Patent
1994-08-30
1997-04-29
Hafiz, Tariq R.
395 21, 395907, G06E 100, G06E 300, G06F 1518
Patent
active
056257511
ABSTRACT:
Analysis and evaluation of outage effects on the dynamic security of power systems is made with a neural network using composite contingency severity indices. A preferably small number of indices describes the power system characteristics immediately post-contingency. These indices are then used as classifiers of the safety of the power system. Using the values of the severity indices, an artificial neural network distinguishes between safe, stable contingencies and potentially unstable contingencies. The severity of the contingency is evaluated based upon a relatively small fixed set of severity indices that are calculated based on a partial time domain simulation. Because a fixed set of severity indices is used, the size and architecture of the neural network is problem independent, thus permitting its use with large scale power systems. Further, the amount of required time domain simulation for the selection of the potentially harmful unstable contingencies is reduced by screening out benign, stable appearing contingencies. The network is trained off-line using training cases that concentrate around the security boundary to reduce the number of cases required to train the neural network.
REFERENCES:
patent: 5469528 (1995-11-01), Douglas et al.
Kumar et al, "Neural networks for dynmaic security assessment of large-scale power systems: requrieiments overview"; Proceedings of hte first international forum on applications of neural networks to power systems, p. 65-71, 23-26 Jul. 1991.
Aggoune et al, "use of artificial neural networks in a dispatcher training simulator for power system dynamic security assessment"; 1990 IEEE international conference on systems, man, and cybernetics conference proceedings, pp. 233-238, 4-7 Nov. 1990.
Pao et al, "combined use of unsupervised and supervised learning for dynamic security assessment"; 1991 Power industry computer application conference, pp. 278-284, 7-10 May 1991.
Djukanovic et al, "neural net based determination of generator-shedding requirements in electrical power systems"; IEEE Proceedings C, vol. 139, iss. 5, pp. 427-436.
Liangzhong et al, "estimation of transient stability limits using artificial neural network"; Proceedings TENCON '93, pp. 87-90 vol. 5, 19-21 Oct. 1993.
Article entitled "Dynamic Security Assessment of Power Systems Using Back Error Propagation Artificial Neural Networks", by M.A. El-Sharkawi, R.J. Marks, M. E. Aggoune, D.C. Park, M.J. Damborg, L.E. Atlas, Second Symposium on Expert Systems Applications to Power Systems, Jul. 17-20, 1989, Seattle, USA.
Article entitled "Artificial Neural-Net Based Dynamic Security Assessment for Electric Power Systems", by Dejan J. Sobajic and Y.H. Pao, IEEE Transactions on Power Systems, vol. 4, No. 1, Feb. 1989.
Article entitled "An Artificial Neural-Net Based Technique for Power System Dynamic Stability with the Kobonen Model", by Hiroyuki Mori, Yoshihito Tamaru, Senji Tsuzuki. Transactions on Power Systems, vol. 7 No. 2, May 1992.
Article entitled "Combined Use of Unsupervised and Supervised Learning for Dynamic Security Assessment", by Y.H. Pao and Dejan J. Sobajic, Transactions on Power Systems, vol. 2, No. 2, May 1992.
Brandwajn Vladimir
Cauley Gerald W.
Ipakchi Ali
Kumar A. B. Ranjit
Electric Power Research Institute
Hafiz Tariq R.
LandOfFree
Neural network for contingency ranking dynamic security indices does not yet have a rating. At this time, there are no reviews or comments for this patent.
If you have personal experience with Neural network for contingency ranking dynamic security indices , we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Neural network for contingency ranking dynamic security indices will most certainly appreciate the feedback.
Profile ID: LFUS-PAI-O-713327