Deep learning and wavelet packet transform for fault diagnosis in double circuit transmission lines
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View abstract on PubMed
Summary
This summary is machine-generated.This study introduces a new directional protection method for double-circuit transmission lines (DCTLs) using wavelet packet transform (WPT) and deep learning (DL). The advanced framework accurately detects, identifies, and locates faults, enhancing grid reliability.
Area Of Science
- Electrical Engineering
- Power Systems
- Artificial Intelligence
Background
- Effective fault diagnosis in double-circuit transmission lines (DCTLs) is crucial for reliable power system operation.
- Existing protection schemes require robust methods for fault detection, section identification, and precise location.
Purpose Of The Study
- To propose an advanced directional protection framework for DCTLs.
- To integrate wavelet packet transform (WPT) with deep learning (DL) models for enhanced fault diagnosis.
- To accurately detect, identify, and locate faults in DCTLs using double-ended measurements.
Main Methods
- Modeling of DCTLs using a distributed parameter line representation with shunt capacitance.
- Application of wavelet packet transform (WPT) with the db10 mother wavelet for signal decomposition.
- Utilizing deep learning (DL) models, including CNNs and RNNs, fed with WPT approximation coefficients for fault analysis.
Main Results
- The proposed WPT-DL framework achieved high accuracy and robustness in diverse fault scenarios (location, resistance, inception angle).
- Demonstrated superior precision compared to WPT-ANN and WPT-ANFIS hybrid approaches, with an average error of only 0.03%.
- The method provides primary protection for most line sections and backup coverage for adjacent areas.
Conclusions
- The integrated WPT and DL approach offers a highly accurate and robust solution for fault diagnosis in DCTLs.
- This advanced directional protection framework significantly improves the reliability and performance of power system protection.
- The method's effectiveness is validated through simulations across various challenging fault conditions.
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