Advancing Fault Detection in Distribution Networks with a Real-Time Approach Using Robust RVFLN
Abstract
:1. Introduction
- 1
- When noise from sensors is not taken into account in real-time operations, various problems occur in both signal processing and machine learning applications. The main problems are misclassification and low accuracy. However, in this study, noise and outlier effects are taken into account for fault detection.
- 2
- When the studies in the research are examined, Matlab is generally used for the realization of fault type and location detection in single-bus transmission systems. However, the work done in Matlab needs to be compared with real-time systems. In this study, the IEEE 39-bus system, which is a small model of a distribution network, is realized using the RTDS simulator and a real-time study is performed.
- 3
- When refs. [18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40] are analyzed, it is found that six new attribute vectors that were not previously used in fault type data detection in distribution networks have been created. Thanks to these feature vectors, the accuracy of fault type and location detection in high-impedance short-circuit faults is improved by 10%.
- 4
2. Fault Detection Method
2.1. Feature Construction
2.1.1. Fundamental Electrical Features (U1–U4)
- U1—Represents the absolute magnitude of the current waveform in each phase. Changes in this value can indicate the presence of a fault.
- U2—Represents the absolute magnitude of the voltage waveform in each phase. A significant drop in voltage is often a sign of a fault.
- U3—The phase angle of the current relative to a reference point, providing insights into power flow and fault-induced distortions.
- U4—The phase angle of the voltage, which can shift significantly during a fault event, particularly in unbalanced conditions.
2.1.2. Derivative-Based Features (U5–U8)
- U5—Measures the rate of change in the current, highlighting rapid variations indicative of fault inception.
- U6—Captures sudden voltage drops or fluctuations caused by fault conditions.
- U7—Provides insights into frequency variations and phase shifts due to faults.
- U8—Tracks dynamic phase shifts in voltage waveforms, particularly useful in detecting unbalanced faults.
2.1.3. Norm-Based Statistical Features (U9–U14)
- U9—Represents the sum of the absolute values of voltage samples over a defined window, effectively capturing the total signal intensity.
- U10—Reflects the root mean square (RMS) value of the voltage signal, emphasizing the dominant frequency components.
- U11—Extracts the maximum absolute value in the voltage signal, highlighting peak disturbances.
- U12—Represents the sum of the absolute values of current samples, useful in quantifying the total current deviation.
- U13—Provides an RMS-like measurement for the current, emphasizing significant fluctuations.
- U114—Extracts the maximum absolute value in the current signal, useful in detecting extreme deviations due to faults.
2.2. RVFLN Algorithm
2.3. R-RVFLN
2.4. RR-RVFLN
2.5. ORR-RVFLN
2.5.1.
2.5.2.
3. Case Study
3.1. Single-Bus Transmission System
3.2. IEEE 39-Busbar System
4. Results and Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Feature | Notation | Formulation |
---|---|---|
U1 | current magnitude | |
U2 | voltage magnitude | |
U3 | current angle | |
U4 | voltage angle | |
U5 | derivative of current magnitude | |
U6 | derivative of voltage magnitude | |
U7 | derivative of current angle | |
U8 | derivative of voltage angle | |
U9 | ||
U10 | ||
U11 | ||
U12 | ||
U13 | ||
U14 |
With Noise | Without Noise | With Noise | Without Noise | With Noise | Without Noise | With Noise | Without Noise | |
---|---|---|---|---|---|---|---|---|
ORR-RVFLN | 0 ohm | 30 ohm | 50 ohm | 100 ohm | ||||
0–50 m | 61 | 59 | 63 | 60 | 66 | 62 | 69 | 65 |
>400 m | 0 | 1 | 4 | 5 | 7 | 9 | 10 | 14 |
Cauchy-M-RVFLN | 0 ohm | 30 ohm | 50 ohm | 100 ohm | ||||
0–50 m | 64 | 62 | 66 | 63 | 69 | 65 | 72 | 68 |
>400 m | 3 | 4 | 6 | 7 | 9 | 11 | 12 | 16 |
RVFLN | 0 ohm | 30 ohm | 50 ohm | 100 ohm | ||||
0–50 m | 66 | 64 | 68 | 65 | 71 | 67 | 73 | 69 |
>400 m | 5 | 5 | 8 | 9 | 11 | 13 | 14 | 18 |
CNN | 0 ohm | 30 ohm | 50 ohm | 100 ohm | ||||
0–50 m | 69 | 67 | 71 | 68 | 74 | 70 | 76 | 72 |
>400 m | 6 | 7 | 9 | 10 | 12 | 14 | 15 | 19 |
LSTM | 0 ohm | 30 ohm | 50 ohm | 100 ohm | ||||
0–50 m | 70 | 67 | 71 | 68 | 74 | 70 | 77 | 73 |
>400 m | 7 | 7 | 10 | 11 | 13 | 15 | 16 | 20 |
SVM | 0 ohm | 30 ohm | 50 ohm | 100 ohm | ||||
0–50 m | 70 | 68 | 72 | 69 | 75 | 71 | 78 | 73 |
>400 m | 8 | 8 | 11 | 12 | 14 | 16 | 17 | 21 |
ELM | 0 ohm | 30 ohm | 50 ohm | 100 ohm | ||||
0–50 m | 72 | 70 | 73 | 70 | 76 | 72 | 79 | 75 |
>400 m | 10 | 11 | 14 | 15 | 17 | 19 | 20 | 24 |
With Noise | Without Noise | With Noise | Without Noise | With Noise | Without Noise | With Noise | Without Noise | |
---|---|---|---|---|---|---|---|---|
ORR-RVFLN | 0 ohm | 30 ohm | 50 ohm | 100 ohm | ||||
0–50 m | 51 | 49 | 54 | 50 | 57 | 53 | 49 | 56 |
>400 m | 0 | 0 | 1 | 2 | 2 | 4 | 5 | 6 |
Cauchy- M-RVFLN | 0 ohm | 30 ohm | 50 ohm | 100 ohm | ||||
0–50 m | 52 | 53 | 58 | 51 | 58 | 54 | 50 | 56 |
>400 m | 1 | 1 | 2 | 3 | 3 | 5 | 6 | 7 |
RVFLN | 0 ohm | 30 ohm | 50 ohm | 100 ohm | ||||
0–50 m | 53 | 51 | 56 | 52 | 59 | 55 | 51 | 57 |
>400 m | 2 | 2 | 3 | 4 | 4 | 6 | 7 | 7 |
CNN | 0 ohm | 30 ohm | 50 ohm | 100 ohm | ||||
0–50 m | 57 | 55 | 59 | 55 | 62 | 58 | 54 | 61 |
>400 m | 3 | 3 | 4 | 5 | 5 | 6 | 7 | 8 |
LSTM | 0 ohm | 30 ohm | 50 ohm | 100 ohm | ||||
0–50 m | 57 | 55 | 60 | 56 | 63 | 59 | 55 | 61 |
>400 m | 4 | 4 | 5 | 6 | 6 | 8 | 8 | 9 |
SVM | 0 ohm | 30 ohm | 50 ohm | 100 ohm | ||||
0–50 m | 58 | 56 | 61 | 56 | 63 | 59 | 55 | 61 |
>400 m | 8 | 8 | 9 | 10 | 10 | 12 | 13 | 14 |
ELM | 0 ohm | 30 ohm | 50 ohm | 100 ohm | ||||
0–50 m | 59 | 57 | 62 | 58 | 65 | 61 | 57 | 64 |
>400 m | 7 | 7 | 8 | 9 | 9 | 11 | 12 | 12 |
With Noise | Without Noise | With Noise | Without Noise | With Noise | Without Noise | With Noise | Without Noise | |
---|---|---|---|---|---|---|---|---|
ORR-RVFLN | 0 ohm | 30 ohm | 50 ohm | 100 ohm | ||||
0–50 m | 55 | 53 | 58 | 56 | 61 | 58 | 63 | 59 |
>400 m | 0 | 0 | 2 | 3 | 4 | 6 | 8 | 9 |
Cauchy-M-RVFLN | 0 ohm | 30 ohm | 50 ohm | 100 ohm | ||||
0–50 m | 57 | 55 | 60 | 58 | 63 | 59 | 64 | 60 |
>400 m | 2 | 2 | 4 | 5 | 6 | 8 | 9 | 10 |
RVFLN | 0 ohm | 30 ohm | 50 ohm | 100 ohm | ||||
0–50 m | 58 | 56 | 61 | 59 | 64 | 61 | 66 | 62 |
>400 m | 3 | 3 | 5 | 6 | 6 | 8 | 10 | 11 |
CNN | 0 ohm | 30 ohm | 50 ohm | 100 ohm | ||||
0–50 m | 61 | 59 | 64 | 62 | 67 | 64 | 69 | 65 |
>400 m | 4 | 4 | 6 | 7 | 8 | 9 | 11 | 12 |
LSTM | 0 ohm | 30 ohm | 50 ohm | 100 ohm | ||||
0–50 m | 62 | 60 | 65 | 63 | 68 | 64 | 69 | 65 |
400 m | 5 | 5 | 7 | 8 | 9 | 10 | 12 | 13 |
SVM | 0 ohm | 30 ohm | 50 ohm | 100 ohm | ||||
0–50 m | 63 | 60 | 65 | 63 | 68 | 65 | 70 | 66 |
>400 m | 6 | 6 | 8 | 9 | 10 | 11 | 13 | 14 |
ELM | 0 ohm | 30 ohm | 50 ohm | 100 ohm | ||||
0–50 m | 64 | 62 | 67 | 65 | 70 | 66 | 71 | 67 |
>400 m | 8 | 8 | 10 | 11 | 12 | 14 | 16 | 17 |
With Noise | Without Noise | With Noise | Without Noise | With Noise | Without Noise | With Noise | Without Noise | |
---|---|---|---|---|---|---|---|---|
ORR-RVFLN | 0 ohm | 30 ohm | 50 ohm | 100 ohm | ||||
0–50 m | 58 | 57 | 60 | 59 | 62 | 60 | 65 | 61 |
>400 m | 0 | 1 | 3 | 4 | 5 | 8 | 9 | 12 |
Cauchy-M-RVFLN | 0 ohm | 30 ohm | 50 ohm | 100 ohm | ||||
0–50 m | 61 | 60 | 63 | 61 | 64 | 62 | 67 | 63 |
>400 m | 2 | 3 | 5 | 6 | 7 | 10 | 11 | 14 |
RVFLN | 0 ohm | 30 ohm | 50 ohm | 100 ohm | ||||
0–50 m | 62 | 61 | 64 | 63 | 66 | 64 | 68 | 64 |
>400 m | 4 | 5 | 7 | 7 | 8 | 11 | 12 | 15 |
CNN | 0 ohm | 30 ohm | 50 ohm | 100 ohm | ||||
0–50 m | 65 | 64 | 67 | 66 | 69 | 67 | 72 | 68 |
>400 m | 5 | 6 | 8 | 8 | 9 | 12 | 13 | 16 |
LSTM | 0 ohm | 30 ohm | 50 ohm | 100 ohm | ||||
0–50 m | 66 | 65 | 68 | 66 | 69 | 67 | 72 | 68 |
>400 m | 6 | 7 | 9 | 9 | 10 | 13 | 14 | 17 |
SVM | 0 ohm | 30 ohm | 50 ohm | 100 ohm | ||||
0–50 m | 66 | 65 | 68 | 67 | 70 | 68 | 73 | 69 |
>400 m | 7 | 8 | 10 | 10 | 11 | 14 | 15 | 18 |
ELM | 0 ohm | 30 ohm | 50 ohm | 100 ohm | ||||
0–50 m | 68 | 67 | 70 | 69 | 71 | 69 | 74 | 70 |
>400 m | 9 | 10 | 12 | 13 | 14 | 17 | 18 | 21 |
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Haydaroğlu, C.; Kılıç, H.; Gümüş, B.; Özdemir, M.T. Advancing Fault Detection in Distribution Networks with a Real-Time Approach Using Robust RVFLN. Appl. Sci. 2025, 15, 1908. https://doi.org/10.3390/app15041908
Haydaroğlu C, Kılıç H, Gümüş B, Özdemir MT. Advancing Fault Detection in Distribution Networks with a Real-Time Approach Using Robust RVFLN. Applied Sciences. 2025; 15(4):1908. https://doi.org/10.3390/app15041908
Chicago/Turabian StyleHaydaroğlu, Cem, Heybet Kılıç, Bilal Gümüş, and Mahmut Temel Özdemir. 2025. "Advancing Fault Detection in Distribution Networks with a Real-Time Approach Using Robust RVFLN" Applied Sciences 15, no. 4: 1908. https://doi.org/10.3390/app15041908
APA StyleHaydaroğlu, C., Kılıç, H., Gümüş, B., & Özdemir, M. T. (2025). Advancing Fault Detection in Distribution Networks with a Real-Time Approach Using Robust RVFLN. Applied Sciences, 15(4), 1908. https://doi.org/10.3390/app15041908