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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-12-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/15/1/194 |
| _version_ | 1797406417349509120 |
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| author | Rytis Maskeliūnas Raimondas Pomarnacki Van Khang Huynh Robertas Damaševičius Darius Plonis |
| author_facet | Rytis Maskeliūnas Raimondas Pomarnacki Van Khang Huynh Robertas Damaševičius Darius Plonis |
| author_sort | Rytis Maskeliūnas |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T03:25:09Z |
| format | Article |
| id | doaj.art-1ef70846b0e742f89fd7a6e08ea295e0 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T03:25:09Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-1ef70846b0e742f89fd7a6e08ea295e02023-12-03T15:03:04ZengMDPI AGRemote Sensing2072-42922022-12-0115119410.3390/rs15010194Power Line Monitoring through Data Integrity Analysis with Q-Learning Based Data Analysis NetworkRytis Maskeliūnas0Raimondas Pomarnacki1Van Khang Huynh2Robertas Damaševičius3Darius Plonis4Center of Excellence Forest 4.0, Department of Multimedia Engineering, Kaunas University of Technology, 51423 Kaunas, LithuaniaDepartment of Electronic Systems, Faculty of Electronics, Vilnius Gediminas Technical University, Sauletekio Ave. 11, 10223 Vilnius, LithuaniaDepartment of Engineering Sciences, University of Agder, Postboks 422, 4604 Kristiansand, NorwayDepartment of Applied Mathematics, Silesian University of Technology, 44-100 Gliwice, PolandDepartment of Electronic Systems, Faculty of Electronics, Vilnius Gediminas Technical University, Sauletekio Ave. 11, 10223 Vilnius, LithuaniaTo 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.https://www.mdpi.com/2072-4292/15/1/194data integrity analysisartificial neural networkQ-learningpower linemonitoring |
| spellingShingle | Rytis Maskeliūnas Raimondas Pomarnacki Van Khang Huynh Robertas Damaševičius Darius Plonis Power Line Monitoring through Data Integrity Analysis with Q-Learning Based Data Analysis Network Remote Sensing data integrity analysis artificial neural network Q-learning power line monitoring |
| title | Power Line Monitoring through Data Integrity Analysis with Q-Learning Based Data Analysis Network |
| title_full | Power Line Monitoring through Data Integrity Analysis with Q-Learning Based Data Analysis Network |
| title_fullStr | Power Line Monitoring through Data Integrity Analysis with Q-Learning Based Data Analysis Network |
| title_full_unstemmed | Power Line Monitoring through Data Integrity Analysis with Q-Learning Based Data Analysis Network |
| title_short | Power Line Monitoring through Data Integrity Analysis with Q-Learning Based Data Analysis Network |
| title_sort | power line monitoring through data integrity analysis with q learning based data analysis network |
| topic | data integrity analysis artificial neural network Q-learning power line monitoring |
| url | https://www.mdpi.com/2072-4292/15/1/194 |
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