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This study introduces a new method for predicting faults in ultra-high voltage direct current (UHVDC) synchronous condensers. The approach enhances fault detection accuracy and efficiency, ensuring system reliability.

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Area of Science:

  • Electrical Engineering
  • Power Systems
  • Artificial Intelligence

Background:

  • Synchronous condensers are critical components in ultra-high voltage direct current (UHVDC) transmission systems.
  • Early fault prediction is essential to prevent catastrophic failures and ensure grid stability.
  • Existing fault detection methods often lack the accuracy and efficiency required for complex power systems.

Purpose of the Study:

  • To develop an innovative framework for predicting faults in UHVDC synchronous condensers.
  • To improve the accuracy, efficiency, and reliability of fault detection in these critical components.
  • To enable timely maintenance interventions and prevent system failures.

Main Methods:

  • Utilized Wavelet Packet Transform (WPT) for intelligent feature extraction from fault signals.
  • Employed Large Language Models (LLM) for intelligent feature selection, enhancing WPT capabilities.
  • Implemented an enhanced Gated Recurrent Unit (GRU) network with multi-head attention mechanisms (MHA-GRU) to capture temporal dependencies.

Main Results:

  • The proposed framework significantly outperformed state-of-the-art methods in classification accuracy, detection time, and false alarm rate.
  • Demonstrated robust stability under varying load conditions.
  • Achieved particularly significant improvements in detecting air-gap eccentricity faults.

Conclusions:

  • The developed WPT and MHA-GRU based framework offers a reliable solution for early fault prediction in UHVDC synchronous condensers.
  • The intelligent feature extraction and selection mechanisms enhance detection performance.
  • The approach facilitates proactive maintenance, preventing minor faults from escalating into major failures.