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Deep-Belief Network Based Prediction Model for Power Outage in Smart Grid

The power outages of the last couple of years around the world introduce the indispensability of technological development to improve the traditional power grids. Early warnings of imminent failures represent one of the major required improvements. Costly blackouts throughout the world caused by the different severe incidents in traditional power grids have motivated researchers to diagnose and investigate previous blackouts and propose a prediction model that enables to prevent power outages. Although, in the new generation of power grid, the smart grid's (SG) real time data can be used from smart meters (SMs) and phasor measurement unit sensors (PMU) to prevent blackout, it demands high reliability and stability against power outages. This paper implements a proactive prediction model based on deep-belief networks that can predict imminent blackout. The proposed model is evaluated on a real smart grid dataset. Promising results are reported in the case study.