Abstract
Recent years have witnessed a growing demand for downsizing various electrical and insulation devices to meet the requirements of smart grids. However, owing to the characteristics of large-sized ultrahigh-voltage insulation devices, it is difficult to guarantee a sufficient insulation distance when downsizing, resulting in a significant degradation of the insulation performance. Therefore, further downsizing and performance improvements are required. Research has been conducted on the design of various high-performance features; however, implementing and applying them is challenging. Furthermore, the application of gradient functionality is difficult to implement accurately based on the dimensions. However, additive manufacturing can be used to apply gradient functionality to insulation spacers with rotational shapes among insulation devices. In this study, nondominated sorting genetic algorithm-II (NSGA-II) and electric field analysis were performed to optimize the permittivity distribution and insulation design for the application of gradient functionality to the existing shape of insulation spacers. Then, a starting material was produced using photocurable material and ceramic fillers for permittivity adjustment. Subsequently, the starting material was evaluated according to the evaluation criteria for actual insulation spacers, and voids and delamination caused using foreign materials were confirmed by X-ray analysis. The insulation and mechanical properties of the composites were also evaluated.
| Original language | English |
|---|---|
| Pages (from-to) | 3736-3744 |
| Number of pages | 9 |
| Journal | IEEE Transactions on Dielectrics and Electrical Insulation |
| Volume | 32 |
| Issue number | 6 |
| DOIs | |
| State | Published - 2025 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 9 Industry, Innovation, and Infrastructure
Keywords
- Electric field stress relaxation
- functionally graded material (FGM)
- high-voltage insulating devices
- nondominated sorting genetic algorithm-II (NSGA-II)
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