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Power flow problem analysis is fundamental for determining real and reactive power flows in network components, such as transmission lines, transformers, and loads. The power system's single-line diagram provides data on the bus, transmission line, and transformer. Each bus k in the system is characterized by four key variables: voltage magnitude Vk​, phase angle δk​, real power Pk​, and reactive power Qk​. Two of these four variables are inputs, while the...
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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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Q-RPL: Q-Learning-Based Routing Protocol for Advanced Metering Infrastructure in Smart Grids.

Carlos Lester Duenas Santos1, Ahmad Mohamad Mezher1, Juan Pablo Astudillo León2,3

  • 1Electrical and Computer Engineering Department, University of New Brunswick, Fredericton, NB E3B 5A3, Canada.

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Summary
This summary is machine-generated.

This study introduces Q-RPL, a Q-learning routing protocol for Smart Grid Advanced Metering Infrastructure (AMI). Q-RPL enhances data routing efficiency and reliability in wireless mesh networks.

Keywords:
machine learningreinforcement learningrouting protocol for low-power and lossy networks (RPL)smart grids

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Network Engineering

Background:

  • Efficient data routing is crucial for Smart Grid Advanced Metering Infrastructure (AMI) performance and resilience.
  • Wireless mesh networks are commonly used in AMI deployments, presenting unique routing challenges.
  • Existing routing protocols may not fully address the dynamic nature of AMI networks.

Purpose of the Study:

  • To introduce Q-RPL, a novel Q-learning-based Routing Protocol for AMI.
  • To enhance routing decisions and network performance in AMI using Reinforcement Learning (RL).
  • To improve the reliability and efficiency of data transmission in Smart Grid networks.

Main Methods:

  • Developed Q-RPL, integrating Q-learning with the IPv6 Routing Protocol for Low-Power and Lossy Networks (RPL).
  • Utilized Reinforcement Learning (RL) principles for dynamic next-hop selection.
  • Conducted extensive simulations in real map scenarios to evaluate performance.

Main Results:

  • Q-RPL demonstrated significant improvements in packet delivery ratio and end-to-end delay.
  • The protocol showed enhanced compliant factor compared to standard RPL and other benchmarks.
  • Simulations confirmed Q-RPL's adaptability and robustness in dynamic network conditions.

Conclusions:

  • Q-RPL offers a significant advancement for routing protocols in Smart Grid AMI.
  • The protocol promises enhanced efficiency and reliability for intelligent energy systems.
  • Reinforcement Learning holds substantial potential for improving future networking protocols.