A Novel Search and Rescue Optimization Algorithm Based Artificial Neural Network for Identification, Classification and Location of DC Fault in Two Terminal HVDC Transmission Systems

Noman Mujeeb Khan; Majad Mansoor; Syed Kumayl Raza Moosavi; Muhammad Hamza Zafar; Umer Amir Khan; Muhammad Shahzad

doi:10.1109/ICETST55735.2022.9922910

A Novel Search and Rescue Optimization Algorithm Based Artificial Neural Network for Identification, Classification and Location of DC Fault in Two Terminal HVDC Transmission Systems

Noman Mujeeb Khan, Majad Mansoor, Syed Kumayl Raza Moosavi, Muhammad Hamza Zafar, Umer Amir Khan, Muhammad Shahzad

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Scopus citations

Abstract

Advance high voltage-DC (HVDC) electric power transmission system provide additional benefits over long distance power transmission. localization of faults is a crucial functionality required in national power grids. This study presents a hybrid artificial neural network (ANN) embedded with search and rescue optimization method (SRA) that employs frequency components of the current signal for DC fault identification, categorization, and localization in two terminal HVDC systems. In an HVDC transmission system, the proposed approach successfully identifies the problem and determines its location. unlike AC grids an accurate and robust site prediction has negligible impact from the fault impatiences/resistances. this study uses VSC-based HVDC system with two terminal. System modeling is done in MATLAB/Simulink 2018a. Several testing conditions for fault occurence have been tested. Results analysis exhibits 100 % accurate detection of the faults with less than 1 % fault location prediction.

Original language	English
Title of host publication	2022 International Conference on Emerging Trends in Smart Technologies, ICETST 2022
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781665459358
DOIs	https://doi.org/10.1109/ICETST55735.2022.9922910
State	Published - 2022
Externally published	Yes
Event	2022 International Conference on Emerging Trends in Smart Technologies, ICETST 2022 - Karachi, Pakistan Duration: 23 Sep 2022 → 24 Sep 2022

Publication series

Name	2022 International Conference on Emerging Trends in Smart Technologies, ICETST 2022

Conference

Conference	2022 International Conference on Emerging Trends in Smart Technologies, ICETST 2022
Country/Territory	Pakistan
City	Karachi
Period	23/09/22 → 24/09/22

Bibliographical note

Publisher Copyright:
© 2022 IEEE.

Keywords

Artificial Neural Network (ANN)
Fault Detection
High Voltage Direct Current (HVDC)
Swarm Intelligence (SI)

ASJC Scopus subject areas

Artificial Intelligence
Computer Networks and Communications
Computer Science Applications
Hardware and Architecture
Human-Computer Interaction
Software
Information Systems and Management
Safety, Risk, Reliability and Quality

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/ICETST55735.2022.9922910

Cite this

Khan, N. M., Mansoor, M., Moosavi, S. K. R., Zafar, M. H., Khan, U. A., & Shahzad, M. (2022). A Novel Search and Rescue Optimization Algorithm Based Artificial Neural Network for Identification, Classification and Location of DC Fault in Two Terminal HVDC Transmission Systems. In 2022 International Conference on Emerging Trends in Smart Technologies, ICETST 2022 (2022 International Conference on Emerging Trends in Smart Technologies, ICETST 2022). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICETST55735.2022.9922910

Khan, Noman Mujeeb ; Mansoor, Majad ; Moosavi, Syed Kumayl Raza et al. / A Novel Search and Rescue Optimization Algorithm Based Artificial Neural Network for Identification, Classification and Location of DC Fault in Two Terminal HVDC Transmission Systems. 2022 International Conference on Emerging Trends in Smart Technologies, ICETST 2022. Institute of Electrical and Electronics Engineers Inc., 2022. (2022 International Conference on Emerging Trends in Smart Technologies, ICETST 2022).

@inproceedings{a06f738aa75048f5bed409c8413b7037,

title = "A Novel Search and Rescue Optimization Algorithm Based Artificial Neural Network for Identification, Classification and Location of DC Fault in Two Terminal HVDC Transmission Systems",

abstract = "Advance high voltage-DC (HVDC) electric power transmission system provide additional benefits over long distance power transmission. localization of faults is a crucial functionality required in national power grids. This study presents a hybrid artificial neural network (ANN) embedded with search and rescue optimization method (SRA) that employs frequency components of the current signal for DC fault identification, categorization, and localization in two terminal HVDC systems. In an HVDC transmission system, the proposed approach successfully identifies the problem and determines its location. unlike AC grids an accurate and robust site prediction has negligible impact from the fault impatiences/resistances. this study uses VSC-based HVDC system with two terminal. System modeling is done in MATLAB/Simulink 2018a. Several testing conditions for fault occurence have been tested. Results analysis exhibits 100 \% accurate detection of the faults with less than 1 \% fault location prediction.",

keywords = "Artificial Neural Network (ANN), Fault Detection, High Voltage Direct Current (HVDC), Swarm Intelligence (SI)",

author = "Khan, \{Noman Mujeeb\} and Majad Mansoor and Moosavi, \{Syed Kumayl Raza\} and Zafar, \{Muhammad Hamza\} and Khan, \{Umer Amir\} and Muhammad Shahzad",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 International Conference on Emerging Trends in Smart Technologies, ICETST 2022 ; Conference date: 23-09-2022 Through 24-09-2022",

year = "2022",

doi = "10.1109/ICETST55735.2022.9922910",

language = "English",

series = "2022 International Conference on Emerging Trends in Smart Technologies, ICETST 2022",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "2022 International Conference on Emerging Trends in Smart Technologies, ICETST 2022",

address = "United States",

}

Khan, NM, Mansoor, M, Moosavi, SKR, Zafar, MH, Khan, UA & Shahzad, M 2022, A Novel Search and Rescue Optimization Algorithm Based Artificial Neural Network for Identification, Classification and Location of DC Fault in Two Terminal HVDC Transmission Systems. in 2022 International Conference on Emerging Trends in Smart Technologies, ICETST 2022. 2022 International Conference on Emerging Trends in Smart Technologies, ICETST 2022, Institute of Electrical and Electronics Engineers Inc., 2022 International Conference on Emerging Trends in Smart Technologies, ICETST 2022, Karachi, Pakistan, 23/09/22. https://doi.org/10.1109/ICETST55735.2022.9922910

A Novel Search and Rescue Optimization Algorithm Based Artificial Neural Network for Identification, Classification and Location of DC Fault in Two Terminal HVDC Transmission Systems. / Khan, Noman Mujeeb; Mansoor, Majad; Moosavi, Syed Kumayl Raza et al.
2022 International Conference on Emerging Trends in Smart Technologies, ICETST 2022. Institute of Electrical and Electronics Engineers Inc., 2022. (2022 International Conference on Emerging Trends in Smart Technologies, ICETST 2022).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - A Novel Search and Rescue Optimization Algorithm Based Artificial Neural Network for Identification, Classification and Location of DC Fault in Two Terminal HVDC Transmission Systems

AU - Khan, Noman Mujeeb

AU - Mansoor, Majad

AU - Moosavi, Syed Kumayl Raza

AU - Zafar, Muhammad Hamza

AU - Khan, Umer Amir

AU - Shahzad, Muhammad

PY - 2022

Y1 - 2022

N2 - Advance high voltage-DC (HVDC) electric power transmission system provide additional benefits over long distance power transmission. localization of faults is a crucial functionality required in national power grids. This study presents a hybrid artificial neural network (ANN) embedded with search and rescue optimization method (SRA) that employs frequency components of the current signal for DC fault identification, categorization, and localization in two terminal HVDC systems. In an HVDC transmission system, the proposed approach successfully identifies the problem and determines its location. unlike AC grids an accurate and robust site prediction has negligible impact from the fault impatiences/resistances. this study uses VSC-based HVDC system with two terminal. System modeling is done in MATLAB/Simulink 2018a. Several testing conditions for fault occurence have been tested. Results analysis exhibits 100 % accurate detection of the faults with less than 1 % fault location prediction.

AB - Advance high voltage-DC (HVDC) electric power transmission system provide additional benefits over long distance power transmission. localization of faults is a crucial functionality required in national power grids. This study presents a hybrid artificial neural network (ANN) embedded with search and rescue optimization method (SRA) that employs frequency components of the current signal for DC fault identification, categorization, and localization in two terminal HVDC systems. In an HVDC transmission system, the proposed approach successfully identifies the problem and determines its location. unlike AC grids an accurate and robust site prediction has negligible impact from the fault impatiences/resistances. this study uses VSC-based HVDC system with two terminal. System modeling is done in MATLAB/Simulink 2018a. Several testing conditions for fault occurence have been tested. Results analysis exhibits 100 % accurate detection of the faults with less than 1 % fault location prediction.

KW - Artificial Neural Network (ANN)

KW - Fault Detection

KW - High Voltage Direct Current (HVDC)

KW - Swarm Intelligence (SI)

UR - https://www.scopus.com/pages/publications/85141459299

U2 - 10.1109/ICETST55735.2022.9922910

DO - 10.1109/ICETST55735.2022.9922910

M3 - Conference contribution

AN - SCOPUS:85141459299

T3 - 2022 International Conference on Emerging Trends in Smart Technologies, ICETST 2022

BT - 2022 International Conference on Emerging Trends in Smart Technologies, ICETST 2022

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2022 International Conference on Emerging Trends in Smart Technologies, ICETST 2022

Y2 - 23 September 2022 through 24 September 2022

ER -

Khan NM, Mansoor M, Moosavi SKR, Zafar MH, Khan UA, Shahzad M. A Novel Search and Rescue Optimization Algorithm Based Artificial Neural Network for Identification, Classification and Location of DC Fault in Two Terminal HVDC Transmission Systems. In 2022 International Conference on Emerging Trends in Smart Technologies, ICETST 2022. Institute of Electrical and Electronics Engineers Inc. 2022. (2022 International Conference on Emerging Trends in Smart Technologies, ICETST 2022). doi: 10.1109/ICETST55735.2022.9922910

A Novel Search and Rescue Optimization Algorithm Based Artificial Neural Network for Identification, Classification and Location of DC Fault in Two Terminal HVDC Transmission Systems

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

UN SDGs

Access to Document

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Cite this