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Summary
This summary is machine-generated.

This study enhances insulator fault diagnosis by combining empirical mode decomposition (EMD) methods with the MiniRocket algorithm. This approach significantly improves the accuracy of detecting faulty electrical insulators, boosting power system reliability.

Keywords:
electric power systemempirical mode decompositionrocket algorithmtime series classification

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Area of Science:

  • Electrical Engineering
  • Signal Processing
  • Machine Learning

Background:

  • Reliable electrical energy supply depends on functional insulators.
  • Ultrasound time series data from insulators can identify faults.
  • Existing methods require enhancement for accurate insulator fault classification.

Purpose of the Study:

  • To propose a novel strategy for insulator fault diagnosis.
  • To evaluate the efficacy of combining empirical mode decomposition (EMD) methods with Rocket algorithms.
  • To improve the accuracy and reliability of electrical insulator fault detection.

Main Methods:

  • Utilized the Random Convolutional Kernel Transform (Rocket) algorithm for feature extraction from time series ultrasound data.
  • Integrated three EMD methods: Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), Empirical Wavelet Transform (EWT), and Variational Mode Decomposition (VMD).
  • Applied logistic regression classifiers to diagnose insulator faults using the extracted features.

Main Results:

  • The combination of EMD methods and MiniRocket significantly boosted logistic regression accuracy.
  • Achieved high diagnostic accuracies: 0.992 with CEEMDAN, 0.995 with EWT, and 0.980 with VMD.
  • Demonstrated superior performance compared to baseline methods without EMD.

Conclusions:

  • Empirical mode decomposition methods, when integrated with MiniRocket, offer a powerful approach for insulator fault diagnosis.
  • The proposed strategy enhances the accuracy and dependability of power system monitoring.
  • This research highlights the potential for improved safety and reliability in electrical infrastructure.