مدلی هوشمند و زمان-تطبیقی برای شناسایی خطاهای متقارن و نامتقارن در شرایط نوسان توان

نوع مقاله : مقاله برق

نویسندگان

1 مهندسی برق قدرت-دانشکده مهندسی برق و کامپیوتر-دانشگاه صنعتی جندی شاپور-دزفول

2 گروه قدرت، دانشکده برق و کامپیوتر، دانشگاه صنعتی جندی‌شاپور، دزفول

چکیده

عملکرد ناخواسته رله دیستانس طی شرایط نوسان توان، می‌تواند به گسترش اغتشاش و وخیم‌تر شدن وضعیت شبکه قدرت منجر شود. بنابراین، تشخیص سریع و دقیق نوسان توان و قفل نمودن رله دیستانس پس از وقوع نوسان توان برای حفظ امنیت و قابلیت اطمینان شبکه قدرت، امری ضروری است. از سویی دیگر، در صورت وقوع خطا طی نوسان توان، به منظور حفظ شاخص قابلیت اتکای سیستم حفاظتی، لازم است تا خطا شناسایی شود. این مقاله الگوریتمی هوشمند و زمان تطبیقی برای تشخیص خطاهای متقارن و نامتقارن در خطوط انتقال جبران شده سری طی نوسان توان مبتنی بر شبکه عصبی بازگشتی حافظه کوتاه مدت ماندگار (Long Short Term Memory (LSTM)) ارائه می‌دهد. این روش از جریان‌های سه فاز در محل رله دیستانس به عنوان ورودی استفاده می‌کند. به منظور بررسی الگوریتم پیشنهادی، شبکه استاندارد برای تست سیستم حفاظت خطوط انتقال که توسط کمیته حفاظت سیستم قدرت IEEE ارائه شده است، در نظر گرفته شد. انواع خطا در شرایط مختلف از جمله مکان خطا، مقاومت خطا، زاویه بار و زمان وقوع خطا در نرم افزار PSCAD شبیه‌سازی شدند. نتایج نشان می‌دهند که روش پیشنهادی دارای میانگین پاسخ زمانی (Average Response Time (ART)) و میانگین دقت (Average Accuracy (AA)) به ترتیب 1004/0 میلی‌ثانیه و 04/99 درصد می‌باشد.

کلیدواژه‌ها

عنوان مقاله [English]

An Intelligent and Time Adaptive Model for Symmetrical and Asymmetrical Faults during Power Swing

نویسندگان [English]

  • Mostafa Sarlak 1
  • Hassan Saeedi 2
1 Ele. & Com. Eng. Department, Jondi-shapur university of technology-Dezful-Iran.
2 Jundi-Shapur University of Technology, Dezful, Iran
چکیده [English]

The unwanted operation of the distance relay during the power swing conditions can lead to increased disturbances and exacerbate of the power grid. Therefore, the rapid and accurate detection of the power swing and blocking of the distance relay after a power swing is necessary to maintain the security and reliability of the power grid. On the other hand, for a fault condition during the power swing, in order to maintain the dependability index of the protective system, it is necessary to identify the fault. This paper presents an intelligent and time adaptive algorithm for detecting symmetric and asymmetric faults in series compensated transmission lines through the long short-term memory (LSTM) recurrent neural network. This method uses three-phase currents in the distance relay point as input. In order to investigate the proposed algorithm, the reference power system for transmission-line relay testing introduced by the IEEE Power System Relaying Committee (PSRC), was considered. Different fault types in different conditions such as fault location, fault resistance, load angle and fault inception time were modeled and simulated in PSCAD software. The results show that the proposed method has an average response time (ART) and an average accuracy (AA) of 0.1004 ms and 99.04%, respectively.

کلیدواژه‌ها [English]

  • Distance Relay
  • Recurrent Neural Network
  • Long Short Term Memory
  • Fault Identification
  • Power Swing

مراجع

[1] IEEE Power System Relaying Committee of the IEEE Power Engineering Society, Power swing and out-of-step considerations on transmission line. Report from PSRC WG D6; July 2005 [Online]. Available: http//www.pes-psrc.org.
[2] R. Dubey, S. R. Samantaray, B. K. Panigrahi. and V. G. Venkoparao,"Phase-Space-Based Symmetrical Fault Detection during Power Swing," IET Generation, Transmission & Distribution, Vol. 10, No. 8, 2016, pp. 1947-1956.
[3] R. Jafari, N. Moaddabi, M. Eskandari-Nasab, G. B. Gharehpetian and M. S. Naderi, "A Novel Power Swing Detection Scheme Independent of the Rate of Change of Power System Parameters," IEEE Transactions on Power Delivery, Vol. 29, No. 3, 2014, pp. 1192-1202.
[4] R. J. Ganeswara and A. K. Pradhan, "Power-Swing Detection Using Moving Window Averaging of Current Signals," IEEE Transactions on Power Delivery, Vol. 30, No. 1, 2015, pp. 368-376.
[5] J. Khodaparast and M. Khederzadeh, "Three-Phase Fault Detection During Power Swing by Transient Monitor," IEEE Transactions on Power Systems, Vol. 30, No. 5, 2015, pp. 2558-2565.
[6] K. Andanapalli, and B. R. Varma, "Park’s Transformation Based Symmetrical Fault Detection during Power Swing", 8th National Power Systems Conference, 2014.
[7] J. Kumar, P. Jena, "Detection of Fault during Power Swing Using Superimposed Negative Sequence Apparent Power Based Scheme", 6th International Conference on Computer Applications In Electrical Engineering-Recent Advances, 2017, pp. 57-62.
[8] P. Gawande and S. Dambhare, "A Novel Unblocking Function for Distance Relay to Detect Symmetrical Faults during Power Swing", IEEE Power and Energy Society General Meeting, 2016, pp. 1-6.
[9] S. Das and B. K. Panigrahi, "Detecting Symmetrical Faults during Power Swing for Deblocking Distance Relays", IEEMA Engineer Infinite Conference, 2018, pp. 1-6.
[10] M. Daryalal and M. Sarlak, "Fast Fault Detection Scheme for Series-Compensated Lines during Power Swing", International Journal of Electrical Power & Energy Systems, Vol. 230-244, 2017, pp. 230-244.
[11] B. Patel and P. Bera, "Detection of Power Swing and Fault during Power Swing Using Lissajous Figure", IEEE Transactions on Power Delivery, Vol. 33, No. 6, 2018, pp. 3019-3027.
[12] S. M. Hashemi, M. Sanaye-Pasand and M. Shahidehpour, "Fault Detection during Power Swings Using the Properties of Fundamental Frequency Phasors", IEEE Transactions on Smart Grid, Vol. 10, No. 2, 2019, pp. 1385-1394.
[13] I. G. Tekdemir and B. Alboyaci, "A Novel Approach for Improvement of Power Swing Blocking and Deblocking Functions in Distance Relays", IEEE Transactions on Power Delivery, Vol. 32, No. 4, 2017, pp. 1986-1994.
[14] J. G. Raol and A. K. Pradhan, "Supervising Distance Relay during Power Swing using Synchrophasor Measurements", IET Generation, Transmission & Distribution, Vol. 11, No. 17, 2017, pp. 4136-4145.
[15] N. G. Chothani, B. R. Bhalja and U. B. Parikh,"New Support Vector Machine-Based Digital Relaying Scheme for Discrimination Between Power Swing and Fault", IET Generation, Transmission & Distribution, Vol. 8, No. 1, 2014, pp. 17-25.
[16] K. Seethalekshmi, S, N. Singh S. N and S. C. Srivastava, "A Classification Approach Using Support Vector Machines to Prevent Distance Relay Maloperation under Power Swing and Voltage Instability", IEEE Transactions on Power Delivery, Vol. 27, No. 3, 2012, pp. 1124-1133.
[17] A. Swetapadma and A. Yadav, "Data-Mining-Based Fault during Power Swing Identification in Power Transmission System", IET Science, Measurement & Technology, Vol. 10, No. 2, 2016, pp. 130-139.
[18] M. J. Reddy and D. K. Mohanta, "Adaptive-Neuro-Fuzzy Inference System Approach for Transmission Line Fault Classification and Location Incorporating Effects of Power Swings", IET Generation, Transmission & Distribution, Vol. 2, No. 2, 2008, pp. 235-244.
[19] Understanding LSTM networks. [Online]. Available:http://colah.github.io/posts/2015-08-Understanding-LSTMs/
[20] زهرا مروج و جواد آذرخش، "شبیه­‌سازی و طبقه­‌بندی وقایع کیفیت توان با استفاده از شبکه­عصبی"، مدل­‌سازی در مهندسی، دوره 13، شماره 41، بهار 1394، صفحه 137-146.
[21] روح­‌الله فیروز­نیا و نیما امجدی، "پیش­بینی بار کوتاه­مدت با استفاده از تجزیه سری زمانی بار وشبکه­عصبی"، مدل­‌سازی در مهندسی، دوره 2، شماره 16، بهار 1387، صفحه 23-32.
[22] J. J. Q. Yu, D. J. Hill, A. Y. S. Lam, J. Gu and DV. O. K. Li, "Intelligent Time Adaptive Transient Stability Assessment System", IEEE Transactions on Power Systems, Vol. 8, No. 5, 2017, pp.1125-1135.
[23] Y. Bengio, P. Simard and P. Frasconi, "Learning Long-Term Dependencies with Gradient Descent is Difficult", IEEE Transactions on Neural Networks, Vol. 5, No. 2, 1994, pp. 157-166.
[24] محمود معلم و علی­اکبر پویان، "کشف نا­هنجاری با استفاده از کد­کننده خودکار مبتنی بر بلوک­‌های LSTM "، مدل­‌سازی در مهندسی، دوره 17 ، شماره 56، بهار ۱۳۹8، صفحه‌ 211-219.
[25] J. B. D. Kingma, "Adam: A Method for Stochastic Optimization", in Proceeding of International Conference for Learning Representations, 2015, pp. 1-15.
[26] Power Systems Relaying Committee, EMTP reference models for transmission line relay testing report, Draft 10a. Technical report, 2004, [Online], Available: http:// www.pserc.org.
[27] R. Zhang, Y. Xu, Z. Y. Dong, and K. P. Wong, "Post-Disturbance Transient Stability Assessment of Power Systems by a Self-Adaptive Intelligent System," IET Generation, Transmission & Distribution, Vol. 9, No. 3, 2015, pp. 296-305.
[28] S. Zhang, Y. Wang, M. Liu and Z. Bao, "Data-Based Line Trip Fault Prediction in Power Systems Using LSTM Networks and SVM", IEEE Access, Vol. 6, 2017, pp. 7675-7686.
[29] Y. LeCun, Y. Bengio and G. Hinton, "Deep learning", Nature International Journal of Science, Vol. 521, No. 7553, 2015, pp. 436-444.
 
مدل سازی در مهندسی
دوره 18، شماره 61
تیر 1399
صفحه 35-47

فایل ها

سابقه مقاله

  • تاریخ دریافت: 06 خرداد 1398
  • تاریخ بازنگری: 06 مهر 1398
  • تاریخ پذیرش: 14 آبان 1398

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