MATLAB Simulation of Neural Network Based MPPT for Solar PV System
Introduction to Neural Network MPPT for Solar PV Systems
MPPT is a technique used in solar energy systems to extract the maximum possible power from solar panels. Traditionally, MPPT algorithms adjust the operating point of the solar panel to the maximum power point, where the output power is optimized. By incorporating neural networks, we can create a more intelligent system that adapts to varying environmental conditions, such as changes in temperature and irradiance.
System Overview: Solar PV Panel and Boost Converter
The system we will be simulating consists of a 250W solar PV panel and a boost converter. The solar panel generates DC power, and the boost converter increases the voltage to match the required load voltage. The key part of this system is the neural network, which adjusts the duty cycle of the boost converter to ensure that the solar panel operates at its maximum power point.
In the boost converter, when the switch is on, the inductor stores energy, and when the switch is off, the energy is released to the load through the forward-biased diode. This process enables power conversion from the solar panel to the load.
How the Neural Network MPPT Algorithm Works
The neural network-based MPPT algorithm uses inputs like solar irradiance and temperature to predict the optimal duty cycle for the boost converter. The neural network consists of three layers: the input layer, hidden layer, and output layer.
Input Layer: The neural network receives irradiance and temperature as inputs.
Hidden Layer: The data is processed and passed through the hidden layer, where the system learns from patterns in the data.
Output Layer: The output of the neural network is the duty cycle, which is then used to control the boost converter.
To make the neural network functional, it must first be trained with historical data. Training involves adjusting the weights in the neural network to minimize error and achieve accurate predictions.
Training the Neural Network
In MATLAB, we use the function fitting option to train the neural network. This option allows us to fit the neural network to our input data, which includes irradiance and temperature. During training, the data is divided into three parts: 70% for training, 15% for validation, and 15% for testing.
Once the data is imported into MATLAB’s workspace, we select the training algorithm. For this simulation, the Levenberg-Marquardt (LM) algorithm is used, which is known for its efficiency in training shallow neural networks. After the network is trained, we can evaluate its performance through regression plots and mean square error (MSE).
Visualizing Neural Network Performance
After training the neural network, MATLAB provides regression plots to show how well the model has learned from the data. The plots display training, validation, and test data, along with the target values. The performance is also evaluated through metrics like MSE, and errors are plotted in histograms for further analysis.
One of the key features of the neural network training process is the ability to export the trained model into Simulink, where it can be used for simulation in the boost converter control system.
Neural Network Structure and Boost Converter Control
The trained neural network generates a duty cycle for the boost converter, which is then used to adjust the switching frequency. The PWM (Pulse Width Modulation) signals are sent to the boost converter switches, which operate at a frequency of 10kHz.
The neural network continuously compares the solar panel's voltage with the maximum power voltage (Vmpp) and processes the error through a PAD controller. The PAD controller adjusts the duty cycle of the boost converter, ensuring the panel operates at its maximum power point.
Simulation Results: Constant Irradiation Condition
In a constant irradiation condition of 1000 W/m², the neural network MPPT algorithm tracks the maximum power point. The output settles at 250W, which is the peak power for the given irradiance. Initially, small oscillations are observed as the system adjusts, but it quickly stabilizes at the maximum power point.
Simulation Results: Varying Irradiance Conditions
When the irradiance varies, the neural network MPPT algorithm continues to track the maximum power point. For example, the irradiance can step from 200W/m² to 400W/m², 600W/m², and 800W/m². As the irradiance increases, the neural network adjusts the duty cycle to optimize the power extracted from the panel.
This ability to adapt to changing environmental conditions is a significant advantage of using a neural network for MPPT. It ensures that the system always operates at peak efficiency, regardless of fluctuations in solar irradiance.
Conclusion
The neural network-based MPPT algorithm provides an intelligent solution for maximizing the power output of a solar PV system. By using input data like irradiance and temperature, the neural network adjusts the duty cycle of the boost converter to ensure the solar panel operates at its optimal power point. Whether under constant or varying irradiance, the system can adapt and track the maximum power efficiently.
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