A novel intelligent fault identification method based on random forests for HVDC transmission lines
Hao Wu1,2, Qiaomei Wang2, Kunjian Yu2
1Artificial Intelligence Key Laboratory of Sichuan Province, Zigong, China.
Plos One
|March 28, 2020
Summary
A new intelligent fault identification method for High Voltage Direct Current (HVDC) transmission lines uses Random Forests (RF) and S transform. This approach accurately identifies internal/external faults and fault poles, improving protection system reliability.
Area of Science:
- Electrical Engineering
- Power Systems
- Signal Processing
Background:
- High Voltage Direct Current (HVDC) transmission lines face challenges balancing protection sensitivity and rapid fault reaction.
- Traditional traveling wave-based fault identification methods can fail due to inaccurate wavefront identification or data loss.
Purpose of the Study:
- To propose a novel intelligent fault identification method for HVDC transmission lines.
- To address the limitations of existing methods in accurately and quickly identifying faults.
Main Methods:
- Utilizing the S transform to extract fault current traveling waves at 8 frequencies.
- Calculating fluctuation index and energy sum ratio for fault characteristic extraction.
- Developing an intelligent fault identification model using Random Forests (RF).
Main Results:
- The proposed method effectively identifies internal and external faults using the wave index.
- Positive and negative pole faults are distinguished using the energy sum ratio.
- The RF model, trained with multi-scale S transform features, demonstrates high accuracy.
Conclusions:
- The Random Forests-based intelligent fault identification method offers a robust solution for HVDC transmission lines.
- It overcomes traditional method failures, providing accurate and fast fault identification under various conditions.
- The method exhibits strong resilience to transitional resistance and interference.
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