Neural Network Based Early Warning System for an Emerging Blackout in Smart Grid Power Networks

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.