|
High impedance faults detection in distribution system using S transform and fuzzy system
|
َAaida Abedzadeh   , Saeid Hasheminejad *1 |
|
|
|
Abstract: (320 Views) |
A new intelligent algorithm is proposed in this paper to identify the high impedance faults (HIF) in distribution systems. After a close analysis on the S transform (ST) and its outputs specifications, all amplitude and phase information of the input signal in both time and frequency domains are extracted. ST output information can be provided by some curves. Then, to quantify the ST output information, four numerical indices named as total harmonic distortion (THD), even harmonic energy (EHE), variation coefficient (VC) and phase difference (PD) are extracted from the ST output curves. Each of the indices represents one of the amplitude or phase information of the signal in time or frequency domains. The numerical indices are used as the inputs of the fuzzy system. Using fuzzy system and its inputs, the HIF is identified from other distribution systems such as the normal situation, load switching, capacitor switching and transformer inrush current. Simulation results show that in the 30dB noisy environment, the accuracy of the proposed algorithm is higher than 98.5%. Using the parameter PD, the algorithm implementation time is near 0.02 seconds. Test signals are extracted from a real 20kV network simulated in PSCAD software. Results of testing the proposed algorithm show its superior performance in real distribution systems. Having high speed and low computational burden of the proposed algorithm, make it a good choice for applying on the protective relays.
|
|
| Keywords: High impedance faults, distribution system, S transform, fuzzy system |
|
|
Full-Text [PDF 1525 kb]
(93 Downloads)
|
Type of Study: Applicable |
Received: 2024/08/9 | Accepted: 2025/04/19 | Published: 2025/05/14
|
|
|
|
|
|
|
| References |
1. Soheili, A., Sadeh, J., Bakhshi, R. (2018). Modified FFT based high impedance fault detection technique considering distribution non-linear loads: simulation and experimental data analysis. Electric power systems Research, vol. 94, pp. 124-140. [ DOI:10.1016/j.ijepes.2017.06.035] 2. Wei, M., Liu, W., Zhang, H., Shi, F., Chen, W. (2021) Distortion-based detection of high impedance fault in distribution systems IEEE transactions on power delivery, vol. 36, no. 3, pp. 1603-1618. [ DOI:10.1109/TPWRD.2020.3011930] 3. Gao, J., Wang, X., Yang, A., Yuan, H., Wei, X. (2022). A high impedance fault detection method for distribution systems based on empirical wavelet transform and differential faulty energy. IEEE transactions on smart grid, vol. 13, no. 2, pp. 900-912. [ DOI:10.1109/TSG.2021.3129315] 4. Wei, M., Shi, F., Zhang, H., Jin, Z., Terzija, V., Zhou, J., Bao, H. (2020). High impedance arc fault detection based on the harmonic randomness and waveform distortion in the distribution system. IEEE transactions on power delivery, vol. 35, no. 2, pp. 837-850. [ DOI:10.1109/TPWRD.2019.2929329] 5. Chakraborty, S., Das, S. (2019). Application of smart meters in high impedance fault detection on distribution systems, IEEE transactions on smart grid, vol. 10, no. 3, pp. 3465-3473. [ DOI:10.1109/TSG.2018.2828414] 6. Lopes, G. N., Menezes, T. S., Santos, G. G., Trondoli, L. H. P. C., Vieira, J. C. M. (2022). High impedance fault detection based on harmonic energy variation via s-transform. vol. 136, 107681. [ DOI:10.1016/j.ijepes.2021.107681] 7. Lopes, G. N., Lacerda, V. A., Vieira, J. C. M., Coury, D. V. (2021). Analysis of signal processing techniques for high impedance fault detection in distribution systems. IEEE transactions on power delivery, vol. 36, no. 6, pp. 3438-3447. [ DOI:10.1109/TPWRD.2020.3042734] 8. Biswal, M., Ghore, S., Malik, O. P., Bansal, R. C. (2021). Development of the time-frequency based approach to detect high impedance fault in an inverter interfaced distribution system. IEEE transactions on power delivery, vol. 36, no. 6, pp. 3825-3833. [ DOI:10.1109/TPWRD.2021.3049572] 9. Wang, X., Gao, J., Wei, X., Song, G., Wu, L., Liu, J., Zeng, Z., Kheshti, M. (2019). High-impedance fault detection method based on the variational mode decomposition and Teager-Kaiser energy operator for distribution network. IEEE transactions on smart grid, vol. 10, no. 6, pp. 6041-6054. [ DOI:10.1109/TSG.2019.2895634] 10. Alaei, S. A., Damchi, Y. (2023). A new method based on the discrete time energy separation algorithm for high and low impedance faults detection in distribution systems. Electric power systems Research, vol. 218, 109200. [ DOI:10.1016/j.epsr.2023.109200] 11. Gu, J. C., Huang, Z. J., Wang, J. M., Hsu, L. C., Yang, M. T. (2021). High impedance fault detection in overhead distribution feeders using a DSP-based feeder terminal unit. IEEE transactions on industry applications, vol. 57, no. 1, pp. 179-186. [ DOI:10.1109/TIA.2020.3029760] 12. Lala, H., Karmakar, S. (2020). Detection and experimental validation of high impedance arc fault in distribution system using empirical mode decomposition. IEEE systems journal, vol. 14, no. 3, pp. 3494-3505. [ DOI:10.1109/JSYST.2020.2969966] 13. Chaitanya, B. K., Yadav, A., Pazoki, M. (2020). An intelligent detection of high impedance faults for distribution lines integrated with distributed generators. IEEE systems journal, vol. 14, no 1, pp. 870-879. [ DOI:10.1109/JSYST.2019.2911529] 14. Xiao, Q. M., Guo, M. F., Chen, D. Y. (2022). High-impedance fault detection method based on one-dimensional variational prototype encoder for distribution networks. IEEE systems journal, vol. 16, no. 1, pp. 966-976. [ DOI:10.1109/JSYST.2021.3053769] 15. Wunderlich, S. S., Bauer, D., Santi, E., Dougal, R. A., Benigni, A., Bennett, R., Zubi, L. E. (2021). Protection scheme for fast detection and interruption of high-impedance faults on rate-limited distribution networks. IEEE journal of emerging and selected topics in power electronics, vol. 9, no. 3, pp. 2540-2549. [ DOI:10.1109/JESTPE.2020.2998056] 16. Sifat, A. I., Mcfadden, F. JS., Bailey, J., Rayudu, R., Hunze, A. (2021). Characterization of 400V high impedance fault with current and magnetic field measurements. IEEE transactions on power delivery, vol. 36, no. 6, pp. 3538-3549. [ DOI:10.1109/TPWRD.2020.3044545] 17. Bhandia, R., Chavez, J. D. J., Cvetkovic, M., Palensky, P. (2020). High impedance fault detection using advanced distortion detection technique. IEEE transactions on power delivery, vol. 35, no. 6, pp. 2598-2611. [ DOI:10.1109/TPWRD.2020.2973829] 18. Wang, B., Geng, J., Dong, X. (2018). High impedance fault detection based on nonlinear voltage-current characteristic profile identification. IEEE transactions on smart grid, vol. 9, no. 4, pp. 3783-3791. [ DOI:10.1109/TSG.2016.2642988] 19. Xiong, Q., Feng, X., Gattozzi, A. L., et al. (2020). Series arc fault detection and localization in DC distribution system. IEEE transactions on instrumentation and measurement, vol. 69, no. 1, pp. 122-134. [ DOI:10.1109/TIM.2019.2890892] 20. Pirmani, S. K., Mahmud, M. A., Islam, S. N., Arif, M. T. (2023). A modified charge similarity approach for detecting high impedance earth faults in resonant grounded power distribution networks. Electric power systems Research, vol. 220, 109264. [ DOI:10.1016/j.epsr.2023.109264] 21. Hojatpanah, F., Badrkhani Ajaei, F., Tiwari, H. (2023). Reliable detection of high impedance faults using mathematical morphology. Electric power systems Research, vol. 216, 109078. [ DOI:10.1016/j.epsr.2022.109078] 22. Cui, L., Liu, Y., Wang, L., Chen, J., Zhang, X. (2023). High-impedance faults detection method based on sparse data divergence discrimination in distribution networks. Electric power systems Research, vol. 223, 109514. [ DOI:10.1016/j.epsr.2023.109514] 23. Stockwell, R. G., Mansinha, L., Lowe, R. P. (1996). Localization of the complex spectrum: The S transform. IEEE transactions on signal processing, vol. 44, no. 4, pp. 998-1001. [ DOI:10.1109/78.492555] |
|
|
