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مدلی هوشمند بر پایه تحلیل فضای فاز برای دسته‌بندی خطا در خطوط انتقال تک‌مداره | ||
| مدل سازی در مهندسی | ||
| مقاله 16، دوره 18، شماره 60، خرداد 1399، صفحه 227-243 اصل مقاله (2.08 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22075/jme.2019.17886.1725 | ||
| نویسندگان | ||
| مصطفی سرلک* 1؛ داریوش فرهادی2 | ||
| 1مهندسی برق قدرت-دانشکده مهندسی برق و کامپیوتر-دانشگاه صنعتی جندی شاپور-دزفول | ||
| 2دانشکده مهندسی برق و کامپیوتر - دانشگاه صنعتی جندی شاپور – دزفول-ایران | ||
| تاریخ دریافت: 06 خرداد 1398، تاریخ بازنگری: 13 شهریور 1398، تاریخ پذیرش: 17 مهر 1398 | ||
| چکیده | ||
| از جمله ملاحظات مهم در حفاظت مدرن خطوط انتقال، سرعت و دقت در دسته‌بندی نوع خطا استکه تاثیر زیادی بر مدت زمان پاک شدن خطا و دقت تشخیص خطا توسط رله دیستانس، دارد. هدف از این مطالعه، استفاده از تحلیل فضای فاز و الگوریتم یادگیری درخت تصمیم برای دسته‌بندی نوع خطا در خطوط انتقال تک‌مداره بوده است. بر این اساس، الگوریتمی طراحی شده استکه در آن، ابتدا سیگنال‌های جریان و ولتاژ سه‌فاز در یک سمت خط انتقال اندازه‌گیری و نمونه‌برداری می‌شوند. سپس، پس از تحلیل فضای فاز نمونه‌های جریان و ولتاژ، بردار ویژگی‌های آماری خروجی تحلیل مذکور محاسبه می‌گردد. در پایان، بردار ویژگی به مدل هوشمند از قبل آموزش داده شده، برای تعیین نوع خطای رخ داده، اعمال می‌شود. الگوریتم پیشنهادی بر روی شبکه نمونه و در شرایط مختلف وقوع خطا شامل مقادیر مختلف مقاومت خطا، لحظه وقوع خطا، میزان توان انتقالی بر روی خط انتقال و مکان وقوع خطا بررسی و مورد آزمایش واقع شده است. نتایج نشان می‌دهند که مدل هوشمند پیشنهادی می‌تواند نوع خطا را با طول پنجره داده پس از وقوع خطای کمتر از 2 میلی‌ثانیه و دقت 100 درصد تعیین نماید. | ||
| کلیدواژه‌ها | ||
| خطوط انتقال تک مداره؛ حفاظت خط انتقال؛ دسته‌بندی خطا؛ مدل هوشمند؛ تحلیل فضای فاز؛ درخت تصمیم گیری | ||
| عنوان مقاله [English] | ||
| An Intelligent Model Based on Phase Space Analysis for Fault Classification in Single Circuit Transmission Lines | ||
| نویسندگان [English] | ||
| Mostafa Sarlak1؛ Daryoush Farhadi2 | ||
| 1Ele. & Com. Eng. Department, Jondi-shapur university of technology-Dezful-Iran. | ||
| 2Jundi-Shapur University of Technology, Dezful, Iran | ||
| چکیده [English] | ||
| Two important issues in the modern transmission lines protection are the speed and accuracy of the fault type classification, which have a great impact on the duration of fault clearing time and the accuracy of fault detection by the distance relay. The purpose of this study was to use the phase space analysis and decision tree-learning algorithm to classify the fault type in single circuit transmission lines. Accordingly, an algorithm is developed in which the three-phase current and voltage signals are measured and sampled on one side of the transmission line, firstly. Then, after the phase space analyzing of the current and voltage samples, the statistical feature vector of the output of the analysis is calculated. In the end, the feature vector is fed to the pre-trained intelligent model, to determine the type of fault occurred. The proposed algorithm has been investigated and tested on the sample network in different fault conditions, including different values of fault resistance, fault inception time, the amount of the transferred power on the transmission line, and the fault location. The results show that the proposed algorithm can determine the fault type with a length of post-fault data window less than 2 ms and accuracy of 100 percent. | ||
| کلیدواژه‌ها [English] | ||
| Single circuit transmission lines, Transmission line protection, Intelligent model, Fault classification, Phase space analysis, Decission tree | ||
| مراجع | ||
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[1] A.G. Phadke and J.S. Thorp, Computer Relaying for Power Systems, New York: Wiley, 1988. [2] ز. مروج، م. قرجه لو و ک. مظلومی، «هماهنگی بهینه رله‌های دیستانس و اضافه جریان جهتی با استفاده از الگوریتم ژنتیک»، مجله مدل‌سازی در مهندسی، دوره 15 ، شماره 48، بهار ۱۳۹6، صفحه 201-216. [3] J.A. Jiang, C.S. Chen and C.W. Liu, "A new protection scheme for fault detection, direction discrimination, classification, and location in transmission lines", IEEE Transactions on Power Delivery, Vol. 18, No. 1, 2003, pp. 34 42. [4] T. Dalstein and B. Kulicke, "Neural network approach to fault classification for high speed protective relaying", IEEE Transactions on Power Delivery, Vol. 10, No. 2, 1995, pp. 1002-1011. [5] W.M. Lin, C.D. Yang, J.H. Lin and M.T. Tsay, "A fault classification method by RBF neural network with OLS learning procedure", IEEE Transactions on Power Delivery, Vol. 16, No. 4, 2001, pp. 473-477. [6] M. Oleskovicz, D. Coury and W.R.K. Aggarwal, "A complete scheme for fault detection, classification and location in transmission lines using neural networks", in Proceeding of 7th International Conference on Developments in Power System Protection, Vol. 9, No. 12, 2001, pp. 335-338. [7] K.M. Silva, B.A. Souza and N.S.D. Brito, "Fault detection and classification in transmission lines based on wavelet transform and ANN", IEEE Transactions on Power Delivery, Vol. 21, No. 4, 2006, pp. 2058–2063. [8] B.Y. Vyas, B. Das and R.P. Maheshwari, "Improved fault classification in series compensated transmission line: comparative evaluation of chebyshev neural network training algorithms", IEEE Transactions on Neural Network and Learning & Systems, Vol. 8, No. 6, 2014, pp. 1-12. [9] B.H. Chowdhury and K. Wang, "Fault classification using kohonen feature mapping", in Proceeding of International Conference on Intelligent Systems Applications to Power Systems, Vol. 28, No. 2, 1996, pp. 194-198. [10] D. Das, N.K. Singh and A.K. Sinha, "A comparison of Fourier transform and wavelet transform methods for detection and classification of faults on transmission lines", IEEE Power India Conference, 2006, pp. 1-7. [11] M. Patel, "Fault detection and classification on a transmission line using wavelet multi-resolution analysis and neural network", International Journal of Computer Applications, Vol. 47, No. 22, 2012, pp. 27-33. [12] D. Guillen, M.R. Arrieta paternina, A. Zamora, J. M. Ramirez and G. ldarraga, "Detection and classification of faults in transmission lines using the maximum wavelet singular value and Euclidean norm", IET Generation, Transmission & Distribution, Vol. 9, No. 15, 2014, pp. 2294-2302. [13] C.Y. Qi, F. Olga and S. Giovanni, "Combined fault location and classification for power transmission lines fault diagnosis with integrated feature extraction", IEEE Transactions on Industrial Electronics, Vol. 65, No. 1, 2018, pp. 561-569. [14] T.S. Abdelgayed, W.G. Morsi and T.S. Sidhu, "A new harmony search approach for optimal wavelets applied to fault classification", IEEE Transactions on Smart Grid, Vol. 9, No. 2, 2018, pp. 521–529. [15] H. Wang and W.W.L. Keerthipala, "Fuzzy-neuro approach to fault classification for transmission line protection", IEEE Transactions on Power Delivery, Vol. 13, No. 4, 1998, pp. 1093-1104. [16] B. Das and J.V. Reddy, "Fuzzy-logic-based fault classification scheme for digital distance protection", IEEE Transactions on Power Delivery, Vol. 20, No. 2, 2005, pp. 609-616. [17] M. Hasmat and S. Rajneesh, "Transmission line fault classification using modified fuzzy Q learning", IET Generation, Transmission & Distribution, Vol. 11 No. 16, 2017, pp. 4041-4050. [18] A. Jamehbozorg and S.M. Shahrtash, "A decision-tree-based method for fault classification in single-circuit transmission lines", IEEE Transactions on Power Delivery, Vol.25, No.4, 2010, pp. 2190-2196. [19] M.K. Jena and S.R. Samantaray, "Intelligent relaying scheme for series-compensated double circuit lines using phase angle of differential impedance", International Journal of Electrical Power & Energy Systems, Vol. 70, 2015, pp. 17-26. [20] A. de Souza Gomes, M.A. Costa, T.G.A. de Faria and W.M. Caminhas, "Detection and classification of faults in power transmission lines using functional analysis and computational intelligence", IEEE Transactions on Power Delivery, Vol. 28, No.1, 2013, pp. 1402-1413. [21] M.K. Jena and S. Samantaray, "Data-mining-based intelligent differential relaying for transmission lines including UPFC and wind farms", IEEE Transactions on Neural Network & Learning Systems, Vol. 27, 2016, pp. 8-17. [22] R. Singh Arvind, R. Patne Nita and S. Kale Vijay, "Digital impedance pilot relaying scheme for STATCOM compensated TL for fault phase classification with fault location", IET Generation, Transmission & Distribution, Vol. 11, No. 10, 2017, pp. 2586-2598. [23] M.M. Taheri, H. Seyedi and B. Mohammadi, "DT-based relaying scheme for fault classification in transmission lines using MODP", IET Generation, Transmission & Distribution, Vol. 11, No. 11, 2017, pp. 2796-2804. [24] M. Salehi and Namdari Farhad, "Fault classification and faulted phase selection for transmission line using morphological edge detection filter", IET Generation, Transmission & Distribution, Vol. 12, No. 7, 2018, pp. 1595-1605. [25] C. Kunjin, H. Jun and H. Jinliang, "Detection and classification of transmission line faults based on unsupervised feature learning and convolutional sparse autoencoder", IEEE Transactions on Smart Grid, Vol. 9, No. 3, 2018, pp. 1748 - 1758. [26] م. پازکی، «روشی مؤثر در تعیین نوع خطا در خطوط انتقال با استفاده از طبقه‌بندی‌کنندۀ بیز مبتنی بر کرنل»، مجله مدل‌سازی در مهندسی، دوره 16، شماره 52، بهار ۱۳۹7، صفحه 119-129. [27] S. Shenxing, S. Mirsaeidi and D. Xinzhou, "Fault classification for transmission lines based on group sparse representation", IEEE Transactions on Smart Grid, (Early Access), 2018, pp. 1-11. [28] F. Takens, "Detecting strange attractors in turbulence", Lecture Notes in Mathematics, Vol. 898, 1980, pp. 366–381. [29] H. Kantz and T. Scheriber, Nonlinear time series analysis, Cambridge University Press, 2nd ed., 2004. [30] M.B. Kennel, R. Brown and H.D.I. Abarbanel, "Determining embedding dimension for phase space reconstruction using a geometrical construction", Physical Review A, Vol. 45, No. 6, 1992, pp. 3403–3411. [31] M. Daryalal and M. Sarlak, "Fast fault detection scheme for series-compensated lines during power swing", International Journal of Electrical Power & Energy Systems, Vol. 92, 2017, pp. 230-244. [32] T.M. Mitchell, Machine Learning, McGraw-Hill International, 1997. [33] L. Breiman, "Random forests", Mach. Learn. Vol. 45, 2001, pp. 5–32, [Online] Available: http://www.stat.berkeley.edu/users/breiman/RandomForests. [34] [Online] Available: http://www.cs. Waikato.ac.nz/ml/weka. [35] Power Systems Relaying Committee, EMTP reference models for transmission line relay testing report, Draft 10a. Technical report, 2004, [Online] Available: http://www.pserc.org. [36] س. ضیاالدینی، م. ابارقی و ز. مروج، «ارائه یک روش جدید برای تخمین مقادیر گمشده در مجموعه داده»، مجله مدل‌سازی در مهندسی، دوره 16 ، شماره 55، زمستان ۱۳۹7، صفحه 155-162. [37] N. Saravanan and V. Gayathri, "Classification of dengue dataset using j48 algorithm and colony based a J48 algorithm", Proceedings of the International Conference on Inventive Computing and Informatics, 2017, pp. 1062-1067. [38] L. Xie, F. Gao, S. He, C. Lin, X. Chen and Z. Zhang, "Application of Kirchhoff formula in prediction of noise level of substation", 11th IEEE International Conference on Anti-counterfeiting, Security, and Identification, 2017, pp. 85-89. [39] M. Brown, M. Biswal, S. Brahma, S. J. Ranade and H. Cao, "Characterizing and quantifying noise in PMU data", IEEE Power and Energy Society General Meeting, 2016, pp. 1-5. [40] K. Fukunaga, Introduction to statistical pattern recognition, Morgan Kaufmann, 2nd ed., 1990. [41] H.A. Darwish and M. Fikri, "Practical considerations for recursive DFT implementation in numerical relays", IEEE Transactions on Power Delivery, Vol. 22, No. 1, 2007, pp. 42-49. [42] User’s Guide, Texas Instruments, TMS320C3x/4x Floating-Point DSP Chip, 1994. | ||
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