Intelligent Sensors for dc Fault Location Scheme Based on Optimized Intelligent Architecture for HVdc Systems
Abstract
:1. Introduction
- Our initial goal is to create a learning-based algorithm that relies on only one end of the communication link for fault location. Hence, eliminating reliance on the communication link.
- In general, a signal detected by a sensor is invariably interfered with by the surrounding environment or modified by the detecting equipment during the detection process, increasing failure chances. The DWT-based signal analysis model is used to eliminate interference from the observed signal to improve signal analysis and recognition.
- The energy or norm of the current and voltage signals at each frequency band gives a unique signature for different fault locations and has been found to be robust against noise. Therefore, it is used as an extracted feature for pattern recognition.
- The proposed algorithm must be able to locate internal faults with high fault impedances at further distances.
2. Proposed Framework
2.1. Feedforward Neural Network (FFNN)
2.2. Backpropagation Algorithm
2.3. Levenberg–Marquardt Backpropagation
2.4. Parameter Optimization
2.4.1. Black-Box Settings
2.4.2. Gaussian Process (GP)
2.4.3. Acquisition Function
2.4.4. Implementation of Proposed Framework
3. System Model
Model Output
4. Data Processing
4.1. Signal Processing
4.1.1. Setting Numbers of Decomposition Layers
4.1.2. Selection of Mother Wavelet Function
4.1.3. Set the Threshold and Filter the Signal
4.2. Feature Extraction Set-Up
4.2.1. Feature Extraction Results
4.2.2. Training Set-Up
5. Simulation Results and Discussions
- A.
- Metric for Evaluation and Testing Set-Up
5.1. Case 1 (Fault Location)
5.2. Case 2 (Fint)
5.3. Case 3 (Noisy Events)
5.4. Case 4 (Comparison with Existing Methods)
6. Comparison and Analysis
6.1. Non-AI-Based Methods
6.2. AI-Based Methods
7. Conclusions
- The wavelet coefficient energies of voltage and current over 10 ms are calculated and denoised during the learning phase for feature extraction. This leads to fewer features yet is robust for the learning model.
- A comprehensive training dataset is collected to train the multilayer FFNN model for different fault locations by varying fault impedance.
- The performance of this model is then evaluated on data points that are not included in the training dataset. The study results show that the fault location can be calculated using the FFNN for fault resistance up to 485 Ω.
- Because the signal and Gaussian noise are integrated into the FFNN training sets, the influence of the noise-contained environment is reduced.
- Due to plug-and-play capability, the suggested intelligent algorithm is tailored for a multi-vendor-based fault location estimation strategy in meshed MT-HVdc grids.
- The case studies show that the proposed scheme performs well against many variables, such as different fault resistances, transmission line lengths, and non-ideal noise events. Thus, that makes it feasible for practical application in the MT-HVdc grid.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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LMBP Algorithm for the Fault Location Process |
---|
|
Station | Rated dc Voltage [kV] | Rated Capacity [MVA] | Arm Capacitance Carm (µF) | Arm Inductance Larm [mH] | Arm Resistance Rarm [Ω] | Bus Filter Reactor [mH] |
---|---|---|---|---|---|---|
MMC1 | ±320 | 900 | 29.3 | 84.8 | 0.885 | 10 |
MMC2 | ±320 | 900 | 29.3 | 84.8 | 0.885 | 10 |
MMC3 | ±320 | 900 | 29.3 | 84.8 | 0.885 | 10 |
MMC4 | ±320 | 1200 | 39.0 | 63.6 | 0.67 | 10 |
dc System | Link12 | Link13 | Link34 | Link24 |
---|---|---|---|---|
Length [km] | 100 | 200 | 100 | 150 |
Inductance [mH] | 100 | 100 | 100 | 100 |
ac system | AC 1 | AC 2 | AC 3 | AC 4 |
Rated voltage [kV] | 400 | 400 | 400 | 400 |
Reactance Xac [Ω] | 17.7 | 17.7 | 17.7 | 13.4 |
Resistance Rac [Ω] | 1.77 | 1.77 | 1.77 | 1.34 |
Transformer µk [pu] | 0.15 | 0.15 | 0.15 | 0.15 |
Cable | Outer Radius [mm] | [Ωm] | Єre1 [-] | µre1 [-] | Link34 |
---|---|---|---|---|---|
Core | 19.5 | 1.7 × 10−8 | -- | 1 | |
Insulation | 48.7 | -- | 2.3 | 150 | 1 |
Sheath | 51.7 | 2.2 × 10−7 | -- | 100 | 1 |
Insulation | 54.7 | -- | 2.3 | AC4 | 1 |
Armor | 58.7 | 1.8 × 10−7 | -- | 400 | 10 |
Insulation | 63.7 | -- | 2.3 | 13.4 | 1 |
Transient Period | Training Samples | Fault Resistance (Ω) | Fault Distance (km) | Noise (dB) |
---|---|---|---|---|
10 ms | 357 | 0.01, 25, 50, …, 375, 400 | 1, 10, 20, …, 180, 190, 198 | 20, 25, 30 |
Total faulty sample = 357/each fault type; dc-link faults are first classified into two parts: pole to pole and pole to ground fault. Therefore, total training samples = k = (Fint = 357 ∗ 2) = 714. Fault distance is noted from MMC1 to MMC 3 and MMC1 to MMC2, respectively. |
Hyperparameters | Range | Fault Location Model |
---|---|---|
Learning Rate | [1 × 10−2–1] | 0.010037 |
Hidden Layers/Neurons (NHL) | [1–40] | 28 |
Momentum | [0.001–0.005] | 0.0028608 |
Epochs | [20–1000] | 994 |
Gradient | [1 × 10−7–10−6] | 1.2925 × 10−7 |
Validation | [0–6] | 4 |
Transient Period [10 ms] | Testing Samples | Fault Resistance (Ω) | Fault Distance (km) | Noise (dB) |
---|---|---|---|---|
10 ms | 400 | 10, 35, 60, 85, …, 435, 460, 485 | 5, 15, 25, …, 175, 185, 195 | 20, 25, 45 |
Total faulty sample = 400/each fault type, Total testing samples = [(400) ∗ 2] = 800, Refer Table 6 for fault distance |
Fault Type | Total Faults | Max Absolute Error (km) | Max Percentage Error (%) | Overall Absolute Error (km) | Overall Percentage Error (%) |
---|---|---|---|---|---|
PTP | 400 | 2.6350 | 1.3174 | 0.9853 | 0.4927 |
PTG | 400 | 2.6412 | 1.3206 | 1.0723 | 0.5361 |
Average Error | NA | NA | NA | 1.0288 | 0.5144 |
Fault Location | Fault Resistance (Ω) | Fault Type | dc-Link Fault Location Results | |||||||
---|---|---|---|---|---|---|---|---|---|---|
Predicted Location | Absolute Error | Percentage Error (%) | ||||||||
PTP | PTG | PTP | PTG | PTP | PTG | |||||
5 km of dc link | 10 | PTP | PTG | 5.02021 | 5.03022 | 0.02021 | 0.03022 | 0.01011 | 0.01511 | |
110 | PTP | PTG | 5.07141 | 5.08142 | 0.07141 | 0.08142 | 0.03571 | 0.04071 | ||
260 | PTP | PTG | 5.63413 | 5.76481 | 0.63413 | 0.76481 | 0.31707 | 0.38241 | ||
35 km of dc link | 35 | PTP | PTG | 35.05123 | 35.07134 | 0.05123 | 0.07134 | 0.025615 | 0.03567 | |
235 | PTP | PTG | 35.62858 | 36.10184 | 0.62858 | 1.10184 | 0.31429 | 0.55092 | ||
285 | PTP | PTG | 35.86144 | 36.31471 | 0.86144 | 1.31471 | 0.43072 | 0.65736 | ||
125 km of dc link | 260 | PTP | PTG | 126.67141 | 126.81487 | 1.67141 | 1.81487 | 0.83571 | 0.90744 | |
385 | PTP | PTG | 126.76175 | 126.91231 | 1.76175 | 1.91231 | 0.88088 | 0.956155 | ||
110 | PTP | PTG | 126.01522 | 126.52812 | 1.01522 | 1.52812 | 0.50761 | 0.76406 | ||
185 km of dc link | 260 | PTP | PTG | 186.94571 | 186.75387 | 1.94571 | 1.75387 | 0.972855 | 0.87694 | |
385 | PTP | PTG | 183.63147 | 186.93141 | 1.36853 | 1.93141 | 0.68427 | 0.96571 | ||
110 | PTP | PTG | 186.34578 | 186.53681 | 1.34578 | 1.53681 | 0.67289 | 0.76841 | ||
Normal operation | X | X | X | X | NOT APPLICABLE | NOT APPLICABLE | NOT APPLICABLE |
Noise (dB) | Fault Location | Fault Resistance (Ω) | Fault Type | dc-Link Fault Location Results | ||||||
---|---|---|---|---|---|---|---|---|---|---|
Predicted Location | Absolute Error | Percentage Error (%) | ||||||||
PTP | PTG | PTP | PTG | PTP | PTG | |||||
25 | 5 km of dc link | 10 | PTP | PTG | 5.04512 | 5.06727 | 0.04512 | 0.06727 | 0.02256 | 0.03364 |
110 | PTP | PTG | 6.01202 | 6.03567 | 1.01202 | 1.03567 | 0.50601 | 0.51784 | ||
260 | PTP | PTG | 6.26783 | 6.15872 | 1.26783 | 1.15872 | 0.63392 | 0.57936 | ||
20 | 45 km of dc link | 35 | PTP | PTG | 46.06982 | 46.23672 | 1.06982 | 1.23672 | 0.53491 | 0.61836 |
235 | PTP | PTG | 46.84612 | 47.03452 | 1.84612 | 2.03452 | 0.92306 | 1.01726 | ||
285 | PTP | PTG | 47.03487 | 46.76324 | 2.03487 | 1.76324 | 1.01744 | 0.88162 | ||
45 | 155 km of dc link | 260 | PTP | PTG | 156.96342 | 154.06853 | 1.96342 | 0.93147 | 0.98171 | 0.46574 |
385 | PTP | PTG | 154.13647 | 153.02356 | 0.86353 | 1.97644 | 0.43177 | 0.98822 | ||
110 | PTP | PTG | 154.43628 | 154.36571 | 0.56372 | 0.63429 | 0.28186 | 0.31715 |
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Yousaf, M.Z.; Tahir, M.F.; Raza, A.; Khan, M.A.; Badshah, F. Intelligent Sensors for dc Fault Location Scheme Based on Optimized Intelligent Architecture for HVdc Systems. Sensors 2022, 22, 9936. https://doi.org/10.3390/s22249936
Yousaf MZ, Tahir MF, Raza A, Khan MA, Badshah F. Intelligent Sensors for dc Fault Location Scheme Based on Optimized Intelligent Architecture for HVdc Systems. Sensors. 2022; 22(24):9936. https://doi.org/10.3390/s22249936
Chicago/Turabian StyleYousaf, Muhammad Zain, Muhammad Faizan Tahir, Ali Raza, Muhammad Ahmad Khan, and Fazal Badshah. 2022. "Intelligent Sensors for dc Fault Location Scheme Based on Optimized Intelligent Architecture for HVdc Systems" Sensors 22, no. 24: 9936. https://doi.org/10.3390/s22249936
APA StyleYousaf, M. Z., Tahir, M. F., Raza, A., Khan, M. A., & Badshah, F. (2022). Intelligent Sensors for dc Fault Location Scheme Based on Optimized Intelligent Architecture for HVdc Systems. Sensors, 22(24), 9936. https://doi.org/10.3390/s22249936