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

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.