Knowledge-based engineering and computer vision for configuration-based substation design
Introduction: As the increase in electrification poses new demands on power delivery, the quality of the distribution system is paramount. Substations are a critical part of power grids that allow for control and service of the electrical distribution system. Substations are currently developed in a...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-04-01
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| Series: | Frontiers in Mechanical Engineering |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fmech.2023.1154316/full |
| _version_ | 1797845549108428800 |
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| author | Erik Nordvall Anton Wiberg Mehdi Tarkian |
| author_facet | Erik Nordvall Anton Wiberg Mehdi Tarkian |
| author_sort | Erik Nordvall |
| collection | DOAJ |
| description | Introduction: As the increase in electrification poses new demands on power delivery, the quality of the distribution system is paramount. Substations are a critical part of power grids that allow for control and service of the electrical distribution system. Substations are currently developed in a project-based and manually intensive manner, with a high degree of manual work and lengthy lead times. Substations are primarily sold through tenders that are accompanied by an inherent need for engineering-to-order activities. Although necessary, these activities present a paradox as tender processes must be agile and fast. To remedy this shortcoming, this article outlines a knowledge capture and reuse methodology to standardize and automate the product development processes of substation design.Methods: A novel framework for substation design is presented that implements knowledge-based engineering (KBE) and artificial intelligence methods in computer vision to capture knowledge. In addition, a product configuration system is presented, utilizing high-level CAD templates. The development has followed the KBE methodology MOKA.Results: The proposed framework has been implemented on several company cases where three (simplified) are presented in this paper. The framework decreased the time to create a 3D model from a basic electric single line diagram by performing the identification and design tasks in an automated fashion.Discussion: Ultimately, the framework will allow substation design companies to increase competitiveness through automation and knowledge management and enable more tenders to be answered without losing engineering quality. |
| first_indexed | 2024-04-09T17:41:49Z |
| format | Article |
| id | doaj.art-8ce1433447794e92830b4912138b73d9 |
| institution | Directory Open Access Journal |
| issn | 2297-3079 |
| language | English |
| last_indexed | 2024-04-09T17:41:49Z |
| publishDate | 2023-04-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Mechanical Engineering |
| spelling | doaj.art-8ce1433447794e92830b4912138b73d92023-04-17T05:13:14ZengFrontiers Media S.A.Frontiers in Mechanical Engineering2297-30792023-04-01910.3389/fmech.2023.11543161154316Knowledge-based engineering and computer vision for configuration-based substation designErik NordvallAnton WibergMehdi TarkianIntroduction: As the increase in electrification poses new demands on power delivery, the quality of the distribution system is paramount. Substations are a critical part of power grids that allow for control and service of the electrical distribution system. Substations are currently developed in a project-based and manually intensive manner, with a high degree of manual work and lengthy lead times. Substations are primarily sold through tenders that are accompanied by an inherent need for engineering-to-order activities. Although necessary, these activities present a paradox as tender processes must be agile and fast. To remedy this shortcoming, this article outlines a knowledge capture and reuse methodology to standardize and automate the product development processes of substation design.Methods: A novel framework for substation design is presented that implements knowledge-based engineering (KBE) and artificial intelligence methods in computer vision to capture knowledge. In addition, a product configuration system is presented, utilizing high-level CAD templates. The development has followed the KBE methodology MOKA.Results: The proposed framework has been implemented on several company cases where three (simplified) are presented in this paper. The framework decreased the time to create a 3D model from a basic electric single line diagram by performing the identification and design tasks in an automated fashion.Discussion: Ultimately, the framework will allow substation design companies to increase competitiveness through automation and knowledge management and enable more tenders to be answered without losing engineering quality.https://www.frontiersin.org/articles/10.3389/fmech.2023.1154316/fullcomputer-aided designknowledge-based engineeringtemplate matchingproduct configuration systemsubstation design |
| spellingShingle | Erik Nordvall Anton Wiberg Mehdi Tarkian Knowledge-based engineering and computer vision for configuration-based substation design Frontiers in Mechanical Engineering computer-aided design knowledge-based engineering template matching product configuration system substation design |
| title | Knowledge-based engineering and computer vision for configuration-based substation design |
| title_full | Knowledge-based engineering and computer vision for configuration-based substation design |
| title_fullStr | Knowledge-based engineering and computer vision for configuration-based substation design |
| title_full_unstemmed | Knowledge-based engineering and computer vision for configuration-based substation design |
| title_short | Knowledge-based engineering and computer vision for configuration-based substation design |
| title_sort | knowledge based engineering and computer vision for configuration based substation design |
| topic | computer-aided design knowledge-based engineering template matching product configuration system substation design |
| url | https://www.frontiersin.org/articles/10.3389/fmech.2023.1154316/full |
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