A new approach to detecting and classifying multiple faults in IEEE 14-bus system

Authors

DOI:

https://doi.org/10.15587/1729-4061.2020.208698

Keywords:

simultaneous faults, artificial neural network, fault detection, classification, two-port network

Abstract

Faults in the power system generally provide considerable changes in its quantities such as under or over-power, over-current, current or power direction, frequency, impedance, and power factor. Reading data related to both currents and voltages is usually involved for detecting and situating the fault on the transmission network. These days, any outage of power in a power grid leads to heavy financial losses for commercial, industrial, and domestic consumers. Random and irregular faults in transmission grids contribute significantly to events of power outages. A significant contribution of this study is a new technique for simulating a multiple simultaneous faults model. The recommended approach is an effective technique for detection, classification and localization of faults in transmission networks of electric power. To attain this objective, a training procedure and a neural network simulation were carried out using m-file in MATLAB. A virtual bus has been proposed to analyze the fault which happens on the transmission line and bus. This technique has been applied on the IEEE 14 bus and multiple simultaneous faults have been mentioned in this study. The fault situations are simulated in m-files through the two-port network performance method, which is a highly enhanced scheme in comparison to the existing methods. The results have been arrived upon by subjecting different buses to varying types of fault. The results provide comprehensive information regarding fault current, post-fault voltages, and fault MVA on all the buses. The values at the bus for voltage, power consumption, and phase angles were specified. As suggested by the findings of the simulation, the proposed methodology is an effective technique for detection, classification and localization of faults

Supporting Agencies

  • Authors would like to thank University of Mosul
  • College of Engineering
  • Electrical Department
  • for the support given during this work.

Author Biographies

Ibrahim Ismael Alnaib, Collage of Engineering University of Mosul Al-Majmoaa str., Mosul, Iraq, 41002

Master of Electrical Engineering, Power and Machines, Assistance Lecturer

Department of Electrical Engineering

Omar Sh. Alyozbaky, Collage of Engineering University of Mosul Al-Majmoaa str., Mosul, Iraq, 41002

Doctor of Electrical Engineering, Power and Machines, Lecture

Department of Electrical Engineering

Ali Abbawi, Collage of Engineering University of Mosul Al-Majmoaa str., Mosul, Iraq, 41002

Master of Electrical Engineering, Power and Machines, Assistance Lecturer

Department of Electrical Engineering

References

  1. Jia, H. (2017). An Improved Traveling-Wave-Based Fault Location Method with Compensating the Dispersion Effect of Traveling Wave in Wavelet Domain. Mathematical Problems in Engineering, 2017, 1–11. doi: https://doi.org/10.1155/2017/1019591
  2. Shaoyu, X., Xiuli, W., Chong, Q., Xifan, W., Jingli, G. (2013). Impacts of different wind speed simulation methods on conditional reliability indices. International Transactions on Electrical Energy Systems, 25(2), 359–373. doi: https://doi.org/10.1002/etep.1851
  3. Han, Z. (1982). Generalized Method of Analysis of Simultaneous Faults in Electric Power System. IEEE Transactions on Power Apparatus and Systems, PAS-101 (10), 3933–3942. doi: https://doi.org/10.1109/tpas.1982.317045
  4. Abbawi, A., Ismael, I., Alyozbaky, O. S. (2020). Comparison between Two Methods to Analyze Multiple Faults in IEEE 14-Bus. 2020 7th International Conference on Electrical and Electronics Engineering (ICEEE). doi: https://doi.org/10.1109/iceee49618.2020.9102491
  5. Al Kazzaz, S. A. S., Ismaeel, I., Mohammed, K. K. (2020). Fault detection and location of power transmission lines using intelligent distance relay. International Journal of Power Electronics and Drive Systems (IJPEDS), 11 (2), 726. doi: https://doi.org/10.11591/ijpeds.v11.i2.pp726-734
  6. Choi, M.-S., Lee, S.-J., Lee, D.-S., Jin, B.-G. (2004). A New Fault Location Algorithm Using Direct Circuit Analysis for Distribution Systems. IEEE Transactions on Power Delivery, 19 (1), 35–41. doi: https://doi.org/10.1109/tpwrd.2003.820433
  7. Girgis, A. A., Fallon, C. M. (1992). Fault location techniques for radial and loop transmission systems using digital fault recorded data. IEEE Transactions on Power Delivery, 7 (4), 1936–1945. doi: https://doi.org/10.1109/61.156997
  8. Coser, J., do Vale, D. T., Rolim, J. G. (2007). Design and Training of Artificial Neural Networks for Locating Low Current Faults in Distribution Systems. 2007 International Conference on Intelligent Systems Applications to Power Systems. doi: https://doi.org/10.1109/isap.2007.4441599
  9. Thukaram, D., Khincha, H. P., Vijaynarasimha, H. P. (2005). Artificial Neural Network and Support Vector Machine Approach for Locating Faults in Radial Distribution Systems. IEEE Transactions on Power Delivery, 20 (2), 710–721. doi: https://doi.org/10.1109/tpwrd.2005.844307
  10. Gracia, J., Mazon, A. J., Zamora, I. (2005). Best ANN Structures for Fault Location in Single- and Double-Circuit Transmission Lines. IEEE Transactions on Power Delivery, 20 (4), 2389–2395. doi: https://doi.org/10.1109/tpwrd.2005.855482
  11. Z Chen, Z., Maun, J.-C. (2000). Artificial neural network approach to single-ended fault locator for transmission lines. IEEE Transactions on Power Systems, 15 (1), 370–375. doi: https://doi.org/10.1109/59.852146
  12. Navaneethan, S., Soraghan, J. J., Siew, W. H., McPherson, F., Gale, P. F. (2002). Automatic fault location for underground low voltage distribution networks. 2002 IEEE Power Engineering Society Winter Meeting. Conference Proceedings (Cat. No.02CH37309). doi: https://doi.org/10.1109/pesw.2002.985114
  13. Ngaopitakkul, A., Pothisarn, C. (2009). Discrete wavelet transform and back-propagation neural networks algorithm for fault location on single-circuit transmission line. 2008 IEEE International Conference on Robotics and Biomimetics. doi: https://doi.org/10.1109/robio.2009.4913242
  14. Tawfik, M. M., Morcos, M. M. (2005). On the use of Prony method to locate faults in loop systems by utilizing modal parameters of fault current. IEEE Transactions on Power Delivery, 20 (1), 532–534. doi: https://doi.org/10.1109/tpwrd.2004.839739
  15. Borghetti, A., Corsi, S., Nucci, C. A., Paolone, M., Peretto, L., Tinarelli, R. (2005). On the use of continuous-wavelet transform for fault location in distribution power networks. 15th Power Syst. Comput. Conf. PSCC 2005.
  16. Borghetti, A., Bosetti, M., Di Silvestro, M., Nucci, C. A., Paolone, M. (2008). Continuous-Wavelet Transform for Fault Location in Distribution Power Networks: Definition of Mother Wavelets Inferred From Fault Originated Transients. IEEE Transactions on Power Systems, 23 (2), 380–388. doi: https://doi.org/10.1109/tpwrs.2008.919249
  17. Kezunovic, M. (1997). A survey of neural net applications to protective relaying and fault analysis. Eng. Intell. Syst., 5 (4), 185–192.
  18. Khorashadi-Zadeh, H., Aghaebrahimi, M. R. (2005). A Novel Approach to Fault Classification and Fault Location for Medium Voltage Cables Based on Artificial Neural Network. International Journal of Computational Intelligence, 2 (2), 90–93.
  19. Sousa Martins, L., Martins, J. F., Fernão Pires, V., Alegria, C. M. (2005). A neural space vector fault location for parallel double-circuit distribution lines. International Journal of Electrical Power & Energy Systems, 27 (3), 225–231. doi: https://doi.org/10.1016/j.ijepes.2004.10.004
  20. Purushothama, G. K., Narendranath, A. U., Thukaram, D., Parthasarathy, K. (2001). ANN applications in fault locators. International Journal of Electrical Power & Energy Systems, 23 (6), 491–506. doi: https://doi.org/10.1016/s0142-0615(00)00068-5
  21. Mazon, A. J., Zamora, I., Miñambres, J. F., Zorrozua, M. A., Barandiaran, J. J., Sagastabeitia, K. (2000). A new approach to fault location in two-terminal transmission lines using artificial neural networks. Electric Power Systems Research, 56 (3), 261–266. doi: https://doi.org/10.1016/s0378-7796(00)00122-x
  22. Chunju, F., Li, K. K., Chan, W. L., Weiyong, Y., Zhaoning, Z. (2007). Application of wavelet fuzzy neural network in locating single line to ground fault (SLG) in distribution lines. International Journal of Electrical Power & Energy Systems, 29 (6), 497–503. doi: https://doi.org/10.1016/j.ijepes.2006.11.009
  23. Jain, A., Kale, V. S., Thoke, A. S. (2006). Application of artificial neural networks to transmission line faulty phase selection and fault distance location. Conference: Proceedings of the IASTED International conference “Energy and Power System”, 262–267.
  24. Carpinelli, G., Lauria, D., Varilone, P. (2006). Voltage stability analysis in unbalanced power systems by optimal power flow. IEE Proceedings - Generation, Transmission and Distribution, 153 (3), 261. doi: https://doi.org/10.1049/ip-gtd:20050011
  25. Vasilic, S., Kezunovic, M. (2005). Fuzzy ART Neural Network Algorithm for Classifying the Power System Faults. IEEE Transactions on Power Delivery, 20 (2), 1306–1314. doi: https://doi.org/10.1109/tpwrd.2004.834676
  26. Silva, K., Dantas, K. M. C., Souza, B., Brito, N. S. D., Costa, F., Silva, J. A. C. B. (2006). Haar Wavelet-Based Method for Fast Fault Classification in Transmission Lines. 2006 IEEE/PES Transmission & Distribution Conference and Exposition: Latin America. doi: https://doi.org/10.1109/tdcla.2006.311465
  27. Dong, X., Kong, W., Cui, T. (2009). Fault Classification and Faulted-Phase Selection Based on the Initial Current Traveling Wave. IEEE Transactions on Power Delivery, 24 (2), 552–559. doi: https://doi.org/10.1109/tpwrd.2008.921144
  28. Silva, K. M., Souza, B. A., Brito, N. S. D. (2006). Fault Detection and Classification in Transmission Lines Based on Wavelet Transform and ANN. IEEE Transactions on Power Delivery, 21 (4), 2058–2063. doi: https://doi.org/10.1109/tpwrd.2006.876659
  29. Dutta, P., Esmaeilian, A., Kezunovic, M. (2014). Transmission-Line Fault Analysis Using Synchronized Sampling. IEEE Transactions on Power Delivery, 29 (2), 942–950. doi: https://doi.org/10.1109/tpwrd.2013.2296788
  30. Mahamedi, B. (2011). A novel setting-free method for fault classification and faulty phase selection by using a pilot scheme. 2011 2nd International Conference on Electric Power and Energy Conversion Systems (EPECS). doi: https://doi.org/10.1109/epecs.2011.6126835
  31. Rakytyanska, H. (2015). Neural-network approach to structural tuning of classification rules based on fuzzy relational equations. Eastern-European Journal of Enterprise Technologies, 4 (2 (76)), 51–57. doi: https://doi.org/10.15587/1729-4061.2015.47124
  32. Kodsi, S. K. M., Canizares, C. A. (2003). Modeling and simulation of IEEE 14-bus system with FACTS controllers.

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Published

2020-10-31

How to Cite

Alnaib, I. I., Alyozbaky, O. S., & Abbawi, A. (2020). A new approach to detecting and classifying multiple faults in IEEE 14-bus system. Eastern-European Journal of Enterprise Technologies, 5(8 (107), 6–16. https://doi.org/10.15587/1729-4061.2020.208698

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Section

Energy-saving technologies and equipment