Power system topological observability analysis using artificial neural networks

2005 IEEE Power Engineering Society General Meeting

Volumn 1, Issue , 2005, Pages 497-502

  Jain, Amit ^a,b,f   Balasubramanian, R ^a,c   Tripathy, S C ^a,d,g,h   Singh, Brij N ^a,e,i   Kawazoe, Yoshiyuki ^b,f,j,k,l  

^a IEEE   (Japan)

^b TOHOKU UNIVERSITY   (Japan)

^c INDIAN INSTITUTE OF TECHNOLOGY DELHI   (India)

^d THE NORTHCAP UNIVERSITY   (India)

^e Tulane University   (United States)

^f TOHOKU UNIVERSITY   (Japan)

^g INSTITUTION OF ENGINEERING AND TECHNOLOGY   (United Kingdom)

^h ITM ^*  (India)

ⁱ TULANE UNIVERSITY   (United States)

^j Materials Database Committee   (Japan)

^k FUDAN UNIVERSITY   (China)

^l Senan University   (China)

more..

Author keywords

Artificial neural network; Back propagation; Quickprop; State estimation; Topological observability

Indexed keywords

POWER SYSTEM CONFIGURATION; QUICKPROP; TOPOLOGICAL OBSERVABILITY;

BACKPROPAGATION; ELECTRIC NETWORK TOPOLOGY; NEURAL NETWORKS; PERSONNEL TRAINING; PROBLEM SOLVING;

ELECTRIC POWER SYSTEMS;

EID: 27144451000     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (12)

1. G. R. Krumpholz, K. A. Clements and P. W. Davis, "Power system observability: a practical algorithm using network topology," IEEE Trans. on Power Apparatus and Systems, vol. PAS-99, pp. 1534-1542, July/Aug. 1980.
2. K. A. Clements, G. R. Krumpholz and P. W. Davis, "Power system state estimation with measurement deficiency: an algorithm using network topology," IEEE Trans. on Power Apparatus and Systems, vol. PAS-100, pp. 1779-1787, April 1981.
3. K. A. Clements, G. R. Krumpholz and P. W. Davis, "Power system state estimation with measurement deficiency: an algorithm that determines the maximal observable subnetwork," IEEE Trans. on Power Apparatus and Systems, vol. PAS-101, pp. 3044-3052, September 1982.
4. K. A. Clements, G. R. Krumpholz and P. W. Davis, "Power system state estimation with measurement deficiency: an observability/measurement placement algorithm," IEEE Transactions on Power Apparatus and Systems, vol. PAS-102, pp. 2012-2020, July 1983.
5. V. H. Quintana, A. Simoes-Costa and A. Mandel, "Power system topological observability using a direct graph-theoretic approach", IEEE Trans Power Apparatus and Systems, Vol. PAS-101, pp. 617-626, April 1982.
6. T. S. Dillon, "Artificial neural network applications to power systems and their relationship to symbolic methods", International Journal of Electrical Power and Energy System, Vol. 13, pp. 66-72. 1991.
7. H. Mori, "Artificial neural net based method for power system state estimation", in Proceedings of International Joint Conference on Neural Networks, Part 2 (of 3), Vol. 2, pp. 1533-1536, 1993.
8. D. Kukoji, F. Kulic, D. Popovic and Z. Gorecan, "Determining topological changes and critical load levels of a power system by means of artificial neural network", Electric Machines and Power systems, Vol. 25, pp. 917-926, Oct. 1997.
9. P. D. Wasserman, Neural computing theory and practice, New York, Van Nostrand Reinhold, 1989.
10. SNNS (Stuttgart Neural Network Simulator) Version 3.2, developed at Institute for Parallel and Distributed High Performance System (IPVR), University of Stuttgart, Postfach, Germany, 1993.
11. N. Deo, Graph theory with applications to engineering and computer science, Englewood Cliffs, N.J., U.S.A., Prentice-Hall, Inc., 1974.
12. George van Schoor, Jacobus Daniel van Wyk and Ian S. Shaw, "Optimal Control of a Hybrid Power Compensator Using an Artificial Neural Network Controller," IEEE Trans. Industry Applications, vol. 38, pp. 467-475, March/April 2002.