Abstract
Various algorithms have been introduced for differential protection of power transformers, each of which has its limitations. One of the causes of unwanted interruption in power transformers is its magnetizing inrush current. This current causes the differential relay to misdiagnose the internal fault of the transformer and leads to an incorrect cutoff command. In this paper, fuzzy systems are used to improve the differential protection of the transformer and to stabilize it against magnetizing inrush current. The basis of the proposed algorithm is that if the cutoff command is sent by the differential relay, by applying Clarke transform to the differential current and using fuzzy logic, the decision to send the cutoff command to the protection relays is made. The proposed algorithm is applied to a sample of the substation transformers model in Iran. The network is simulated in PSCAD/EMTDC software and the output of the simulation is transferred to MATLAB software to apply the proposed algorithm. The simulation results show the efficiency of the proposed algorithm in improving the differential relay.
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References
Afrasiabi, M., Afrasiabi, S., Parang, B., & Mohammadi, M. (2019). Power transformers internal fault diagnosis based on deep convolutional neural networks. Journal of Intelligent & Fuzzy Systems, 37(1), 1165–1179.
Ali, E., Helal, A., Desouki, H., Shebl, K., Abdelkader, S., & Malik, O. P. (2018). Power transformer differential protection using current and voltage ratios. Electric Power Systems Research, 154, 140–150.
Balaga, H., Gupta, N., & Vishwakarma, D. N. (2015). GA trained parallel hidden layered ANN-based differential protection of three-phase power transformer. International Journal of Electrical Power & Energy Systems, 67, 286–297.
Bejmert, D., Kereit, M., Mieske, F., Rebizant, W., Solak, K., & Wiszniewski, A. (2020). Power transformer differential protection with integral approach. International Journal of Electrical Power & Energy Systems, 118, 105859.
Dashtdar, M, Rahman D, and Hamid R. S., (2018) Distribution network fault section identification and fault location using artificial neural network. In 2018 5th International conference on electrical and electronic engineering (ICEEE), pp. 273–278. IEEE.
Dashtdar, M. (2018). Fault location in distribution network based on fault current analysis using artificial neural network. Mapta Journal of Electrical and Computer Engineering (MJECE), 1(2), 18–32.
Dashtdar, M., & Dashtdar, M. (2019a). Fault location in the transmission network based on extraction of fault components using wavelet transform. The Scientific Bulletin of Electrical Engineering Faculty, 19(2), 1–9.
Dashtdar, M., & Dashtdar, M. (2019b). Fault location in distribution network based on phasor measurement units (PMU). The Scientific Bulletin of Electrical Engineering Faculty, 19(2), 38–43.
Deshmukh, M.S., and Barhate V., T. (2017) Microcontroller-based differential relay using fuzzy logic for transformer protection. In 2017 International conference on intelligent computing and control systems (ICICCS), pp. 712–717. IEEE.
Eissa, M. M. (2005). A novel digital directional transformer protection technique based on wavelet packet. IEEE Transactions on Power Delivery, 20(3), 1830–1836.
Faiz, J., Bashir, M. E., & Tahere, N. (2008). Three-and two-dimensional finite-element computation of inrush current and short-circuit electromagnetic forces on windings of a three-phase core-type power transformer. IEEE Transactions on Magnetics, 44(5), 590–597.
Hosseinimoghadam, S. M. S., Dashtdar, M., Dashtdar, M., & Roghanian, H. (2020). Security control of islanded micro-grid based on adaptive neuro-fuzzy inference system. Scientific Bulletin: Series C Electrical Engineering and Computer Science, 1, 189–204.
Jazebi, S., Vahidi, B., Hosseinian, S. H., & Faiz, J. (2009). Magnetizing inrush current identification using wavelet-based Gaussian mixture models. Simulation Modelling Practice and Theory, 17(6), 991–1010.
Liu, P., Huang, C., Jiang, Y., & Yang, Y. (2018). A digital scheme to minimize the influence of transformer magnetizing inrush or CT saturation on line protection. International Journal of Electrical Power & Energy Systems, 101, 394–402.
Milani, A. R., Asadzadeh, B., Mokhtari, H., and Tarafdarhagh, M. (2014) Performance of an Over Voltage Relay at Harmonic Polluted Conditions. In the 2014 9th International conference on technical and physical problems of electrical engineering (ICTPE), pp. 1–6.
Parihar, V. R., Nimkar, S. D., Warudkar, S., Deshmukh, R., & Thakare, M. (2017). Power transformer protection using fuzzy logic based controller. International Journal of Engineering Research (IJER), 6(7), 366–370.
Qais, M., Khaled, U., & Alghuwainem, S. (2018). Improved differential relay for bus bar protection scheme with saturated current transformers based on second-order harmonics. Journal of King Saud University-Engineering Sciences, 30(4), 320–329.
Roy, A., Singh, D., Misra, R. K., & Singh, A. (2019). Differential protection scheme for power transformers using matched wavelets. IET Generation, Transmission & Distribution, 13(12), 2423–2437.
Roy, N., & Bhattacharya, K. (2015). Detection, classification, and estimation of fault location on an overhead transmission line using S-transform and neural network. Electric Power Components and Systems, 43(4), 461–472.
Saravanan, B., & Rathinam, A. (2017). Inrush blocking scheme in transformer differential protection. Energy Procedia, 117, 1165–1171.
Tripathy, M. (2010). Power transformer differential protection using neural network principal component analysis and radial basis function neural network. Simulation Modelling Practice and Theory, 18(5), 600–611.
Tripathy, M., Maheshwari, R. P., & Verma, H. K. (2008). Neuro-fuzzy technique for power transformer protection. Electric Power Components and Systems, 36(3), 299–316.
Wiszniewski, A., Solak, K., Rebizant, W., & Schiel, L. (2018). Calculation of the lowest currents caused by turn-to-turn short-circuits in power transformers. International Journal of Electrical Power & Energy Systems, 95, 301–306.
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Hosseinimoghadam, S.M.S., Dashtdar, M. & Dashtdar, M. Improving the Differential Protection of Power Transformers Based on Clarke Transform and Fuzzy Systems. J Control Autom Electr Syst 33, 610–624 (2022). https://doi.org/10.1007/s40313-021-00814-w
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DOI: https://doi.org/10.1007/s40313-021-00814-w