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Particle Swarm Optimization and Modular Multilevel Converter Communication in Electrical Applications with Machine

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This study introduces a novel unified power flow controller (UPFC) using machine learning and particle swarm optimization to mitigate harmonic distortions and improve voltage stability in power systems with nonlinear loads.

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Area of Science:

  • Electrical Engineering
  • Power Systems Analysis
  • Control Theory

Background:

  • Power electronic devices with nonlinear loads can cause harmonic problems and voltage instability.
  • Maintaining power system consistency and dependability is crucial.
  • Existing series controllers may lead to distortions.

Purpose of the Study:

  • To mitigate harmonic distortions and voltage instability in power systems.
  • To enhance power handling capability and modularity.
  • To improve overall power system efficiency and reduce costs.

Main Methods:

  • Replaced series controller with a multilevel modular controller.
  • Utilized a shunt compensator for voltage stability.
  • Employed the DQ theory and machine learning for harmonic separation.
  • Implemented a PI controller and optimized parameters using Particle Swarm Optimization (PSO).
  • Developed a simulation model using time-fluctuating characteristics and MATLAB.

Main Results:

  • The proposed unified power flow controller (UPFC) effectively reduces harmonic distortions.
  • Watt-less power components are significantly decreased.
  • Power system efficiency is enhanced.
  • Costs associated with power quality issues are reduced.

Conclusions:

  • The novel UPFC demonstrates persuasive capacity in improving power quality.
  • The integration of machine learning and PSO offers an effective approach to power system stabilization.
  • This research provides a cost-effective solution for stable and reliable power flow.