Sammendrag
To monitor and handle big data obtained from electrical, electronic, electro-mechanical,
and other equipment linked to the power grid effectively and efficiently, it is important to monitor
them continually to gather information on power line integrity. We propose that data transmission
analysis and data collection from tools like digital power meters may be used to undertake predictive
maintenance on power lines without the need for specialized hardware like power line modems and
synthetic data streams. Neural network models such as deep learning may be used for power line
integrity analysis systems effectively, safely, and reliably. We adopt Q-learning based data analysis
network for analyzing and monitoring power line integrity. The results of experiments performed
over 32 km long power line under different scenarios are presented. The proposed framework may
be useful for monitoring traditional power lines as well as alternative energy source parks and large
users like industries. We discovered that the quantity of data transferred changes based on the
problem and the size of the planned data packet. When all phases were absent from all meters, we
noted a significant decrease in the amount of data collected from the power line of interest. This
implies that there is a power outage during the monitoring. When even one phase is reconnected, we
only obtain a portion of the information and a solution to interpret this was necessary. Our Q-network
was able to identify and classify simulated 190 entire power outages and 700 single phase outages.
The mean square error (MSE) did not exceed 0.10% of the total number of instances, and the MSE
of the smart meters for a complete disturbance was only 0.20%, resulting in an average number of
conceivable cases of errors and disturbances of 0.12% for the whole operation.
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