Nonlinear robust integral backstepping based MPPT control for stand-alone photovoltaic system

Area of Science:

• Electrical Engineering
• Renewable Energy Systems
• Control Theory

Background:

• Photovoltaic (PV) cells exhibit nonlinear current-voltage (I-V) and power-voltage (P-V) characteristics.
• The maximum power point (MPP) of PV cells is highly sensitive to meteorological conditions like solar irradiance and temperature.
• Efficiently extracting maximum power requires sophisticated Maximum Power Point Tracking (MPPT) control strategies.

Purpose of the Study:

• To develop and evaluate a novel, hybrid nonlinear MPPT control strategy for enhanced PV energy extraction.
• To ensure continuous operation of PV systems at their MPP under dynamic environmental conditions.
• To improve the efficiency and responsiveness of MPPT controllers.

Main Methods:

• A hybrid nonlinear MPPT strategy combining robust integral backstepping (RIB) control with an adaptive neuro-fuzzy inference system (ANFIS) was formulated.
• The control scheme features a two-loop structure: ANFIS for real-time MPP voltage reference generation and RIB for control signal generation.
• The system was simulated using MATLAB/Simulink, incorporating a non-inverting buck-boost (NIBB) DC-DC converter, PV array, and dynamic load.

Main Results:

• The proposed RIB-based MPPT strategy demonstrated no overshoot, fast convergence, and excellent transient response.
• The controller achieved fast rising and settling times with minimal output tracking error.
• Performance was validated against varying meteorological conditions, faults, and parametric uncertainties.

Conclusions:

• The hybrid nonlinear RIB control strategy offers a highly efficient and robust solution for MPPT in PV systems.
• The ANFIS integration provides accurate real-time MPP voltage tracking.
• Comparative analysis confirmed the superiority of the proposed strategy over conventional PID and P&O methods.