# Adaptive PSO MPPT for Solar PV System

## Introduction to Adaptive PSO MPPT

The Adaptive PSO algorithm enhances the conventional PSO by dynamically updating parameters to achieve better performance in solar PV systems. Unlike traditional PSO, where the inertia weight (w) and cognitive (c1) and social (c2) coefficients are constant, the Adaptive PSO varies these parameters based on certain conditions and iteration performance.

## Adaptive PSO Algorithm

### Key Differences

**Traditional PSO**: Constant values for w, c1, and c2.**Adaptive PSO**: Dynamic adjustment of w, c1, and c2 based on iteration performance.

### Inertia Weight (w)

The inertia weight (w) is adjusted based on the particleâ€™s fitness compared to the average fitness:

If the particle's fitness is greater than the average fitness, w takes a maximum value (e.g., 0.1).

If the particle's fitness is less than or equal to the average fitness, w is calculated using the following formula: w=wminâˆ’(wmaxâˆ’wmin)Ã—particleÂ fitnessâˆ’minÂ fitnessmaxÂ fitnessâˆ’minÂ fitnessw = w_{\text{min}} - (w_{\text{max}} - w_{\text{min}}) \times \frac{\text{particle fitness} - \text{min fitness}}{\text{max fitness} - \text{min fitness}}w=wminâ€‹âˆ’(wmaxâ€‹âˆ’wminâ€‹)Ã—maxÂ fitnessâˆ’minÂ fitnessparticleÂ fitnessâˆ’minÂ fitnessâ€‹

### Cognitive (c1) and Social (c2) Coefficients

These coefficients are updated in each iteration based on the total number of iterations (T) and the current iteration (k):

c1=1.3+1.2Ã—(kT)c1 = 1.3 + 1.2 \times \left(\frac{k}{T}\right)c1=1.3+1.2Ã—(Tkâ€‹)c2=1.3+1.2Ã—(kT)c2 = 1.3 + 1.2 \times \left(\frac{k}{T}\right)c2=1.3+1.2Ã—(Tkâ€‹)

## Simulink Model of Solar PV System

### Model Overview

The Simulink model includes a solar PV panel, a boost converter, and a load. The PV panel consists of four series-connected cells, each with a maximum power rating of 62 watts, resulting in a total voltage of 30.96 volts under standard testing conditions.

### Simulation Setup

The system measures the current and voltage from the PV panel and provides these values to the Adaptive PSO MPPT algorithm, which calculates the optimal duty cycle for the boost converter to maximize power extraction.

### Simulation Results

#### Uniform Irradiance

**Irradiance Setting**: 1000 W/mÂ²**Results**: The system achieves a maximum power extraction of approximately 250 watts.

#### Partial Shading Conditions

**Irradiance Setting**: Varying levels**Results**: The Adaptive PSO MPPT successfully extracts the maximum power under different partial shading conditions, demonstrating its effectiveness in dynamic environments.

### Dynamic Change in Irradiance

**Irradiance Setting**: Initially set to 1000 W/mÂ² for all panels; then, the second panelâ€™s irradiance is reduced to 800 W/mÂ².**Results**: The system continues to extract maximum power, showcasing the robustness of the Adaptive PSO MPPT algorithm.

## Conclusion

The Adaptive PSO MPPT algorithm offers significant improvements over traditional PSO by dynamically adjusting key parameters. This results in more efficient power extraction from solar PV systems, especially under varying environmental conditions such as partial shading.

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