A reduced sensor-based efficient and robust MPPT nonlinear controller for grid-integrated photovoltaic energy systems...

Area of Science:

• Electrical Engineering
• Renewable Energy Systems
• Control Theory

Background:

• Grid-integrated photovoltaic (PV) systems require efficient Maximum Power Point Tracking (MPPT) for optimal energy harvest.
• Rapidly changing climatic conditions pose challenges to conventional MPPT controllers, often necessitating expensive irradiance sensors.
• Maintaining DC-link stability and grid power quality is crucial for reliable PV system operation.

Purpose of the Study:

• To propose a reduced sensor-based nonlinear MPPT controller for grid-integrated PV systems.
• To achieve fast and robust MPPT performance under dynamic environmental conditions.
• To enhance DC-link stability and grid power quality without relying on irradiance sensors.

Main Methods:

• Developed a two-stage controller combining mathematical irradiance estimation with a radial basis function neural network.
• Utilized a backstepping nonlinear controller to enforce optimal reference voltages.
• Validated the controller on a 100 kW MATLAB/Simulink-based grid-tied PV system.

Main Results:

• The controller achieved rapid Maximum Power Point (MPP) tracking in as little as 7 ms under step irradiance changes.
• DC-link stability was restored within 42 ms, and total harmonic distortion (THD) remained below 0.1%.
• Outperformed Perturb & Observe (P&O), Improved Differential Evolution (IDE), and Particle Swarm Optimization (PSO) in power yield, active power delivery, and DC-link voltage regulation.

Conclusions:

• The proposed reduced sensor-based nonlinear MPPT controller offers superior performance compared to existing methods.
• It provides faster convergence, improved voltage regulation, and enhanced grid stability for large-scale PV systems.
• The controller is a promising solution for real-world deployment in PV systems operating under variable conditions.