Transformer-Based Fault Diagnosis for Large-Scale Standby Power Generators: Partial Discharge Pattern Recognition at Hyperscale Data Center Installations
Keywords:
Transformer-based fault diagnosis,Partial discharge detection,Standby power generators,Hyperscale data centers,Predictive maintenance,Deep learning for power systems,Electrical insulation monitoring,Fault pattern recognition,Intelligent condition monitoring,High-voltage equipment diagnostics.Abstract
The standby power generators and associated systems in hyperscale data centers are subject to high exposure to partial discharge. The presence of severe partial discharge cases in large-scale, high-quality sensor datasets motivates the development of periodiccondition monitoring based on attention Transformer architectures. The approach harnesses large amounts of monitoring data
References
Pamisetty, A. (2024). Leveraging Big Data Engineering for Predictive Analytics in Wholesale Product Logistics. Available at SSRN 5231473.
Pamisetty, V. (2024). AI-Driven Decision Support for Taxation and Unclaimed Property Management: Enhancing Efficiency through Big Data and Cloud Integration. Available at SSRN 5250776.
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Published
2024-08-20
How to Cite
Rajesh Mattaparthi. (2024). Transformer-Based Fault Diagnosis for Large-Scale Standby Power Generators: Partial Discharge Pattern Recognition at Hyperscale Data Center Installations . Journal of Computational Analysis and Applications (JoCAAA), 33(08), 8781–8799. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/5482
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