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This paper introduces an enhanced fault prediction framework for synchronous condensers in UHVDC transmission systems, utilizing Large Language Models (LLMs) and optimized Wavelet Packet Transform (WPT) for improved diagnostic accuracy. The innovative approach allows for automatic optimization of WPT parameters and incorporates a Multi-Head Attention Gated Recurrent Unit (MHA-GRU) network for superior temporal feature learning and fault classification. Experimental results demonstrate the framework

Author

visioneletricprojects

Language

EN