Smart Microgrid QoS and Network Reliability Performance Improvement using Reinforcement Learning
A Smart Microgrid consists of physical and communication layered networks. It provides communication services to each connected component and resource through multi-agent system. This paper proposes a reinforcement learning based methodology, Q-reinforcement Learning based Multi-agent based Bellmanf...
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Institute of Electrical and Electronics Engineers Inc.
2021
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my.utp.eprints.292232022-03-25T01:12:04Z Smart Microgrid QoS and Network Reliability Performance Improvement using Reinforcement Learning Singh, N. Elamvazuthi, I. Nallagownden, P. Badruddin, N. Ousta, F. Jangra, A. A Smart Microgrid consists of physical and communication layered networks. It provides communication services to each connected component and resource through multi-agent system. This paper proposes a reinforcement learning based methodology, Q-reinforcement Learning based Multi-agent based Bellmanford Routing (QRL-MABR), using multiple agents communicating over the microgrid network. It strengthens the decision-making core of the microgrid by improving Quality of service and network reliability of the smart microgrid. The performance analysis of the algorithm is tested over small-scale IEEE microgrid models i.e. IEEE 9 and IEEE 14. The work is tested and compared with four routing oriented decision-making algorithms, Open shortest path first (OSPF), Optimized link state routing (OLSR), Routing information protocol (RIP) and Multi-agent based Bellmanford routing (MABR). The results validate the productivity and learning capabilities of the proposed QRL-MABR algorithm. © 2021 IEEE. Institute of Electrical and Electronics Engineers Inc. 2021 Conference or Workshop Item NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85124122304&doi=10.1109%2fICIAS49414.2021.9642596&partnerID=40&md5=9e9acf69b67deadb43bd568a208b166b Singh, N. and Elamvazuthi, I. and Nallagownden, P. and Badruddin, N. and Ousta, F. and Jangra, A. (2021) Smart Microgrid QoS and Network Reliability Performance Improvement using Reinforcement Learning. In: UNSPECIFIED. http://eprints.utp.edu.my/29223/ |
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A Smart Microgrid consists of physical and communication layered networks. It provides communication services to each connected component and resource through multi-agent system. This paper proposes a reinforcement learning based methodology, Q-reinforcement Learning based Multi-agent based Bellmanford Routing (QRL-MABR), using multiple agents communicating over the microgrid network. It strengthens the decision-making core of the microgrid by improving Quality of service and network reliability of the smart microgrid. The performance analysis of the algorithm is tested over small-scale IEEE microgrid models i.e. IEEE 9 and IEEE 14. The work is tested and compared with four routing oriented decision-making algorithms, Open shortest path first (OSPF), Optimized link state routing (OLSR), Routing information protocol (RIP) and Multi-agent based Bellmanford routing (MABR). The results validate the productivity and learning capabilities of the proposed QRL-MABR algorithm. © 2021 IEEE. |
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Conference or Workshop Item |
author |
Singh, N. Elamvazuthi, I. Nallagownden, P. Badruddin, N. Ousta, F. Jangra, A. |
spellingShingle |
Singh, N. Elamvazuthi, I. Nallagownden, P. Badruddin, N. Ousta, F. Jangra, A. Smart Microgrid QoS and Network Reliability Performance Improvement using Reinforcement Learning |
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Singh, N. Elamvazuthi, I. Nallagownden, P. Badruddin, N. Ousta, F. Jangra, A. |
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Singh, N. |
title |
Smart Microgrid QoS and Network Reliability Performance Improvement using Reinforcement Learning |
title_short |
Smart Microgrid QoS and Network Reliability Performance Improvement using Reinforcement Learning |
title_full |
Smart Microgrid QoS and Network Reliability Performance Improvement using Reinforcement Learning |
title_fullStr |
Smart Microgrid QoS and Network Reliability Performance Improvement using Reinforcement Learning |
title_full_unstemmed |
Smart Microgrid QoS and Network Reliability Performance Improvement using Reinforcement Learning |
title_sort |
smart microgrid qos and network reliability performance improvement using reinforcement learning |
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Institute of Electrical and Electronics Engineers Inc. |
publishDate |
2021 |
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https://www.scopus.com/inward/record.uri?eid=2-s2.0-85124122304&doi=10.1109%2fICIAS49414.2021.9642596&partnerID=40&md5=9e9acf69b67deadb43bd568a208b166b http://eprints.utp.edu.my/29223/ |
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