Performance Analysis of grey wolf optimization in photovoltaic

Performance Analysis of grey wolf optimization for maximum power point tracking in photovoltaic system

Implementation plan
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Scenario 1: (using PSO Algorithm)
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Step 1: Initially, we constructed a PV system model integrated with a hybrid storage system in Simulink.

Step 2: Then, we simulate and collect the simulated data

Step 3: Next, we analyze the varying solar irradiance conditions using Particle Swarm Optimization algorithm .

Step 4: Next, we Validate that the hybrid storage system has stable DC-link voltage, reduced battery stress, and dynamic energy balance under load fluctuations.

Step 5: Finally, we plot performance metrics for the following

5.1: Time vs. Power Output (W)
5.2: Time vs. Voltage (V)
5.3: Time vs. State of Charge (SOC%)
5.4: Time vs. Tracking Error (W)
5.5: Time vs. Converter Duty Cycle (d)
5.6: Iteration vs. Fitness Function (Convergence Curve)
5.7: Time vs. Energy Flow Distribution
5.8: Comparison Curve Graph(PV extracted power (PPV), Load Power (PL),. Battery Power (Pba) and Supercapacitor Power (Psc))

Scenario 2: (using Modified PSO)
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Step 1: Initially, we constructed a PV system model integrated with a hybrid storage system in Simulink.

Step 2: Then, we simulate and collect the simulated data

Step 3: Next, we analyze the varying solar irradiance conditions using the Modify the PSO algorithm by integrating inertia weight decay to form a Modified PSO (MPSO) .

Step 5: Next, we Validate that the hybrid storage system has stable DC-link voltage, reduced battery stress, and dynamic energy balance under load fluctuations.

Step 5: Finally, we plot performance metrics for the following

Scenario 3: (using GWOAlgorithm)
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Step 1: Initially, we constructed a PV system model integrated with a hybrid storage system in Simulink.

Step 2: Then, we simulate and collect the simulated data

Step 3: Next, we analyze the varying solar irradiance conditions using the Grey Wolf Optimization algorithm .

Step 4: Next, we Validate that the hybrid storage system has stable DC-link voltage, reduced battery stress, and dynamic energy balance under load fluctuations.

Step 5: Finally, we plot performance metrics for the following

Scenario 4: (using ACO Algorithm)
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Step 1: Initially, we constructed a PV system model integrated with a hybrid storage system in Simulink.

Step 2: Then, we simulate and collect the simulated data

Step 3: Next, we analyze the varying solar irradiance conditions using the Ant Colony Optimization algorithm .

Step 4: Next, we Validate that the hybrid storage system has stable DC-link voltage, reduced battery stress, and dynamic energy balance under load fluctuations.

Step 5: Finally, we plot performance metrics for the following

Scenario 5: (using CG-GWOAlgorithm)
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Step 1: Initially, we constructed a PV system model integrated with a hybrid storage system in Simulink.

Step 2: Then, we simulate and collect the simulated data

Step 3: Next, we analyze the varying solar irradiance conditions using the Cauchy-Gaussian Grey Wolf Optimizer algorithm .

Step 4: Next, we Validate that the hybrid storage system has stable DC-link voltage, reduced battery stress, and dynamic energy balance under load fluctuations.

Step 5: Finally, we plot performance metrics for the following

Software Requirements:
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1. Development Tool: Matlab-R2023a/Simulink or above
2. Operating System: Windows-10 (54-bit) or above

Note:
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1) If the plan does not meet your requirements, provide detailed steps, parameters, models, or expected results in advance. Once implemented, changes won’t be possible without prior input; otherwise, we’ll proceed as per our implementation plan.

2) If the plan satisfies your requirement, Please confirm with us.

3) Project based on Simulation only, not a real time project.

4) Please understand that any modifications made to the confirmed implementation plan will not be made after the project development.