Intelligent MPPT-based energy management for hybrid renewable energy grids using trans Z-source quadratic boost converter

The growing demand for renewable energy necessitates efficient power conversion and grid integration solutions. This work proposes an energy management system (EMS) using a trans Z-source quadratic boost converter (TZSQBC) and puffer fish optimized fuzzy neural network (PF-OFNN)-maximum power point tracking (MPPT). The system integrates photovoltaic (PV) and wind system to enhance reliability and mitigate variability issues. The TZSQBC significantly improves voltage boosting efficiency, ensuring seamless grid integration. The PF-OFNN MPPT algorithm optimizes power extraction from PV system under dynamic environmental conditions. A doubly fed induction generator (DFIG) wind system is incorporated to enhance flexibility and stability. Additionally, a bidirectional DC-DC converter with battery storage ensures efficient energy flow management. The proposed system is evaluated through MATLAB simulations under various test cases, including constant and varying environmental conditions, analysing power efficiency, voltage gain, and total harmonic distortion (THD). A hardware prototype further validates system feasibility, demonstrating effective grid synchronization and improved power quality. Comparative analysis with existing topologies highlights reduced component count, improved duty cycle utilization, and lower voltage stress. The results confirm the proposed system's effectiveness in achieving stable, high-efficiency renewable energy integration with enhanced power management and minimal harmonic distortion.