Time-Domain Protection of Superconducting Cables Based on Artificial Intelligence Classifiers
Fault detection and protection of Superconducting Cables (SCs) is considered a challenging task due to the effects of the quenching phenomenon of High Temperature Superconducting (HTS) tapes and the prospective magnitude of fault currents in presence of highly-resistive faults and converter-interfac...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9678383/ |
| _version_ | 1830176494927740928 |
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| author | Eleni Tsotsopoulou Xenofon Karagiannis Panagiotis Papadopoulos Adam Dysko Mohammad Yazdani-Asrami Campbell Booth Dimitrios Tzelepis |
| author_facet | Eleni Tsotsopoulou Xenofon Karagiannis Panagiotis Papadopoulos Adam Dysko Mohammad Yazdani-Asrami Campbell Booth Dimitrios Tzelepis |
| author_sort | Eleni Tsotsopoulou |
| collection | DOAJ |
| description | Fault detection and protection of Superconducting Cables (SCs) is considered a challenging task due to the effects of the quenching phenomenon of High Temperature Superconducting (HTS) tapes and the prospective magnitude of fault currents in presence of highly-resistive faults and converter-interfaced generation. This paper presents a novel, time-domain method for discriminative detection of faults in a power system incorporating SCs and high penetration of renewable energy sources. The proposed algorithms utilizes feature extraction tools based on Stationary Wavelet Transform (SWT), as well as artificial intelligence (AI) classifiers to discriminate between external and internal faults, and other network events. The performance of the proposed schemes has been validated in electromagnetic transient simulation environment using a verified model of SC. Simulation results revealed that the proposed algorithms can effectively and within short period of time discriminate internal faults occurring on SC, while remain stable to external faults and other disturbances. The suitability of the proposed algorithms for real-time implementation has been verified using software and hardware in the loop testing environment. To determine the best options for real-time deployment, two different artificial intelligence classifiers namely Artificial Neural Network (ANN) and Support Vector Machine (SVM) have been deployed. The extensive assessment of their performance revealed that the ANN classifier is advantageous in term of prediction speed. |
| first_indexed | 2024-12-17T19:07:54Z |
| format | Article |
| id | doaj.art-f55046e421b5419ab2270a13a853fa8c |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-17T19:07:54Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-f55046e421b5419ab2270a13a853fa8c2022-12-21T21:35:57ZengIEEEIEEE Access2169-35362022-01-0110101241013810.1109/ACCESS.2022.31425349678383Time-Domain Protection of Superconducting Cables Based on Artificial Intelligence ClassifiersEleni Tsotsopoulou0https://orcid.org/0000-0001-9118-3743Xenofon Karagiannis1Panagiotis Papadopoulos2https://orcid.org/0000-0001-7343-2590Adam Dysko3Mohammad Yazdani-Asrami4https://orcid.org/0000-0002-7691-3485Campbell Booth5https://orcid.org/0000-0003-3869-4477Dimitrios Tzelepis6https://orcid.org/0000-0003-4263-7299Department of Electronic and Electrical Engineering, Institute for Energy and Environment, University of Strathclyde, Glasgow, U.K.Department of Electrical and Computer Engineering, Democritus University of Thrace, Xanthi, GreeceDepartment of Electronic and Electrical Engineering, Institute for Energy and Environment, University of Strathclyde, Glasgow, U.K.Department of Electronic and Electrical Engineering, Institute for Energy and Environment, University of Strathclyde, Glasgow, U.K.Department of Electronic and Electrical Engineering, Institute for Energy and Environment, University of Strathclyde, Glasgow, U.K.Department of Electronic and Electrical Engineering, Institute for Energy and Environment, University of Strathclyde, Glasgow, U.K.Department of Electronic and Electrical Engineering, Institute for Energy and Environment, University of Strathclyde, Glasgow, U.K.Fault detection and protection of Superconducting Cables (SCs) is considered a challenging task due to the effects of the quenching phenomenon of High Temperature Superconducting (HTS) tapes and the prospective magnitude of fault currents in presence of highly-resistive faults and converter-interfaced generation. This paper presents a novel, time-domain method for discriminative detection of faults in a power system incorporating SCs and high penetration of renewable energy sources. The proposed algorithms utilizes feature extraction tools based on Stationary Wavelet Transform (SWT), as well as artificial intelligence (AI) classifiers to discriminate between external and internal faults, and other network events. The performance of the proposed schemes has been validated in electromagnetic transient simulation environment using a verified model of SC. Simulation results revealed that the proposed algorithms can effectively and within short period of time discriminate internal faults occurring on SC, while remain stable to external faults and other disturbances. The suitability of the proposed algorithms for real-time implementation has been verified using software and hardware in the loop testing environment. To determine the best options for real-time deployment, two different artificial intelligence classifiers namely Artificial Neural Network (ANN) and Support Vector Machine (SVM) have been deployed. The extensive assessment of their performance revealed that the ANN classifier is advantageous in term of prediction speed.https://ieeexplore.ieee.org/document/9678383/Superconducting cablesfault detectionartificial intelligence |
| spellingShingle | Eleni Tsotsopoulou Xenofon Karagiannis Panagiotis Papadopoulos Adam Dysko Mohammad Yazdani-Asrami Campbell Booth Dimitrios Tzelepis Time-Domain Protection of Superconducting Cables Based on Artificial Intelligence Classifiers IEEE Access Superconducting cables fault detection artificial intelligence |
| title | Time-Domain Protection of Superconducting Cables Based on Artificial Intelligence Classifiers |
| title_full | Time-Domain Protection of Superconducting Cables Based on Artificial Intelligence Classifiers |
| title_fullStr | Time-Domain Protection of Superconducting Cables Based on Artificial Intelligence Classifiers |
| title_full_unstemmed | Time-Domain Protection of Superconducting Cables Based on Artificial Intelligence Classifiers |
| title_short | Time-Domain Protection of Superconducting Cables Based on Artificial Intelligence Classifiers |
| title_sort | time domain protection of superconducting cables based on artificial intelligence classifiers |
| topic | Superconducting cables fault detection artificial intelligence |
| url | https://ieeexplore.ieee.org/document/9678383/ |
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