Knowledge-based Neural Network for Line Flow Contingency Selection and Ranking

Malik, Mr. Nitin and Srivastava, Dr. Laxmi (2006) Knowledge-based Neural Network for Line Flow Contingency Selection and Ranking. [Conference Paper]

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The Line flow Contingency Selection and Ranking (CS & R) is performed to rank the critical contingencies in order of their severity. An Artificial Neural Network based method for MW security assessment corresponding to line outage events have been reported by various authors in the literature. One way to provide an understanding of the behaviour of Neural Networks is to extract rules that can be provided to the user. The domain knowledge (fuzzy rules extracted from Multi-layer Perceptron model trained by Back Propagation algorithm) is integrated into a Neural Network for fast and accurate CS & R in an IEEE 14-bus system, for unknown load patterns and are found to be suitable for on-line applications at Energy Management Centers. The system user is provided with the capability to determine the set of conditions under which a line-outage is critical, and if critical, then how severe it is, thereby providing some degree of transparency of the ANN solution.

Item Type:Conference Paper
Keywords:MW security assessment, Energy Management Centers, Fuzzy rules, Domain Knowledge, IEEE 14-bus system
Subjects:Neuroscience > Neural Modelling
Computer Science > Neural Nets
ID Code:4362
Deposited By: Malik, Mr. Nitin
Deposited On:19 May 2005
Last Modified:30 Aug 2011 04:20

References in Article

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[1] Sandilya, A., Gupta, H.O., Sharma, J.D.,"A method for automatic contingency selection, ranking and line outage simulation", Proceedings, IASTED, Int. Symposium on high technology in the power industry, Scottsdale, Arizona, March 1-4, 1998, pp 88-91

[2] Pai, M A, Aggarwal, R P and Armugam, N, “A fast algorithm for on-line load flow contingency evaluation.”, IFAC Symp. on Computer Applications in large scale power systems, New Delhi (Aug 1979)

[3] V.S.S.Vankayala and N.D.Rao, “Power system security enhancement using a coupled ANN-ES scheme.”, Proc. 4th Expert system applications to power system, Melbourne, 1993.

[4] Lo, K. L., Peng, L. J., Macqueen, J.F., Ekwue, A. O. and Cheng, D.T. Y.,”Fast Real Power Contingency Ranking using a Counter Propagation Network" IEEE, 1997, pp. 1259-1264.

[5] Sobjic, D. J. and Pao, Y. H., "An artificial intelligence system for power system contingency screening, " IEEE Trans.Power Syst. Vol PWRS-3 No 2 (1988) pp. 647-65324.

[6] Lauby, M G, Mikolinnas, T A and Reppen, N D, "Contingency selection of branch outages causing voltage problems" IEEE Trans. Power Appar. Syst. Vol. PAS-102 No 12 (1983) 3899-3904

[7] Nitin Malik, “Artificial Neural Networks and their applications.” National Conf. on ‘Unearthing Technological Developments & their Transfer for Serving Masses’, GLA ITM, Mathura, India 17-18 April 2005

[8] L. Srivastava, S. N. Singh and J. Sharma, “ANN applications in power systems: an overview and key issues,” International Conf. on computer applications in electrical engineering, recent advances, pp. 397-403, 8-11 Sept, 1997.

[9] Task force 38-06-06 of study committee 38, “Artificial neural networks for power systems,” Electra No.159, pp. 78-101, April 1995.

[10] V.Brandwaji, B.R.Kumar, A.Bose and S.D.Kuo, “Voltage security indices for contingency screening in dynamic security assessment,.” IEEE Trans. Power Systems, Vol. 12, No 2, August1993

[11] S.B. Tham, “Extracting provably correct rules from artificial neural networks.” Institute for Informatik III, Universitat Bonn, Tech. Rep. IAI-TR pp. 93-95, 1993.

[12] G.Towell and J.W. Shavlik, “Extracting refined rules from knowledge-based neural networks,” Machine learning, vol.13, pp. 71-101, 1993.

[13] Esukimoto and C. Morita, “The discovery of propositions in noisy data,” In Machine Intelligence 13, Oxford, U.K. Oxford Univ. Press. 1994, pp. 143-167

[14] L. Srivastava, S.N. Singh and J.Sharma: “Knowledge-based neural network for voltage contingency selection and ranking.” IEE Proc. Gener. Transm.Distrib., Vol.146, No. 6, November 1999.

[15] Buckley, J.J., Hayashi, Y., and Czogala, E.: “On the equivalence of neural nets and fuzzy expert systems.”, Fuzzy Sets Syst., 1993, 53, pp.129-134

[16] Huang, S.H., and Endsley, M.R.: “Providing understanding of the behaviour of feedforward neural networks,.” IEEE Trans. Syst. Man Cybern., 1997,27, pp. 465-474.


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