Insulation Defect Diagnosis Using a Random Forest Algorithm with Optimized Feature Selection in a Gas-Insulated Line Breaker
Abstract
:1. Introduction
2. PD Simulation Experimental Setup and Method
2.1. PD Simulation Test Setup
2.2. Random Forest (RF) Algorithm
- The number of estimators N:
- 2.
- The maximum depth of decision trees D:
- 3.
- The minimum number of samples per split:
3. Statistical PD Feature Extraction
3.1. Time-Domain Features
- Rising time (Tr, RT): time durations increase from 10% to 90% of peak magnitudes.
- Falling time (Tf, FT): time durations decrease from 90% to 10% of peak magnitudes.
- Pulse width (Tw, PW): time durations between 50% of peak magnitudes.
3.2. Frequency-Domain Features
- First peak frequency (1st Pk): the frequency at the highest FFT spectral magnitude
- Second peak frequency (2nd Pk): the frequency at the second highest FFT spectral magnitude
3.3. Physical Shape-Domain Features
3.4. Phase Distribution-Domain Features
4. Insulation Defect Recognition Using Random Forest Algorithm
4.1. Feature Extraction
4.2. PD Defect Recognition
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Types of PD Defect Models | 1st Half Cycle | 2nd Half Cycle | Frequency | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
1-RT | 1-FT | 1-PW | 1-KUR | 1-SKE | 2-RT | 2-FT | 2-PW | 2-KUR | 2-SKE | 1st Pk | 2nd Pk | |
FMP | 2.98 ns | 7.51 ns | 4.89 ns | −1.13 | 0.2 | 1.51 ns | 1.86 ns | 2.22 ns | −0.49 | 0.53 | 34.65 MHz | 30.42 MHz |
POC | 7.43 ns | 7.96 ns | 6.42 ns | −0.93 | −0.41 | 7.46 ns | 7.24 ns | 8.48 ns | −1.2 | −0.21 | 34.13 MHz | 35.25 MHz |
POE | 5.79 ns | 7.18 ns | 5.93 ns | −1.24 | −0.09 | 10.57 ns | 5.96 ns | 10.96 ns | −0.44 | −0.23 | 34.03 MHz | 30.53 MHz |
Delamination | 25.12 ns | 37.74 ns | 39.48 ns | −1.1 | −0.01 | 21.42 ns | 36.86 ns | 38.23 ns | −1.02 | 0.06 | 8.02 MHz | 7.18 MHz |
Types of PD Defect Models | Positive Half Cycle | Negative Half Cycle | ||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
PO-1 | PO-2 | PO-3 | PO-4 | PO-5 | PO-6 | PO-KUR | PO-SKE | NE-1 | NE-2 | NE-3 | NE-4 | NE-5 | NE-6 | NE-KUR | NE-SKE | |
FMP | 5.1% | 6.3% | 10.1% | 10.5% | 9.1% | 7.6% | 7.62 | 2.62 | 5.2% | 6.5% | 10.3% | 11.2% | 10.0% | 7.7% | 7.00 | −2.51 |
POC | 0.0% | 1.0% | 53.5% | 45.4% | 0.0% | 0.0% | 2.08 | 1.78 | - | |||||||
POE | - | 0.0% | 0.0% | 1.7% | 43.0% | 55.3% | 0.0% | 15.44 | −3.57 | |||||||
Delamination | 16.8% | 31.5% | 0.5% | 0.1% | 0.1% | 0.0% | 11.04 | 2.71 | 26.4% | 21.0% | 2.8% | 0.5% | 0.3% | 0.0% | 8.90 | −2.58 |
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Lee, G.-Y.; Kil, G.-S. Insulation Defect Diagnosis Using a Random Forest Algorithm with Optimized Feature Selection in a Gas-Insulated Line Breaker. Electronics 2025, 14, 1940. https://doi.org/10.3390/electronics14101940
Lee G-Y, Kil G-S. Insulation Defect Diagnosis Using a Random Forest Algorithm with Optimized Feature Selection in a Gas-Insulated Line Breaker. Electronics. 2025; 14(10):1940. https://doi.org/10.3390/electronics14101940
Chicago/Turabian StyleLee, Gyeong-Yeol, and Gyung-Suk Kil. 2025. "Insulation Defect Diagnosis Using a Random Forest Algorithm with Optimized Feature Selection in a Gas-Insulated Line Breaker" Electronics 14, no. 10: 1940. https://doi.org/10.3390/electronics14101940
APA StyleLee, G.-Y., & Kil, G.-S. (2025). Insulation Defect Diagnosis Using a Random Forest Algorithm with Optimized Feature Selection in a Gas-Insulated Line Breaker. Electronics, 14(10), 1940. https://doi.org/10.3390/electronics14101940