RBF neural network based backstepping terminal sliding mode MPPT control technique for PV system.

Area of Science:

• Renewable Energy Systems
• Control Theory
• Artificial Intelligence

Background:

• Global energy demand necessitates efficient alternative resources like solar power.
• Photovoltaic (PV) system efficiency is challenged by environmental factors like temperature and solar irradiance.
• Maximum Power Point Tracking (MPPT) algorithms are crucial for optimizing PV energy extraction.

Purpose of the Study:

• To propose a novel nonlinear backstepping terminal sliding mode control (BTSMC) for enhanced maximum power extraction from PV systems.
• To ensure finite-time stability of the proposed MPPT controller.
• To improve tracking performance and convergence speed under dynamic environmental conditions.

Main Methods:

• A nonlinear backstepping terminal sliding mode control (BTSMC) algorithm was developed for MPPT.
• A DC-DC buck-boost converter was utilized to interface the PV system with the load.
• A radial basis function neural network (RBF NN) was employed for generating reference voltages.
• System stability was validated using Lyapunov functions.
• Simulations were conducted using MATLAB/Simulink.

Main Results:

• The proposed BTSMC controller demonstrated superior tracking performance compared to P&O, PID, and a standard backstepping controller.
• The controller achieved fast convergence in finite time, even under rapidly changing environmental conditions.
• Finite-time stability of the system was mathematically validated.

Conclusions:

• The developed BTSMC offers a robust and efficient solution for MPPT in PV systems.
• The integration of RBF NN for reference voltage generation further enhances controller performance.
• The proposed control strategy significantly improves the reliability and efficiency of solar energy utilization.