Advanced Fault Detection in Power Transformers Using Improved Wavelet Analysis and LSTM Networks Considering Current Transformer Saturation and Uncertainties
Abstract
:1. Introduction
1.1. Research Importance
1.2. Research Literature Review
1.3. Shortcoming of Previous Research
- Dependence on Time Domain Features:
- -
- Susceptibility to Noise: Features extracted from the differential current in the time domain can be highly sensitive to noise [3].
- Intelligent Techniques for Internal Fault Detection
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- Artificial Neural Networks:
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- Support Vector Machines:
- Signal Analysis Tools
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- Wavelet Transform:
- -
- -
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- S-Transform:
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- Shortcomings of Harmonic Blocking and Restraint Techniques
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- Harmonic Restraint:
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- Low Security: Often lacks security when dealing with inrush currents that have low harmonic content [21].
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- Unexpected Blocking: Can unexpectedly block the relay during the energization of a faulty transformer, especially with high harmonics in healthy phases [21].
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- Harmonic Cross-Blocking:
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- Low Reliability: Exhibits low reliability during the energization of a faulty transformer [21].
- -
- -
- Modern Transformers:
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- Low Harmonic Ratios: Modern power transformers may exhibit very low second harmonic ratios during energization, challenging harmonic-based methods [21].
- Challenges with New Techniques
- -
- Artificial Intelligence and Signal Processing Methods:
- -
- -
- -
- -
- Machine Learning Algorithms:
- -
- Advanced Signal Processing Techniques:
- -
- Hybrid Approaches:
1.4. Research Contribution
- Introduction of Advanced Feature Extraction:
- ○
- Utilizes wavelet transform analysis to derive novel features from the differential current, significantly enhancing fault detection accuracy.
- Integration with Deep Learning for Real-Time Application
- ○
- Implements long short-term memory (LSTM) networks for training, thereby boosting the system’s capability to accurately identify internal transformer faults in real time.
- ○
- Comprehensive Fault Detection:
- ○
- Integrates differential current amplitude and bias current in the detection process, resulting in a more robust and reliable fault detection mechanism.
- Consideration of CT Saturation and Measurement Uncertainty
- ○
- Addresses the challenges posed by CT saturation and measurement uncertainties, often overlooked in traditional methods.
- Improved Relay Operations
- ○
- Mitigates issues caused by even harmonics and DC components during CT saturation, thereby reducing the likelihood of incorrect relay activations.
- Enhanced Reliability and Security
- ○
- Demonstrates superior performance in detecting internal faults in power transformers, even under challenging conditions such as CT saturation.
1.5. Research Structure
2. Conceptual Model and Problem Procedure
Algorithm 1. Procedure of the Proposed Framework for Internal Fault Detection in Power Transformers |
1. Training Stage: |
1.1. For k = 1:2000 |
1.2. If k ≤ 500 |
1.3. Run the Simulation file for sampling the differential currents of external fault |
1.4. SignalVector = Differential currents of external fault |
1.5. ElseIf 500 < k ≤ 1000 |
1.6. Run the Simulation file for sampling the differential currents of inrush current |
1.7. SignalVector = Differential currents of inrush current |
1.8. ElseIf 1000 < k ≤ 1500 |
1.9. Run the Simulation file for sampling the differential currents of internal fault without CT saturation |
1.10. SignalVector = Differential currents of inrush current internal fault without CT saturation |
1.11. ElseIf 1500 < k ≤ 2000 |
1.12. Run the Simulation file for sampling the differential currents of internal fault with CT saturation |
SignalVector = Differential currents of inrush current internal fault with CT saturation |
1.13. Call the Improved RTBSWT Function () |
|
1.14. End Call RTBSWT Function |
1.15. Call Proposed LSTM Network Function |
|
1.16. End LSTM Function |
1.17. End for (K = 2000) |
2. Test and Verification Stage |
2.1. Call an unknown differential current (, ) |
2.2. Call the Improved RTBSWT Function () 2.3. Feature selection of using the 1.13.1 and 1.13.2 |
2.4. End Call RTBSWT Function |
2.5. Call Proposed LSTM Network Function |
2.6. = predict the signal type using the trained LSTM Network |
2.7. End LSTM Function |
2.8. Calculate the RSME (, ) |
2.9. End Stage 2 |
3. Problem Statement and Mathematical Formulation
3.1. Structure of Differential Current Measuring
3.2. Wavelet Transform (Stationary State)—SWT
3.2.1. Real-Time Stationary Wavelet Transform (RT-SWT)
3.2.2. Improved Wavelet Transform (Real-Time Boundary Stationary)
3.3. Enhanced LSTM Neural Network
- : Binary coding vector using (0,1) for various types of faults in power transformers.
4. Result Analysis and Discussion
- Running the power system with varying power supply phase angles (0–360°).
- Modifying fault ground resistance within a range (0.001, 0.01, 0.1, 1, 2, 10, …, 75, 125, 150).
- Adjusting transformer load from 5 MVA to 33 MVA.
- Manipulating fault location timing on both the LV side (33 kV) and HV side (132 kV) as per the provided table.
Cases | Power Source Phase Angle | Number of Cases | Fault Location | Fault Ground Resistance | Transformer Load |
---|---|---|---|---|---|
Inrush current | (0–360°) | 500 | - | - | - |
Internal faults with and without CT saturation | (0–360°) | 500 | HV and LV | (0.001, 0.01, 0.1, …, 75), | (5 MVA to 33 MVA) |
500 | HV and LV | ||||
External faults under CT saturation | (0–360°) | 250 | HV | (0.001, 0.01, 0.1, …, 75, 125, 150), | (5 MVA to 33 MVA) |
250 | LV |
4.1. Feature Extraction Using Improved RBSWT
4.2. Training and Verification of the Proposed LSTM Neural Network
4.3. Analysis of Results and Comparison with State-of-the-Art Research
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
Abbreviations
LSTM | Long Short-Term Memory | ELSTM | Enhanced Long Short-Term Memory |
RT-SWT | Real-Time Stationary Wavelet Transform | GRU | Gated Recurrent Unit |
RTBSWT | Real-Time Boundary Stationary Wavelet Transform | BPTT | Backpropagation Through Time |
ANN | Artificial Neural Network | SMOTE | Synthetic Minority Over-Sampling Technique |
DNN | Deep Neural Network | RMSE | Root Mean Square Error |
RNN | Recurrent Neural Network |
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Fault Type | Binary Code |
---|---|
Internal faults | (0,0) |
External faults | (0,1) |
Inrush current | (1,0) |
Internal faults and inrush current (under CT saturation) | (1,1) |
Connection of Transformer | YnD11 |
---|---|
Nominal apparent power (MVA) | 63 |
Voltage ratio (kV) | 132/33 |
Rated frequency (Hz) | 50 |
Percentage impedance (%) | 10 |
CT ratio primary side | 300:5 |
CT ratio secondary side | 1200:5 |
Epoch | Iteration | Time Elapsed (hh:mm:ss) | Mini-Batch RMSE | Mini-Batch Loss | Base Learning Rate |
---|---|---|---|---|---|
1 | 1 | 00:00:03 | 191 × 10−2 | 18 × 10−1 | 1 × 10−2 |
50 | 50 | 00:00:04 | 7 × 10−2 | 26 × 10−4 | 1 × 10−2 |
100 | 100 | 00:00:05 | 3 × 10−2 | 4 × 10−4 | 1 × 10−2 |
150 | 150 | 00:00:06 | 1 × 10−2 | 57 × 10−7 | 1 × 10−3 |
200 | 200 | 00:00:07 | 933 × 10−5 | 44 × 10−7 | 1 × 10−3 |
Fault Type | Analysis of Correct Diagnosis by Different Methods | ||||||||
---|---|---|---|---|---|---|---|---|---|
[5,24,27] | [10,25] | [9,14] | Present Method | ||||||
Internal fault | Number of cases | Number | % | Number | % | Number | % | Number | % |
500 | 489 | 97 | 476 | 95 | 482 | 96 | 498 | 99 | |
Average detection time (ms) | 340 | 380 | 410 | 130 | |||||
External fault | Number of cases | Number | % | Number | % | Number | % | Number | % |
500 | 469 | 93 | 471 | 94 | 463 | 92 | 499 | 99 | |
Average detection time (ms) | 362 | 310 | 510 | 110 | |||||
Inrush currents | Number of cases | Number | % | Number | % | Number | % | Number | % |
500 | 486 | 97 | 477 | 95 | 471 | 94 | 497 | 99 | |
Average detection time (ms) | 360 | 379 | 525 | 120 | |||||
Internal faults with CT saturation | Number of cases | Number | % | Number | % | Number | % | Number | % |
500 | 462 | 92 | 420 | 84 | 431 | 86 | 496 | 99 | |
Average detection time (ms) | 390 | 428 | 610 | 170 |
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Alhamd, Q.; Saniei, M.; Seifossadat, S.G.; Mashhour, E. Advanced Fault Detection in Power Transformers Using Improved Wavelet Analysis and LSTM Networks Considering Current Transformer Saturation and Uncertainties. Algorithms 2024, 17, 397. https://doi.org/10.3390/a17090397
Alhamd Q, Saniei M, Seifossadat SG, Mashhour E. Advanced Fault Detection in Power Transformers Using Improved Wavelet Analysis and LSTM Networks Considering Current Transformer Saturation and Uncertainties. Algorithms. 2024; 17(9):397. https://doi.org/10.3390/a17090397
Chicago/Turabian StyleAlhamd, Qusay, Mohsen Saniei, Seyyed Ghodratollah Seifossadat, and Elaheh Mashhour. 2024. "Advanced Fault Detection in Power Transformers Using Improved Wavelet Analysis and LSTM Networks Considering Current Transformer Saturation and Uncertainties" Algorithms 17, no. 9: 397. https://doi.org/10.3390/a17090397