How do integrated PSO MATLAB CODE with Backward forward sweep method for power loss minimization by network reconfiguration.

How do integrated PSO MATLAB CODE with Backward forward sweep method for power loss minimization by network reconfiguration.

Can you give me a full MATLAB code of PSO integrated with BFS method.

[TB]

Now I have some clue how I can integrate PSO algorizim with Back ward forward sweep method to minimize power loss by network reconfiguration.

But I want full MATLAB CODE of PSO by using BFS method to minimize power loss in my network reconfiguration. My project has 78 bus, 5 tie switchs and it is radial. so the out put will be

1.Active power loss before and after reconfiguration

2.Reactive power loss before and after reconfiguration

3. Voltage magnitude (p.u) before and after reconfiguration

4. voltage profile ( plot voltage) before and after reconfiguration,

5. Tie switch before and after reconfiguration. ( base case testing tie switch is 78,79,80,81 and 82)

[John D'Errico]

@TB

Answers is not a service where we write code for you on demand. If you need code, then start writing.

And don't post your comments/demands as answers to your question.

[TB]

ok

Answer 1

[Jaimin]

Hi @TB

Integrating Particle Swarm Optimization (PSO) with the Backward/Forward Sweep (BFS) method for power loss minimization through network reconfiguration in MATLAB involves several steps.

Kindly refer following algorithm for understanding.

Initialize Parameters:

• Set PSO parameters: number of particles, iterations, inertia weight, cognitive and social components.
• Define network parameters: number of buses, lines, and line data (resistance, reactance).

Particle Initialization:

• Randomly initialize particle positions and velocities.
• Set personal best (pBest) to initial positions.
• Initialize global best (gBest) as the best of initial positions.

Objective Function (BFS):

• Calculate power loss using BFS:
• Initialize bus voltages and loads.
• Perform backward and forward sweeps to update currents and voltages.
• Compute total power loss across lines.

PSO Iteration:

• For each iteration:
• Evaluate fitness of each particle using the objective function.
• Update pBest and gBest based on fitness.
• Update particle velocities and positions using PSO formulas.
• Ensure positions remain within valid bounds.

Output Results:

• After all iterations, output the optimal line configuration (gBest) and the corresponding minimum power loss.

For more information kindly refer following MathWorks documentation.

PSO: https://www.mathworks.com/help/gads/particleswarm.html

I hope this will be helpful.