ANN-based MPPT (Artificial Neural Network-based Maximum Power Point Tracking) is a technique used in solar photovoltaic (PV) systems to maximize energy extraction from solar panels. Here's a brief explanation of how it works:
How ANN MPPT Works: Objective:
Solar panels have a non-linear power-voltage (P-V) curve, and the Maximum Power Point (MPP) is the point where the panel produces the maximum power.
MPPT algorithms are used to track this point under varying environmental conditions (e.g., sunlight intensity, temperature).
Role of ANN:
An Artificial Neural Network (ANN) is trained to predict the optimal voltage or duty cycle for the MPP based on inputs like solar irradiance, temperature, and panel voltage/current.
The ANN learns the complex relationship between environmental conditions and the MPP during the training phase.
Process:
Inputs: The ANN takes real-time data (e.g., irradiance, temperature, voltage, current) as inputs.
Prediction: The ANN predicts the optimal operating point (voltage or duty cycle) for the MPP.
Control: The predicted value is fed to a DC-DC converter (e.g., buck-boost converter) to adjust the operating point of the solar panel.
Advantages:
Adaptability: ANN can handle non-linearities and adapt to changing environmental conditions.
Efficiency: It can converge to the MPP faster than traditional methods like Perturb and Observe (P&O) or Incremental Conductance (IC).
Robustness: ANN-based MPPT performs well under partial shading conditions, where multiple local maxima exist.
Challenges:
Requires a well-trained ANN model, which depends on the quality and quantity of training data.
Computational complexity may be higher compared to traditional methods.
In summary, ANN-based MPPT leverages the learning capability of neural networks to dynamically and efficiently track the maximum power point in solar PV systems, making it a powerful tool for optimizing energy harvest.