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Related Concept Videos

Maximum Power Flow and Line Loadability01:23

Maximum Power Flow and Line Loadability

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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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Power system distribution involves delivering electrical energy from power plants to consumers through a network of transmission and distribution systems. The process begins at power plants, where energy from coal, gas, nuclear, water, and wind is converted into electrical energy. These plants use three-phase generators, typically rated between 50 to 1300 MVA, with terminal voltages ranging from a few kV to 20 kV, depending on the size and age of the units.
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Distributed Loads01:19

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Distributed loads are a common type of load that engineers and scientists encounter in various practical situations. Distributed loads often refer to a type of load spread over a surface or a structure and can be modeled as continuous force per unit area.
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Distributed Loads: Problem Solving01:21

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Beams are structural elements commonly employed in engineering applications requiring different load-carrying capacities. The first step in analyzing a beam under a distributed load is to simplify the problem by dividing the load into smaller regions, which allows one to consider each region separately and calculate the magnitude of the equivalent resultant load acting on each portion of the beam. The magnitude of the equivalent resultant load for each region can be determined by calculating...
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The fast decoupled power flow method addresses contingencies in power system operations, such as generator outages or transmission line failures. This method provides quick power flow solutions, essential for real-time system adjustments. Fast decoupled power flow algorithms simplify the Jacobian matrix by neglecting certain elements, leading to two sets of decoupled equations:
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Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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Cloud- and Fog-Integrated Smart Grid Model for Efficient Resource Utilisation.

Junaid Akram1,2, Arsalan Tahir3, Hafiz Suliman Munawar4

  • 1School of Computer Science, The University of Sydney, Camperdown, NSW 2006, Australia.

Sensors (Basel, Switzerland)
|December 10, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a cloud and fog computing architecture for smart grids, enhancing efficiency. The novel BPSOSA method optimizes virtual machine allocation, significantly reducing response and processing times.

Keywords:
binary particle swarm optimisationcloud computingfog computingmakespan minimisationsmart grid

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Information Technology

Background:

  • The smart grid (SG) requires enhanced performance, reliability, and energy efficiency.
  • Integrating cloud and fog computing offers potential for increased SG efficiency and optimized resource allocation.
  • Fog computing provides location awareness, low latency, and mobility, crucial for SG data management.

Purpose of the Study:

  • To propose a cloud and fog-based architecture for information management in smart grids.
  • To enhance system efficiency through optimized virtual machine (VM) allocation using load balancing.
  • To introduce and evaluate a novel optimization technique for resource allocation.

Main Methods:

  • Developed a cloud and fog computing architecture for smart grid information management.
  • Implemented a load-balancing mechanism for efficient VM allocation.
  • Proposed the Binary Particle Swarm Optimization with Simulated Annealing (BPSOSA) technique for optimizing inertia weight and search space.

Main Results:

  • The BPSOSA technique demonstrated significant improvements in response time compared to Round Robin, Odds Algorithm, and Ant Colony Optimization (53.99 ms, 82.08 ms, and 81.58 ms reductions, respectively).
  • BPSOSA also outperformed these algorithms in processing time (52.94 ms, 81.20 ms, and 80.56 ms reductions, respectively).
  • While Ant Colony Optimization showed slightly better cost efficiency, the difference was insignificant.

Conclusions:

  • The proposed cloud and fog-based architecture with the BPSOSA optimization technique effectively enhances smart grid information management.
  • BPSOSA offers a superior approach for VM allocation, leading to reduced response and processing times.
  • The integration of fog computing is vital for minimizing cloud burden and optimizing smart grid operations.