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The maximum power flow for lossy transmission lines is derived using ABCD parameters in phasor form. These parameters create a matrix relationship between the sending-end and receiving-end voltages and currents, allowing the determination of the receiving-end current. This relationship facilitates calculating the complex power delivered to the receiving end, from which real and reactive power components are derived.
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Maximum Power Transfer01:16

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Numerous practical applications within engineering disciplines, such as telecommunications, necessitate optimizing power delivery to a connected load. This pursuit, however, entails inherent internal losses, which can either equal or exceed the power supplied to the load. The Thevenin equivalent circuit is helpful in finding the maximum power a linear circuit can deliver to a load. It is assumed in this context that the load resistance can be adjusted.
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Aluminum has become the material of choice for overhead transmission lines, surpassing copper due to its abundance and cost-effectiveness. The most prevalent type is the aluminum conductor, steel-reinforced (ACSR), which combines aluminum strands around a steel core. Other variants include all-aluminum conductors (AAC), all-aluminum alloy conductors (AAAC), aluminum conductor alloy-reinforced (ACAR), and aluminum-clad steel conductors. Advanced designs, such as aluminum conductors with steel...
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The Maximum Power Transfer Theorem01:20

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Consider a linear AC Thevenin equivalent circuit connected to a load impedance.
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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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Updated: May 9, 2025

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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Optimization of multi-user scheduling in WPCNs.

Lina Yuan1, Huajun Chen2,3, Tingsui Liu2

  • 1School of Data Science, Tongren University, Tongren, 554300, China. dsjyln@gztrc.edu.cn.

Scientific Reports
|April 29, 2025
PubMed
Summary
This summary is machine-generated.

This study optimizes scheduling in Wireless Powered Communication Networks (WPCNs) by balancing energy and data transmission. The new algorithm significantly cuts energy use and boosts network throughput for better performance.

Keywords:
Energy minimizationLagrange multiplier algorithmMulti-user schedulingNon-linear energy collection efficiencyThroughput maximizationWireless powered communication networks

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Wireless Communication

Background:

  • Internet of Things (IoT) and sensor networks drive the need for Wireless Powered Communication Networks (WPCNs).
  • Energy-constrained devices benefit from wireless energy transfer, but face challenges in multi-user scheduling.
  • Optimizing energy collection efficiency and resource allocation is critical for WPCN performance.

Purpose of the Study:

  • To address the multi-user scheduling challenge in WPCNs.
  • To optimize network performance by maximizing total weighted throughput and minimizing energy consumption.
  • To develop a model considering non-linear energy collection efficiency.

Main Methods:

  • A network model incorporating non-linear energy collection efficiency was developed.
  • A Lagrange multiplier algorithm was employed to balance energy consumption and data transmission.
  • Simulations were conducted using MATLAB to evaluate the proposed algorithm.

Main Results:

  • The proposed algorithm reduced total energy consumption by 25%.
  • Network throughput was increased by 15% compared to traditional methods.
  • The algorithm effectively balanced energy consumption and data transmission.

Conclusions:

  • The study provides theoretical and practical insights for WPCN optimization and deployment.
  • The developed algorithm offers superior performance in energy efficiency and throughput.
  • Future work will focus on algorithm optimization, practical deployment, and network security.