Transmission Line Fault Classification of Multi-Dataset Using CatBoost Classifier
Abstract
:1. Introduction
2. Modelling of 330 kV Transmission Line
3. Methodology
3.1. Data Preparation and Extraction
3.2. The Use of CatBoost in Fault Classification
3.3. Training of Datasets Using CatBoost Algorithm
4. Results and Discussion
5. Discussion
The Effect of Noise and Disturbance in the Proposed Algorithm
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Sequence | Parameter | Value | Unit |
---|---|---|---|
Positive and negative sequence resistance | , | 0.01273 | /km |
Zero sequence resistance | 0.3864 | /km | |
Positive and negative sequence inductance | , , | 0.9337 × | H/km |
Zero sequence inductance | 4.1264 × | H/km | |
Positive and negative sequence capacitance | , , | 12.74 × | F/km |
Zero sequence capacitance | 7.751 × | F/km |
System Components | Parameters/Units | Value |
---|---|---|
Phase to phase voltage | voltage | 330 |
Source resistance Rs | Ohms | 0.8929 |
Source inductance | H | 16.58 × |
Fault incipient angle | in degree | 0° and −30° |
Fault resistance | Ohms (Ω) | 0.001 |
Ground resistance | Ohms (Ω) | 0.01 |
Snubber resistance | Ohms (Ω) | 1.0 × |
Fault capacitance | F | infinite |
Switching time | seconds | 0.2 |
Class | Fault Type | (a) | (b) | (c) | G (g) |
---|---|---|---|---|---|
1 | a-g | 1 | 0 | 0 | 1 |
2 | b-g | 0 | 1 | 0 | 1 |
3 | c-g | 0 | 0 | 1 | 1 |
4 | a-b | 1 | 1 | 0 | 0 |
5 | a-c | 1 | 0 | 1 | 0 |
6 | b-c | 0 | 1 | 1 | 0 |
7 | a-b-g | 1 | 1 | 0 | 1 |
8 | b-c-g | 0 | 1 | 1 | 1 |
9 | a-c-g | 1 | 0 | 1 | 1 |
10 | a-b-c | 1 | 1 | 1 | 0 |
11 | a-b-c-g | 1 | 1 | 1 | 1 |
12 | No fault | 0 | 0 | 0 | 0 |
CatBoost Model is Fitted | True |
---|---|
Iterations | 1000 |
Depth | 10 |
Loss function | Multiclass |
Leaf estimation method | Newton |
Class weight | 0.001, 0.01, 0.9, 0.001 |
Random strength | 0.1 |
True Class | 0 | 0 | 0 | 6955 | 0 |
1 | 0 | 4862 | 2051 | 0 | |
2 | 0 | 0 | 7048 | 0 | |
3 | 0 | 0 | 2013 | 5073 | |
0 | 1 | 2 | 3 | ||
Predicted Class |
Fault Type | Classes | Precision | Recall | F1-Score | Support |
---|---|---|---|---|---|
A-G | 1 | 1.00 | 0.70 | 0.83 | 6913 |
B-C-G | 2 | 0.39 | 1.00 | 0.56 | 7048 |
A-B-C-G | 3 | 1.00 | 0.72 | 0.83 | 7086 |
No fault | 0 | 0 | 0 | 0 | 0 |
Micro Avg | 0.61 | 0.81 | 0.69 | 21,047 | |
Macro Avg | 0.80 | 0.81 | 0.74 | 21,047 | |
Weighted Avg | 0.80 | 0.81 | 0.74 | 21,047 |
Technique Used | Input Parameter | Fault Types | Data Size | % Accuracy | Strength | Weakness |
---|---|---|---|---|---|---|
WT, CNN [20] | vibration signal | 400 different fault condition | 2400 | 97.78% | speed of 8 s to execute and easy to use | The WT Packet is not theoretically proven. |
ANN [37] | Three-phase voltage and current waveform | 10 different fault condition | 7920 | 78.1% | Easy to use and implement, Repro- gramming is not needed | Requires a system with a high processor, Longer training time. |
Proposed CatBoost Classifier | Voltage and current signal | Single-phase fault, double phase fault, three-phase fault and no-fault | 93,340 X 6 | 99.54% | Higher accuracy, speed and low training time.multiple feature classification | It needs a high- performance operating system to train the data. |
BPNN [11] | Voltage and current | AG, BG, CG, ABG, ACG, BCG, AB, AC, BC, ABC, ABCG | 1188 | 97.3% | easy to execute, it requires less number of neurons for training. | Slow to use, computationally expensive, can’t be used to solve complex andlarge problems. Slow convergence. |
RBFNN [11] | Voltage and current | AG, BG, CG, ABG, ACG, BCG, AB, AC, BC, ABC, ABCG | 1188 | 99.3% | Faster than BPNN, easy to use. | Not suitable for non-linear systems and large dataset |
PNN [11] | Voltage and current | AG, BG, CG, ABG, ACG, BCG, AB, AC, BC, ABC, ABCG | 1188 | 99.4% | It can handle multi-dataset to classify faults. Also, no learning process is required. | Expensive to implement, and learning can be slow, high processing time if the network is extensive. |
RDRP [19] | Voltage signal of 10 dB, 20 dB and 30 dB | Single, double and three-phase fault | 480 | 93.9, 96.8% and 96.8% | It can work well with small datasets | Not suitable for multiple datasets and low prediction accuracy. |
CNN [16] | Three phase Voltage and current | 10 different fault condition | 92,077 | 99% | Used to solve multi-channel sequence recognition problem | The computational cost of offline mode is expensive |
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Ogar, V.N.; Hussain, S.; Gamage, K.A.A. Transmission Line Fault Classification of Multi-Dataset Using CatBoost Classifier. Signals 2022, 3, 468-482. https://doi.org/10.3390/signals3030027
Ogar VN, Hussain S, Gamage KAA. Transmission Line Fault Classification of Multi-Dataset Using CatBoost Classifier. Signals. 2022; 3(3):468-482. https://doi.org/10.3390/signals3030027
Chicago/Turabian StyleOgar, Vincent Nsed, Sajjad Hussain, and Kelum A. A. Gamage. 2022. "Transmission Line Fault Classification of Multi-Dataset Using CatBoost Classifier" Signals 3, no. 3: 468-482. https://doi.org/10.3390/signals3030027