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# Comparison of GWO MPPT, Fuzzy MPPT, and P&O MPPT for Solar PV Systems

Comparison of GWO MPPT, Fuzzy MPPT, and P&O MPPT for Solar PV Systems

The Simulink model is designed to test and compare the performance of GWO MPPT, Fuzzy MPPT, and P&O MPPT under partial shading conditions:

• Four solar panels connected in series to simulate partial shading.

• Each panel rated at 249 watts with specific maximum power point voltage and current characteristics.

Gray Wolf Optimization (GWO) MPPT

Operation

• GWO MPPT receives PV voltage (Vpv) and current (Ipv) as inputs.

• Utilizes the GWO algorithm to optimize the duty cycle of a boost converter.

• Goal: Extract maximum power from PV panels under varying shading conditions by dynamically adjusting the duty cycle based on GWO optimization.

Key Features

• Quick response time and efficient tracking of global maximum power points.

• Demonstrates superior performance in scenarios with uniform irradiance and partial shading.

Fuzzy MPPT

Operation

• Fuzzy MPPT receives Vpv and Ipv inputs.

• Uses fuzzy logic to generate the duty cycle of the boost converter.

• Incorporates fuzzy inference rules to adaptively adjust the duty cycle based on PV panel characteristics and environmental conditions.

Key Features

• Adaptive and robust performance under varying environmental conditions.

• Requires tuning of fuzzy logic rules for optimal performance in complex shading scenarios.

Perturb and Observe (P&O) MPPT

Operation

• P&O MPPT adjusts the duty cycle of the boost converter based on changes in PV panel voltage and power.

• Perturbs the operating point and observes the resulting change in power output to track the maximum power point.

Key Features

• Simple implementation and widely used in various PV systems.

• Prone to oscillations around the maximum power point, especially under partial shading conditions.

Testing Conditions Explanation

The Simulink model is tested under different irradiance scenarios to evaluate each MPPT technique's performance:

1. Uniform Irradiance (1000, 1000, 1000, 1000 watts per square meter)

• Each panel receives equal irradiance levels to simulate uniform sunlight conditions.

• Evaluates response time and accuracy in tracking maximum power points under consistent irradiance.

1. Partial Shading (1000, 1000, 1000, 300 watts per square meter)

• Tests the ability to handle non-uniform shading conditions and track global maximum power points effectively.

Simulation Results

Results for Uniform Irradiance (1000, 1000, 1000, 1000)

• GWO MPPT: Quickly reaches global maximum power point with minimal rise time.

• Fuzzy MPPT: Slightly slower response compared to GWO, but still effective in maximizing power output.

• P&O MPPT: Demonstrates steady-state performance but may oscillate around the maximum power point.

Results for Partial Shading (1000, 1000, 1000, 300)

• GWO MPPT: Efficiently tracks and reaches the global maximum power point despite partial shading.

• Fuzzy MPPT: Shows competitive performance but struggles to consistently achieve the global maximum due to fuzzy rule limitations.

• P&O MPPT: Slower to adapt and may get stuck at local maximum points under partial shading conditions.

Conclusion

Based on the comparison under uniform and partial shading conditions, the Gray Wolf Optimization (GWO) MPPT proves to be the most effective in maximizing power output from solar PV systems. Its quick response time and robust optimization capabilities make it suitable for environments with varying irradiance levels and partial shading effects.