PSO (Particle Swarm Optimization) to minimize power losses and voltage deviation by optimally placing and sizing DG

Project Objective

This research page presents PSO (Particle Swarm Optimization) to minimize power losses and voltage deviation by optimally placing and sizing DG as a structured Optimization-Based EV Charging simulation project within EV, Battery & E-Mobility. The objective is to explain the model, demonstrate the local research video and support scholars with a clear workflow for thesis, paper implementation, presentation and academic discussion.

System Scope

The project is organized around the main model blocks, input conditions, controller or algorithm logic, measured outputs and result interpretation. The page is written for engineering researchers who need a trustworthy overview before discussing deeper customization.

Methodology & Simulation Workflow

• Define the research problem, model assumptions, input parameters and expected publication-style outputs.
• Build the system model using MATLAB Simulink with domain-specific blocks, equations, geometry, controller logic or datasets.
• Integrate controller, optimization, AI, converter, machine, grid, communication or multiphysics logic depending on the topic.
• Run simulation scenarios and compare the response under nominal, transient, fault, loading or parameter-variation conditions.
• Export publication-ready graphs, video demonstrations and explanation notes for research discussion.

Expected Simulation Outputs

• tracking, overshoot, settling-time and error plots
• controller comparison under disturbances
• optimization convergence and parameter-tuning curves
• robustness response under uncertainty or faults

Research Applications

• control-system thesis implementation
• dynamic-system controller tuning
• optimization-based parameter selection
• robust and adaptive control validation

Trust & Academic Integrity

PhD Research Labs presents simulation support as a research-assistance workflow. The content is intended for learning, implementation guidance, result explanation and model customization. Researchers should validate assumptions, cite appropriate literature and follow their university's academic-integrity rules.