High resistance fault detection in DC microgrid using Hilbert Huang transform and vector-based ensemble optimized
H K Prashanth Kumar1, Ritesh Dash1, K Jyotheeswara Reddy1
1School of Electrical and Electronics Engineering, REVA University, Bangalore, India.
Scientific Reports
|May 9, 2025
Summary
This study introduces a novel Long Short-Term Memory (LSTM) and Hilbert-Huang Transform (HHT) model for advanced fault detection in DC microgrids. The LSTM-HHT approach significantly improves accuracy, especially for challenging high-resistance faults.
Area of Science:
- Electrical Engineering
- Computer Science
- Signal Processing
Background:
- DC microgrids are increasingly adopted, requiring robust fault detection.
- Traditional methods like FT and DFT struggle with non-stationary signals and high-resistance faults.
Purpose of the Study:
- To develop and evaluate an advanced fault detection mechanism for DC microgrids.
- To overcome limitations of traditional methods in detecting high-resistance faults.
Main Methods:
- Integration of Long Short-Term Memory (LSTM) networks with the Hilbert-Huang Transform (HHT) model.
- Implementation and testing using MATLAB Simulink in simulated DC microgrid environments.
Main Results:
- The proposed LSTM-HHT approach significantly enhances fault detection accuracy and reliability.
- Demonstrated effectiveness in accurately detecting and localizing challenging high-resistance faults.
Conclusions:
- The LSTM-HHT model offers a more resilient and intelligent solution for DC microgrid fault detection.
- This research supports the integration of renewable energy and the development of safer, more stable microgrids.
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