Application of Grey Wolf Optimizer for Predictive Maintenance and Optimization of High Voltage Insulators

High-voltage insulator has an important role in ensuring the reliability and longevity of electrical systems. This study investigates the optimization of corona ring design and parameters H,R,r, using the Grey Wolf Optimizer (GWO), aiming to minimize electric field concentrations and enhance insulator performance. By strategically placing corona rings, the model developed predicts optimal configurations that mitigate peak electric field levels, thereby improving durability and preventing unexpected failures. Numerical simulations, conducted via COMSOL 4.3 and Finite Element analysis, validate the effectiveness of the optimized design in significantly reducing electric field intensities near end fittings. This research underscores the transformative potential of AI-driven optimization techniques, particularly the GWO, in advancing predictive maintenance strategies for high voltage insulators. Such advancements are crucial for fostering a sustainable and resilient electrical infrastructure.

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Publication Details

Published
2024-10-01
DOI
https://doi.org/10.1109/icaecot62402.2024.10828968
Citations
1
Primary Topic
Power System Reliability and Maintenance
Type
article
Field-Weighted Citation Impact
0.36
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article

Application of Grey Wolf Optimizer for Predictive Maintenance and Optimization of High Voltage Insulators

Lyamine Ouchen, Mohammed Amroune, Khaled Belhouchet
1 citations
Power System Reliability and Maintenance
0.36
article

Application of Grey Wolf Optimizer for Predictive Maintenance and Optimization of High Voltage Insulators

Lyamine Ouchen, Mohammed Amroune, Khaled Belhouchet
article en
1 citations

Abstract

High-voltage insulator has an important role in ensuring the reliability and longevity of electrical systems. This study investigates the optimization of corona ring design and parameters H,R,r, using the Grey Wolf Optimizer (GWO), aiming to minimize electric field concentrations and enhance insulator performance. By strategically placing corona rings, the model developed predicts optimal configurations that mitigate peak electric field levels, thereby improving durability and preventing unexpected failures. Numerical simulations, conducted via COMSOL 4.3 and Finite Element analysis, validate the effectiveness of the optimized design in significantly reducing electric field intensities near end fittings. This research underscores the transformative potential of AI-driven optimization techniques, particularly the GWO, in advancing predictive maintenance strategies for high voltage insulators. Such advancements are crucial for fostering a sustainable and resilient electrical infrastructure.

University Ferhat Abbas of Setif (DZ), University Mohamed Boudiaf of M'sila (DZ)
Affordable and clean energy
Openalex Percentile: Top 36%
Power System Reliability and Maintenance
0.36
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