Application of Artificial Intelligence Techniques on Computational Electromagnetics for Power System Apparatus: An Overview
This paper provides a review of the most recent advances in artificial intelligence (AI) as applied to computational electromagnetics (CEM) to address challenges and unlock opportunities in power system applications. It is intended to provide readers and practitioners in electromagnetics (EM) and re...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2024-01-01
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| Series: | IEEE Open Access Journal of Power and Energy |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10473758/ |
| _version_ | 1797243385204965376 |
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| author | Dinusha Maramba Gamage Madhawa Ranasinghe Venkata Dinavahi |
| author_facet | Dinusha Maramba Gamage Madhawa Ranasinghe Venkata Dinavahi |
| author_sort | Dinusha Maramba Gamage |
| collection | DOAJ |
| description | This paper provides a review of the most recent advances in artificial intelligence (AI) as applied to computational electromagnetics (CEM) to address challenges and unlock opportunities in power system applications. It is intended to provide readers and practitioners in electromagnetics (EM) and related applicable fields with valuable perspectives on the efficiency and capabilities of machine learning (ML) techniques used with CEM tools, offering unparalleled computational advantage. The discussion begins with an overview of traditional computational methods in EM, highlighting their strengths and limitations. The paper then delves into the integration of AI techniques, including ML, deep learning, and optimization algorithms, into CEM frameworks. Emphasis is placed on how AI enhances the accuracy and efficiency of EM simulations, enabling rapid analysis and optimization of power system components and configurations. Case studies and examples illustrate the successful application of AI-based CEM in solving practical challenges in electrical machine modeling, condition monitoring, and design optimizations in power systems. This paper conducts a comprehensive assessment of AI-based CEM techniques, critically evaluating their merits, addressing open issues, and examining the technical implementations within the context of power system applications. |
| first_indexed | 2024-04-24T18:54:16Z |
| format | Article |
| id | doaj.art-b615d4e1c64f4e60a6ab5792162f9302 |
| institution | Directory Open Access Journal |
| issn | 2687-7910 |
| language | English |
| last_indexed | 2024-04-24T18:54:16Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Access Journal of Power and Energy |
| spelling | doaj.art-b615d4e1c64f4e60a6ab5792162f93022024-03-26T17:47:10ZengIEEEIEEE Open Access Journal of Power and Energy2687-79102024-01-011113014010.1109/OAJPE.2024.337857710473758Application of Artificial Intelligence Techniques on Computational Electromagnetics for Power System Apparatus: An OverviewDinusha Maramba Gamage0https://orcid.org/0009-0000-5789-6360Madhawa Ranasinghe1https://orcid.org/0009-0003-2298-1348Venkata Dinavahi2https://orcid.org/0000-0001-7438-9547Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, CanadaDepartment of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, CanadaDepartment of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, CanadaThis paper provides a review of the most recent advances in artificial intelligence (AI) as applied to computational electromagnetics (CEM) to address challenges and unlock opportunities in power system applications. It is intended to provide readers and practitioners in electromagnetics (EM) and related applicable fields with valuable perspectives on the efficiency and capabilities of machine learning (ML) techniques used with CEM tools, offering unparalleled computational advantage. The discussion begins with an overview of traditional computational methods in EM, highlighting their strengths and limitations. The paper then delves into the integration of AI techniques, including ML, deep learning, and optimization algorithms, into CEM frameworks. Emphasis is placed on how AI enhances the accuracy and efficiency of EM simulations, enabling rapid analysis and optimization of power system components and configurations. Case studies and examples illustrate the successful application of AI-based CEM in solving practical challenges in electrical machine modeling, condition monitoring, and design optimizations in power systems. This paper conducts a comprehensive assessment of AI-based CEM techniques, critically evaluating their merits, addressing open issues, and examining the technical implementations within the context of power system applications.https://ieeexplore.ieee.org/document/10473758/Artificial neural networks (ANN)deep learningfinite difference time domain (FDTD)finite element method (FEM)machine learning (ML)method of moments (MoM) |
| spellingShingle | Dinusha Maramba Gamage Madhawa Ranasinghe Venkata Dinavahi Application of Artificial Intelligence Techniques on Computational Electromagnetics for Power System Apparatus: An Overview IEEE Open Access Journal of Power and Energy Artificial neural networks (ANN) deep learning finite difference time domain (FDTD) finite element method (FEM) machine learning (ML) method of moments (MoM) |
| title | Application of Artificial Intelligence Techniques on Computational Electromagnetics for Power System Apparatus: An Overview |
| title_full | Application of Artificial Intelligence Techniques on Computational Electromagnetics for Power System Apparatus: An Overview |
| title_fullStr | Application of Artificial Intelligence Techniques on Computational Electromagnetics for Power System Apparatus: An Overview |
| title_full_unstemmed | Application of Artificial Intelligence Techniques on Computational Electromagnetics for Power System Apparatus: An Overview |
| title_short | Application of Artificial Intelligence Techniques on Computational Electromagnetics for Power System Apparatus: An Overview |
| title_sort | application of artificial intelligence techniques on computational electromagnetics for power system apparatus an overview |
| topic | Artificial neural networks (ANN) deep learning finite difference time domain (FDTD) finite element method (FEM) machine learning (ML) method of moments (MoM) |
| url | https://ieeexplore.ieee.org/document/10473758/ |
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