تشخیص خطای اتصال کوتاه امپدانس بالا در سیستم‌های توزیع با استفاده از یک روش مبتنی بر اندازه گیری مشابهت

محورهای موضوعی : مهندسی برق قدرت

عمار عبدالعظیم احمد دیبس ¹ , محمد مهدی رضایی ²

1 - دانشکده مهندسي برق، واحد اصفهان (خوراسگان)، دانشگاه آزاد اسلامی، خوراسگان، اصفهان، ايران
2 - دانشکده مهندسي برق، واحد خمینی شهر، دانشگاه آزاد اسلامی، خمینی شهر، اصفهان، ايران

تاریخ دریافت : 1402/12/05 تاریخ پذیرش : 1403/03/04 تاریخ انتشار : 1404/03/30

کلید واژه: خطای امپدانس بالا, شبکه‌های توزیع, اندازه گیری مشابهت, کلید زنی بار, کلید زنی خازن,

چکیده مقاله :

قوس الکتریکی یکی از شدیدترین رخدادهای الکتریکی است. این پدیده به دلیل تخلیه بارهای الکتریکی بین هادی¬ها یا بین هادی و زمین، از طریق هوا رخ می¬دهد. هنگامی که شدت جریان اتصال کوتاه زیاد باشد، می‌توان آن را به راحتی با تجهیزات حفاظتی سنتی تشخیص داد. با این حال، روش‌های حفاظت سنتی نمی¬توانند این خطاها را زمانی که جریان اتصال کوتاه کم است، تشخیص دهند. خطاهایی که جریان خطای کافی برای شناسایی توسط تجهیزات حفاظتی معمولی تولید نمی¬کنند، خطاهای امپدانس بالا نامیده می‌شوند. خطاهای امپدانس بالا در سیستم‌های توزیع برق می‌توانند خطرات جدی ایمنی و آسیب به تجهیزات را به دلیل خطر اشتعال ناشی از قوس الکتریکی ایجاد کنند. این مقاله یک طرح تشخیص جدید برای خطاهای امپدانس بالا در سیستم‌های توزیع الکتریکی بر اساس اندازه گیری مشابهت ارائه می‌کند. در این روش بر اساس شکل موج دو نیم سیکل متوالی جریان، شاخصی استخراج می‌شود که با استفاده از آن می‌توان خطاهای امپدانس بالا را تشخیص داد. الگوریتم پیشنهادی تشخیص خطای امپدانس بالا می‌تواند این رخدادها را از سایر رخدادهای بدون خطا با شکل موج‌هایی که ممکن است مشابه شکل موج‌های خطای امپدانس بالا باشند، متمایز کند. در این مقاله، چهار مورد مطالعاتی برای تأیید الگوریتم پیشنهادی تشخیص خطاهای امپدانس بالا شبیه‌سازی شده است. نتایج شبیه‌سازی، توانایی قابل قبول عملکرد روش پیشنهادی در تشخیص خطا با امپدانس بالا و تفکیک آنها از دیگر رخدادها را به نمایش می‌گذارد.

چکیده انگلیسی:

The electric arc is one of the most intense electrical events. This phenomenon occurs due to the electric discharge between two conductors or between a conductor and the ground, through the air. When the short-circuit current intensity is high, it can be easily detected by traditional protection equipment. However, when the short-circuit current is low, traditional protection methods cannot detect these faults. Faults that do not generate enough fault current to be detected by conventional protective equipment are called high-impedance faults (HIFs). HIFs can cause serious safety hazards in power distribution systems and damage to equipment due to the risk of arc ignition. This paper presents a new detection scheme for HIFs in electrical distribution systems based on similarity measurement. In this method, based on the waveform of two consecutive half-cycles of the current, an index is extracted that can be used to detect HIFs. The proposed HIF detection algorithm can distinguish these events from other non-fault events with waveforms that may be similar to HIF waveforms. In this paper, four case studies are simulated to verify the proposed HIF detection algorithm. The simulation results demonstrate the acceptable performance of the proposed method in detecting HIFs and distinguishing them from other events.

منابع و مأخذ:

[1] A. Khoshnami and I. Sadeghkhani, “Sample entropy‐based fault detection for photovoltaic arrays,” IET Renewable Power Generation, vol. 12, no. 16, pp. 1966–1976, Oct. 2018, doi: https://doi.org/10.1049/iet-rpg.2018.5220
[2] B. Fani, H. Bisheh, and I. Sadeghkhani, “Protection coordination scheme for distribution networks with high penetration of photovoltaic generators,” IET Generation, Transmission & Distribution, vol. 12, no. 8, pp. 1802–1814, Apr. 2018, doi: https://doi.org/10.1049/iet-gtd.2017.1229
[3] S. Chakraborty and S. Das, “Application of Smart Meters in High Impedance Fault Detection on Distribution Systems,” IEEE Transactions on Smart Grid, vol. 10, no. 3, pp. 3465–3473, May 2019, doi: https://doi.org/10.1109/tsg.2018.2828414
[4] K. Sarwagya, S. De, and P. K. Nayak, “High‐impedance fault detection in electrical power distribution systems using moving sum approach,” IET Science, Measurement & Technology, vol. 12, no. 1, pp. 1–8, Jan. 2018, doi: https://doi.org/10.1049/iet-smt.2017.0231
[5] A. Soheili and J. Sadeh, “Evidential reasoning based approach to high impedance fault detection in power distribution systems,” IET Generation, Transmission & Distribution, vol. 11, no. 5, pp. 1325–1336, Mar. 2017, doi: https://doi.org/10.1049/iet-gtd.2016.1657
[6] M. Farajollahi, Alireza Shahsavari, and Hamed Mohsenian-Rad, “Location identification of high impedance faults using synchronized harmonic phasors,” IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT), Apr. 2017, doi: https://doi.org/10.1109/isgt.2017.8086048
[7] Sh. Nezamzadeh-Ejieh and I. Sadeghkhani, “Cross Entropy-Based High-Impedance Fault Detection Algorithm for Distribution Networks,” Iranian Electric Industry Journal of Quality and Productivity, vol. 8, no. 1, pp. 62–71, Sep. 2019
[8] E. Baharozu, S. Ilhan, and G. Soykan, “High impedance fault localization: A comprehensive review,” Electric Power Systems Research, vol. 214, p. 108892, Jan. 2023, doi: https://doi.org/10.1016/j.epsr.2022.108892
[9] M. R. Lukowicz and L. Kang, “High-Impedance Fault Detection in Distribution Networks with Use of Wavelet-Based Algorithm,” IEEE Transactions on Power Delivery, vol. 21, no. 4, pp. 1793–1802, Oct. 2006, doi: https://doi.org/10.1109/tpwrd.2006.874581
[10] N. Elkalashy, M. Lehtonen, H. Darwish, M. Izzularab, and A. Taalab, “Modeling and experimental verification of high impedance arcing fault in medium voltage networks,” IEEE Transactions on Dielectrics and Electrical Insulation, vol. 14, no. 2, pp. 375–383, Apr. 2007, doi: https://doi.org/10.1109/tdei.2007.344617
[11] P. H. Schavemaker and L. van der Slui, “An improved Mayr-type arc model based on current-zero measurements [circuit breakers],” IEEE Transactions on Power Delivery, vol. 15, no. 2, pp. 580–584, Apr. 2000, doi: https://doi.org/10.1109/61.852988
[12] H. A. Darwish and N. I. Elkalashy, “Universal Arc Representation Using EMTP,” IEEE Transactions on Power Delivery, vol. 20, no. 2, pp. 772–779, Apr. 2005, doi: https://doi.org/10.1109/tpwrd.2004.838462
[13] A. E. Emanuel, D. Cyganski, J. A. Orr, S. Shiller, and E. M. Gulachenski, “High impedance fault arcing on sandy soil in 15 kV distribution feeders: contributions to the evaluation of the low frequency spectrum,” IEEE Transactions on Power Delivery, vol. 5, no. 2, pp. 676–686, Apr. 1990, doi: https://doi.org/10.1109/61.53070
[14] A.M. Sharat, L. A. Snider, and K. Debnath, “A neural network based back error propagation relay algorithm for distribution system high impedance fault detection,” in: 1993 2nd International Conference on Advances in Power System Control, Operation and Management, APSCOM-93 2, 1993, pp. 613–620.
[15] I. Baqui, I. Zamora, J. Mazón, and G. Buigues, “High impedance fault detection methodology using wavelet transform and artificial neural networks,” Electric Power Systems Research, vol. 81, no. 7, pp. 1325–1333, Jul. 2011, doi: https://doi.org/10.1016/j.epsr.2011.01.022.
[16] Y.-Y. Hong and W.-S. Huang, “Locating High-Impedance Fault Section in Electric Power Systems Using Wavelet Transform,k-Means, Genetic Algorithms, and Support Vector Machine,” Mathematical Problems in Engineering, vol. 2015, pp. 1–9, Jan. 2015, doi: https://doi.org/10.1155/2015/823720
[17] A. Bakar, Mohammed, Chee Keong Tan, Hazlie Mokhlis, Hamzah Arof, and Hazlee Azil Illias, “High impedance fault location in 11kV underground distribution systems using wavelet transforms,” International Journal of Electrical Power & Energy Systems, vol. 55, pp. 723–730, Feb. 2014, doi: https://doi.org/10.1016/j.ijepes.2013.10.003
[18] M. S. Ali, A. H. Abu Bakar, H. Mokhlis, H. Arof, and H. Azil Illias, “High-impedance fault location using matching technique and wavelet transform for underground cable distribution network,” IEEJ Transactions on Electrical and Electronic Engineering, vol. 9, no. 2, pp. 176–182, Jan. 2014, doi: https://doi.org/10.1002/tee.21953
[19] Mini Shaji Thomas, N. Bhaskar, and A. Prakash, “Voltage Based Detection Method for High Impedance Fault in a Distribution System,” Journal of The Institution of Engineers (India): Series B, vol. 97, no. 3, pp. 413–423, Jun. 2015, doi: https://doi.org/10.1007/s40031-015-0203-7
[20] M. Sarlak and S. M. Shahrtash, “High-Impedance Faulted Branch Identification Using Magnetic-Field Signature Analysis,” IEEE Transactions on Power Delivery, vol. 28, no. 1, pp. 67–74, Jan. 2013, doi: https://doi.org/10.1109/tpwrd.2012.2222056
[21] Z. Moravej, S. H. Mortazavi, and S. M. Shahrtash, “DT-CWT based event feature extraction for high impedance faults detection in distribution system,” International Transactions on Electrical Energy Systems, vol. 25, no. 12, pp. 3288–3303, Dec. 2014, doi: https://doi.org/10.1002/etep.2035
[22] A. Ghaderi, H. L. Ginn, and H. A. Mohammadpour, “High impedance fault detection: A review,” Electric Power Systems Research, vol. 143, pp. 376–388, Feb. 2017, doi: https://doi.org/10.1016/j.epsr.2016.10.021
[23] M. Salehi, M. Zolfaghari, and J. M. Maritz, “A Simple Approach to Detect High Impedance Fault Using Morphological Gradient Edge Detector,” IEEE Access, vol. 12, pp. 11024–11034, Jan. 2024, doi: https://doi.org/10.1109/access.2024.3351565.
[24] A. Chandra, Girish Kumar Singh, and V. Pant, “A Novel High Impedance Fault Detection Strategy for Microgrid Based on Differential Energy Signal of Current Signatures and Entropy Estimation,” Electric Power Components and Systems, pp. 1–23, Jun. 2023, doi: https://doi.org/10.1080/15325008.2023.2227193.
[25] W. Sheng, K. Liu, D. Jia, and Y. Wang, “An improved high-impedance fault identification scheme for distribution networks based on kernel extreme learning machine,” International Journal of Electrical Power & Energy Systems, vol. 155, pp. 109543–109543, Jan. 2024, doi: https://doi.org/10.1016/j.ijepes.2023.109543
[26] K.-Y. Lien, S. Chen, C. Liao, T. Guo, T.-M. Lin, and J.-S. Shen, “Energy variance criterion and threshold tuning scheme for high impedance fault detection,” IEEE Transactions on Power Delivery, vol. 14, no. 3, pp. 810–817, Jul. 1999, doi: https://doi.org/10.1109/61.772319
[27] V. Ashok and A. Yadav, “Fault Diagnosis Scheme for Cross-Country Faults in Dual-Circuit Line With Emphasis on High-Impedance Fault Syndrome,” IEEE Systems Journal, pp. 1–11, 2020, doi: https://doi.org/10.1109/jsyst.2020.2991770
[28] I. Sadeghkhani and S. Nezamzadeh-Ejieh, “High-Impedance Fault Detection in Distribution Networks Based on Kullback-Leibler Divergence,” IET Generation, Transmission & Distribution, Sep. 2019, doi: https://doi.org/10.1049/iet-gtd.2019.0001
[29] A. A. Amer and H. I. Abdalla, “A set theory-based similarity measure for text clustering and classification,” Journal of Big Data, vol. 7, no. 1, Sep. 2020, doi: https://doi.org/10.1186/s40537-020-00344-3
[30] “Distribution test feeders”. IEEE PES Distribution System Analysis Subcommittee. Available: http://sites.ieee.org/pes-testfeeders/resources, 2000.