Authors :
Sathish Kumar N; Kannan B; Harish K G; Jaya Anand N
Volume/Issue :
Volume 8 - 2023, Issue 7 - July
Google Scholar :
https://bit.ly/3TmGbDi
Scribd :
https://tinyurl.com/2ezaeupk
DOI :
https://doi.org/10.5281/zenodo.8216844
Abstract :
Discovery of electricity by Benjamin Franklin
in 1752 from lightning has made a great changes in all the
world. Industrial revolution 1.0 to Industrial revolution
4.0 completely depends on electricity. This study proposes
a novel approach for predicting three-phase power
failures using artificial intelligence and machine learning
techniques. The proposed system is designed to monitor
real-time data from a power system and analyze it using a
hybrid machine learning model consisting of random
forest algorithm. The model is trained using historical
data on power system conditions, including voltage,
current. The system uses the trained model to make
predictions on whether a three-phase power failure is
likely to occur in the near future. The proposed approach
is evaluated on a large-scale power system dataset, and
the results demonstrate that the proposed approach
achieves high accuracy in predicting three-phase power
failures. The proposed approach has the potential to
significantly improve the reliability of power systems and
reduce the risk of power outages, which can have serious
economic and social consequences.
Keywords :
Artificial Intelligence, Machine Learning, Three Phase, Random Forest, Power Failure.
Discovery of electricity by Benjamin Franklin
in 1752 from lightning has made a great changes in all the
world. Industrial revolution 1.0 to Industrial revolution
4.0 completely depends on electricity. This study proposes
a novel approach for predicting three-phase power
failures using artificial intelligence and machine learning
techniques. The proposed system is designed to monitor
real-time data from a power system and analyze it using a
hybrid machine learning model consisting of random
forest algorithm. The model is trained using historical
data on power system conditions, including voltage,
current. The system uses the trained model to make
predictions on whether a three-phase power failure is
likely to occur in the near future. The proposed approach
is evaluated on a large-scale power system dataset, and
the results demonstrate that the proposed approach
achieves high accuracy in predicting three-phase power
failures. The proposed approach has the potential to
significantly improve the reliability of power systems and
reduce the risk of power outages, which can have serious
economic and social consequences.
Keywords :
Artificial Intelligence, Machine Learning, Three Phase, Random Forest, Power Failure.
