Background:
Enhancing the resiliency of electric power grids is becoming a crucial issue
due to the outages that have recently occurred. One solution could be the prediction of imminent failure
that is engendered by line contingency or grid disturbances. Therefore, a number of researchers
have initiated investigations to generate techniques for predicting outages. However, extended
blackouts can still occur due to the frailty of distribution power grids.
Objective:
This paper implements a proactive prediction model based on deep-belief networks to
predict the imminent outages using previous historical blackouts, trigger alarms, and suggest solutions
for blackouts. These actions can prevent outages, stop cascading failures and diminish the
resulting economic losses.
Methods:
The proposed model is divided into three phases: A, B and C. The first phase (A) represents
the initial segment that collects and extracts data and trains the deep belief network using the collected
data. Phase B defines the Power outage threshold and determines whether the grid is in a
normal state. Phase C involves detecting potential unsafe events, triggering alarms and proposing
emergency action plans for restoration.
Results:
Different machine learning and deep learning algorithms are used in our experiments to
validate our proposition, such as Random forest, Bayesian nets and others. Deep belief Networks
can achieve 97.30% accuracy and 97.06% precision.
Conclusion:
The obtained findings demonstrate that the proposed model would be convenient
for blackouts’ prediction and that the deep-belief network represents a powerful deep learning
tool that can offer plausible results.
Sentiment Analysis using Deep Belief Network for User Rating Classification