Nonlinear adaptive NeuroFuzzy feedback linearization based MPPT control schemes for photovoltaic system in microgrid

Area of Science:

• Electrical Engineering
• Control Systems
• Renewable Energy Systems

Background:

• Renewable energy integration into smart grids necessitates advanced control for stability and efficiency.
• Nonlinear control algorithms are crucial for managing power output and stability in photovoltaic (PV) systems under dynamic conditions.

Purpose of the Study:

• To comparatively analyze adaptive Feedback Linearization (FBL) embedded Full Recurrent Adaptive NeuroFuzzy (FRANF) control schemes for Maximum Power Point Tracking (MPPT) in PV subsystems.
• To evaluate the performance of different FRANF structures within FBL models under various operating scenarios.

Main Methods:

• Development and simulation of multiple adaptive FBL-FRANF control schemes.
• Comparative analysis based on power error, efficiency, and performance indexes.
• Testing under step changes in solar irradiation/temperature, partial shading conditions, and daily field data.
• Simulation using Matlab/Simulink and comparison with adaptive PID controllers.

Main Results:

• All proposed FRANF-FBL schemes demonstrate enhanced convergence, stability, and efficiency compared to conventional MPPT.
• The Mexican hat wavelet-based FRANF-FBL scheme exhibits superior performance over other proposed schemes and adaptive PID.
• Performance evaluation using spider charts highlights multivariate advantages.

Conclusions:

• Adaptive FBL-FRANF control schemes provide robust MPPT for PV systems in smart microgrids.
• The Mexican hat wavelet-based FRANF-FBL is a highly effective control strategy for solar energy optimization.
• The research validates the superiority of advanced neuro-fuzzy control over traditional methods for renewable energy integration.