Open-Circuit Fault Detection and Classification of Modular Multilevel Converters in High Voltage Direct Current Systems (MMC-HVDC) with Long Short-Term Memory (LSTM) Method
Abstract
:1. Introduction
- (1)
- The proposed method has the ability to achieve accurate detection and classification of IGBT open-circuit faults but also can reduce the computational cost of sensing and learning from a large number of measurements.
- (2)
- Without data preprocessing or post-operations, the fault detection accuracy of 100% and excellent classification accuracy are achieved.
- (3)
- Performance comparisons of LSTM, bidirectional LSTM (BiLSTM), CNN, and AE-based DNN in terms of fault detection, classification accuracy, and time spent on training and testing for IGBT Open-circuit fault diagnosis of MMC-HVDC are provided.
2. Preliminaries on MMC Open-Circuit Faults and Simulation Experiments
2.1. MMC Sub-Module and Open-Circuit Faults
2.2. Simulation Experiments
3. RNN and LSTM
4. Fault Diagnosis of MMC-HVDC Systems with LSTM
4.1. Design of LSTM
4.2. Results and Analysis
4.2.1. Parameters Selection of LSTM
4.2.2. Detection and Classification of MMC-HVDC System with LSTM
5. Comparison
5.1. Comparison with BiLSTM
5.2. Comparison with CNN and AE-DNN
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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SM State | Normal | Fault | Fault |
---|---|---|---|
Parameters | Value |
---|---|
number of SMs per arm | 9 |
SM capacitor | 1000 μF |
arm inductance | 50 mH |
AC frequency | 50 Hz |
Faulty Bridge | Label Value |
---|---|
Normal | 1 |
A-phase lower SMs | 2 |
A-phase upper SMs | 3 |
B-phase lower SMs | 4 |
B-phase upper SMs | 5 |
C-phase lower SMs | 6 |
C-phase upper SMs | 7 |
Testing Data Proportion | Detection Accuracy (%) |
---|---|
0.1 | 100 |
0.2 | 100 |
0.3 | 100 |
0.4 | 100 |
0.5 | 100 |
0.6 | 100 |
0.7 | 100 |
0.8 | 100 |
0.9 | 100 |
Testing Data Proportion = 0.2 | |||||||
Normal | A-Phase Lower SMs | A-Phase Upper SMs | B-Phase Lower SMs | B-Phase Upper SMs | C-Phase Lower SMs | C-Phase Upper SMs | |
Normal | 100 | 0 | 0 | 0 | 0 | 0 | 0 |
A-phase lower SMs | 0 | 96.25 | 0 | 1.25 | 0 | 2.5 | 0 |
A-phase upper SMs | 0 | 0 | 98.75 | 0 | 1.25 | 0 | 0 |
B-phase lower SMs | 0 | 2.75 | 0 | 95.75 | 0 | 1.5 | 0 |
B-phase upper SMs | 0 | 0 | 0 | 0 | 98.75 | 0 | 1.25 |
C-phase lower SMs | 0 | 1.25 | 0 | 2.25 | 0 | 96.5 | 0 |
C-phase upper SMs | 0 | 0 | 0.25 | 0 | 3.75 | 0 | 96 |
Testing Data Proportion = 0.5 | |||||||
Normal | A-Phase Lower SMs | A-Phase Upper SMs | B-Phase Lower SMs | B-Phase Upper SMs | C-Phase Lower SMs | C-Phase Upper SMs | |
normal | 100 | 0 | 0 | 0 | 0 | 0 | 0 |
A-phase lower SMs | 0 | 96.2 | 0 | 2.7 | 0 | 1 | 0.1 |
A-phase upper SMs | 0 | 0 | 98.5 | 0 | 1.1 | 0 | 0.4 |
B-phase lower SMs | 0 | 1.8 | 0 | 97.3 | 0 | 0.9 | 0 |
B-phase upper SMs | 0 | 0.2 | 0.2 | 0 | 97.4 | 0 | 2.2 |
C-phase lower SMs | 0 | 0.2 | 0 | 1.6 | 0 | 98 | 0.2 |
C-phase upper SMs | 0 | 1.9 | 0 | 0 | 3.1 | 0 | 95 |
Testing Data Proportion = 0.8 | |||||||
Normal | A-Phase Lower SMs | A-Phase Upper SMs | B-Phase Lower SMs | B-Phase Upper SMs | C-Phase Lower SMs | C-Phase Upper SMs | |
normal | 100 | 0 | 0 | 0 | 0 | 0 | 0 |
A-phase lower SMs | 0 | 95.88 | 0 | 0.69 | 0 | 3.12 | 0.31 |
A-phase upper SMs | 0 | 0 | 93.12 | 0.44 | 3.25 | 1.13 | 2.06 |
B-phase lower SMs | 0 | 1.69 | 0 | 93.69 | 0 | 4.62 | 0 |
B-phase upper SMs | 0 | 0.25 | 1.56 | 0 | 94.81 | 0.88 | 2.50 |
C-phase lower SMs | 0 | 2.06 | 0 | 2.44 | 0 | 94.88 | 0.62 |
C-phase upper SMs | 0 | 3.88 | 1.81 | 0 | 2.81 | 0.50 | 91 |
Testing Data Proportion | Detection Accuracy | Classification Accuracy | Training Time Spent | Testing Time Spent | ||||
---|---|---|---|---|---|---|---|---|
LSTM | BiLSTM | LSTM | BiLSTM | LSTM | BiLSTM | LSTM | BiLSTM | |
0.1 | 100 | 100 | 0.984 | 0.974 | 942.4 | 2169.1 | 0.41 | 0.97 |
0.2 | 100 | 100 | 0.974 | 0.974 | 863.8 | 1519.0 | 0.69 | 1.40 |
0.3 | 100 | 100 | 0.979 | 0.980 | 794.4 | 1469.8 | 0.92 | 1.89 |
0.4 | 100 | 100 | 0.974 | 0.970 | 724.5 | 1362.9 | 1.29 | 2.63 |
0.5 | 100 | 100 | 0.975 | 0.973 | 677.9 | 1249.0 | 1.51 | 3.10 |
0.6 | 100 | 100 | 0.963 | 0.967 | 590.9 | 1147.0 | 1.83 | 3.93 |
0.7 | 100 | 100 | 0.959 | 0.962 | 531.6 | 1068.1 | 2.05 | 4.56 |
0.8 | 100 | 100 | 0.948 | 0.951 | 490.6 | 951.4 | 2.45 | 5.26 |
0.9 | 100 | 100 | 0.926 | 0.924 | 417.5 | 863.5 | 2.62 | 5.88 |
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Wang, Q.; Yu, Y.; Ahmed, H.O.A.; Darwish, M.; Nandi, A.K. Open-Circuit Fault Detection and Classification of Modular Multilevel Converters in High Voltage Direct Current Systems (MMC-HVDC) with Long Short-Term Memory (LSTM) Method. Sensors 2021, 21, 4159. https://doi.org/10.3390/s21124159
Wang Q, Yu Y, Ahmed HOA, Darwish M, Nandi AK. Open-Circuit Fault Detection and Classification of Modular Multilevel Converters in High Voltage Direct Current Systems (MMC-HVDC) with Long Short-Term Memory (LSTM) Method. Sensors. 2021; 21(12):4159. https://doi.org/10.3390/s21124159
Chicago/Turabian StyleWang, Qinghua, Yuexiao Yu, Hosameldin O. A. Ahmed, Mohamed Darwish, and Asoke K. Nandi. 2021. "Open-Circuit Fault Detection and Classification of Modular Multilevel Converters in High Voltage Direct Current Systems (MMC-HVDC) with Long Short-Term Memory (LSTM) Method" Sensors 21, no. 12: 4159. https://doi.org/10.3390/s21124159
APA StyleWang, Q., Yu, Y., Ahmed, H. O. A., Darwish, M., & Nandi, A. K. (2021). Open-Circuit Fault Detection and Classification of Modular Multilevel Converters in High Voltage Direct Current Systems (MMC-HVDC) with Long Short-Term Memory (LSTM) Method. Sensors, 21(12), 4159. https://doi.org/10.3390/s21124159