PROJECT TITLE :
Support-Vector-Machine-Based Proactive Cascade Prediction in Smart Grid Using Probabilistic Framework
The worldwide major blackout events of power network are highlighting the necessity for technology upgradation in traditional grid. One among the foremost upgradations needed is in the world of early warning generation in case of any grid disturbances such as line contingency leading to cascade failure. This paper proposes a proactive blackout prediction model for a smart grid early warning system. The proposed model evaluates system performance probabilistically, in steady state and under dynamical (line contingency) state, and prepares a historical database for traditional and cascade failure states. A support vector machine (SVM) has been trained with this historical database and is used to predict blackout events in advance. The key contribution of this paper is to capture the essence of the cascading failure using probabilistic framework and integration of SVM machine learning tool to make a prediction rule, which would be in a position to predict the scenarios of the blackout as early as potential. The proposed model is validated using the IEEE 30-bus take a look at-bed system. Proactive prediction of cascade failure using the proposed model might facilitate in realizing the grid resilience feature of good grid.
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