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 particle’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=wmin−(wmax−wmin)×particle fitness−min fitnessmax fitness−min 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=wmin−(wmax−wmin)×max fitness−min fitnessparticle fitness−min fitness
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+1.2×(kT)c1 = 1.3 + 1.2 \times \left(\frac{k}{T}\right)c1=1.3+1.2×(Tk)c2=1.3+1.2×(kT)c2 = 1.3 + 1.2 \times \left(\frac{k}{T}\right)c2=1.3+1.2×(Tk)
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/m²
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/m² for all panels; then, the second panel’s irradiance is reduced to 800 W/m².
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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