Fault Detection on Power Transmission Line Based on Wavelet Transform and Scalogram Image Analysis
Abstract
:1. Introduction
2. System Requirements and Challenges
- Fault impedance.
- Fault inception angle (FIA).
- Compensation (series and/or shunt).
- Other transmission factors such as loads, branches, transformers, etc.
- Only shunt faults are considered, while series faults are ignored. While the shunt faults have a high impact on the system, the series faults have less impact. However, both have an affect on the power system equipment life.
- Using a very simple network consisting of TL and fixed generators. Using only a simple network does not represent the real-world TL system, and the fault on such systems is not as complex as the real system. Thus, the TL that has additional components suffers their contribution to the fault as well.
- Theories are based on the TL protection to the threshold point, which differs from one network to another. This happens when the threshold is a fixed value which is different from one network to another, and may not be valuable if the system has changed by adding or removing components, load, …etc.
- Some theories utilize a supervised approach that requires a significant quantity of data and training. In general, increasing the number of datasets has the advantage of increasing the efficiency of these models, which means that it has been trained on most of the probabilistic scenario and vice versa. However, this is costly and requires time for training.
- Noise is a significant obstacle to the protection system as it can lead to misdiagnosis. This can be a major point in the diagnosis of signals. Therefore, not considering the noise in the fault analysis method causes a malfunction in the protection system as this noise is considered as a disturbance in the system, viz, it is a faulty signal.
- Fault inception angle (FIA) has an impact on the TL protection system. Without considering this factor FIA in any model lead to be a weak point for the model because the fault may occur in any FIA. That means, the future prediction results will be improper and unreliable.
- Computational cost which can be time consuming. Taking data from TL both ends costs resources and time for the fault diagnosis due to the fact that collecting data from two sides of a long transmission line needs a communication system, data preprocess, and devices besides the time to analyze.
3. The Proposed Methodology
3.1. Data Collection
3.2. Contious Wavelet-Based Algorithm
- Time factor: Time does not exist for the Fourier transform, which makes it difficult to apply this theory to power systems that rely on signals traveling through time [49].
- Window limitation cannot be employed when the signal changes in a very short time or even slightly, such as when the signal passes through a transitory period [50].
- Time: it depicts the waveform with three signal spectrum fields (time, magnitude, and frequency).
- Window: the segmentation of the signal is treated as if it were a stationary signal. Consequently, STFT has a pre-defined restricted multi-window configuration.
- The window: once specified, it cannot be altered.
- Resolution pinpoint: this indicates that the window is directly proportional to frequency resolution and inversely proportional to time, and vice versa. This means that any component can result in either excellent frequency resolution or time resolution, but not both. .
- Time interval and frequency: this is regarded as the most difficult and demanding aspect of using STFT in power system analysis, particularly for transmission line protection.
- a (binary dilation or scale parameter): can compress or stretch the wavelet signal [51].
- b (binary position or translation parameter): used for shifting the wavelet signal over the original signal.
- Collect data from sending end.
- Sampling data collected.
- Applying wavelet for the signals’ feature extraction.
- Converting results in a 2D visualizing scalogram image.
3.3. Scalogram Analysis
3.3.1. Wavelet Transform Limitations
3.3.2. Role of Scalogram Analysis
3.3.3. Scalogram Generation and Interpretation
3.3.4. Machine Learning Integration
3.3.5. Validation and Performance Metrics
- Sensitivity:
- 2.
- Specificity:
- 3.
- Precision:
- 4.
- Recall:
- 5.
- F1 Score
3.3.6. Experimental Setup
- Classification based on the image features:
- 2.
- Classification based on high ranked image features:
4. Simulation Results and Discussion
4.1. Introduction to Results and Discussion
4.2. Effect of Noise
4.3. Discussion of Results
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
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System Condition | Parameters |
---|---|
Shunt fault | AG, BG, CG, AB, AC, BC, ABG, ACG, BCG, ABCG and NORMAL |
Series fault | One line open, two lines open |
Noise level | Ideal, 10, 20 and 30 dB |
Fault inception angle | 30, 60, 90, 180, 270, 360 |
Fault location | 20, 30, 40, 50, 75, 100, 150, 200, 280 |
Fault resistance | 0.001, 0.005, 1, 10, 50, 75, 100, 200 |
‘bior’ Biorthogonal wavelets-3.1 | ‘coif’ Coiflets-2 | ‘db’ Daubechies wavelets-7 | ‘dmey’ Discrete approximation of Meyer wavelet-14 |
‘fk’ Fejér-Korovkin filters-14 | ‘gaus’ Gaussian wavelets-7 | ‘haar’ Haar wavelet- | ‘mexh’ Mexican hat wavelet (also known as Ricker wavelet)- |
‘meyr’ Meyer wavelet- | ‘morl’ Morlet wavelet- | ‘rbio’ Reverse biorthogonal wavelets-4.4 | ‘sym’ Symlets-5 |
Classifier Type | According to Image Embedding Inputs | According to Image Features | According to 250 High Ranked Image Features | |||
---|---|---|---|---|---|---|
Accuracy % | Accuracy % | Accuracy % | ||||
Ideal | With Noise 10, 20, and 30 dB | Ideal | With Noise 10, 20, and 30 dB | Ideal | With Noise 10, 20, and 30 dB | |
Logistic regression | 100 | 100 | 100 | 100 | 100 | 100 |
SVM | 100 | 100 | 100 | 100 | 100 | 100 |
Random forest | 100 | 100 | 100 | 100 | 100 | 100 |
Neural Network | 100 | 100 | 100 | 100 | 100 | 100 |
kNN | 100 | 100 | 100 | 100 | 100 | 100 |
AdaBoost | 100 | 100 | ||||
Decision Tree | 100 | 100 |
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Altaie, A.S.; Majeed, A.A.; Abderrahim, M.; Alkhazraji, A. Fault Detection on Power Transmission Line Based on Wavelet Transform and Scalogram Image Analysis. Energies 2023, 16, 7914. https://doi.org/10.3390/en16237914
Altaie AS, Majeed AA, Abderrahim M, Alkhazraji A. Fault Detection on Power Transmission Line Based on Wavelet Transform and Scalogram Image Analysis. Energies. 2023; 16(23):7914. https://doi.org/10.3390/en16237914
Chicago/Turabian StyleAltaie, Ahmed Sabri, Ammar Abbas Majeed, Mohamed Abderrahim, and Afaneen Alkhazraji. 2023. "Fault Detection on Power Transmission Line Based on Wavelet Transform and Scalogram Image Analysis" Energies 16, no. 23: 7914. https://doi.org/10.3390/en16237914