Smart fault detection and classification for distribution grid hybridizing ST and MLP-NN

Abdulaziz Aljohani; Abdulrahman Aljurbua; Md Shafiullah; M. A. Abido

doi:10.1109/SSD.2018.8570582

Smart fault detection and classification for distribution grid hybridizing ST and MLP-NN

Abdulaziz Aljohani, Abdulrahman Aljurbua, Md Shafiullah, M. A. Abido^*

^*Corresponding author for this work

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

11 Scopus citations

Abstract

Power quality disturbances become a major issue in modern commercial distribution grids, hence an innovative attempt to diagnose the faults is necessary for optimal management of power distribution grids and associated assets. This paper presents a hybrid approach using Stockwell transform (ST) and multilayer perceptron neural network (MLP-NN) to detect, and classify the faults in a simulated IEEE 13-node test distribution feeder in Real Time Digital Simulator (RTDS). In the proposed technique, the three-phase current waveforms are measured from different points in the feeder and then processed using ST to extract useful statistical features. The features are later fed into the MLP-NN system to detect and classify the faults. The approach proved to be highly efficient in terms of accuracy under both noisy and non-noisy measurements. In addition, the proposed approach is independent of pre-fault operating conditions as well as fault resistance and inception angle.

Original language	English
Title of host publication	2018 15th International Multi-Conference on Systems, Signals and Devices, SSD 2018
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	94-98
Number of pages	5
ISBN (Electronic)	9781538653050
DOIs	https://doi.org/10.1109/SSD.2018.8570582
State	Published - 7 Dec 2018

Publication series

Name	2018 15th International Multi-Conference on Systems, Signals and Devices, SSD 2018

Bibliographical note

Publisher Copyright:
© 2018 IEEE.

Keywords

Distribution grids
Fault classification
Fault detection
IEEE 13-node test distribution feeder
Multilayer perceptron neural network
Statistical features
Stockwell transform

ASJC Scopus subject areas

Computer Networks and Communications
Signal Processing
Control and Optimization
Instrumentation

Access to Document

10.1109/SSD.2018.8570582

Cite this

Aljohani, A., Aljurbua, A., Shafiullah, M., & Abido, M. A. (2018). Smart fault detection and classification for distribution grid hybridizing ST and MLP-NN. In 2018 15th International Multi-Conference on Systems, Signals and Devices, SSD 2018 (pp. 94-98). Article 8570582 (2018 15th International Multi-Conference on Systems, Signals and Devices, SSD 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SSD.2018.8570582

Aljohani, Abdulaziz ; Aljurbua, Abdulrahman ; Shafiullah, Md et al. / Smart fault detection and classification for distribution grid hybridizing ST and MLP-NN. 2018 15th International Multi-Conference on Systems, Signals and Devices, SSD 2018. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 94-98 (2018 15th International Multi-Conference on Systems, Signals and Devices, SSD 2018).

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title = "Smart fault detection and classification for distribution grid hybridizing ST and MLP-NN",

abstract = "Power quality disturbances become a major issue in modern commercial distribution grids, hence an innovative attempt to diagnose the faults is necessary for optimal management of power distribution grids and associated assets. This paper presents a hybrid approach using Stockwell transform (ST) and multilayer perceptron neural network (MLP-NN) to detect, and classify the faults in a simulated IEEE 13-node test distribution feeder in Real Time Digital Simulator (RTDS). In the proposed technique, the three-phase current waveforms are measured from different points in the feeder and then processed using ST to extract useful statistical features. The features are later fed into the MLP-NN system to detect and classify the faults. The approach proved to be highly efficient in terms of accuracy under both noisy and non-noisy measurements. In addition, the proposed approach is independent of pre-fault operating conditions as well as fault resistance and inception angle.",

keywords = "Distribution grids, Fault classification, Fault detection, IEEE 13-node test distribution feeder, Multilayer perceptron neural network, Statistical features, Stockwell transform",

author = "Abdulaziz Aljohani and Abdulrahman Aljurbua and Md Shafiullah and Abido, \{M. A.\}",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE.",

year = "2018",

month = dec,

day = "7",

doi = "10.1109/SSD.2018.8570582",

language = "English",

series = "2018 15th International Multi-Conference on Systems, Signals and Devices, SSD 2018",

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Aljohani, A, Aljurbua, A, Shafiullah, M & Abido, MA 2018, Smart fault detection and classification for distribution grid hybridizing ST and MLP-NN. in 2018 15th International Multi-Conference on Systems, Signals and Devices, SSD 2018., 8570582, 2018 15th International Multi-Conference on Systems, Signals and Devices, SSD 2018, Institute of Electrical and Electronics Engineers Inc., pp. 94-98. https://doi.org/10.1109/SSD.2018.8570582

Smart fault detection and classification for distribution grid hybridizing ST and MLP-NN. / Aljohani, Abdulaziz; Aljurbua, Abdulrahman; Shafiullah, Md et al.
2018 15th International Multi-Conference on Systems, Signals and Devices, SSD 2018. Institute of Electrical and Electronics Engineers Inc., 2018. p. 94-98 8570582 (2018 15th International Multi-Conference on Systems, Signals and Devices, SSD 2018).

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

TY - GEN

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AU - Aljohani, Abdulaziz

AU - Aljurbua, Abdulrahman

AU - Shafiullah, Md

AU - Abido, M. A.

PY - 2018/12/7

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N2 - Power quality disturbances become a major issue in modern commercial distribution grids, hence an innovative attempt to diagnose the faults is necessary for optimal management of power distribution grids and associated assets. This paper presents a hybrid approach using Stockwell transform (ST) and multilayer perceptron neural network (MLP-NN) to detect, and classify the faults in a simulated IEEE 13-node test distribution feeder in Real Time Digital Simulator (RTDS). In the proposed technique, the three-phase current waveforms are measured from different points in the feeder and then processed using ST to extract useful statistical features. The features are later fed into the MLP-NN system to detect and classify the faults. The approach proved to be highly efficient in terms of accuracy under both noisy and non-noisy measurements. In addition, the proposed approach is independent of pre-fault operating conditions as well as fault resistance and inception angle.

AB - Power quality disturbances become a major issue in modern commercial distribution grids, hence an innovative attempt to diagnose the faults is necessary for optimal management of power distribution grids and associated assets. This paper presents a hybrid approach using Stockwell transform (ST) and multilayer perceptron neural network (MLP-NN) to detect, and classify the faults in a simulated IEEE 13-node test distribution feeder in Real Time Digital Simulator (RTDS). In the proposed technique, the three-phase current waveforms are measured from different points in the feeder and then processed using ST to extract useful statistical features. The features are later fed into the MLP-NN system to detect and classify the faults. The approach proved to be highly efficient in terms of accuracy under both noisy and non-noisy measurements. In addition, the proposed approach is independent of pre-fault operating conditions as well as fault resistance and inception angle.

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Aljohani A, Aljurbua A, Shafiullah M , Abido MA. Smart fault detection and classification for distribution grid hybridizing ST and MLP-NN. In 2018 15th International Multi-Conference on Systems, Signals and Devices, SSD 2018. Institute of Electrical and Electronics Engineers Inc. 2018. p. 94-98. 8570582. (2018 15th International Multi-Conference on Systems, Signals and Devices, SSD 2018). doi: 10.1109/SSD.2018.8570582

Smart fault detection and classification for distribution grid hybridizing ST and MLP-NN

Abstract

Publication series

Bibliographical note

Keywords

ASJC Scopus subject areas

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