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Particle Swarm Optimization Trained Neural Network MPPT for Solar PV Systems

Particle Swarm Optimization Trained Neural Network MPPT for Solar PV Systems

Introduction to PSO and ANN

The integration of PSO with artificial neural networks (ANNs) has shown promising results in optimizing the performance of solar PV systems. The structure of an ANN consists of three primary layers: input, hidden, and output. Each layer is interconnected by weights that determine how information flows through the network.

In our approach, the ANN is trained to predict the maximum power point (MPP) based on input variables such as solar irradiation and temperature. This method aims to maximize the energy harvested from solar panels.

Data Collection and Training Process

To effectively train the neural network, we gather input data, which includes solar radiation and temperature measurements. The target for training is set as the voltage at the maximum power point.

Instead of relying solely on classical training methods, we utilize PSO to optimize the weights and biases of the neural network. This technique allows for more efficient convergence during the training process, improving the network's accuracy in predicting the MPP.

Simulation Implementation

The next step involves executing the trained neural network within a Simulink block model. During the execution, we implement a training process that aims to minimize the root mean square error (RMSE). This is achieved through a maximum of 100 iterations, during which the PSO algorithm continuously updates the weights based on the network's performance.

By tracking the cost function throughout the iterations, we can observe the effectiveness of the PSO in refining the neural network's predictions.

Performance Testing Under Varying Conditions

Once the training is complete, we conduct performance tests under various load conditions and different solar irradiance levels. The goal is to evaluate how effectively the PSO-trained neural network can extract maximum power from the solar panels under changing environmental conditions.

The results indicate that the neural network is capable of maintaining optimal power extraction, even when subjected to abrupt changes in load or variations in solar irradiation. This adaptability is crucial for maximizing energy output in real-world applications.

Conclusion and Resources

In conclusion, the implementation of a PSO-trained neural network for MPPT in solar PV systems proves to be an effective method for optimizing energy extraction. The combination of advanced computational techniques with traditional PV system design enhances performance and reliability.

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