This paper's approach evaluates the effect of faults on stability parameters, acknowledging the crucial role of power system stability. This integration aims to provide a thorough grasp of the relationship between defect detection and system stability. Phase-to-phase and phase-to-ground fault detection in power systems is the main emphasis of this research. Through the use of Wavelet Transform (WT), Hilbert-Huang Transform (HHT), and Short-Time Fourier Transform (STFT), our study offers a thorough analysis by capturing both time and frequency features. We detail the technique's WT, HHT, and STFT application principles, highlighting the significance of real-time sampling of voltage and current behaviors during faults. This improves the depth of our fault detection analysis. We use a pertinent dataset to investigate phase-to-phase and phase-to-ground faults, adopting preprocessing for strong data quality. Including faults makes it possible to sample and observe voltage and current behaviors in real-time, giving information about the power system's dynamic reaction. The method's performance in fault identification is illustrated using visual aids, and the results are given and debated. The effects of dynamic variations in voltage and current behaviors on the stability of the power system are emphasized during failure situations. Our findings are more significant when seen in the larger context of creating a stable and resilient power grid, thanks to the inclusion of power system stability analysis.
Banner, C. L., & Don Russell, B. (1997). Practical high-impedance fault detection on distribution feeders. IEEE Transactions on Industry Applications, 33(3), 635 640. https://doi.org/10.1109/28.585852
Basir, M. S. S. M., Ismail, R. C., Yusof, K. H., Katim, N. I. A., Isa, M. N. M., & Naziri, S. Z. M. (2021). An implementation of Short Time Fourier Transform for Harmonic Signal Detection. Journal of Physics: Conference Series, 1755(1), 012013. https://doi.org/10.1088/1742-6596/1755/1/012013
Daubechies, I. (1993). Ten Lectures on Wavelets - Preface. SIAM Review, 35(4), 666 669.
Dogan, Z., & Tetik, K. (2021). Diagnosis of Inter-Turn Faults Based on Fault Harmonic Component Tracking in LSPMSMs Working under Nonstationary Conditions. IEEE Access, 9, 92101 92112.
https://doi.org/10.1109/ACCESS.2021.3092605
Grainger, J. J., & Stevenson, W. D. (1994). Power System Analysis. New York. : McGraw-Hill Book Co.
Huang, N. E., Shen, Z., & Long, S. R. (1999). A new view of nonlinear water waves: The Hilbert spectrum. Annual Review of Fluid Mechanics, 31(Volume 31, 1999), 417 457.
https://doi.org/10.1146/ANNUREV.FLUID.31.1.417/CITE/REFWORKS
Kehtarnavaz, N. (2008). Digital signal processing system design: LabVIEW-based hybrid programming. Digital Signal Processing System Design: LabVIEW-Based Hybrid Programming, 1 325. https://doi.org/10.1016/B978-0-12-374490-6.X0001-3
Li, Y., Lin, J., Niu, G., Wu, M., & Wei, X. (2021). A Hilbert–Huang Transform-Based Adaptive Fault Detection and Classification Method for Microgrids. Energies, 14(16), 5040. https://doi.org/10.3390/en14165040
MATLAB Documentation. (s.d.). Repéré à https://www.mathworks.com/help/matlab/
Meyer, Y., & Salinger, D. H. (1993). Wavelets and Operators. Wavelets and Operators. https://doi.org/10.1017/CBO9780511623820
Ray, P. K., Panigrahi, B. K., Rout, P. K., Mohanty, A., & Dubey, H. (2016). Detection of Faults in Power System Using Wavelet Transform and Independent Component Analysis. Dans First International Conference on Advancement of Computer Communication & Electrical Technology. Murshidabad, India.
Ray, Prakash K., Dubey, H. C., Mohanty, S. R., Kishor, N., & Ganesh, K. (2010). Power quality disturbance detection in grid-connected wind energy system using wavelet and S-transform. ICPCES 2010 - International Conference on Power, Control and Embedded Systems. https://doi.org/10.1109/ICPCES.2010.5698664
Ruiz Florez, H. A., López, G. P., Jaramillo-Duque, Á., López-Lezama, J. M., & Muñoz-Galeano, N. (2022). A Mathematical Modeling Approach for Power Flow and State Estimation Analysis in Electric Power Systems through AMPL. Electronics 2022, Vol. 11, Page 3566, 11(21), 3566. https://doi.org/10.3390/ELECTRONICS11213566
Stevenson, W. D. (1982). Elements of power system analysis. (S.l.) : McGraw-Hill.
Ukil, A., & Živanović, R. (2006). Abrupt change detection in power system fault analysis using adaptive whitening filter and wavelet transform. Electric Power Systems Research, 76(9 10), 815 823. https://doi.org/10.1016/J.EPSR.2005.10.009
Vachtsevanos, G., & Wang, P. (2001). Fault prognosis using dynamic wavelet neural networks. AUTOTESTCON (Proceedings), 857 870. https://doi.org/10.1109/AUTEST.2001.949467
Wang, J., Liu, X., Li, W., Liu, F., & Hancock, C. (2021). Time–Frequency Extraction Model Based on Variational Mode Decomposition and Hilbert–Huang Transform for Offshore Oil Platforms Using MIMU Data.
Wang, Y. S., Ma, Q. H., Zhu, Q., Liu, X. T., & Zhao, L. H. (2014). An intelligent approach for engine fault diagnosis based on Hilbert–Huang transform and support vector machine. Applied Acoustics, 75, 1 9. https://doi.org/https://doi.org/10.1016/j.apacoust.2013.07.001
Farklı Zaman-Frekans Boyutu Yöntemleri Kullanılarak Güç Sistemi Arızaları Tespiti
Bu makalenin yaklaşımı, güç sistemi kararlılığının kritik rolünü kabul ederek, arızaların kararlılık parametreleri üzerindeki etkisini değerlendirmektedir. Bu entegrasyon, kusur tespiti ile sistem kararlılığı arasındaki ilişkinin kapsamlı bir şekilde anlaşılmasını sağlamayı amaçlamaktadır. Güç sistemlerinde faz-faz ve faz-toprak arıza tespiti bu araştırmanın ana vurgusunu oluşturmaktadır. Dalgacık Dönüşümü (WT), Hilbert-Huang Dönüşümü (HHT) ve Kısa Zamanlı Fourier Dönüşümü (STFT) kullanımı sayesinde çalışmamız hem zaman hem de frekans özelliklerini yakalayarak kapsamlı bir analiz sunmaktadır. Tekniğin WT, HHT ve STFT uygulama prensiplerini detaylandırarak, arızalar sırasında gerilim ve akım davranışlarının gerçek zamanlı örneklemesinin önemini vurguluyoruz. Bu, arıza tespit analizimizin derinliğini artırır. Güçlü veri kalitesi için ön işlemeyi benimseyerek fazdan faza ve fazdan toprağa arızaları araştırmak için uygun bir veri seti kullanıyoruz. Arızaların dahil edilmesi, gerilim ve akım davranışlarının gerçek zamanlı olarak örneklenmesine ve gözlemlenmesine olanak tanıyarak, güç sisteminin dinamik tepkisi hakkında bilgi verir. Yöntemin arıza tespitindeki performansı görsel araçlarla gösterilmiş, sonuçlar verilmiş ve tartışılmıştır. Arıza durumlarında gerilim ve akım davranışlarındaki dinamik değişimlerin güç sisteminin kararlılığı üzerindeki etkileri vurgulanmaktadır. Bulgularımız, güç sistemi stabilite analizinin dahil edilmesi sayesinde istikrarlı ve dayanıklı bir enerji şebekesi yaratmaya yönelik daha geniş bir bağlamda görüldüğünde daha anlamlıdır.
Banner, C. L., & Don Russell, B. (1997). Practical high-impedance fault detection on distribution feeders. IEEE Transactions on Industry Applications, 33(3), 635 640. https://doi.org/10.1109/28.585852
Basir, M. S. S. M., Ismail, R. C., Yusof, K. H., Katim, N. I. A., Isa, M. N. M., & Naziri, S. Z. M. (2021). An implementation of Short Time Fourier Transform for Harmonic Signal Detection. Journal of Physics: Conference Series, 1755(1), 012013. https://doi.org/10.1088/1742-6596/1755/1/012013
Daubechies, I. (1993). Ten Lectures on Wavelets - Preface. SIAM Review, 35(4), 666 669.
Dogan, Z., & Tetik, K. (2021). Diagnosis of Inter-Turn Faults Based on Fault Harmonic Component Tracking in LSPMSMs Working under Nonstationary Conditions. IEEE Access, 9, 92101 92112.
https://doi.org/10.1109/ACCESS.2021.3092605
Grainger, J. J., & Stevenson, W. D. (1994). Power System Analysis. New York. : McGraw-Hill Book Co.
Huang, N. E., Shen, Z., & Long, S. R. (1999). A new view of nonlinear water waves: The Hilbert spectrum. Annual Review of Fluid Mechanics, 31(Volume 31, 1999), 417 457.
https://doi.org/10.1146/ANNUREV.FLUID.31.1.417/CITE/REFWORKS
Kehtarnavaz, N. (2008). Digital signal processing system design: LabVIEW-based hybrid programming. Digital Signal Processing System Design: LabVIEW-Based Hybrid Programming, 1 325. https://doi.org/10.1016/B978-0-12-374490-6.X0001-3
Li, Y., Lin, J., Niu, G., Wu, M., & Wei, X. (2021). A Hilbert–Huang Transform-Based Adaptive Fault Detection and Classification Method for Microgrids. Energies, 14(16), 5040. https://doi.org/10.3390/en14165040
MATLAB Documentation. (s.d.). Repéré à https://www.mathworks.com/help/matlab/
Meyer, Y., & Salinger, D. H. (1993). Wavelets and Operators. Wavelets and Operators. https://doi.org/10.1017/CBO9780511623820
Ray, P. K., Panigrahi, B. K., Rout, P. K., Mohanty, A., & Dubey, H. (2016). Detection of Faults in Power System Using Wavelet Transform and Independent Component Analysis. Dans First International Conference on Advancement of Computer Communication & Electrical Technology. Murshidabad, India.
Ray, Prakash K., Dubey, H. C., Mohanty, S. R., Kishor, N., & Ganesh, K. (2010). Power quality disturbance detection in grid-connected wind energy system using wavelet and S-transform. ICPCES 2010 - International Conference on Power, Control and Embedded Systems. https://doi.org/10.1109/ICPCES.2010.5698664
Ruiz Florez, H. A., López, G. P., Jaramillo-Duque, Á., López-Lezama, J. M., & Muñoz-Galeano, N. (2022). A Mathematical Modeling Approach for Power Flow and State Estimation Analysis in Electric Power Systems through AMPL. Electronics 2022, Vol. 11, Page 3566, 11(21), 3566. https://doi.org/10.3390/ELECTRONICS11213566
Stevenson, W. D. (1982). Elements of power system analysis. (S.l.) : McGraw-Hill.
Ukil, A., & Živanović, R. (2006). Abrupt change detection in power system fault analysis using adaptive whitening filter and wavelet transform. Electric Power Systems Research, 76(9 10), 815 823. https://doi.org/10.1016/J.EPSR.2005.10.009
Vachtsevanos, G., & Wang, P. (2001). Fault prognosis using dynamic wavelet neural networks. AUTOTESTCON (Proceedings), 857 870. https://doi.org/10.1109/AUTEST.2001.949467
Wang, J., Liu, X., Li, W., Liu, F., & Hancock, C. (2021). Time–Frequency Extraction Model Based on Variational Mode Decomposition and Hilbert–Huang Transform for Offshore Oil Platforms Using MIMU Data.
Wang, Y. S., Ma, Q. H., Zhu, Q., Liu, X. T., & Zhao, L. H. (2014). An intelligent approach for engine fault diagnosis based on Hilbert–Huang transform and support vector machine. Applied Acoustics, 75, 1 9. https://doi.org/https://doi.org/10.1016/j.apacoust.2013.07.001
Bin Shafique, H., & Doğan, Z. (2024). The Detection of Power System Faults Using Different Time-Frequency Domain Methods. Gaziosmanpaşa Bilimsel Araştırma Dergisi, 13(1), 126-134.
AMA
Bin Shafique H, Doğan Z. The Detection of Power System Faults Using Different Time-Frequency Domain Methods. GBAD. Haziran 2024;13(1):126-134.
Chicago
Bin Shafique, Hamza, ve Zafer Doğan. “The Detection of Power System Faults Using Different Time-Frequency Domain Methods”. Gaziosmanpaşa Bilimsel Araştırma Dergisi 13, sy. 1 (Haziran 2024): 126-34.
EndNote
Bin Shafique H, Doğan Z (01 Haziran 2024) The Detection of Power System Faults Using Different Time-Frequency Domain Methods. Gaziosmanpaşa Bilimsel Araştırma Dergisi 13 1 126–134.
IEEE
H. Bin Shafique ve Z. Doğan, “The Detection of Power System Faults Using Different Time-Frequency Domain Methods”, GBAD, c. 13, sy. 1, ss. 126–134, 2024.
ISNAD
Bin Shafique, Hamza - Doğan, Zafer. “The Detection of Power System Faults Using Different Time-Frequency Domain Methods”. Gaziosmanpaşa Bilimsel Araştırma Dergisi 13/1 (Haziran 2024), 126-134.
JAMA
Bin Shafique H, Doğan Z. The Detection of Power System Faults Using Different Time-Frequency Domain Methods. GBAD. 2024;13:126–134.
MLA
Bin Shafique, Hamza ve Zafer Doğan. “The Detection of Power System Faults Using Different Time-Frequency Domain Methods”. Gaziosmanpaşa Bilimsel Araştırma Dergisi, c. 13, sy. 1, 2024, ss. 126-34.
Vancouver
Bin Shafique H, Doğan Z. The Detection of Power System Faults Using Different Time-Frequency Domain Methods. GBAD. 2024;13(1):126-34.