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MATLAB-Based ANFIS MPPT for Solar PV Systems

MATLAB-Based ANFIS MPPT for Solar PV Systems

Overview of ANFIS and MPPT

  • What is ANFIS? ANFIS combines neural networks and fuzzy logic to create a system capable of approximating complex functions. It consists of multiple layers, each performing specific functions, from input specification to output defuzzification.

  • Understanding MPPT The primary goal of MPPT is to optimize the output power from solar panels by adjusting operating conditions in response to varying environmental factors such as temperature and solar radiation.


Data Collection for Training

  • Gathering Input Data The system requires data on temperature and solar irradiance to determine the maximum power point voltage, current, or power. Data is modeled and collected to train the ANFIS.

  • Parameters for Data Collection Key parameters include short circuit current, current at maximum power point, open circuit voltage, and temperature coefficients. Random values are generated for temperature and radiation within specified ranges.

Implementation Steps

  1. Data Preparation Using the collected data, the ANFIS is trained to learn the relationship between input conditions (temperature and irradiance) and the corresponding output power.

  2. Training the ANFIS The training involves utilizing hybrid algorithms to minimize the error between the predicted and actual output. The system is tested to ensure that the trained data aligns closely with the collected data.

  3. Modeling in MATLAB The trained ANFIS model is integrated into a simulation environment in MATLAB, where it interacts with the PV system to generate control signals for a DC-DC converter based on real-time input conditions.

Testing the ANFIS MPPT Algorithm

  • Performance Under Varying Conditions The algorithm's effectiveness is evaluated under two scenarios: varying solar irradiance while maintaining a constant load, and sudden changes in load while keeping irradiance constant.

  • Results and Observations The simulation demonstrated that the ANFIS MPPT successfully extracted maximum power even during fluctuations in irradiance and load changes, adjusting duty cycles appropriately to maintain optimal performance.

Conclusion

The video successfully illustrates the MATLAB implementation of an ANFIS-based MPPT algorithm for solar PV systems. By effectively adapting to changes in environmental conditions, the algorithm ensures maximum energy extraction from solar panels.

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