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Hybrid signal processing and machine learning algorithm for adaptive fault classification of wind farm integrated transmision line protection

The technological advancement in integration of Renewable Green Energy Sources (RGES) like Wind Farm Generators (WFG), and Photovoltaic (PV) system into conventional power system as a future solution to meet the increase in global energy demands in order to reduce the cost of power generation, and i...

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Main Authors: Olufemi, Osaji Emmanuel, Othman, Mohammad Lutfi, Hizam, Hashim, Othman, Muhammad Murtadha, Ammar, Aker Elhadi Emhemed Alhaaj, Okeke, Chidiebere Akachukwu, Onuabuchi, Nwagbara Samuel
Format: Article
Language:English
Published: UTHM Publisher 2019
Online Access:http://psasir.upm.edu.my/id/eprint/80112/1/Hybrid%20signal%20processing%20and%20machine%20learning%20algorithm%20for%20adaptive%20fault%20classification%20of%20wind%20farm%20integrated%20transmision%20line%20protection.pdf
http://psasir.upm.edu.my/id/eprint/80112/
https://1library.net/document/y6xr277y-processing-learning-algorithm-adaptive-classification-integrated-transmission-protection.html
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spelling my.upm.eprints.801122020-10-25T00:14:47Z http://psasir.upm.edu.my/id/eprint/80112/ Hybrid signal processing and machine learning algorithm for adaptive fault classification of wind farm integrated transmision line protection Olufemi, Osaji Emmanuel Othman, Mohammad Lutfi Hizam, Hashim Othman, Muhammad Murtadha Ammar, Aker Elhadi Emhemed Alhaaj Okeke, Chidiebere Akachukwu Onuabuchi, Nwagbara Samuel The technological advancement in integration of Renewable Green Energy Sources (RGES) like Wind Farm Generators (WFG), and Photovoltaic (PV) system into conventional power system as a future solution to meet the increase in global energy demands in order to reduce the cost of power generation, and improve on the climate change impact. This innovation also introduces challenges in the power system protection by it being compromised due to injected fault current infeeds on existing facilities. These infeed leadto the undesired trip of a healthy section of the line, and protection system failure. This paper presents a soft computational approach to adaptive fault classification model on High VoltageTransmission Line (HVTL) with and without RGES-WFG integration topologies, using extracted one-cycle fault signature of voltage and current signals with wavelet statistical approach in Matlab. The results are unique signatures across all fault types and fault distances with distinct entropy energy values on proposed network architecture. The supervised machine learning algorithm from Bayesian network classified 99.15 % faults correctly with the operation time of 0.01 s to produced best-generalized model with an RMS error value of 0.05 for single line-to-ground (SLG) fault identification and classification. Best suitable for adaptive unit protection scheme integration. UTHM Publisher 2019 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/80112/1/Hybrid%20signal%20processing%20and%20machine%20learning%20algorithm%20for%20adaptive%20fault%20classification%20of%20wind%20farm%20integrated%20transmision%20line%20protection.pdf Olufemi, Osaji Emmanuel and Othman, Mohammad Lutfi and Hizam, Hashim and Othman, Muhammad Murtadha and Ammar, Aker Elhadi Emhemed Alhaaj and Okeke, Chidiebere Akachukwu and Onuabuchi, Nwagbara Samuel (2019) Hybrid signal processing and machine learning algorithm for adaptive fault classification of wind farm integrated transmision line protection. International Journal of Integrated Engineering, 11 (4). pp. 91-100. ISSN 2229-838X; ESSN: 2600-7916 https://1library.net/document/y6xr277y-processing-learning-algorithm-adaptive-classification-integrated-transmission-protection.html 10.30880/ijie.2019.11.04.010
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
language English
description The technological advancement in integration of Renewable Green Energy Sources (RGES) like Wind Farm Generators (WFG), and Photovoltaic (PV) system into conventional power system as a future solution to meet the increase in global energy demands in order to reduce the cost of power generation, and improve on the climate change impact. This innovation also introduces challenges in the power system protection by it being compromised due to injected fault current infeeds on existing facilities. These infeed leadto the undesired trip of a healthy section of the line, and protection system failure. This paper presents a soft computational approach to adaptive fault classification model on High VoltageTransmission Line (HVTL) with and without RGES-WFG integration topologies, using extracted one-cycle fault signature of voltage and current signals with wavelet statistical approach in Matlab. The results are unique signatures across all fault types and fault distances with distinct entropy energy values on proposed network architecture. The supervised machine learning algorithm from Bayesian network classified 99.15 % faults correctly with the operation time of 0.01 s to produced best-generalized model with an RMS error value of 0.05 for single line-to-ground (SLG) fault identification and classification. Best suitable for adaptive unit protection scheme integration.
format Article
author Olufemi, Osaji Emmanuel
Othman, Mohammad Lutfi
Hizam, Hashim
Othman, Muhammad Murtadha
Ammar, Aker Elhadi Emhemed Alhaaj
Okeke, Chidiebere Akachukwu
Onuabuchi, Nwagbara Samuel
spellingShingle Olufemi, Osaji Emmanuel
Othman, Mohammad Lutfi
Hizam, Hashim
Othman, Muhammad Murtadha
Ammar, Aker Elhadi Emhemed Alhaaj
Okeke, Chidiebere Akachukwu
Onuabuchi, Nwagbara Samuel
Hybrid signal processing and machine learning algorithm for adaptive fault classification of wind farm integrated transmision line protection
author_facet Olufemi, Osaji Emmanuel
Othman, Mohammad Lutfi
Hizam, Hashim
Othman, Muhammad Murtadha
Ammar, Aker Elhadi Emhemed Alhaaj
Okeke, Chidiebere Akachukwu
Onuabuchi, Nwagbara Samuel
author_sort Olufemi, Osaji Emmanuel
title Hybrid signal processing and machine learning algorithm for adaptive fault classification of wind farm integrated transmision line protection
title_short Hybrid signal processing and machine learning algorithm for adaptive fault classification of wind farm integrated transmision line protection
title_full Hybrid signal processing and machine learning algorithm for adaptive fault classification of wind farm integrated transmision line protection
title_fullStr Hybrid signal processing and machine learning algorithm for adaptive fault classification of wind farm integrated transmision line protection
title_full_unstemmed Hybrid signal processing and machine learning algorithm for adaptive fault classification of wind farm integrated transmision line protection
title_sort hybrid signal processing and machine learning algorithm for adaptive fault classification of wind farm integrated transmision line protection
publisher UTHM Publisher
publishDate 2019
url http://psasir.upm.edu.my/id/eprint/80112/1/Hybrid%20signal%20processing%20and%20machine%20learning%20algorithm%20for%20adaptive%20fault%20classification%20of%20wind%20farm%20integrated%20transmision%20line%20protection.pdf
http://psasir.upm.edu.my/id/eprint/80112/
https://1library.net/document/y6xr277y-processing-learning-algorithm-adaptive-classification-integrated-transmission-protection.html
_version_ 1681490885273976832
score 13.211869

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