Abstract
Worldwide power blackouts have attracted great attention of researchers towards early warning techniques for cascading failure in power grid. The key issue is how to analyse, predict and control cascading failures in advance and prevent system against emerging blackouts. This paper proposes a model which analyse power flow of the grid and predict cascade failure in advance with the integration of Artificial Neural Network (ANN) machine learning tool. The Key contribution of this paper is to introduce machine learning concept in early warning system for cascade failure analysis and prediction. Integration of power flow analysis with ANN machine learning tool has a potential to make present system more reliable which can prevent the grid against blackouts. An IEEE 30 bus test bed system has been modeled in powerworld and used in this paper for preparation of historical blackout data and validation of proposed model. The proposed model is a step towards realizing smart grid via intelligent ANN prediction technique.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
A Report of The enquiry committee on grid disturbance in Northen region on 30th July 2012 and in Northern, Eastern and North-Eastern region on 31 st July 2012, 16th August 2012 New Delhi
Borkowska, S.B.: Probabilistic load flow. IEEE Trans. on Power Apparatus and Systems PAS-93(3), 752–755 (1974)
Allan, R.N., Borkowska, B., Grigg, C.H.: Probabilistic analysis of power flows. In: Proceedings of the Institution of Electrical Engineers, London, vol. 121(12), pp. 1551–1556 (December 1974)
Min, L., Zhang, P.: Probabilistic load flow with consideration of network topology uncertainties. In: The 14th International Conference on Intelligent System Applications to Power Systems, ISAP 2007, Kaohsiung, Taiwan, November 4-8, pp. 7–11 (2007)
Zhang, P., Lee, S.T.: Load flow computation using the method of combined cumulants and Gram-Charlier expansion. IEEE Trans. on Power Systems 19(1), 676–682 (2004)
Gupta, S.R., Kazi, F.S., Wagh, S.R., Singh, N.M.: Probabilistic framework for evaluation of smart grid resilience of cascade failure. In: IEEE Innovative Smart Grid Technologies Conference (ISGT) Asia, Kuala Lumpur, Malaysia, May 20-23, pp. 264–269 (2014)
Guo, C.-X., Jiang, Q.-Y., Cao, X., Cao, Y.-J.: Recent developments on applications of neural networks to power systems operation and control: An overview. In: Yin, F.-L., Wang, J., Guo, C. (eds.) ISNN 2004. LNCS, vol. 3174, pp. 188–193. Springer, Heidelberg (2004)
Sobajic, D.J., Pao, Y.-H.: Artificial neural -netbased dynamic security. IEEE Trans. on Power Systems 4(1), 220–228 (1989)
Aggoune, M., El-Sharkawi, M.A., Park, D.C., Damborg, M.J., Marks, R.J.: Preliminary results on using artificial neural networks for security assessment. IEEE Trans. on Power Systems 6(2), 890–896 (1991)
Swamp, K.S., Prasad Reddy, K.V.: Neural Network based Pattern Recognition for Power System Security Assessment. In: CISIP 2005 (2005)
Hassan, L.H., Moghavvemi, M.: Current state of neural networks applications in power system monitoring and control. International Journal of Electrical Power and Energy Systems 51, 134–144 (2013)
Bourguet, R.E., Antsaklis, P.J.: Artificial Neural Networks In Electric Power Industry. Technical Report of the ISIS (Interdisciplinary Studies of Intelligent Systems) Group, No. ISIS-94-007, Univ of Notre Dame (April 1994)
Wong, K.P.: Artificial intelligence and neural network applications in power systems. In: 2nd International Conference on Advances in Power System Control, Operation and Management, December 7-10, vol. 1, pp. 37–46 (1993)
Bashier, E., Tayeb, M.: Faults Detection in Power Systems Using Artificial Neural Network. American Journal of Engineering Research (AJER) 2(6), 69–75 (2013)
Sullivan, R.L., Lee, R.: Power system planning. McGraw-Hill International Book Company
Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning representations by back-propagating errors. Nature 323(6088), 533–536 (1986)
Moller, M.F.: A Scaled conjugate gradient algorithm for fast supervised learning. Neural Networks 6(4), 525–533 (1993)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Gupta, S., Kazi, F., Wagh, S., Kambli, R. (2015). Neural Network Based Early Warning System for an Emerging Blackout in Smart Grid Power Networks. In: Buyya, R., Thampi, S. (eds) Intelligent Distributed Computing. Advances in Intelligent Systems and Computing, vol 321. Springer, Cham. https://doi.org/10.1007/978-3-319-11227-5_16
Download citation
DOI: https://doi.org/10.1007/978-3-319-11227-5_16
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-11226-8
Online ISBN: 978-3-319-11227-5
eBook Packages: EngineeringEngineering (R0)