Enhanced power system fault detection using quantum‐AI and herd immunity quantum‐AI fault detection with herd immunity optimisation in power systems
Abstract Quantum computing and deep learning have recently gained popularity across various industries, promising revolutionary advancements. The authors introduce QC‐PCSANN‐CHIO‐FD, a novel approach that enhances fault detection in electrical power systems by combining quantum computing, deep learn...
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| Format: | Article |
| Language: | English |
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Wiley
2024-12-01
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| Series: | IET Quantum Communication |
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| Online Access: | https://doi.org/10.1049/qtc2.12106 |
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| author | M. L. Sworna Kokila V. Bibin Christopher G. Ramya |
| author_facet | M. L. Sworna Kokila V. Bibin Christopher G. Ramya |
| author_sort | M. L. Sworna Kokila |
| collection | DOAJ |
| description | Abstract Quantum computing and deep learning have recently gained popularity across various industries, promising revolutionary advancements. The authors introduce QC‐PCSANN‐CHIO‐FD, a novel approach that enhances fault detection in electrical power systems by combining quantum computing, deep learning, and optimisation algorithms. The network, based on a Pyramidal Convolution Shuffle Attention Neural Network (PCSANN) optimised with the Coronavirus Herd Immunity Optimiser, shows promising results. Initially, historical datasets are used for fault detection. Preprocessing, which includes handling missing data and outliers using Adaptive Variational Bayesian Filtering is followed by Dual‐Domain Feature Extraction to extract grayscale statistical features. These features are processed by PCSANN to detect faults. The Coronavirus Herd Immunity Optimisation Algorithm is proposed to optimise PCSANN for precise fault detection. Performance of the proposed QC‐PCSANN‐CHIO‐FD approach attains 24.11%, 28.56% and 22.73% high specificity, 21.89%, 23.04% and 9.51% lower computation Time, 25.289%, 15.35% and 19.91% higher ROC and 8.65%, 13.8%, and 7.15% higher Accuracy compared with existing methods, such as combining deep learning based on quantum computing for electrical power system malfunction diagnosis (QC‐ANN‐FD), electrical power system fault diagnostics using hybrid quantum‐classical deep learning (QC‐CRBM‐FD), applications of machine learning to the identification of power system faults: Recent developments and future directions (QC‐RF‐FD). |
| format | Article |
| id | doaj-art-3e0439f2913b42a5bda2e85e19c841f2 |
| institution | Kabale University |
| issn | 2632-8925 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Quantum Communication |
| spelling | doaj-art-3e0439f2913b42a5bda2e85e19c841f22024-12-29T13:34:29ZengWileyIET Quantum Communication2632-89252024-12-015434034810.1049/qtc2.12106Enhanced power system fault detection using quantum‐AI and herd immunity quantum‐AI fault detection with herd immunity optimisation in power systemsM. L. Sworna Kokila0V. Bibin Christopher1G. Ramya2Department of Computing Technologies Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur, Chennai IndiaDepartment of Computing Technologies Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur, Chennai IndiaDepartment of Computing Technologies Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur, Chennai IndiaAbstract Quantum computing and deep learning have recently gained popularity across various industries, promising revolutionary advancements. The authors introduce QC‐PCSANN‐CHIO‐FD, a novel approach that enhances fault detection in electrical power systems by combining quantum computing, deep learning, and optimisation algorithms. The network, based on a Pyramidal Convolution Shuffle Attention Neural Network (PCSANN) optimised with the Coronavirus Herd Immunity Optimiser, shows promising results. Initially, historical datasets are used for fault detection. Preprocessing, which includes handling missing data and outliers using Adaptive Variational Bayesian Filtering is followed by Dual‐Domain Feature Extraction to extract grayscale statistical features. These features are processed by PCSANN to detect faults. The Coronavirus Herd Immunity Optimisation Algorithm is proposed to optimise PCSANN for precise fault detection. Performance of the proposed QC‐PCSANN‐CHIO‐FD approach attains 24.11%, 28.56% and 22.73% high specificity, 21.89%, 23.04% and 9.51% lower computation Time, 25.289%, 15.35% and 19.91% higher ROC and 8.65%, 13.8%, and 7.15% higher Accuracy compared with existing methods, such as combining deep learning based on quantum computing for electrical power system malfunction diagnosis (QC‐ANN‐FD), electrical power system fault diagnostics using hybrid quantum‐classical deep learning (QC‐CRBM‐FD), applications of machine learning to the identification of power system faults: Recent developments and future directions (QC‐RF‐FD).https://doi.org/10.1049/qtc2.12106quantum computingquantum computing techniques |
| spellingShingle | M. L. Sworna Kokila V. Bibin Christopher G. Ramya Enhanced power system fault detection using quantum‐AI and herd immunity quantum‐AI fault detection with herd immunity optimisation in power systems IET Quantum Communication quantum computing quantum computing techniques |
| title | Enhanced power system fault detection using quantum‐AI and herd immunity quantum‐AI fault detection with herd immunity optimisation in power systems |
| title_full | Enhanced power system fault detection using quantum‐AI and herd immunity quantum‐AI fault detection with herd immunity optimisation in power systems |
| title_fullStr | Enhanced power system fault detection using quantum‐AI and herd immunity quantum‐AI fault detection with herd immunity optimisation in power systems |
| title_full_unstemmed | Enhanced power system fault detection using quantum‐AI and herd immunity quantum‐AI fault detection with herd immunity optimisation in power systems |
| title_short | Enhanced power system fault detection using quantum‐AI and herd immunity quantum‐AI fault detection with herd immunity optimisation in power systems |
| title_sort | enhanced power system fault detection using quantum ai and herd immunity quantum ai fault detection with herd immunity optimisation in power systems |
| topic | quantum computing quantum computing techniques |
| url | https://doi.org/10.1049/qtc2.12106 |
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