Application of probabilistic neural network for differential relaying of power transformer
Application of probabilistic neural network for differential relaying of power transformer
- Author(s): M. Tripathy ; R.P. Maheshwari ; H.K. Verma
- DOI: 10.1049/iet-gtd:20050273
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- Author(s): M. Tripathy 1 ; R.P. Maheshwari 1 ; H.K. Verma 1
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View affiliations
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Affiliations:
1: Department of Electrical Engineering, Indian Institute of Technology Roorkee, Roorkee, India
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Affiliations:
1: Department of Electrical Engineering, Indian Institute of Technology Roorkee, Roorkee, India
- Source:
Volume 1, Issue 2,
March 2007,
p.
218 – 222
DOI: 10.1049/iet-gtd:20050273 , Print ISSN 1751-8687, Online ISSN 1751-8695
Investigations towards the applicability of probabilistic neural networks (PNNs) as core classifiers to discriminate between magnetising inrush and internal fault of power transformer are made. An algorithm has been developed around the theme of conventional differential protection of transformer. It makes use of the ratio of the voltage-to-frequency and the amplitude of differential current for the detection of the operating condition of the transformer. The PNN has a significant advantage in terms of a much faster learning capability because it is constructed with a single pass of exemplar pattern set and without any iteration for weight adaptation. For the evaluation of the developed algorithm, transformer modelling and simulation of fault are carried out in power system computer-aided designing PSCAD/EMTDC. The operating condition detection algorithm is implemented in MATLAB.
Inspec keywords: learning (artificial intelligence); power transformer protection; fault simulation; neural nets; relay protection; power engineering computing
Other keywords:
Subjects: Protection apparatus; Neural computing techniques; Power engineering computing; Transformers and reactors
References
-
-
1)
- D.F. Specht . Probabilistic neural network. Neural Netw. , 1 , 190 - 118
-
2)
- A.G. Phadke , J.S. Thorp . A new computer based flux-restrained current differential relay for power transformer protection. IEEE Trans. Power Appar. Syst. , 11 , 3624 - 3629
-
3)
- P. Bastard , M. Meunier , H. Regal . Neural network based algorithm for power transformer differential relays. IEE Proc., Gener. Transm. Distrib. , 4 , 386 - 392
-
4)
- Specht, D.F., Shapiro, P.D.: `Generalization accuracy of probabilistic neural networks compared with back-propagation networks', IEEE Int. Conf. on Neural Networks, 1991, Seattle, 1, p. 887–892.
-
5)
- M.H. Hammond , C.J. Riedel , S.L. Rose-Pehrsson , F.W. Williams . Training set optimization methods for a probabilistic neural network. Elsevier Sci., Chemometr. Intell. Lab. Syst. , 1 , 73 - 78
-
6)
- T. Cover , P. Hart . Nearest neighbor pattern classification. IEEE Trans. Inf. Theory , 1 , 21 - 27
-
7)
- M.R. Berthold , J. Diamond . Constructive training of probabilistic neural networks. Elsevier Sci. Neurocomput. , 167 - 183
-
8)
- M.-C. Shin , C.-W. Park , J.-H. Kim . Fuzzy logic-based relaying for large power transformer protection. IEEE Trans. Power Deliv. , 3 , 718 - 728
-
9)
- A.R. Web . (2002) Statistical pattern recognition.
-
10)
- Washburne, T.P., Okamura, M.M., Specht, D.F., Fisher, W.A.: `The lockheed probabilistic neural network processor', IEEE Int. Joint Conf. on Neural Networks, 1991, Seattle, 1, p. 513–518.
-
11)
- H.K. Verma , A.M. Basha . A microprocessor-based inrush restrained differential relay for transformer protection. J. Microcomput. Appl. , 4 , 313 - 318
-
12)
- D.F. Specht . Probabilistic neural networks and the polynomial adeline as complementary techniques for classification. IEEE Trans. Neural Netw. , 1 , 111 - 121
-
13)
- Specht, D.F.: `Enhancements to probabilistic neural networks', Proc. IEEE Int. Conf. on Neural Networks, 1992, 1, p. 761–767.
-
14)
- P. Torf , R. Wojcik . Local probabilistic neural networks in hydrology. Elsevier Sci., Phys. Chem. Earth (B) , 1 , 9 - 14
-
15)
- X. Ma , J. Shi . A new method for discrimination between fault and magnetizing inrush current using H.M.M.. Elsevier Sci. Electr. Power Syst. Res. , 1 , 43 - 49
-
16)
- Aibe, N., Yasunaga, M., Yoshihara, I., Kim, J.H.: `A probabilistic neural network hardware system using a learning parameter parallel architecture', Proc. IEEE Int. Conf. on Neural Networks, 12–17 May 2002, 3, p. 2270–2275.
-
17)
- M.S. Sachdev . (1988) Microprocessor relays and protection systems, IEEE Tutorial Course Text.
-
18)
- J. Pihler , B. Grcar , D. Dolinar . Improved operation power transformer protection using artificial neural network. IEEE Trans. Power Delivery , 3 , 1128 - 1136
-
19)
- R.P. Maheshwari , H.K. Verma . Adaptive digital differential relay of parabolic characteristic for transformer protection. Taylor and Francis, Electr. Mach. Power Syst. , 5 , 459 - 473
-
20)
- H.K. Verma , G.C. Kakoti . Algorithm for harmonic restraint differential relaying based on the discrete Hartley transform. Electr. Power Syst. Res. , 2 , 125 - 129
-
21)
- P. Burrascano . Learning vector quantization for the probabilistic neural network. IEEE Trans. Neural Netw , 4 , 111 - 121
-
22)
- N.K. Bose , P. Liang . (1996) Neural network fundamentals with graphs, algorithms, andapplications.
-
23)
- M.R. Zaman , M.A. Rahman . Experimental testing of the artificial neural network based protection of power transformers. IEEE Trans. Power Delivery , 2 , 510 - 517
-
24)
- M. Rahman , B. Jeyasurya . A state-of-the-art review of transformer protection algorithms. IEEE Trans. Power Deliv. , 2 , 534 - 544
-
25)
- Z. Moravej , D.N. Vishwakarma , S.P. Singh . Application of radial basis function neural network for differential relaying of a power transformer. Elsevier Sci., Comput. Electric. Eng. , 3 , 421 - 434
-
26)
- Minchin, G., Zaknich, A.: `A design for implementation of the probabilistic neural network', Proc. IEEE TCONI ‘99, 1999, p. 556–559.
-
27)
- Z. Moravej . ANN-based harmonic restraint differential protection of power transformer. IE (I) J. Electron. Lett. , 1 , 1 - 6
-
28)
- M.J. Heathcote . (2007) The J&P transformer book.
-
29)
- Specht, D.F.: `Probabilistic neural networks for classification, mapping, or associative memory', Proc. IEEE Int. Conf. on Neural Networks, July 1988, 1, p. 525–532.
-
30)
- Y.V.V.S. Murty , W.J. Smolinski . Kalman filter based digital percentage differential and ground fault relay for a 3-phase power transformer. IEEE Trans. Power Delivery , 3 , 1299 - 1308
-
31)
- Musavi, M.T., Kalantri, K., Ahmed, W.: `Improving the performance of probabilistic neural networks', Proc. IEEE Int. Conf. on Neural Networks, 1992, 1, p. 595–600.
-
32)
- K.C. Tan , H.J. Tang . New dynamical optimal learning for linear multilayer FNN. IEEE Trans. Neural Netw. , 6 , 1562 - 1568
-
33)
- L.G. Perez , A.J. Flechsig , J.L. Meador , Z. Obradovic. . Training an artificial neural network to discriminate between magnetizing inrush and internal faults. IEEE Trans. Power Deliv. , 1 , 431 - 441
-
1)
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