords

Artificial neural network
Fault current signal
Fault detection
Fault location
Transmission line
VSC-HVDC system

Access to Document

10.1016/j.epsr.2017.03.024

Post-PrintAccepted author manuscript, 704 KBLicence: CC BY-NC-ND

https://researchportal.bath.ac.uk/en/publications/new-ann-method-for-multi-terminal-hvdc-protection-relayingLicence: CC BY-NC-ND

Cite this

@article{e6f87822ef00467998daf6f8e09652f1,

title = "New ANN method for multi-terminal HVDC protection relaying",

abstract = "This paper proposes a comprehensive novel multi-terminal HVDC protection scheme based on artificial neural network (ANN) and high frequency components detected from fault current signals only. The method is shown to accurately detect, classify and locate overhead line faults. Unlike existing travelling wave based methods which must capture the initial wavefront and require high sampling rates, the new approach is more robust since it gives accurate fault detection and fault location over a range of windowed post-fault signals. Furthermore, the proposed method is fault resistance independent meaning even a very high fault impedance has no effect on accurate fault location. A three-terminal VSC-HVDC system is modelled in PSCAD/EMTDC, which is used for obtaining the fault current data for transmission line terminals. The method is verified by studying different cases with a range of fault resistances in various fault locations, and in addition, external faults. The results show that the proposed method gives fast (<5 ms) and reliable (100%) fault detection and classification and accurate location (<1.16%) for DC line faults.",

keywords = "Artificial neural network, Fault current signal, Fault detection, Fault location, Transmission line, VSC-HVDC system",

author = "Qingqing Yang and Blond, {Simon Le} and Raj Aggarwal and Yawei Wang and Jianwei Li",

note = "NOTICE: this is the author{\textquoteright}s version of a work that was accepted for publication in Electric Power Systems Research. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Electric Power Systems Research, 148 (2017) DOI: 10.1016/j.epsr.2017.03.024 {\textcopyright} 2017, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/ ",

year = "2017",

month = jul,

doi = "10.1016/j.epsr.2017.03.024",

language = "English",

volume = "148",

pages = "192--201",

journal = "Electric Power Systems Research",

issn = "0378-7796",

publisher = "Elsevier",

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TY - JOUR

T1 - New ANN method for multi-terminal HVDC protection relaying

AU - Yang, Qingqing

AU - Blond, Simon Le

AU - Aggarwal, Raj

AU - Wang, Yawei

AU - Li, Jianwei

N1 - NOTICE: this is the author’s version of a work that was accepted for publication in Electric Power Systems Research. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Electric Power Systems Research, 148 (2017) DOI: 10.1016/j.epsr.2017.03.024 © 2017, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/

PY - 2017/7

Y1 - 2017/7

N2 - This paper proposes a comprehensive novel multi-terminal HVDC protection scheme based on artificial neural network (ANN) and high frequency components detected from fault current signals only. The method is shown to accurately detect, classify and locate overhead line faults. Unlike existing travelling wave based methods which must capture the initial wavefront and require high sampling rates, the new approach is more robust since it gives accurate fault detection and fault location over a range of windowed post-fault signals. Furthermore, the proposed method is fault resistance independent meaning even a very high fault impedance has no effect on accurate fault location. A three-terminal VSC-HVDC system is modelled in PSCAD/EMTDC, which is used for obtaining the fault current data for transmission line terminals. The method is verified by studying different cases with a range of fault resistances in various fault locations, and in addition, external faults. The results show that the proposed method gives fast (<5 ms) and reliable (100%) fault detection and classification and accurate location (<1.16%) for DC line faults.

AB - This paper proposes a comprehensive novel multi-terminal HVDC protection scheme based on artificial neural network (ANN) and high frequency components detected from fault current signals only. The method is shown to accurately detect, classify and locate overhead line faults. Unlike existing travelling wave based methods which must capture the initial wavefront and require high sampling rates, the new approach is more robust since it gives accurate fault detection and fault location over a range of windowed post-fault signals. Furthermore, the proposed method is fault resistance independent meaning even a very high fault impedance has no effect on accurate fault location. A three-terminal VSC-HVDC system is modelled in PSCAD/EMTDC, which is used for obtaining the fault current data for transmission line terminals. The method is verified by studying different cases with a range of fault resistances in various fault locations, and in addition, external faults. The results show that the proposed method gives fast (<5 ms) and reliable (100%) fault detection and classification and accurate location (<1.16%) for DC line faults.

KW - Artificial neural network

KW - Fault current signal

KW - Fault detection

KW - Fault location

KW - Transmission line

KW - VSC-HVDC system

U2 - 10.1016/j.epsr.2017.03.024

DO - 10.1016/j.epsr.2017.03.024

M3 - Article

SN - 0378-7796

VL - 148

SP - 192

EP - 201

JO - Electric Power Systems Research

JF - Electric Power Systems Research

ER -

New ANN method for multi-terminal HVDC protection relaying

Abstract

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Keywords

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