Power Line Monitoring through Data Integrity Analysis with Q-Learning Based Data Analysis Network

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.

Automated summary

To effectively monitor and handle big data from equipment linked to the power grid, it is important to continually gather information on power line integrity. Data transmission analysis and collection can be done with tools like digital power meters to perform predictive maintenance on power lines without specialized hardware. Neural network models, specifically deep learning, can be used safely and reliably for power line integrity analysis. We used a Q-learning based data analysis network for monitoring and analyzing power line integrity, with experiments conducted on a 32 km long power line. The proposed framework is applicable to traditional power lines, alternative energy parks, and large users like industries. The quantity of transferred data changes based on the problem and data packet size, and a power outage affects the amount of data collected from the line of interest. The Q-network successfully identified and classified simulated power outages, with low mean square error and a small number of errors and disturbances.