{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,8]],"date-time":"2026-03-08T22:50:55Z","timestamp":1773010255990,"version":"3.50.1"},"reference-count":107,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2022,12,29]],"date-time":"2022-12-29T00:00:00Z","timestamp":1672272000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"EEA Grants","award":["S-BMT-21-5 (LT08-2-LMT-K-01-040)"],"award-info":[{"award-number":["S-BMT-21-5 (LT08-2-LMT-K-01-040)"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/rs15010194","type":"journal-article","created":{"date-parts":[[2022,12,30]],"date-time":"2022-12-30T03:18:18Z","timestamp":1672370298000},"page":"194","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Power Line Monitoring through Data Integrity Analysis with Q-Learning Based Data Analysis Network"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2809-2213","authenticated-orcid":false,"given":"Rytis","family":"Maskeli\u016bnas","sequence":"first","affiliation":[{"name":"Center of Excellence Forest 4.0, Department of Multimedia Engineering, Kaunas University of Technology, 51423 Kaunas, Lithuania"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6669-4445","authenticated-orcid":false,"given":"Raimondas","family":"Pomarnacki","sequence":"additional","affiliation":[{"name":"Department of Electronic Systems, Faculty of Electronics, Vilnius Gediminas Technical University, Sauletekio Ave. 11, 10223 Vilnius, Lithuania"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0480-6859","authenticated-orcid":false,"given":"Van","family":"Khang Huynh","sequence":"additional","affiliation":[{"name":"Department of Engineering Sciences, University of Agder, Postboks 422, 4604 Kristiansand, Norway"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9990-1084","authenticated-orcid":false,"given":"Robertas","family":"Dama\u0161evi\u010dius","sequence":"additional","affiliation":[{"name":"Department of Applied Mathematics, Silesian University of Technology, 44-100 Gliwice, Poland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6579-1526","authenticated-orcid":false,"given":"Darius","family":"Plonis","sequence":"additional","affiliation":[{"name":"Department of Electronic Systems, Faculty of Electronics, Vilnius Gediminas Technical University, Sauletekio Ave. 11, 10223 Vilnius, Lithuania"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,29]]},"reference":[{"key":"ref_1","first-page":"9379789","article-title":"Design of a Power Transmission Line Monitoring System Based upon Edge Computing and Zigbee Wireless Communication","volume":"2022","author":"Hou","year":"2022","journal-title":"Mob. 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